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Tissue Remodeling and Steroidogenesis in the Preovulatory Follicle of Cycling Pony Mares


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TISSUE REMODELING AND STEROIDOGENESIS IN THE PREOVULATORY FOLLICLE OF CYCLING PONY MARES By ANDRIA L. DESVOUSGES A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2004

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Copyright 2004 By Andria L. Desvousges

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iii ACKNOWLEDGMENTS The road to this destinati on has been a very long and wi nding one. Over the course of the last year I realized how fragile we are as people and th at it is okay to ask for a bit of help when you really need it. I have had a very difficult last year. If not for the people who helped me along the way, I never would ha ve made it. First and foremost, I must thank Dr. Sharp for taking a chance on me, and helping me become a better researcher. He taught me how to survive on my own, and forced me to think outside of the box to find the answers I was looking for. From him I gained a greater knowledge of science and research techniques. He encouraged me to think independently and to question everything. His support the confiden ce to present this work. I w ould also like to thank the members of my graduate committee, Dr. Lokenga Badinga, Dr. William Buhi, and Dr. Gregory Schultz. They helped me in nay way possible achieve this goal of finishing my research and thesis. I would especially like to thank Dr. Badinga for coming in to help me at the last minute and offering me his patient technician, his students, and his laboratory resources. Additionally I would like to thank Dr. William Thatcher, who helped me with all of my last minute statistics. Idania Alvarez ra n many reverse zymography gels for me at the end, when I was incapable of doing them my self. I thank the wonderful AMCB faculty for their continued support a nd guidance when I needed it most. I thank Dr, Michael Smith (and his students from the University of Missouri), for their help with all the

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iv cloning and sequencing of my MMP-2 and TIMP-1 inserts. They accepted me as a part of their lab and I still have a place to go to if I am ever in Missouri. I must also thank all of my family and friends for putting up with me over the last year. Things have been very difficult. They all are wonderful for being so patient, loving and kind. I must thank my brother and my pare nts, who always believed in me no matter what, and drove me to dust myself off and try again. They support me in whatever I do, no matter how long it has taken me to get to this point. I appreciat e their unconditional love and know how lucky I am to have them al l in my life. Last but not least, I thank my husband, and best friend, Dan. He has seen me at my worst and best, and has always been there for me no matter what. I am truly blessed because of him. He helped me push through, at the end of this long strange tr ip that has been my masters program.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF FIGURES...........................................................................................................ix ABSTRACT......................................................................................................................x ii CHAPTER 1 INTRODUCTION........................................................................................................1 2 LITERATURE REVIEW.............................................................................................4 Reproduction in the Mare.............................................................................................4 Seasonality.............................................................................................................4 Photoperiod............................................................................................................5 Winter Anestrus.....................................................................................................6 Vernal Transition...................................................................................................9 The Breeding Season...........................................................................................12 Autumnal Transition............................................................................................13 Endocrinology of the Estrous Cycle....................................................................13 Ovarian Anatomy................................................................................................14 Folliculogenesis...................................................................................................15 Ovulation.............................................................................................................17 Monitoring the Estrous Cycle.....................................................................................18 The Extracellular Matrix.............................................................................................20 Matrix Metalloproteinases Structure and function.....................................................24 Matrix Metalloproteinases...................................................................................24 Matrix Metalloproteinases Structure...................................................................24 Membrane Bound Matrix Metalloproteinases (MT-MMPs)..............................26 Regulation of MMPs..........................................................................................27 Activation of MMPs...........................................................................................27 Activation of MMP-2..........................................................................................28 Tissue Inhibitor of Metalloproteinases (TIMPs).......................................................29 TIMP Structure....................................................................................................29 TIMP Expression and Regulation.......................................................................29 MMPs/TIMPs and Their Role in the Ovulatory Cycle............................................30 Follicular Growth and Development...................................................................30 Follicular Atresia and Apoptosis.........................................................................30

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vi Follicular Rupture and Ovulation........................................................................31 Types of MMPs: Role in Ovulation and Response to Gonadotropin Stimulation....32 Collagenases........................................................................................................32 Gelatinases...........................................................................................................32 Membrane Type MMPs (MT-MMPs)..............................................................34 TIMPs: Role in Ovulation and Rela tionship to Gonadotropin Stimulation.............35 3 MMP-2, TIMP-1 AND STEROIDOGENEIS IN THE PREOVULATORY FOLLICLE.................................................................................................................37 Introduction.................................................................................................................37 Materials and Methods...............................................................................................40 General Procedures..............................................................................................40 Monitoring of the mares...............................................................................40 Blood processing..........................................................................................40 Follicular fluid processing............................................................................40 Hormone assays............................................................................................41 Experiment 1: Effect of hCG on pre ovulatory steroids E2, P4 and Matrix Metalloproteinase-2 and Tissue Inhib itor of Metalloproteinase-1 in the Preovulatory Follicle of Cycling Pony Mares.......................................................41 Materials and Methods........................................................................................41 Statistical Analysis..............................................................................................42 Experiment 2 : Time trends of follicular P4, MMP-2 and TIMP-1 in untreated mares by folliculocentesis................................................................................................42 Methods and Materials........................................................................................42 Statistical Analysis..............................................................................................43 Experiment 3: The time dependent effect of hCG administration on steriodogenesis and tissue remodeling in th e preovulatory follicle of cycling pony mares............44 Methods and Materials........................................................................................44 Statistical Analysis..............................................................................................44 Experiment 4: Tissue expression of MMP-2 and TIMP-1 mRNA in gonadotropin stimulated ovarian tissue........................................................................................45 Methods and Materials........................................................................................45 Statistical Analysis..............................................................................................46 Experiment 1: Results and Discussion.......................................................................46 Experiment 2: Results and Discussion.......................................................................49 Experiment 3: Results and Discussion.......................................................................53 Experiment 4: Results and Discussion.......................................................................56 Conclusions.................................................................................................................58 4 INHIBITION OF THE TISSU E REMODELING SYSTEM AND STERIODOGENESIS IN THE PREOVULATORY FOLLICLE OF CYCLING PONY MARES...........................................................................................................61 Introduction.................................................................................................................61 Methods and Materials...............................................................................................62 General Procedures..............................................................................................62

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vii Monitoring of the mares...............................................................................62 Blood processing..........................................................................................63 Follicular fluid processing............................................................................63 Hormone assays............................................................................................63 Experiment 1: Inhibition of follicular P4 concentrations and its effects on MMP-2 and TIMP-1............................................................................................................64 Methods and Materials........................................................................................64 Statistical Analysis..............................................................................................65 Experiment 2: The Effects of MMP-2/9 cyclic inhibitor III on Follicular Steriodogenesis and Tissue Remodeling in the Preovulatory Follicle of Cycling Pony Mares............................................................................................................65 Methods and Materials........................................................................................65 Statistical Analysis..............................................................................................66 Experiment1: Results and Discussion........................................................................66 Experiment 2: Results and Discussion.......................................................................69 Conclusions.................................................................................................................72 5 INTRAFOLLICULAR ADMINISTRATION OF GNRH, P4 AND MELATONIN AND THEIR EFFECTS ON TISSUE REMODELING AND STEROIDOGENESIS IN THE PREOVULATORY FOLLICLE.............................74 Introduction.................................................................................................................74 Methods and Materials...............................................................................................75 General Procedures..............................................................................................75 Monitoring of the mares...............................................................................75 Blood processing..........................................................................................75 Follicular fluid processing............................................................................76 Hormone assays............................................................................................76 Experiment 1: Intrafollicular Administra tion of GnRH, P4 or Melatonin and Their Effects on MMP-2, TIMP-1, and Steroidogene sis in the Preovulatory Follicle of Cycling Pony Mares...............................................................................................77 Materials and Methods........................................................................................77 Statistical Analysis..............................................................................................78 Experiment 1: Results and Discussion.......................................................................78 Conclusions.................................................................................................................82 6 BLOCKADE OF LH AND/OR FSH AND THE EFFECTS ON TISSUE REMODELING AND STEROIDOGENESIS IN THE PREOVULATORY FOLLICLE OF CYCLING PONY MARES..............................................................84 Introduction.................................................................................................................84 Materials and Methods...............................................................................................86 General Procedures..............................................................................................86 Monitoring of the mares...............................................................................86 Follicular fluid processing............................................................................86 Hormone assays............................................................................................87

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viii Experiment 1: The effect of Exogeno us P4 and E2+P4 Administration on Follicular P4 Concentrations, and Matrix Metalloproteinase-2 and Tissue Inhibitor of Metalloproteinase -1 Activity within the Preovulatory Follicle of the Cycling Pony Mare..........................................................................................87 Methods and Materials........................................................................................87 Statistical Analysis..............................................................................................89 Experiment 1: Results and Discussion.......................................................................89 Conclusions.................................................................................................................96 7 CONCLUSION...........................................................................................................99 APPENDIX A PICTURES OF GELS AND BLOTS.......................................................................104 B GELATIN ZYMOGRAPHY AND REVERSE ZYMOGRAPHY..........................111 C DOT (NORTHERN) BLOT PROCEDURE............................................................119 D ESTROGEN RADIOIMMUNOASSAY..................................................................124 E PROGESTERONE RADIOIMMUNOASSAY.......................................................126 F ANOVA TABLES....................................................................................................128 LIST OF REFERENCES.................................................................................................245 BIOGRAPHICAL SKETCH...........................................................................................258

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ix LIST OF FIGURES Figure page 3.1 Mean follicular P4 concentrations by treatment Experiment 1................................47 3.2 Mean follicular MMP-2 activity by treatment, Experiment 1..................................47 3.3 Mean TIMP-1 follicular activity by treatment, Experiment 1.................................48 3.4 Mean follicular P4 concentrations for groups 1, 2, &3 (single aspiration groups) Experiment 2............................................................................................................50 3.5 Mean follicular P4 concentrations Group 4 (serial aspiration group) Experiment 2............................................................................................................50 3.6 Mean follicular MMP-2 activity Gro ups 1,2 &3 (single aspiration groups) Experiment 2............................................................................................................51 3.7 Mean follicular MMP-2 activity in Group 2(serial as piration group) Experiment 2............................................................................................................51 3.8 Mean TIMP-1 activity in Groups 1, 2 &3 (single aspiration groups) Experiment 2............................................................................................................52 3.9 Mean TIMP-1 activity in Group 4 (ser ial aspiration group) Experiment 2..............53 3.10 Mean follicular P4 concentrations by group and time Experiment 3.......................54 3.11 Mean follicular MMP-2 levels by group and time Experiment 3............................55 3.12 Mean follicular TIMP-1 levels by group and time Experiment 3............................56 3.13 MMP-2 mRNA expression levels by ti ssue and treatment, Experiment 4...............57 3.14 TIMP-1 mRNA expression levels by ti ssue and treatment, Experiment 4..............58 4.1 Mean Follicular P4 concentrations by group Experiment 1.....................................67 4.2 Mean MMP-2 activity by group, Experiment 1.......................................................67 4.3 Mean follicular TIMP-1 levels by group, Experiment 1..........................................68

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x 4.4 Mean follicular P4 concentrations by group, Experiment 2.....................................70 4.5 Mean follicular MMP-2 levels by group, Experiment 2..........................................70 4.6 Mean follicular TIMP-1 levels by group, Experiment 2..........................................71 5.1 Mean follicular E2 concentrations by group, Experiment 1....................................78 5.2 Mean follicular P4 concentrations by treatment and time, Experiment 1................80 5.3 Mean MMP-2 activity by group and time, Experiment 1........................................81 5.4 Mean TIMP-1 activity by group and time, Experiment 1........................................82 6.1 Mean follicular P4 concentrations by treatment, Experiment 1...............................90 6.2 Mean MMP-2 follicular activity by treatment, Experiment 1..................................90 6.3 Mean follicular TIMP-1 activ ity by treatment, Experiment 1..................................91 6.4 Mean MMP-2 mRNA expression by tissue and Treatment (In Vivo), Experiment 1............................................................................................................92 6.5 Mean Follicular TIMP-1 mRNA expression by tissu e type and treatment (In Vivo), Experiment 1...........................................................................................93 6.6 Mean P4 concentration in cultu re by treatment, Experiment 1................................94 6.7 Mean MMP-2 production In Vitro by Treatment, Experiment 1.............................95 6.8 Mean TIMP-1 production In Vitro by treatment, Experiment 1..............................96 A.1 Hypothetical model of the remodeling syst em in the preovulatory follicle of the mare based upon this series of experiments.....................................................104 A.2 Sample Gelatin Zymography gel............................................................................105 A.3 Messenger RNA Dot Blot probed with TIMP-1 in Control and hCG treated mares......................................................................................................................106 A.4 Messgenger RNA Dot Blot probed with MMP-2 in control and hCG treated mares......................................................................................................................107 A.5 Messenger RNA Dot Blot probed with TI MP-1 in control P4 and P+E treated mares......................................................................................................................108

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xi A.6 Messenger RNA Dot Blot probed with MMP -2 in control, P4 and P+E treated mares......................................................................................................................109 A.7 Messenger RNA Dot Blot probed with 18s Bovine mRNA for standardization...110

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xii Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science TISSUE REMODELING AND STEROIDOGENESIS IN THE PREOVULATORY FOLLICLE OF CYCLING PONY MARES By Andria L Desvousges December 2004 Chair: Dan C. Sharp Major Department: Animal Science Ovulation is a poorly understood series of events that likely involves tissue remodeling and steroidogenesis within the pr eovulatory follicle. We sought a better understanding of the mechanisms by which the ovulatory process occurs in the horse, by examining the effects of stimulation and inhibition of this remodeling system in the preovulatory follicle. In response to administ ration of gonadotropin (hCG), there was an increase in follicular progesterone (P4) concentrations, matrix metalloproteinase-2 (MMP-2), and tissue inhibitor of metallo proteinases-1 (TIMP-1) at 24 hours post treatment (p<0.0001* for all factors) compared with controls. In untreated animals sampled cross-sectionally at one of 3 time poi nts (0, 48, or 72 afte r detection of a 30 mm follicle), or serially every day (0,24,48, and 72 after detection of a 30 mm follicle), there was no adverse effect on multiple sampling vs. single sampling on the microenvironment of the follicle. Time trends of the response to gonadotropin administration were

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xiii examined. Results indicated different time tr ends for follicular P4, MMP-2 and TIMP-1 in treated animals compared with controls (p<0.0001*). In another experiment, we examined mRNA expression of MMP-2 and TIMP-1 in response to gonadotropin administration. Re sults showed different responses by tissue type and time in hCG treate d versus control animals. The next series of experiments examined the effects of blockade of MMP-2 and P4 on the remodeling system and steroidogenesi s. Results showed that there is a causal relationship between follicular P4, MMP2 and TIMP-1 demonstrated by blocking follicular P4 with Ru486, or blocking MMP-2 with an inhibitor. The next experiment examined the effects of intrafollicular administration of hormones (GnRH 10g/100l, P4 1g/100l, or Melatonin 10g/100l) on tissue remodeling and steroidogenesis. Results sugge st that different hor mones had different effects on the remodeling system and steroidogenesis. The last experiment examined the eff ects of blockade of LH and FSH on the remodeling system and steroidogenesis. Result s suggest that blockade of LH and FSH significantly reduced the amount of tissu e remodeling and steroidogenesis in the preovulatory follicle of the cycling pony mare s, both in vivo and in vitro. Based on this series of experiments, it is our belief th at the tissue remodeling system within the preovulatory follicle of the cycling pony ma re is dependent on a positive gonadotropinMMP-2 interaction, for ovulation to occur.

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1 CHAPTER 1 INTRODUCTION There is something fascinating about scien ce that can be traced back to ancient times. The overall study of science goes back far into our ancient history as humans. The Greeks began with accepting philosophy as a tr ue science, and it continued with the discovery of medicine and natural causes fo r ailments that ancient people were suffering from rather that the will of the gods. The a pplication of logic and reason to the overall study of science has become one of its strongest foundations. Truly the discipline of science as we know it today was born out of the renaissance (for example, Leonardo Da Vinci and his studies of anatomy, physiology, chemistry, engineering, and metallurgy). Overall resear ch has remained at the forefront of study through the ages. Originally observation was a scientist’s main tool of the trade. True observational scientists or natu ralists began their scientific studies through observation of the natural world, by observing and collecti ng living plants and animals and studying behavior. Yet observation had limits, and new and exciting tools were developed like the microscope to delve deeper into the unseen world of cells and organisms. In today’s society we often take for grante d the basic area of observational science. Through basic observational techniques, scientis ts were able to extrapolate the reason behind specific actions and behaviors, and appl y this information to the physiology of an animal. Today we use a plethora of techni ques to understand the microenvironments of the body, signal transduction systems, and cellula r processes. We ofte n take for granted the tools such as polymerase chain reac tion (PCR), immunohistochemistry, Northern

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2 blotting, Southern blotting, West ern Blotting, and genetic engin eering (which are part of our bag of tricks so to speak). Yet all the well developed techniques at our disposal are useless without a basic unders tanding of how the system we study works. The pursuit of basic science works hand in hand with th e development and use of these everyday techniques. Horse reproduction has become a large bus iness. It has helped the scientific community to gain a better understanding of the reproductive phys iology of the horse, and to develop new techniques that can be used to improve reproductive efficiency. Today the farms are split between mares and st allions and it is current practice to collect and ship semen from one stallion to be sent to a mare thousands of miles away. To make this process more efficient and economical fo r farm owners and veterinarians alike, we need to understand the patterns and purpose of the physiological changes that occur throughout the estrus cycle. The keys to unlocking the doors to the reproductive mysteries of the horse are like those for any other anim al; an understanding of the hormone levels, sexual behavior, and the an atomical changes of the ovaries and the uterus is needed to understand how these para meters work together in the whole animal. In addition and understanding of the basic phys iology can help us decipher the norm from the abnormal. This will allow us to progress further into the areas of preventive medicine, and timed Artificial insemination; and allows us to help solve infertility issues. As we look back on the foundations laid fo r us by the hard work and inquisitive nature of the scientists before us, it is amazing to discover how far we have come and how far we have yet to go. The study of science is an endless journey with many roads to travel down, and many unanswered questions to ask. To date, each new discovery opens

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3 a thousand new questions to be answered, and there is no end in sight to this journey. I ask myself, if there will ever be a day when we have nothing new to research. A limitless wealth of knowledge is out there to be f ound. One must stop and discover the answers, and pass them on to someone else.

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4 CHAPTER 2 LITERATURE REVIEW Reproduction in the Mare Seasonality Horses are often considered to be in the same category as other farm species, yet they are different in many as pects of their reproductive cycl e. A mare’s estrous cycle is typically 21 days in length and they are in st anding estrus for anywhere from 2-7 days. The typical estrus cycle is somewhat longe r than other farm animal species, likely reflecting the prolonged period of estrus. Thus, the “typical” length of the estrous cycle is 21 to 23 days, consisting of approximately 7 da ys of estrus and 14-16 days of diestrus. The length of estrus is usually more vari able (composite mean 6.5 days 2.6 [SD]; composite of 26 references) than the length of diestrus (composite mean 14.9 days 2.8 [SD]; composite of 10 references) (Ginth er, 1992). Mares usua lly ovulate 24-48 hours before going out of heat, making the predic tion of ovulation time based on the beginning of estrus uncertain. However, two separate studies, reported by (G inther 1992) showed a tendency for repeatability of length of estrus in individual mares. This suggests that the root cause of estrus variation may be inhere nt to individual mare s. Nonetheless, the variability of this time period makes predic ting the timing of ovulat ion difficult for farm managers and veterinarians alike. The mare is considered a seasonal long-day or (summer) breeder. The annual reproductive cycle of the mare can be divided into four segments: anestrus, vernal transition, the breeding season, and the autumnal transition.

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5 Photoperiod Regulation of gonadotropin secretion througho ut the year has been shown to be independent of ovarian influence (Freeman et al. 1979, Affleck et al. 1991, Sharp et al. 1993 Porter et al. 1997.). Ovariectomized mares exhibit a spont aneous increase in Luteinizing Hormone (LH) and Follicle St imulating Hormone (FSH) in the spring, continuously elevated concentrations during the summer, and a spontaneous decline in the fall (Freedman et al 1979). This is in di stinct contrast to sheep, in which negative feedback by ovarian estrogen is required fo r reduced secretion of gonadotropins during anestrus. Furthermore, Freedman et al. (1979) demonstrated that exposure to artificially lengthened day resulted in earlier increase in LH and FSH in ovariectomized mares, indicating that photoperi od is the determining factor of the annual gonadotropin secretory pattern. It has been shown by many researchers that artificial increase in day length during the anestrus period hastens the onset of th e breeding season in ma res (Burkhart J. 1947, Cleaver et al. 1995, Sharp et al. 1975, Sharp, 1980; Ginther, 1992). Resu lts indicate that increasing photoperiod hastened first ovulat ion of the breeding season by 2-3 months (Sharp et al, 1975). Addition of artificial light at sunset was shown to be more effective than addition at sunrise (Sha rp 1980). However, other researchers showed that exposing anestrous mares to 1h of artificial light dur ing the scotophase (dar k phase of photoperiod) also stimulates earlier sexual recrudescence (Palmer et al. 1981) These latter authors reported that this light schedule, a “night interruption” study, was effective only when the light exposure began 9.5 h af ter onset of darkness. For light stimulus to affect the seasonal reproductive cycle of the mare, the light signal must be converted to an endocrine si gnal. This process invol ves the pineal gland,

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6 and the hormone melatonin. Denervating th e pineal gland, by removing the superior cervical ganglia, ablated the secretory patter n of melatonin, with consequent delay of entry into the breeding season (Sharp et al. 1979). Of interest, the delay was not observed until the second post-surgery breeding season. The surgeries were performed during the winter, and time of onset of the breedi ng season the following springtime was not different among non-operated, sham operated, an d superior cervical ganglionectomized mares. However, the following year, onset of the breeding season was delayed by over two months in the ganglion ectomized mares compared with nonoperated or sham operated controls. The authors s uggested that the dela y in onset of the breeding season in superior cervical ganglionectomized mares likely reflected loss of photoperiodic timing cues, and consequent expression of an e ndogenously controlled rhythm of reproductive function Sharp et al. 1979). Results were si milar following removal of the pineal gland itself (Sharp 1982). Thus, these results suggest that pineal su bstances, such as melatonin may convey photoperiodic cues to the central ne rvous system which then act to regulate reproductive function directly or indirectly through phase cont rol of an endogenous rhythm. As a result of this research, it is now common practice on breeding farms to expose mares to lights for 2.5 h after sunset nightly beginning in November to advance the onset of the breeding season. Winter Anestrus The winter anestrus period is a time of reproductive quiescence which occurs usually from the end of October through January In the winter time mares are considered to be in deep anestrus, with little-tono sexual receptivity and little follicular development. Of course as with all things th ere is great variability to the length of this anestrus period between individual mares. Roughly about 20% of horse mares exhibit

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7 continuous estrous cycles year round, whereas the percentage of pony mares exhibiting complete estrous cyclicity (ovulatory cycles), during the winter months is considerably less, (Ginther, 1992). During anestrus, ovarian activity is decreased. There is little follicular development, with most mares exhibiting follicles that are less than 10 mm in size during this time period. If the ovaries are removed at this time they appear to be mainly composed of ovarian stroma (Ginth er, 1992). Accordingly along with a reduction in follicular activity there is an overall reduction in the production of ovarian hormones, estrogen and progesterone (Oxender, N oden & Hafs 1977). Usually these hormones remain low until the onset of sexual awakeni ng in the spring which is paralleled by an increase in sexual receptivity (Sharp et al 1993). A decreased receptivity response or indifferent response by the mare towards the stallion is seen during this time (Ginther, 1992, Sharp, 1980). During the early vernal transition mares may exhibit estrus be havior without any associated ovulations. These behavioral si gns can thus be decei ving, and should not be used exclusively as breeding indications. Rather, the breeder or equine practitioner should make use of ovarian changes to judge whether or not a mare has entered the breeding season, and is displaying appropriate be havioral estrus. The overall lack of gonadal function during winter time reflects the decrease in hypothalamic-pituitary secretion activity, with greatly reduced gonadotropin secretion. Of special note, the pituitary has been shown to have consider ably reduced LH prot ein during the winter months (Hart et al. 1984) which reflects the loss of messenger ri bonucleic acid (mRNA) encoding the subunits of LH (Sherman et al. 1992). The rise in gonadotropin levels associated with onset of the breeding season is poorly understood. These seasonal

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8 changes in gonadotropin levels occur with or without ovaries and shows that seasonal influences, predominantly photoperiod, affect the CNS to regulat e the secretion of gonadotropins. Pelletier et al (1998a ) demonstrated that the initial rise in LH prior to the first ovulation of the year in intact pony mares occurred at the same time as in ovariectomized pony mares, indicating that en vironmental influences likely played a regulatory role. Alternatively, the timing of the increase in LH might reflect expression of internal rhythm of some kind. Studies have shown that overall circulat ing Luteinizing Hormone (LH) and Follicle Stimulating Hormone (FSH) are markedly lo w during this time period. Strauss et al. (1979) and Silvia et al. (1986) showed that the GnRH content of the hypothalamus in mares was significantly lower during anes trus than during the breeding season, suggesting a potential mechanism for the lack of pituitary gonadot ropin. Hart et al (1984), using pituitaries collected from a local slaughter house throughout the year, reported that the concentration of LH protei n in the pituitaries was significantly lower during the months of December and March compared with the months of July, and October, reflecting the reduced LH secretion during anestrus and vernal transition. FSH, on the other hand, was not significantly diffe rent throughout the y ear. Sherman et al. (1992) demonstrated that the gene that re gulates the synthesis of LH subunits was undetectable during the months of December and March, indicating that the relative loss of expression of the LH subunits serves as an explanation for the loss of circulating LH. The failure of FSH secretion du ring winter anestrus, on the ot her hand does not appear to reflect loss of gene expression, as Hart et al. (1984) dem onstrated abundant presence of the protein in pituitaries. Therefore, it is likely that a two-phase re gulatory system is in

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9 effect during anestrus: 1) FSH secretion is reduced because of the paucity of GnRH secretion from the hypothalamus ( Strauss et al. 1979; Silvia et al. 1986; Sharp and Grubaugh, 1987), and 2) LH is reduced because of the reduction in GnRH, but is also not available for release, even if GnRH were introduced. Due to the reduction in both LH and FSH, ovarian follicular development is reduced, and consequently, steroid secretion is reduced, Studies of Douglas et al. (1974) and Lapin and Ginther (1977) indicated that, although ovaries are inactive duri ng the winter anestrus, they are capable of responding to gonadotropins exogenously administered. Thes e authors administered purified equine pituitary extract to anestrus mares, and demonstrated follicular development and ovulation. However, if they failed to c ontinue providing pituitary extract, such exogenously-treated mares revert ed to the anestrus state. Vernal Transition As day length increases in the springtime, mares enter into a transitional period referred to as “vernal transition”. Vernal tr ansition is the time period between winter anestrus and the breeding season, during whic h sexual function is renewed. Shortly after the shortest day of the year there is an increase in hypothalamic GnRH content and secretion (Sharp and Grubaugh, 1987, Silvia et al. 1992. In response to the increased GnRH secretion, peripheral FSH increases, gene rally in the month of January in mares under ambient photoperiod, and remains elevat ed, but highly variable, throughout the vernal transition period. Despite the elevat ed FSH concentrations, LH remains low until just before the first ovulation of the year so metime in April or May, in horse mares or pony mares, respectively (Ginther, 1992, Garc ia et al.1979, Sharp et al. 1975, Sylvia, 1986). This unusual hormonal environment l eads to development of a succession of follicles that are: 1) slow growing, although they achieve pre-ovulatory follicle size, 2)

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10 poorly steroidogenic, lacking key steroidogeni c enzymes, 3) poorly vascularized, 4) poorly invested with granulosa cells, and most significantly, 5) anovul atory (Sharp et al. 1975, Davis and Sharp, 1991, Tucker, 1993). In support of this finding, it has been shown that the FSH receptor levels remain re latively constant independent of season and stage of the mare’s estrous cycle (Fay a nd Douglas, 1987). Furthermore, as previously stated, administration of pituit ary extract to mares in anestrus or early vernal transition also leads to follicular development (Douglas 1974). During the vernal transition pony mares develop an average of 3.7 0.9 anovulat ory or transitional follicles (>30mm in size) before the first ovulati on of the year (Davis and Shar p, 1991; Watson et al. 2003). Upon palpation or ultrasonic examination th ese transitional follicl es are essentially indistinguishable from pre-ovulatory fo llicles present during the breeding season. Although mares during this time develop larg e follicles and may demonstrate estrus behavior, peripheral E2 levels remain low, which suggests that thes e transitional follicles are steroidogenically incomp etent (Seamans, 1982, Davis 1987) In one study, the early transitional follicles lacked androgens and estrogens whereas follicles late in the vernal transition (presumably the last anovulatory fo llicles before the appearance of the first ovulatory follicle), displayed incr eased androgens and estrogens, in follicular fluid, and in in vitro culture, (Davis & Sharp, 1991). Seam ans et al. (1982) repor ted that transitional follicles cultured in vitro, were capable of converting both progesterone and androgen to estrogen, indicating the presence of adequate amounts of aromatase enzyme. These data suggest that the lack of estroge ns in the transitional follicl es may be due to a lack of substrate earlier in the steroi d biosynthetic pathway. In that regard, Tucker et al. (1992) reported that follicles during early vernal transition did not exhibit 17-hydroxylase

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11 enzymes, suggesting that failure to produce es trogens reflected this lack of steroidogenic catalysis. Watson et al. (2003) re ported, in further support of th at idea, that the side chain cleavage enzyme is also undetectable in early vernal transition follicles. Therefore, the main characterization of the vernal transiti on follicle is its poor steroidogenic capability, likely due to the unusual hi gh FSH to low LH ratio. Just before the first ovulation of the year, there is a large surge of estrogen in the peripheral plasma that reflects a large increase in intrafollicular estrogen, as well as an increase in the in vitro estrogen synthetic cap acity of the follicles. This estrogen surge, which precedes the first ovulati on of the year by approximately 5 to 6 days, is in close temporal association with the first signifi cant increase in periphe rally circulating LH. This secretion of LH indi cates the reappear ance of mRNA encoding the LH subunit synthesis, but the signal for this gene e xpression is unknown. Estrogen secretion may be an important signal in the regul ation of LH synthesis and secr etion, the E2 surge precedes increased levels of LH by a few days (Davis, 1987). The increase in E2 concentration could th en act as a positive feedback mechanism to stimulate the release of LH, and is certain ly appropriately timed temporally for such a role. Furthermore, Sharp et al dem onstrated that estrogen administration to ovariectomized pony mares during the equivalent time of early vernal transition resulted in significantly increase d mRNA encoding both the and the LH subunits (Sharp et al. 2001). Therefore, it is clear th at estrogen can stimulate rene wal of LH biosynthesis in vernal transition, but the questi on remains as to whether that is the natural signal or not. Pelletier et al (1998a 1998b) reported that the time of incr eased LH in ovary-intact and ovariectomized pony mares was identical. Furt hermore, the pre-ovulatory increase in

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12 estrogen in the ovary-intact group was, of course not observed in the ovariectomized mare group. Thus, in that study, the essential role of estrogen in LH biosynthesis renewal is questionable. Thus, the question still rema ins as to what signal stimulates LH biosynthesis renewal in the springtime. The fact that timing is similar in ovary-intact and ovariectomized mares indicates that ovarian feedback is not like ly the major signal, leading to speculation about the potentia l role of environmental factors. These large transitional follicles pres ent a problem to farm owners and veterinarians. For the Thoroughbred industry, foals that are born earlier in the year (close to January 1st) bring the most money at year ling sales. Many of these owners attempt to breed mares during this transitional period when the follicles are incompetent. This overall practice has lead to the usually in appropriate use of ov ulation stimulating compounds to get mares bred earlier in the year. Yet breeding mare s during this time period not only exposes them to the possibili ty of uterine infecti on, but ends up costing the breeder more money and lost time. The Breeding Season At the end of the vernal transition th e first ovulation of the year marks the beginning of the breeding season. The br eeding season extends from April through October in the northern hemisphere (Ginth er, 1974, 1992). The mare’s typical estrous cycle is approximately 21 days in length (Daels and Hughes 1993). Th e cycle is divided into an estrus and diestrus phase. The follicular phase, or estrus, is a time when the mare is sexually receptive and develops large steroidogenically competent preovulatory follicles. The estrus period lasts from 2-7 days, whereas diestrus lasts from 14-15 days (Sharp, 1980). There is a lot of variation to the length of the estrus period as compared to

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13 diestrus for individual mares (Ginther, 1974). Generally most mares (approximately 80%) ovulate within 24-48 hours pr ior to the end of estr us (Hughes et al. 1975, 1980). As ovulation occurs the follicle releases the oocyte into the oviduct. The follicle cavity fills with blood and the granulosa cells begin to lute inize. A corpus hemmoragicum (blood filled follicle) of ten forms, and is evident by ultrasound examination for 2-4 days before becoming a mature corpus luteum. Loss of sexual receptivity is temporally associated with pr ogesterone concentrations above 1 ng/ml. The luteal phase which lasts 14-15 days, ends with regression of the corpus luteum and a return to estrus 2-4 days later Autumnal Transition It is a very poorly defined phase th at is characterized by gradual loss of reproductive function the end of the breeding s eason and the beginning of winter anestrus (Sharp and Davis, 1993). This phase of the cycl e begins with a rise in FSH, but is not accompanied by an LH surge or ovulation (Ginther 1979, 1992, Snyder et al. 1978). There is a tremendous amount of variability among mares entering into the autumnal transition. It is believed that the transition involves a decline in GnRH secretion, and or LH synthesis and secretion in response to se asonal or visual light changes (Sharp and Davis, 1993). It may also be due to the failu re of follicular growth and E2 production to lead to luteolysis and the corpus lutem’s overall life span may be extended (King and Evans, 1988). Endocrinology of the Estrous Cycle The mares’ entrance to the breeding season is induced by an increase in day length, and an increase in GnRH secretion. This Gn RH release triggers a gonadotropin release, which has downstream effect on the ovarian pr oduction of E2 and P4. FSH occurs in two

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14 surges one during early diestrus (postovulator y rise), and again around mid-diestrus (days 10-13) (Evans and Irvine 1975, Ginther 1992, Ir vine 1981). FSH begins to decline about 5-7 days prior to ovulation, and there is a concomitant rise in LH secretion which promotes ovulation (Pierson 1993). There is 12-1 8 hour LH surge prior to ovulation as in other domestic farm species such as the cow and sheep. In contrast there is a slow rise in LH over the course of 5-7 days, which r eaches peak concentrations 1-2 days postovulation and declines by approximately 5 days after ovulation (Pattison et al. 1974 Greaves et al. 2000). Growth of the dominant follicle stimulates the stimulates, the secretion of E2 which then peaks 1-2 days prior to ovulatio n (Hughes et al. 1972, Hi llman and Loy 1975, Ginther 1992, Palmer and Jousset 1975, Palmer and Terqui 1977, Plotka et al. 1975). Estrogen generally declines before ovulati on, although one report suggested that estrogen remained elevated for 1-2 days post ovulation (Pelehach 2000). In estrus P4 levels are low, generally less than 1ng/ml, and rise within 24 to 48 hours after ovulation, with maximal P4 production by 6 days post ovulation. (Stabenfeldt et al. 1972, Plotka et al. 1972). It has been shown that P4 levels increase 140-160pg/ml 10 hour post ovulatory and again increase up to 346pg/ml by 26 hour s post ovulation (Plotka et al. 1975). Progesterone remains elevated until regression of the CL at day 14-15 of the cycle (Stabenfeldt et al. 1981). Anot her interesting point is that in the mare P4 does not block follicular development (Daels and Hughes 1993). Ovarian Anatomy A distinctive and somewhat unusual feature in mares is their ovarian anatomy. The mare has an inside-out ovary unlike other domestic farm species, companion animals or laboratory animals. The ovary itself is kidney shaped with a prominent depression

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15 (ovulation fossa) on the ventra l border (Ginther 1992). The co rtex and medullary tissue are reversed in the mare, with the cortex centr al and the medulla peripheral. The cortical tissue is almost completely surrounded by medullary tissue and only a small portion of the ovary, the ovulation fossa, is invested with germinal ep ithelium (Stabenfeldt et al. 1975, Ginther 1992). This area of germinal epith elium covers the surface of the ovulation fossa and it is the only locati on on the ovary where ovulation can occur. This anatomical limitation may preclude more than one or two follicles from ovulating on each ovary. Hence attempts to superovulate mares w ith exogenous gonadotropins is relatively unsuccessful, with an average of only 4 to 5 embryos resulting from such attempts (Squires et al. 2003) Another an atomical difference is that the CL when fully formed does not project from the greater surface of the ovary as in other farm species (Ginther 1992). Folliculogenesis Follicular activity becomes suppressed dur ing short days, but as the day length increases in the spring there is an overall increase in the number and size of follicles developing within the ovary (G inther 1990). Mares have fol licular waves that are less defined than cattle and usually develop one dominant follicle, although some breed-types, especially Thoroughbred, exhibit a propensity for development of more than one dominant follicle (Stabenfeldt et al.. 1975, Ginther 1990, 1992, 2001). The follicle wave that is initiated during the lute al phase consists of 3 to 6 follicles in a so-called “cohort”, which continues to grow throughout the luteal phase, until selection and deviation occur. In the “selection” phase, a pool of small follicles is recruited to grow out of the primordial pool. These follicles continue to develop until one of the cohort continues to develop at the same or accelerated rate. At this time, follicular growth slows in the

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16 remainder of the cohort, with subsequent de cline in follicle diameter, and loss through atresia. This point is call ed the point of deviation. Deviation in mares occurs when the tw o largest follicles are on average 19 and 22.5mm in diameter, or approximately three days after the FSH peak (Ginther et al. 2003). The deviation mechanism is thought to re flect declining FSH concentrations in response to inhibin produced by the entire follicle cohort (Ginther, 2003). E2, inhibin, activin, and free IGF-1 increase in the dominant follicle approximately one day prior to deviation (Ginther et al. 2003). Overall these changes may be due to a greater responsiveness to LH and FSH or the earlier development of LH or FSH receptors, in the future dominant follicle. As this dominant follic le grows the rest of the recruited follicles undergo atresia. On average, more than 95% of all follicles be come atretic (Pierson 1993). Folliculogenesis is also associated with a rise in E2 which leads to ovulation. There are differences between the follicular and lute al phases, with respect to E2 and P4. The dominant follicle is the mo st steroidogenically compet ent and gonadotropin responsive follicle (Pierson, 1993Tucker, 1992). The domin ant follicle produces E2 approximately 1-3 days before ovulation (Hughes et al., 1972). This responsiveness of the pre-ovulatory follicle to LH has been suggested to reflect an increase in the number of LH receptors in the theca. This overall response may in f act be augmented by E2 (Pierson, 1993). The periovulatory follicle is very highly vascular ized compared with subordinate follicles. The follicular fluid content of E2 is also increased within the periovulatory follicle compared with other follicles (Tucker, 1993). Studies have suggested that the increased

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17 vasculature allows the dominant follicle to accumulate more gonadotropins (Pierson, 1993) On average the equine preovulatory fo llicle can grow up to 45-50mm prior to ovulation. The preovulatory follicle in cows grows to almost 36,000 times larger in square surface area from a primordial to a pr eovulatory follicle (Smith et al., 2000). You can imagine the overall increase in surface area in an equine follicle as compared to a bovine follicle. The dominant preovulatory fo llicle is usually the largest on the ovary from about 6-7 days prior to ovulation, and it can grow on average 2-4mm per day or more (Tucker, 1992) in size, as ovulati on approaches (Pierson and Ginther, 1985, Ginther, 1992). This provides an interesti ng dilemma for the mare, in that the only portion of the ovary where ovula tion can occur is the ventral pole at the ovulation fossa. Wherever a dominant follicle gr ows and develops, within the internal cortex of the ovary it must expand and/or migrate to contact the fossa in order to ovul ate (Ginther, 1992). As this follicle continues to grow, it often becomes conical or triangular in shape, as ovulation becomes imminent. Ovulation As ovulation approaches in most species the dominant follicle expands internally to the point of protruding from a consid erable portion of the ovarian surface, and ultimately contacting the ovulation fossa. At this time both LH and E2 are elevated. Increased LH at the time of ovulation is a ssociated with releas e of the oocyte and follicular fluid, followed by luteinization of the remaining follicle. After the follicle is evacuated, it fills with blood from the su rrounding vasculature and forms a CH. During palpation one can differentiate between a pre-ovulatory follicle and a CH only with great difficulty. (Daels and Hughes 1993, Ginther 1992) It has been shown that changes in

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18 follicle turgidity occur in 90% of preovulatory follicles (Parker 1971, Ginther 1992). Luteinization of the granulosa cells and theca intern a cells into large luteal cells is a process of differentiation, marked by both bi ochemical and morphological changes. This formation of the CL and it subsequent pr oduction of P4 begins the diestrus period (Senger 1997) Monitoring the Estrous Cycle Ultrasonography. During the early 1980s an important technology became available to researchers and veterinarians, the use of ultrasonic monitoring of the reproductive tract. The advent of ultr asound has provided both researchers and veterinarians with a diagnostic tool to monitor the changes in the reproductive organs of the mare throughout the estrous cycle. U ltrasound allows for evaluation of the reproductive organs in real time. Ultrasound is used for many things, such as monitoring follicular changes, examining the process of ovulation, CL morphology, and many others (Squires et al. 1988, Ginther 1992). Ultrasound is an efficient way to examine th e reproductive tract of the mare using a linear array transducer, and a forward facing tr ansducer for transvaginal techniques. The typical linear array transducer has a flat probe with the crystals arranged in a linear fashion along the length of th e transducer. The crystals themselves are energized via an electric current that causes them to vibrat e. Placement of the probe face against the mare’s rectal wall allows the transduction of sound waves through the tissues. The sound waves are then reflected back to the probe with a greater or lesser energy depending on the type of tissue. These sound waves are then converted to a grey and displayed in 2 dimensions on the screen (Ginther 1988, 1992).

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19 Sound waves pass easily through fluid, and ar e not reflected back toward the sound generating source. With B-mode ultrasound e quipment, the reflected sound is expressed electronically on a gray scale, with highest intensity echoes displayed as white, and lowest intensity sound displayed as black. T hus, dense tissue reflects sound with a high energy which is translated as white pixels on a video monitor. Such tissues or structures are often referred to as echogenic. Other tissues appear in varying shades of grey which depends on the amount of sound wave refl ected (Ginther 1988). The use of high frequency scanners (5-7.5Mz) permits good reso lution, i.e. discrimination of two closely located structures, yet such instruments do not penetrate very deeply in tissues. Therefore, ultrasound scanners with a lower frequency of ge nerated sound may be more utilitarian for examination of tissues that ar e farther away from the probe, as near term pregnancies. One must also consider the or ientation of the transducer when proceeding with an ultrasonic examination. If one place s the probe toward the medial portion of the ovary that is the image that appears on the top portion of the sc reen (Ginther, 1988). The use of ultrasound has become an impor tant research tool to examine the ovaries collect oocytes for IVF (mostly bovi ne), and has potential for follicular fluid collection and injection of various compounds into the follicular microenvironment. Before the development of ultrasound, rectal palpation was the only means to determine the reproductive status of the mare. One of th e most important developments to research is that it has aided is examination of the ovaries and pregnancy diagnosis. Now researchers are able to accura tely measure and map both fol licular growth and regression. Additionally it has also allowed for more precise ovulation detec tion. As follicles are fluid-filled, they appear black on the monitor due to the fact that low intensity echoes are

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20 interpreted electronically as black on a gray scale. The CL appears mottled in color, because it is a mixture of dense and less dens e tissues. The great profluence of cellular margins presents highly echogenic surfaces wh ich reflects sound with higher intensity, hence the mixture of white and black pixe ls. Ultrasound also allows researchers to accurately measure follicle size and examin e small developing follicles of 5mm or smaller. In a study by Squires et al. (1988) ovulation was determined by palpation and also by ultrasound, the ultrasound operator was mo re accurate in 88% of the cases. It has also helped clear up the ident ity of a CH, and CL. Blood is visualized as semi-echogenic with fibrin lines, and this can now be distinguished from echogenic luteal cells (Squires et al. 1988). The Extracellular Matrix Normal ovarian function it is dependent upon the dynamic and cyclic remodeling of the extracellular matrix (ECM). The extracellular matrix is a network of molecules that are bordered by cells, which benefit by ch anges in the compositi on and structure of the ECM. In dynamic tissues such as the ova ry, the thecal and gra nulosa cells are in contact with ligands that bind these receptors (integrins) as the ECM is remodeled. The process of differentiation both morphologi cally and biochemically, of granulosa and thecal cells into larg e luteal cells which are steroi dogenically competent may require contact with ECM components (Smith et al. 2002). There are also many types of interactions between the ECM and growth factors and cyt okines, which may protect them from proteolysis or alter their activ ity, yet are beyond the sc ope of this thesis (Flaumenhaft and Rifkin 1992). The turnover of the ECM around these cells releases both specific cytokines and growth factors (Logan and Hill 1992).

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21 The ECM supports adhesion of cells and f unctions to transmit signals through cellsurface adhesion receptors. The ECM c ontains collagens, glycoproteins and proteoglycans. The basement membrane is a specialized ECM that separates epitheial cells and the surrounding stroma, providing a barrier (Werb 1997). Collagen is composed of 3 alpha chains that form a triple heli x. In the ECM there are the typical fibrillar collagens (I, II, III, V, and XI) which form fibrils that influence cellular function by their interactions with specific integrins. Th e basement membrane (BM) is composed primarily of collagen type IV and form s a network throughout the ECM. Cells can interact with these type IV fibrils through intehrins, laminins and proteoglycans (Egeblab et al. 2002).The overall product of the diges tion of collagen by collagenase enzymes is gelatin. The glycoprotein component of the ECM is mainly made up of laminins, which are heterotrimeric glycoproteins made up of 3 ch ains. Laminins are primarily located in the BM where they also form a network with coll agen type IV fibers. Laminins themselves can affect cellular functions by binding direc tly to integrins and non integrin receptors (Sakasi et al. 1999). The fibronectins are also glycoprotein’s that are present in the ECM and in the blood. They too form fibrils that have effects on cellular morphology, migrations, adhesion, and differentiation via thei r interactions with integrins (Hynes et al. 1999, Gustafsson et al. 2000). Overall the prot eoglycans also compose parts of the ECM and help “decorate” collagen fibers yet much of their roles remain to be discovered. Basal Components of the ECM are often altered via cleavage by various matrix metalloproteinases (MMP’s) (Nagase et al. 1999). When the ECM is remodeled by MMP’s this can have effects on cellular pro liferation, differentiati on and cell survival

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22 (Whitelock et al., Manes et al. 1999, Boudr eau et al. 1995, Sympson et al. 1994). The control of MMP’s by tissue inhi bitors of matrix metalloprotei nases (TIMP’s) can help to maintain the overall matrix stability Ovarian Extracellular Matrix and Remodeling The ovarian ECM itself provides a unique microenvironment. The ECM of the pr eovulatory follicle consists of the theca externa, and tunica albuginia tissue layers which are rich in type I, III, and IV collagen. (Luck 1994). These collagen fibers provide the tensile strength and support to the follicle wall. Ovarian follicles are composed of both epithelial and stromal tissue where cell migration, division, differentiation and death occur. In cattle it has been shown that as the antrum develops and at ovulation the ep ithelial cells of th e follicle undergo a morphological and biochemical transition to lu teal cells (Rodgers et al. 2003). As the follicle itself develops there are marked changes in the follicular basal lamina composition from collagen type IV6 alpha ch ains to mainly alpha one and two chains (Rodgers et al. 2003). In another study by Rodgers et al. (2002) in ca ttle they found that there were changes in the gra nulosa membrane layer of the BM which was similar to the formation of Call-Exner Bodies. Call-Exner bodies are small fluid fills spaces that develop between granulosa cells in ovarian follic les, and they form rosette like structures, during the beginning stages of tumor formati on. The overall expression of this newly reformed matrix was discovered to coincide with the exposure of granulosa cells to steroidogenic enzymes (Rodgers et al. 2002). In the same study it was suggested that as the granulosa cells mature and undergo di fferentiation this is accompanied by novel changes in the composition of the ovarian ma trix. In a comparative study by Bortolussim et al. (1989) in pigs, rats, and cattle, they suggest that in al l the follicles both laminin and

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23 collagen type IV are localized to the basal la mina that separates th e granulosa and thecal cell layers. They also suggest that this basal lamina or ba sement membrane of growing follicles is undergoing regular and co ntinuous modification in its makeup. The localized degradation of these collage fibers at the apex of the follicle wall is required for ovulation in sheep, cattle, mi ce, rats, and primates. Breakdown of the basement membrane is required for the release of the oocyte from the follicle cavity (Espey and Linper 1994). It has been s hown that MMP inhibitors prevented the ovulatory process and that these enzymes play a critical role in follicular rupture associated with ovulation, yet which specific one is required is s till under investigation (Brannnstrom et al. 1988, Butler et al. 1991). The ratio of degradation and inhibition by MMP’s and TIMP’s can influence many cellu lar processes associated with ovarian function, such as, differentiation, cellular migration, prolifera tion and apoptosis (Boudreau et al. 1996, Brooks et al. 1996). In a study by Aten et al. (1995) it was shown that rat granulosa cells cultured on a lamini n matrix, promoted differentiation of these cells into luteal cells, and this differentiation was blocked with an antibody to the integrin beta 1 subunit. As follicles proceed through growth they dramatically increase in surface area within the ovarian stroma (Smith et al 2002). The process of ovulation itself is characterized by local ECM degradation of the follicle wall of the preovulatory follicle. This usually occurs at the apex of the stigma of the antral cavity in sheep, cattle, mice and primates (Lui et al. 1999). In the horse howev er this only occurs at the ovulatory fossa, where the follicle contacts the germinal ep ithelium. After rupture of the ovulatory follicle, the remaining thecal and granulos a steroidogenic cells become terminally

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24 differentiated and reorganized. The overall mechanism of follicular rupture and CL reformation are equivalent to the processes of wound healing and tumor formation (Smith et al. 1994b). After ovulation the remaini ng cells within the follicular cavity are remodeled into a CL, this process is equivale nt to a 20 fold increase in tissue mass (Smith et al. 2002). Dynamic remode ling of the ECM is found thr oughout the follicular cycle, and is characteristic of growth, atresi a, ovulation CL formation and regression. Matrix Metalloproteinase s Structure and function Matrix Metalloproteinases Matrix metalloproteinases are a super fa mily of zinc metallo-endopeptidase which are responsible for the turnover of extrace llular matrix component s. These are included within the "MB Clan" of metallopeptidas es, which contain a HEXXHXXGXXH motif that acts as the zinc binding active site. Th ey are commonly referred to as "Metzincins" due to the fact that they all contain a conser ved methionine reside that forms a turn eight residues downstream from the active site. This group contains a number of families and the MMP's are within the M10 group, which is further subdivided into subfamilies A and B. MMP's belong to the A subfamily and are collectively known as matrixins, they are usually designated with numbers. The MMP fa mily currently consists of at least 26 members all of which share a common catalytic core, with a zinc mol ecule at their active site (Stamenkovic I 2000). Matrix Metalloproteinases Structure All MMP family members are composed of a prodomain, a catalytic domain and a highly conserved active site domain. The ac tive site consists of an HEXGHXXGXXG motif in which the three histidine residues cons titute three zinc ligands and the glutamic acid residue sits within the active site of th e enzyme. There are glutamic and aspartic

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25 rich sequences at both the N and C terminal ends of the domain that are thought to be CA++ binding sites. MMP's contain a very distinctive PRCGVPD motif within the prodomain which is responsible for mainta ining the enzymes latency. All the MMP's except MMP-7 and 26 contain a homeopexin domain which promotes the substrate specificity of the enzymes. MMP-2/9 also contains a fibronectin gelatin binding domain. The MMP family members are categorized ba sed on both their struct ural and functional characteristics. MMP's are either secreted as in 1-13, and 18-20, or they are anchored to the cell membrane via a transmembrane domai n 14-17 where they are referred to as MTMMP's. The MMP's are also functionally divide d into categories based on the substrates that they degrade, such as collagenases, st romelysins, and gelatinases (Chambers, et al. 1997, Van den Steen et al. 2002, Stamenkovic I 2000). All of the MMP's demonstrate a high de gree of sequence and structural homology and are derivatives of the five domain archetypal collagenase enzyme, with either insertions or deletions of specific domains. In all the enzymes, the N-terminal domain consists of a short stretch of hydrophobic amino acid residues that form the signaling sequence for secretion of the protein into the extracellular space. This dom ain is cleaved off for activation to occur. MMP's are maintained in a latent inactive state by a 77-78 residue propeptide which is part of the N-terminal domain. This controls part of the regulation of the enzyme activity. The pro-peptide is centered ar ound the cysteine residue and in teracts with the zinc atom at the active site. This interaction excludes water from the active site and maintains the inactive state of the pro-enzymes.

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26 MMP's are produced as zymogens, they c ontain a signal sequence that must be removed for activation. In the propeptide fo rm of the enzymes there is a conserved cysteine residue that chelates the active zinc s ite of the enzyme. The conserved sequence PRCGVP surrounding this cysteine has been co ined the "cysteine switch". Within the MMP family there is a subset of MMP's including the MT-MMP's, as well as MMP-11, 21, 23, and 28 that contain a basic prohor mone cleavage sequence RRKR, RRRR, or RKRR, which is believed to be cleaved by th e PACE/ furin family of enzymes. MMP's differ in the placement of the active site groove which allows for different substrate and inhibitor specificities. All the MMP's except MMP-7 and 26 c ontain a regulatory hemopexin domain which is separated from the catalytic doma in by a hinge region. The homeopexin domain is believed to confer much of the enzymes substrate specificity. The MMP's also are often found to be associated with heparin sulfat e gycosaminoglycans on the cell surface. The overall structure of the hemopexin domain is a "four bladed propeller" with a calcium binding site within the domai n. The catalytic activation of the enzymes often includes shedding the hemopexin domain and this domai n is suggested to feedback on the enzyme directly to down regulate it' s activity( Itoh et al. 2002, Se iki M., 2002, Troederg, et al. 2002). Membrane Bound Matrix Metalloproteinases (MT-MMP’s) MT-MMP's are localized to the cell surf ace, four of these MT-MMP 14, 15, 16, and 24 contain a hydrophobic transmembrane domai n followed by a cytoplasmic domain. MT-MMP 17 and 25 lacks the cytoplasmic doma ins are thought to be anchored to the cells surface. The cytoplasmic domain is beli eved to be involved in cyto skeletal signaling cascade systems, and may be di rectly phosphorylated by various protein

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27 kinases. All of the MT-MMP's contain the transmembrane sequence downstream of the homeopexin domain. MT-MMP 14 has been sh own to provide the activation mechanism for pro MMP-2, and both MMP-14 and 15 can activate pro MMP-13. MMP-14 degrades native collagen type I, fibronectin, and la minin (Egeblad et al. 2002, Itoh et al. 2002, Seiki 2002, Stamenkovic 2000). Regulation of MMP’s The activity of MMP enzymes is regulated at thre e levels: transcri ption, protelytic activation of the zymogen, and inhibition of th e active enzyme. Data that suggest that a variety of external stimuli such as cytokines, growth factors, and changes in the cell to cell and cell to ECM interactions also help regulate enzyme activity. MMP expression is primarily regulated at the transcriptional leve l. The 5' regions of the regulatory elements of the MMP's contain an AP-1 cis element th at is found to be proximal to the promoter, roughly 70 nucleotides upstream of the initiati on site. The promoter re gion itself contains a PEA3 site which is another cis element th at interacts with the AP-1 motif for optima activation of the enzymes (Chamber s et al. 1997, Seiki 2002, Stamenkovic 2000, Yoshizaki, et al. 2002). Activation of MMP’s MMP's are mostly secreted as latent proenzymes, the zymogen form, and they are activated in the extracellular space. The latency is maintained by the "cysteine switch" which is formed through the action of the sulfhydryl group of the conserved cysteine residue which is within the propeptide and cat alytic zinc binding s ite. This interaction blocks the zinc dependent act ivation of a water molecule which mediates the nucleophilic attack on the peptide bonds of the enzyme.

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28 Activation of MMP-2 The activation of MMP-2 is the most widely elucidated and well understood mechanism of MMP activation. The model for th is activation relies on the formation of a ternary complex on the cell surface made up of MT-MMP-14 (otherwise refered to as MT-MMP-1), pro MMP-2 and the tissue inhib itor of matrix metalloproteinase 2, (TIMP2). TIMP-2 serves as the receptor for th e homeopexin COOH terminal domain of MMP2, and the binding of pro MMP-2 to the MT-MMP 14/ TIMP-2 complex localizes it to the cell surface. Then proteolytic cleavage of the MMP-2 propeptide specifically at the ASN37-LEU38 bond, results in MMP-2 activati on, which is mediated by another TIMP2 free MT-MMP14 molecule (Seiki 2002, Stamenkovic 2000). Assembly of the trimolecular complex of MT-MMP, MMP-2 and TIMP-2 is critical for the docking, activa tion, and cross talk of MMP's and integrins. This is involved in the regulation of focal matrix degradation and cell locomotion. MT-MMP-14 is in immediate proximity to integrin v 3. In this case both the integrin and MT-MMP14 are associated with the cytoskeleton. TI MP-2 links MT-MMP-14 (the receptor) and the secretory MMP-2 proenzyme, together. Th e second molecule of MT-MMP-14 that is free of TIMP-2 activates the integrin v 3 through a limited pr oteolysis of the 3 subunit. This activator initiates the activa tion or pro MMP-2 by cl eaving the N-terminal portion of the 68kDa latent zymogen. The activation intermediate a 64kDA molecule associates with the activated integrin through the C-terminal domain of the enzyme itself. The full maturation of the active enzyme from 64 to 62 kDa autocatalytic activation occurs only if MMP-2 is complexed with the integrin (Ratin kov et al. 2002).

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29 Tissue Inhibitor of Metalloproteinases (TIMP’s) TIMP Structure The overall proteolytic activity of the enzy mes is primarily controlled by the tissue inhibitors of metalloproteinases (TIMP's). TIMP's are small protein molecules ranging from 21-28 kDa, and they specifically block MMP's by binding to the zinc binding site of active MMP's. There are currently 4 identified TIMP's 1-4. All TIMP's possess a formation of 12 conserved cysteine residues th at are required for the formation of six disulphide bonds. The amino terminal domain is required for the inhibition of the active MMP's. TIMP-1 is very effective at inhibi ting specifically collagenase activity. TIMP-1 and 2, are responsible for the inhibition of most of the MMP enzymes, and TIMP-1 can also form a complex with MMP-9. This TI MP-1/MMP-9 complex is thought to be responsible for the recruitmen t of MMP-3 and results in th e inhibition of this enzyme. TIMP-3 inhibits the activity of MMP-1,2,3,7,9, and 13. TIMP-4 also inhibits the activity of MMP-2, 7, 1, 3 and 9 (Brew et al. 2000, Henriet et al. 1999). TIMP Expression and Regulation TIMP's are produced by a variety of di fferent cell types including fibroblasts, osteoblasts, keratinocytes and endothelial cells They are differentially expressed in different tissues and they follow the influx of the MMP's. TIMP-2 is constituently expressed and normally paired with MMP-2. TI MP-3 is localized to the ECM and TIMP4 is mostly found in the vascular tissue. The TI MP's are slow tight bindi ng inhibitors with low nanomolar concentrations. TIMP-1 e xpression is usually regulated at the transcription level by several cytokine s and growth factors, such as TGF, TNF, EGF, IL-1 and IL-6. TIMP-3 expression is regulat ed in a cell cycle dependent manner in many cell types and in enhanced by phorbolesters, and TGF, while it is inhibited by TNF.

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30 TIMP's 1, 2, and 4 are secreted by the cel ls in a soluble form, while TIMP-3 is sequestered to the preicellular matrix (Bre w et al. 2000, Henriet et al. 1999, Stamenkovic 2000). Yet interestingly it has been shown in a study by Ro eb et al. (1999 utilizing transfected TIMP-1 hepatoma cells, that an in crease in TIMP-1 expression or treatement to cells caused an increase in the activity of MMP-2 and 9 specifically. These results are interesting due to the fact that there may be a complex activation mechanism of TIMP-1 to enhance MMP-2/9 proteolysis (Roeb et al. 1999). There may be more than one regulatory effect of the TIMP molecules to e ither promote degradation or enhance matrix stability, these complex systems remain to be elucidated. MMP’s/TIMP’s and Their Role in the Ovulatory Cycle Follicular Growth and Development It has been shown that there may be an indirect role of MMP’s in follicular growth and development. In one study by Bagava ndoss et al.(1998) it was shown that rat preovulatory follicles that were stimulated with equine chrorionic gonadotropin (eCG), resulted in increased gelatinase A (MMP-2 ) and B (MMP-9) immunoreactivity. It has also been shown in other studies in rats that expression of th e mRNA encoding MMP-2, MMP-9 and TIMP-1 activity, along with gelatin olytic and collagenolytic activity was increased in response to eCG administrati on (Cooke et al.1999, Kenne dy et al. 1996). It has been elucidated that MMP expression is associated with follicular growth and development but the biological role of this in follicular expansion remains to be investigated. Follicular Atresia and Apoptosis The overall increase of some MMP’s may help with the process of atresia and apoptosis of non ovulatory follicles. In a study by Huet et al. (1998), there was an

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31 increase in MMP-2 and 9 following hypophysect omy in sheep. It may be that the enzymatic activity of MMP’s breaks down the basement membrane, a characteristic sign of follicular atresia. A stromeolysin-3 knock-out (KO) mouse model was utilized to investigate the functional role of this MMP in atresia and granulosa cell apoptosis. Hagglund et al. (2001) found that expression of stromelysin-3 mRNA is induced in wild type mouse ovaries, during follicular atresia, yet there was no atresia or granulosa cell apoptosis in the KO mouse model. It may be that MMP-3, (stromeolysin-3), may play a role in ECM ovarian remodeli ng during follicular atresia. Follicular Rupture and Ovulation Follicular rupture and release of the oocy te are required for reproductive success. The preovulatory surge of LH is the signal that triggers ovulation in many species, such as sheep, cattle, mice, and primates. It has been believed that degradation of the ECM surrounding the preovulatory follicle is a critical step in the ovulatory process that may be triggered by the LH surge (Espey and Lipne r 1994). Many studies have identified MMP’s that play role as the mediators of this process (Curry and Osten 2001). There must be breakdown of the thecal collagenous layer, a nd the surface germinal epithelium at the apex of the follicle for rupture to occur. Al so there must be a breakdown of the basement membrane which separates the thecal and gran ulosa cells for the release of the oocyte. The basement membrane that separates these ce lls is predominately collagen type IV. In 2 studies it was shown that the administra tion of synthetic MMP inhibitors blocked follicular rupture and ovulation (Butler et al. 1991, Brannstrom et al. 1988). The overall regulation of these MMP’s is important to ma intain tissue homeostasis of the ovary. It has been proposed that this occurs through three possible mechanisms: 1) the rate of

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32 synthesis of the enzyme, 2) activation of the enzyme and 3) synthesis of their specific inhibitors (Kane 1985). Types of MMP’s: Role in Ovulation a nd Response to Gonadotropin Stimulation Collagenases It has been shown that collagenolytic act ivity is increased in the preovulatory follicles of rats and sheep, in response to an ovulatory stimulus (Curry et al. 1985, Murdoch and McCormick 1992). The rise in th e collagenolytic activity may be due to an increase in either interstitially expressed collag enase or specifically collagenase III. It has been shown in rats, macaque granulosa cells, and cattle, that there is an increase in interstitial collagenase mRNA from preovulatory follicles, after an ovulatory stimulus (Reich et al. 1991, Chaffin and Stouffer 1999, Ba kke et al. 2000). Collagenase-3 on the other hand may be more species specific in its regulation. It was f ound to be undetectable in the mouse ovary during the periovulatory period (Hagglund et al. 1999). Yet in rats and Cattle, it was expressed and different ially regulated by an ovulatory dose of hCG (rats) or following the LH surge (cattle) (Balbi n et al. 1996, Bakke et al. 2002). It may be that an increase in the expre ssion of either collagenase-3 or interstitial collagenase in the preovulatory follicle may mediate the degradatio n of type I and III collagen fibers within the ECM of the theca externa and tu nica albuginea for ovulation to occur. Gelatinases In the ovulatory process it is thought that the gelatinases may play a key role in the degradation of the basement membrane betw een the granulosa and thecal cells and may further degrade the denatured collagen fibers after the initial reac tion by the collagenase enzymes. Overall the regulation and role of gelatinase A (MMP-2) and gelatinase B ( MMP-9) in the ovulatory process have been seen to be species specifi c. In rat and bovine

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33 preovulatory follicles MMP-2 mRNA has been shown to be expressed constituently (Bakke et al. 1999, Hagglund et al. 1999). Yet in contrast Cooke et al. (1996) demonstrated increased in MMP-2 activity following an ovulatory stimulus with hCG administration in the mouse. While in cont rast to the rat and bovine model both MMP-2 mRNA and gelatinolytic activ ity increased following an ovulatory stimulus in mice (Reich et al. 1991, Curry et al. 1992). In sheep it has also been shown that MMP -2 may be a regulator of the ovulatory process. Gottsch et al. (2000, 2001) Gotts ch et al. (2001) demonstrated that administration of antibodies to MMP-2 blocked ovulation in sheep. Ru ssell et al. (1995) immunized sheep against the N-terminal peptide of alpha-N-i nhibin and showed a significant decrease in MMP-2 activity in follicular fluid and the follicles failed to ovulate. In another study in primates it was shown that there was an increase in both MMP-2 and MMP-9 mRNA in response to an ovulatory stimulus by hCG (Chaffin and Stouffer 1999). In rats it was also found that MMP-9 mRNA was increased following administration of hCG and it increased in mouse ovaries following gonadotropin stimulus (Curry and Osteen 2001 Robker et al. 2000). In a study by Tsafriri, A. (1995) utilizing ra t ovarian extracts, he demonstrated that PMSG/hCG primed ovaries produced a signi ficant increase in MMP-2 activity as compared to controls. In a study by Curry et al. (2001) in mice it wa s shown that during follicular development in rats MMP-2/9 was lo calized to the thecal and stromal layers of the developing follicles. In the same study th ey also found that after hCG stimulation MMP-2 mRNA increased in the granulosa cells as they underwent luteinization. They also localized the overall gela tinalytic activity via in situ zymography, to the surrounding

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34 tissue of developing follicles and the apex of preovulatory follicles, suggesting that both MMP-2 and 9 play a role in growth of th e follicles and ovulation (Curry et al. 2001). In a study by Song et al. (1999) it was show n that equine ovarian stromal cells in culture produced both pro a nd active MMP-2 and 9. In another study examining equine follicular fluid it was shown that the predominant MMP in follicular fluid was both pro and active MMP-2 from follicles of differe nt sizes. MMP-9 was also present in the follicular fluid and was significantly increased as the size of the follicles increased from less than 10 mm to over 20 mm (Riley et al. 2001). Riley et al. (2001) also localized both MMP-2 and 9 and found them to be present in the stromal, thecal and granulosa cell layers. It has been suggested by Imai et al. (2003) that both pro-MMP-2 and Active MMP-2 in the follicular fluid of cattle may serve as a marker for follicular health. Membrane Type MMP’s (MT-MMP’s) It is thought that pretenses that are loca lized to the cellular surface may promote or assist in the breakdown of the collagen fibers within the ECM of the theca externa and tunica albuginea during ovulation in sheep, cattle, and rodents (Espey and Lipner 1994). The MT-MMP’s may play a role in the ovulat ory process by activating other MMP’s and maintaining their activity at the cell surface-ECM interface. (Knauper et al. 1996, Ohuchi et al. 1997). MT-MMP-1 or 14 is the most widely studied memb er of this family in the ovary. In a study by Bakke et al. (1999) mRNA expression was increased in bovine preovulatory follicles following the LH surge. Ye t in rodents, such as rats and mice, the mRNA has been localized primarily to th e thecal and granulosa cells within the preovulatory follicle (Lui et al. 1998, Hagglund et al. 1999). It has been demonstrated that there is a change in the regulation of cell type expression of the MT-MMP-1 in response to gonadotropin stimulus. MT-MMP-1 both

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35 mRNA and protein was shown to be down -regulated in the granulosa cells and upregulated in the thecal cells in response to hCG administratio n to rodents (Liu et al. 1998, Hagglund et al. 1999). Yet in contrast in the bovine preovulator y follicle MT-MMP-1 mRNA is localized to the th ecal cells prior to the LH surg e and to the granulosa cells after the LH surge (Bakke et al. 1999). It ma y be that the regulation is involved in the activation of the trimolecular complex of MMP -2 and plays a role in the breakdown of the basement membrane between the thecal and granulosa cells. TIMP’s: Role in Ovulation and Rela tionship to Gonadotropin Stimulation Like the MMP’s, TIMP expression is dyna mically regulated throughout the ovarian cycle. It has been shown th at TIMP-1 mRNA and protein increase in the preovulatory follicle of sheep following a preovulatory surge of LH (Smith et al. 1994a). Yet in another study TIMP-2 expression was shown to be secreted constitutively throughout the cycle (Smith et al. 1995). Utilizing immunohist ochemical and in situ studies TIMP-1 was found to be present in the granul osa cell layer of the preovulatory follicle in sheep (Smith et al. 1994a, McIntush et al 1996). When Smith et al. (1995) examined TIMP-2 they discovered that it was localized primarily to the thecal cell layer in preovulatory follicles, and it was constituently expressed after the pr eovulatory LH surge. Smith et al. (1999) suggest that the localization and expression of TIMP-1 and TIMP-2 may indicate distinct roles for each inhibitor during the preovulat ory period. They suggest that TIMP-1 may regulate the proteolysis within the granulosa cell layer a nd promote differentiation of these cells into luteal cells, while TIMP-2 in the thecal cells enhances proteolysis via localization of pro-MMP-2 to the thecal cell surface wh ich expresses MT-MMP-1. It has been shown in rats that there is an increased in TIMP-1 mRNA expression following hCG stimulation (Mann et al. 1993). Curry et al. (2001) localized TIMP-1,2

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36 and 3 to the stroma and thecal layers of deve loping follicles in rats. They also showed that at 12 hours after hCG ad ministration to rats that Luteinizing granulosa cells expressed and increase in TIMP-1 and 3 mR NA, while TIMP-2 mRNA levels remained unchanged (Curry et al. 2001). In a study by Cha ffin et al. (1999) they also showed an increase in TIMP-1 and TIMP-2 by 12 hour s in primate granulosa cells following administration of hCG. Song et al. (1999) demo nstrated that cultured equine stromal cells can produce TIMP-1 and -2 in the conditione d medium. They also found that treatment with phorbol ester increased TIMP-1 production by these st romal cells significantly (Song et al. 1999). In a study by Riley et al. (2001) it was shown that follicular fluid from various sized equine follicles contained TIMP-1-4 yet their content remained unchanged with the size of the follicle. They also immunolocalized TIMP-1-4 in the ovarian tissue and found that all 4 TIMP’s were present in the thecal and granulosa cell layers of the follicles and that they were also associated with th e ovarian stromal ECM (Riley et al. 2001). It has been suggested that these 2 dynamic TIMP’s are im portant in the process of ovulation, both in their roles as inhibitors to MMP’s and TIMP -2’s role in the activation of MMP-2. Yet TIMP-1 may play a role in the process of biochemical and morphological differentiation of the granul osa and thecal cells into luteal cells.

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37 CHAPTER 3 MMP-2, TIMP-1 AND STEROIDOGENEIS IN THE PREOVULATORY FOLLICLE Introduction The statement that “structure implies function” is a basic tenet of physiology research. The equine ovary is “inside-out ” compared with other mammals in that ovulation is restricted to a band of germinal epithelium at the vent ral pole of the ovary, the ovulation fossa ( Ginther 1992). Due to this “inside-out” configuration and the requirement for ovulation to occur at a partic ular location, we propose that the prolonged duration of estrus reflects the considerable amount of tissue remodeling required for ovulation. Ovulation in the equine ovary, involv es a complex series of events which are not yet well understood. Mamma lian ovulation is a process tr iggered by the preovulatory surge of LH, resulting in th e release of a mature ovum from the preovulatory follicle (Hagglund et al. 1999). This process requires extensive ti ssue remodeling of the ECM and degradation of the connective tissue of the follicle wall. In mares the unusually prolonged estrus is accompanied by prolonged LH elevation, which lasts 5-7 days. It is our belief that the prolonge d estrus of mares reflects continued development of the preovulatory follicles to reach the fossa and for ovulation to occur. LH stimulates MMP’s such as collagenas e, and gelatinase, which likely play an important role in the overall tissue remodeling associated with ovulation (Smith et al. 1999). Simultaneous expression of the enzyme and the inhibitor is believed to be the mechanism that allows focal control of tissu e degradation (Nothnick et al. 1996). In rats administration of human chorionic gona dotropin (hCG ), an LH like compound,

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38 increased ovarian gelatinase and collagenase expression as well as enzymatic activity. Cooke et al (1999) showed that gonadotropi n induction of folliculogenesis stimulated metalloproteinase activity concomitant with follicle growth and ovulation. In another study it was shown that follicular levels of MMP-2 increased betw een the preovulatory LH surge and the time of ovulation in ewes (Russel et al. 1995). It has been shown in many species that gonadotropin stimulation can increase MMP activity (Smith et al. 2001). LH-like compounds such as hCG are commonly used throughout the horse industry to stimulate ovulation, yet very little is known a bout how the use of gonadotropins affects follicular tissu e remodeling to promote ovulation. In primates studies suggest that P4 c oncentrations and P4 receptor expression increase with an ovulatory stimulus of LH supporting the idea of a role for P4 in preovulatory events associated with ovulation (Chaffin et al 1999). It has also been shown in primates that follicular steroi d production, particularly P4, and MMP-2 and TIMP-1 demonstrated a temporal relationshi p (Chaffin et al. 1999, 2000). In mares, large luteal cells (biochemically and morphologically differentiated granulos a cells) within the preovulatory follicle have been shown to secr ete P4 during estrus, which indicates that the P4 producing capacity (luteinization) begins prior to ovulation (Ginther 1992). Follicular fluid P4 increases prior to ovulati on in horses, suggesting that luteinization precedes follicular rupture (Watson et al. 1999). Such follicular P4 could possibly play a role in the tissue remode ling process in horses. Current knowledge of other species establishe s the model for a seri es of projects to examine the process of tissue remodeling and steroidogenesis in the preovulatory follicle of pony mares. Further examination of the relationship of the steroid hormones and

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39 MMP-2 and TIMP-1, may help us understa nd how this dynamic process occurs. This series of experiments will help bring to light the remodeling system and its relationship to steroid production using the preovulatory foll icle of the pony mare as an experimental model. This series of experiments will also allow for the development of intrafollicular (singular and multiple) sampling of follicular fluid for further studies and research uses. It has been previously shown in pony ma res that hCG administration increases steroid production in vivo but the effect s of hCG on the expression of the tissue remodeling system specifically MMP-2 and TI MP-1 have not been demonstrated (Song et al.1999). Based upon other species models th ere appear to be time dependent changes in the remodeling dynamics of the preovulat ory follicle and some studies suggest that there may be a relationship between the rem odeling system and steriodogenesis (Chaffin et al. 1999, Gottesh et al. 2000, Smith et al. 2000). Furthermore there is evidence to suggest that there may be concerted stimul atory effects of E2 or P4 on MMP-2 and TIMP-1, depending on the species that was exam ined. There appear to be conflicting data as to the overall relationshi p of steroids and tissue remodeling enzymes within the preovulatory follicle, depending on the species examined. The hypotheses to be tested in this seri es of experiments are as follows: 1). Gonadotropins will stimulate in trafollicular MMP-2, TIMP-1 E2, and P4. 2). Tissue remodeling enzymes change as the time of ovulation approaches and these changes can be assessed by folliculocentesis. 3). There are significant time trends for these factors in response to hCG administration over a 24 hour period and these time trends are similar.

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40 Materials and Methods General Procedures Monitoring of the mares All mares were maintained on pastur e with ample water provided and supplementation of hay when necessary. Daily monitoring of the mares involved collection of jugular vein blood samples and a te aser stallion was used to detect signs of estrus. Teasing responses were recorded to k eep track of the onset and duration of estrus for each individual mare. Utilizing rectal palpation and ultrasonography the number, location and size of ovarian follicles were measured and tracked and recorded daily. Blood processing Blood samples were collected daily in 10 ml heparinzed vacutainer tubes and temporarily stored on ice. Upon arrival at the lab, all blood samples were centrifuged at 4oC. at 3000 rpm for 15 minutes. Plasma was d ecanted and stored in plastic vials labeled with the date and mare number at -20oC. until being assayed for E2 and P4 via radioimmunoassay. Follicular fluid processing Follicular fluid samples were collected at the appropriate times according to experimental protocols via tr ansvaginal ultrasound guided needle aspiration. This was accomplished with a forward facing transvag inal ultrasound probe f itted with a 16 Ga needle guide. Mares were tranquilized with Xylazine at a dosage of (1.5mg/kg IV). Their tails were wrapped and the genital area cleans ed and scrubbed with betadine solution and rinsed clean. The probe was in serted vaginally while the appropriate ovary was held rectally to guide the needle puncture into th e follicle of interest Follicular fluid was completely or partially sampled and evacuat ed with a large 35ml syringe or a 1 ml

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41 syringe and placed into a 50ml or a 1.5ml labeled conical tube. The conical tube was placed on ice for transport and stored at -80oC. until further analysis for steroid hormones E2, P4 via radioimmunoassay, and enzyme and inhi bitor protein gels were run for MMP-2 and TIMP-1. Hormone assays Commercially available radioimmunoa ssay kits from Diagnostic Products Corporation (DPC) were used to determine es trogen and progesterone concentrations in the blood samples and follicular fluid. Progest erone concentrations were determined using DPC Coat-A-Count kit. The sensitivity, in ter-assay, and intra-as say coefficients of variation were 0.03ng/ml, 3.2% and 2.2%. Estrog en (estradiol-17b) concentrations were determined using DPC double antibody kit. Th e sensitivity, inter-a ssay and intra-assay coefficients of variation were 2.2pg/ml, 5.7% and less than 5%. Experiment 1: Effect of hCG on pre ovulatory steroids E2, P4 and Matrix Metalloproteinase-2 and Tissue Inhibitor of Metalloproteinase-1 in the Preovulatory Follicle of Cycling Pony Mares Materials and Methods This experiment utilized 10 intact cy cling pony mares. Beginning in May, mares were monitored daily and the luteal pha se was shortened by administration of prostagalindin-2 to achieve estrus synchronization. Ev aluation of mares involved rectal palpation and examination via ultrasonogra phy daily. Each mare was evaluated for follicle size, number of follicles on each ovary, and location of each follicle. Blood samples were collected daily from the jugular vein at first detection of estrus for all mares. Mares were randomly assigned to a control or treatment group at the first detection of a 25mm follicle. Mares were exam ined daily to monito r the growth of the dominant follicle until it ach ieved a diameter of 30mm. At first detection of a 30mm

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42 follicle accompanied by estrus, mares were tr eated with either saline control vehicle 2.5mls, or 2500IU hCG via jugu lar injection. Twenty four hours after treatment the dominant follicle was completely evacuated of follicular fluid using a transvaginal probe. Follicular fluid was evaluated for steroi ds, via radioimmunoassay and MMP-2 and TIMP-1 were determined by gelatin zymogra phy and reverse zymography. Quantification of MMP-2 and TIMP-1 was done with densit ometric analysis by the Alpha Imaging 3.0 software system. Statistical Analysis Data from this experiment were analyzed using Statistical Analysis System (SAS) by least squares analys is of variance (ANOVA) for the ma in effect of group (control vs. hCG). Plasma and follicular fluid estroge n and progesterone concentrations, and follicular fluid MMP-2 Adjusted densito metric values, and TIMP-1 adjusted densitometric values were the dependent variables examined in this experiment.. Least squares means were calculated for group and tested for significance. Experiment 2 : Time trends of follicular P4, MMP-2 and TIMP-1 in untreated mares by folliculocentesis. Methods and Materials This experiment utilized 20 intact cycling mares. Beginning in May mares were monitored daily and treated with prostaglandin-2 to achieve estrus synchronization, and to lyse any corpus lutem present on the ova ries. Evaluation of mares involved rectal palpation and examination via ultrasonogra phy daily. Each mare was evaluated for follicle size, number of follicles on each ova ry and location of each follicle. Follicular fluid (200-500l) was collected via tran svaginal ultrasound guided folliculocentesis either one time as assigned or 4 consecutive sa mples over 4 days ( Group 4). Mares were

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43 randomly assigned to one of 4 groups as follows: Group 1 folliculocentesis at first detection of a 30 mm follicle; time 0); Group 2 folliculocentesis 48 h af ter first detection of a 30 mm follicle); Group 3 folliculocentesi s 72 h after first detection of a 30 mm follicle); or Group 4 sequential serial as pirations of the same follicle over time (aspirations at time 0, 24 48 and 72 h after first detection of a 30 mm follicle). Follicular fluid was collected by folliculocentesis with a 25ga needle and a total volume of 200500l of follicular fluid was collected for analys is in all groups. Follicular fluid was then analyzed for intrafollicular P4, by radioimmunoassay. MMP-2 activity was determined by gelatin zymography and densitometric anal ysis through Alpha imaging 3.0 software. TIMP-1 activity was determined by revers e zymography and densitometric analysis through Alpha imaging 3.0 software. Statistical Analysis Data from this experiment were analyzed using Statistical Analysis System (SAS) by least squares analysis of variance (ANOVA). Due to the dissimilar numbers of observations, data from Groups and 1, 2 & 3 were analyzed separately (analysis 1) from data in Group 4 (analysis 2) and comparis ons between the two me thods (cross sectional sampling, Groups 1, 2 &3 and serial sampli ng (Group 4) were made visually only. Progesterone concentrations, MMP-2 adjust ed densitometric values, and TIMP-1 adjusted densitometric values were the depende nt variables examined in this experiment. The main effect tested in analysis 1 was group (Group 1, 2, &3). On analysis 2 potential differences among consecutive sampling (0, 24, 48 72 h) were tested as the main effect. Least squares means were calculated fo r groups and tested for significance.

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44 Experiment 3: The time dependent effect of hCG administration on steriodogenesis and tissue remodeling in the preovulat ory follicle of cycling pony mares Methods and Materials Mares were randomly assigned to a control or treatment group, at the first detection of a 25 mm follicle within group assignments mares were further randomly assigned to a specific time of follicular fluid collecti on, 4, 9, or 24 h respectively. Mares were examined daily to track the growth of th e dominant follicle until the enlargement to 30mm. At the first detection of a 30mm fo llicle that was accompanied by estrus, mares were treated with either saline control vehicle 2.5mls, or 2500IU hCG via jugular injection. Depending on random assignment of mares to group the dominant follicle for both groups was aspirated at 4, 9, or 24 h following saline or hCG injection ( Grps 1, 3 & 5 control injection at 4, 9 or 24 h, Grps 2,4,& 6 hCG injection at 4, 9 or 24 h). The dominant follicle was aspirate d after treatment using a transvaginal probe for complete evacuation of the dominant follicle. Follicular fluid was evaluated for steroids, via radioimmunoassay and MMP-2 and TIMP-1 wa s determined by gelatin zymography and reverse zymography. All blood samples were assayed for steroid hormones via RIA. Statistical Analysis Data from this experiment were analyzed using Statistical Analysis System (SAS) by least squares analysis of variance (ANOVA).Experimental model was a 2*3 factorial with main effects of treatment (control vs. hCG) and time (4, 9 or 24 h) and the interaction of treatment by time. E2, P4 concentrations, MMP-2 and TIMP-1 adjusted densitometric values were the dependent variables examined in this experiment. Additionally regressi on analysis was used to examine the time trends with tests for

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45 homogeneity of regression Least square m eans were calculated for group, time and the group*time interactions. Experiment 4: Tissue expression of MMP-2 and TIMP-1 mRNA in gonadotropin stimulated ovarian tissue. Methods and Materials This experiment utilized 9 intact cycling horse mares. Beginning in May, mares were monitored daily and estrus wa s synchronized with prostagalindin-F2 regressing any corpus lutem present on the ovaries. Eval uation of mares involved rectal palpation and examination via ultrasonography. Each mare was evaluated for follicle size, number of follicles on each ovary, location of each fo llicle. Mares were randomly assigned to a control or treatment group at th e first detection of a 25mm fo llicle. Mares were examined daily to track the growth of the dominan t follicle until a diameter of 30mm was achieved. At the first detection of a 30mm follicle accompanied by estrus mares were treated with either a saline control vehicle 2.5mls, or 2500I U hCG via jugular injection. Mares were paired into control and treatme nt groups as follows; Group 1 n=2; control 24 h, Group 2 n=2; hCG 24 h, Group 3 n=2; cont rol 9 h, Group 4 n=2; hCG 9 h. Mares were then sacrificed at either 9 or 24 h after treatment. Ovaries were collected under sterile conditions and tissues were dissected into th e following types, follicular wall and thecal tissues. All tissues were snap frozen on liquid nitrogen a nd stored at -800 C for RNA extraction. RNA was then dot blotted at a c oncentration of 5 g per sample. RNA was then probed with equine MMP-2 or TIMP-1 radiolabled with P32. Blots were then exposed to X-Ray film to quantify mRNA leve ls. All blots were then normalized to the 18s RNA Subunit for the bovine, this was acceptable due to species homology.

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46 Quantification of MMP-2 and TIMP-1 was done with densitometric analysis by the Alpha Imaging 3.0 software system. Statistical Analysis Data from this experiment were analyzed using Statistical Analysis System (SAS) by least squares analysis of variance (ANOVA). Experiment al model was a 2*2 factorial with main effects of treatment (control or hC G) and time (9 or 24 h) and the interaction of treatment by time. MMP-2 Adjusted dens itometric values and TIMP-1 adjusted densitometric values were the dependent variables examined in this experiment. The main effect tested was group. Least squares m eans were calculated for groups and tested for significance. Experiment 1: Results and Discussion There was a significant group effect (p< 0.0001 see appendix table F.1) for the hCG treatment group that was characterized by an increase in intrafollicular P4 concentrations compared with control animals (Figure 3.1). There was no significant difference between control and hCG treatment in peripheral P4 (see a ppendix table F.2), and E2 (see appendix table F.3), or intrafollicular E2 (see appendix table F.4). There was a significant effect of gr oup on MMP-2 (p<0.001 see appendix table F.5) characterized by higher MMP-2 activity following hCG compared with control animals (Figure 3.2). There was also a signifi cant effect of group on TIMP-1 (p<0.0001 see appendix table F.6) characterized by hi gher TIMP-1 activity in the follicular fluid following hCG compared with control animals (Figure 3.3).

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47 Mean Follicular P4 Concentrations P 4 n g / m l 0 200 400 600 800 1000 1200 1400 control hCG *p<0.0001 Figure 3.1: Mean follicular P4 concentr ations by treatment Experiment 1 Mean Follicular MMP-2 Levels IDV/CV densitometric units 0 1 2 control hCG p<0.0001 Figure 3.2: Mean follicular MMP-2 ac tivity by treatment, Experiment 1

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48 Mean Follicular TIMP-1 levelsIDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Control hCG p>0.0001* Figure 3.3: Mean TIMP-1 follicular activity by treatment, Experiment 1 These results suggest that there may be a relationship among intrafollicular P4, MMP-2 and TIMP-1. There were no differences in peripheral steroid hormones or in intrafollicular E2, in response to hCG (gonadotropin) administration which suggests that whatever remodeling events within th e preovulatory follicle that involve E2 and/or P4 are ot reflected in the peripher al circulation. There was an increase in intrafollicular P4 concentrations, and an increase in active MMP -2 and TIMP-1 protein level twenty four hours after hCG administration. These data indicate that there is a mark ed response to gonadotropin administration reflected by increased intrafo llicular P4, MMP-2 a nd TIMP-1 in the preovulatory follicle of the pony mare. Gonadotropin stimulation increased MMP-2 which in turn may stimulate remodeling of the basement memb rane and other unknown events which may aid in follicular apposition to the germinal epithelium where ovulation can occur. In turn TIMP-1 may act either as a control point fo r MMP-2 or as a stimulator of biochemical and morphological changes of the thecal and gr anulosa cells. The latt er could contribute

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49 to an increase in P4 steroidogenesis. It has been demons trated in other species that TIMP1 can lead to premature luteinization of th e granulosa and thecal cells and differentiation to large luteal cells leading to an increased production of P4 within the preovulatory follicle (Smith et al.1999). Experiment 2: Results and Discussion There was no significant group effect in follicular P4 concentrations (p<0.13 see appendix table F.7) in analysis one (Grp 1, 2, &3) over time figure 3.4. The 48 and 72 h folliculocentesis showed an arit hmetic decline in follicular P4 concentrations compared with time 0, it approached significance by pdiff an alysis. In the serially sampled animals (Grp 4) there was a significant group eff ect (p<0.0001 see appendix table F.8) Figure 3.5 Pdiff analysis revealed that P4 concentrations decreased ove r time in the 24, 48 and 72 h groups compared with time 0. Of interest, th e serially aspirated groups showed the same basic profile as the single aspi ration group with respect to P4 concentrations by visual inspection. There was a significant group effect (Grps 1, 2 & 3) in follicular MMP-2 levels (p<0.0001 see appendix table F.9) for analysis 1 over time (Figure 3.6) Group 3 showed a significant increase in MMP-2 activity co mpared with both time 0 and 48 h by pdiff analysis. In the serially sampled animals (G rp 4) there was a si gnificant time effect (p<0.0005 see appendix table F.10), (Figure 3.7) Pdiff analysis demonstrated that MMP-2 levels increased over time from 48 to 72 h. By visual inspection collecting follicular fluid for analysis cross sectionally and serially appeared sim ilar and suggests that serial sampling may be a viable research technique.

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50 * P4 Follicular Concentrations Single Aspirations by Group P4 ng/ml 0 200 400 600 800 1000 1200 0 hours 24 hours48 hours 72 hoursb Group=p<0.13 Individual group by time comparisons by pdiff analysis Bars with different superscript letters were different p<0.06* b a Figure 3.4: Mean follicular P4 concentrations for groups 1, 2, &3 (single aspiration groups) Experiment 2 P4 Follicular Concentrations Multiple Aspirations by Time P4 ng/ml 0 200 400 600 800 1000 1200 0 hours24 hours48 hours72 hoursa b c Group=p<0.0001* Individual group by time comparisons by pdiff analysis Bars with different superscript letters were different p<0.01* Figure 3.5: Mean follicular P4 concentrations Group 4 (serial aspiration group) Experiment 2

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51 MMP-2 Follicular Levels Single Aspirate Group by Time IDV/CV Densitometric Units 0.0 0.5 1.0 1.5 2.0 2.5 0 hours 48 hours 72 hours 24 hoursb Group=p<0.0001* a a Individual group by time comparisons by pdiff analysis Bars with different superscript letters were different p<0.0001* Figure 3.6: Mean follicular MMP-2 activity Groups 1,2 &3 (singl e aspiration groups) Experiment 2 MMP-2 Follicular Levels Multiple Aspirates over Time IDV/CV Densitometric Units 0.0 0.5 1.0 1.5 2.0 2.5 0 hours24 hours48 hours72 hoursb a a a Group=p<0.0005* Individual group by time comparisons by pdiff analysis Bars with different superscript letters were different p<0.0001* Figure 3.7: Mean follicular MMP-2 activity in Group 2(serial aspiration group) Experiment 2 There was a significant group effect (Grp 1, 2, & 3) in follicular TIMP-1 (p<0.0001 see appendix table F.11) (Figure 3.8). TIMP-1 levels increased from 0 to 48 and from 48

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52 to 72 h by Pdiff analysis. In Grp 4 animals there was a significant time effect (p<0.0001 see appendix table F.12)(Figur e 3.9), which demonstrated th at TIMP-1 levels increased over time from time 0 to 24, 48 and 72 h. Pdiff an alysis also showed that there was also a significant increase in TIMP-1 levels at all 3 times when compared with time 0. Group 4 showed the same basic profile for TIMP-1 levels as Grps 1, 2, & 3. * TIMP-1 Follicular Levels Single Aspirates by Time IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0 hours 48 hours 72 hours 24 hoursbcaGroup=p<0.0001* Individual group by time comparisons by pdiff analysis Bars with different superscript letters were different p<0.0001* Figure 3.8: Mean TIMP-1 activity in Gr oups 1, 2 &3 (single aspiration groups) Experiment 2

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53 TIMP-1 Follicular Levels Multiple Aspirates by Time IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0 hours24 hours48 hours72 hoursb c d a Group=p<0.0001* Individual group by time comparisons by pdiff analysis Bars with different superscript letters were different p<0.004* Figure 3.9: Mean TIMP-1 activity in Group 4 (serial aspiration group) Experiment 2 Experiment 3: Results and Discussion Data from experiment one data indicate d that there was a relationship between intrafollicular P4 concentrations, MMP-2 active form and TIMP-1 levels. All rose by 24 hours after hCG. In this experiment we examin ed the potential time trends of these three factors within the preovulatory follicle of the cycling pony mare. There was a significant effect of group characterized by increased P4, MMP-2 and TIMP-1 compared with c ontrols (p<0.005 see appendix table F.13). There was a significant group by time interaction ch aracterized by sharply increased P4 in hCG treated vs. control mares by 24 (p<0.0001* see append ix table F.13) (Figure 3.10). Regression analysis indicated that the time trends for control and hCG treatment were not homogeneous and the data were best repr esented by 2 separate curves. (p>0.001* see appendix table F.14).There were no significant time trends in P4 or MMP-2 in control

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54 mares whereas TIMP-1 in control mare s was significantly elevated by 24 hours (p<0.0001* see appendix table F .15). Mean Follicular P4 Concentrations P 4 n g / m l0 500 1000 1500 2000 2500 4 hrs 9 hrs 24 hrs control hCG Group=p<0.005* Group by Time interaction=p<0.001* Individual group by time comparisons by pdiff analysis Bars with different superscript letters were different p<0.001* a a a a a b Figure 3.10: Mean follicular P4 concentrations by group and time Experiment 3 Statistical analysis indicated there was a significant effect of group, time and a group*time interaction (p>0.0001* for group, and time g*t p<0.002* see appendix table F.16) for intrafollicular MMP-2 levels. hCG treated mares exhibited markedly elevated MMP-2 activity compared with controls (Figure 3.11). Pdiff an alysis exhibitied significant differences at 24 hours, with MMP-2 levels increased compared to 4 or 9 h. Regression analysis of the time trends indica ted that data were best represented by 2 separate curves, with the cont rols showing no significant tim e trends and the hCG-treated animals best represented by a second order curve with a sharp upswing from 9 to 24 h (p>0.001* see appendix table F.17).

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55 Mean Follicular MMP-2 Levels IDV/CV densitometric units 0 1 2 3 4 4 hrs 9 hrs 24 hrs control hCG Group by Time interaction=p<0.002* Group=p<0.0001* Individual group by time comparisons by pdiff analysis Bars with different superscript letters were different p<0.003* a a a b b d Figure 3.11: Mean follicular MMP-2 levels by group and time Experiment 3 Statistical analysis of follicular fluid TI MP-1 indicated significant group time and group*time interactions (p>0.0001*: see appendix table F.18) (Figure 3.12). The control group showed a significant incr ease in TIMP-1 levels from 9 to 24 h, while the treated group showed a significant increas e in TIMP-1 over all 3 time points. Regression analysis of time trend data indicated homogeneity of regression which demonstrated that these data were best represented by one single line which was an approximate average of the 2 groups over time (p>0.005* see appendix table F.19).

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56 Mean Follicular TIMP-1 Levels IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 4 hours 9 hours 24 hours Control hCG b Group=p<0.0001* Individual group by time comparisons by pdiff analysis Bars with different superscript letters were different p<0.001* Group by Time interaction=p<0.0001* a a c d e I DV/CV Densitometric Unit s Figure 3.12: Mean follicular TIMP-1 levels by group and time Experiment 3 Experiment 4: Results and Discussion Data indicated that there was a significan t effect of treatment on the levels of MMP-2 expression within the ovarian tissu e that was collected (p<0.05* see appendix table F.19). Data also indi cate that there was a significan t effect of time (p<0.05* see appendix table F.20) (Figure 3.13).Results indi cate that there was a significant group by time interaction (p<0.01* see appendix tabl e F.20) Results suggest that there were different levels of MMP-2 mRNA expression in follicular wall and theca interna tissue in response to hCG administration as compared with control ti ssue collected at the same time point There was a signi ficant decrease in the amount of MMP-2 mRNA expression in control animals in both ti ssue types at 24 h when comp ared to the 9 h controls (p<0.05* see appendix table F.20) (Figure 3.13) Data also indicated that there was significantly less MMP-2 mRNA expression in hCG treated mares at both time points

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57 compared to the 9 h control tissues (p<0.05* see appendix table F.20). There were also significant differences in the MMP-2 mRNA expression at 24 h following hCG as compared to 9 h after hCG administrati on (p<0.05* see appendi x table F.20)(Figure 3.13). Yet interestingly there were no signi ficant differences between the 24 h control animals or the 24 hCG treated animal levels of MMP-2 mRNA expression. Individual group comparisons by pdiffanalysis Bars with different superscript letters are different p<0.05* MMP-2 mRNA Expression by Tissue and TX MMP-2 mRNA relative units 0 2 4 6 8 10 Follicle Wall Theca Control 9 hrsControl 24 hrshCG 9 hrshCG 24 hrs a a b b c d b b Group p<0.05* Time p<0.05* Group by Time p<0.01* Figure 3.13: MMP-2 mRNA expression levels by tissue and treatment, Experiment 4 TIMP-1 mRNA levels overall were not significantly different between groups (p<0.117 see appendix table F.21) (Figure 3.14) Data indicated that there were not significant differences in time (p<0.1159 s ee appendix table F.21),and there were no significant group by time interactions (p< 0.276 see appendix table F.21). Yet Pdiff analysis indicated that there were signifi cant differences among tissue types and by time in response to treatment when compared with the control animal ti ssue expression at 9 h (p<0.05* see appendix tabl e F.21) (Figure3.14).

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58 Individual group comparisons by pdiffanalysis Bars with different superscript letters are different p<0.05* TIMP-1 mRNA Expression by Tissue and Tx TIMP-1 mRNA relative units 0 5 10 15 20 25 Follicle wall Theca Control 9 hrs Control 24 hrs hCG 9 hrshCG 24 hrs b Group p<0.117* Time p<0.1159* Group by TIme p<0.276* a a a b b b b Figure 3.14: TIMP-1 mRNA expression levels by tissue and treatment, Experiment 4 Conclusions Gonadotropin administration (h CG) stimulated follicular P4 concentrations, MMP-2 and TIMP-1 levels. There appears to be a relationship among these three factors. Follicular E2, and both peripheral E2 and P4 showed no differences in response to treatment, but P4 exhibited marked changes in the follicle, suggesting that P4 may be involved in, or affected by th e remodeling system at the loca l level. This contrasts to E2 which has been shown to be the main factor in other species such as cattle, and rodents (Hagglund et al., 1999, Bakke et al., 2000). Th e temporal relationship among follicular P4 concentrations, MMP-2 and TIMP-1 leve ls in response to treatment with hCG suggests an interrelationshi p between intrafollicular P4 and the tissue remodeling system. Visual inspection of results from two diffe rent sampling methods, serially and cross sectionally suggests that th ere is no difference, and th erefore may provide a more

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59 efficient sampling tool via se rial sampling. It appears that intrafollicular sampling of small volumes is a viable means to examine small volumes of follicular fluid without disrupting the follicular environment. The data obtained from this experiment obtained with two different sampling methods may represent the microenvironment of the preovulatory follicle of the mare with respect to P4 concentrations, MMP-2 and TIMP-1 levels. These techniques are viable tools fo r further research by u tilizing a small 25 Ga needle for follicular sampling or for microinj ection. We have demonstrated that there is no adverse effect to the follicle, any appare nt tissue disruption or visible ultrasonic changes. Experiment three suggests that gonado tropin administration hastens ovulation by stimulating tissue remodeling enzymes. The increase in P4 at 24 h suggests that this increase is delayed compared with MMP-2 and TIMP-1 levels. MMP-2 and TIMP-1 levels showed an increase in treated an imals compared to controls. MMP-2 levels increased significantly from 9 to 24 h and TIMP -1 levels increased over all 3 time points in response to treatment. The time trends in P4, MMP-2 and TIMP-1 suggest that MMP-2 and TIMP-1 rose steadily while P4 rose rapidly between 9 and 24 h. One interpretation of this observation is that the increase in P4 in response to exogenous gonadotropin may lag behind the changes in MMP-2 and TIMP-1. If th at is true, it is interesting to speculate whether MMP-2 and/or TIMP-1 are causally associated with the increase in P4. Experiment 4 demonstrated that there ar e changes in the expr ession of both MMP-2 and TIMP-1 mRNA levels in both follicular wall and theca interna tissue. These changes appear to occur most significantly by 9 h in both MMP-2 and TIMP-1 mRNA levels. Yet interestingly there appears to be no si gnificant differences in the MMP-2 mRNA

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60 expression in control or trea ted animals at 24 h, but there were differences in MMP-2 mRNA expression levels in both tissue type s and in response to hCG treatment at 9 h. This suggests that changes in MMP-2 mR NA expression in response to gonadotropin administration occur before 24 h. Howeve r TIMP-1 mRNA expression levels were different by the tissue type in response to tr eatment and time. Overall these experiments demonstrate that hCG stimulated the tissue re modeling system in the equine preovulatory follicle, and that P4 is likely a key player in th is 3 part regulat ory relationship.

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61 CHAPTER 4 INHIBITION OF THE TISSUE REMOD ELING SYSTEM AND STERIODOGENESIS IN THE PREOVULATORY FOLLICLE OF CYCLING PONY MARES Introduction Based on results from experiments one and three, a relationship exists among follicular P4 concentrations, MMP-2 and TIMP-1 levels within the preovulatory follicle. In this series of experiments we examined the effects of inhibition of follicular P4 concentrations and MMP-2 activity. These e xperiments examine the remodeling system (MMP-2 and TIMP-1) a nd steroidogenesis (P4) to better understand how these 3 factors function together. We chose to examine this relationship through the use of mifepristone (Ru486) and a cyclic inhibitor of MMP-2. It has been shown in pregna nt rats that administration of Ru486 at a concentration of (2mg/kg) on day 12 of pregnancy induced preterm delivery and si gnificantly reduced the luteal 3 -HSD enzyme activity by 72 hours after in jection (Telleria et al. 1995). In another study by Telleria et al. 1994, they examined the effects of both Ru486 and a specific rat progesterone antibody. Their resu lts suggested that both the antibody and Ru486 act by decreasing 3 -HSD enzyme activity. Furthermore Ru486 action was exerted through an antiprogesterone action, th at is Ru486 may have acted by directly blocking the effects of P4. Results from a study examining Ru486 effects in ovulation in the rat, after gonadotropin administ ration, suggested that ovarian 3 -HSD enzyme activity was inhibited in hCG stimulated an imals when 20mg/kg Ru486 was administered 2 hours prior to hCG stimulation (Tanaka et al. 1993). The results from this study

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62 indicated that ovarian 3 -HSD activity depended on P4 concentrations and suggest that there may be an autocrine regulation of P4 production during ovulation in stimulated animals (Tanaka et al. 1993). Uilenbroek et al. 1992, examined the effects of Ru486 on preovulatory follicles isolated from rats and cultured in the presence or absence of LH. Their results suggested that in the absence of LH there were no significant differences in the accumulation of E2, P4 and T in the medium. Yet in the presence of LH, E2, P4 and T were significantly reduced in culture in the Ru486 treated animals, as monitored by the inability to produce P4. Overall the data suggested that 3 -HSD activity was reduced by administration of RU486 in vivo (Uilenbroek et. al. 1992). MMP-2/9 cyclic inhibitor III from calbiochem is a cyclic peptide that acts as strong inhibitor of MMP-2 and 9. It s a heterocyclic inhibitor with the molecular formula C52H71N13O14S2, that has an inhibitory concentration of 50% (IC50), specifically to MMP2 and MMP-9 in a concentration of 10M/ml in culture. It has been shown to prevent the activation of the enzyme through preventing clea vage of the pro-form to the active form of the enzyme. It is supplied as a lyoph ilized solid that is water soluble, and has a 2 month stability when r econstituted and frozen at -20oC (Koivunen et al. 1999). We propose to test the dual hypotheses that: 1. blockade of intrafollicular P4 results in associated blockade of MMP-2 a nd TIMP-1, and 2. blockade of MMP-2 results in an associated blockade of intrafollicular P4. Methods and Materials General Procedures Monitoring of the mares All mares were maintained on pastur e with ample water provided and supplementation of hay when necessary. Monito ring of the mares involved collection of a

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63 jugular vein blood sample and a teaser stallion was used to de tect signs of estrus daily. Teasing responses were recorded to keep tr ack of the onset and duration of estrus for each individual mare. Utilizing rectal palpat ion and ultrasonography the number, location and size of ovarian follicles were meas ured and tracked and recorded daily. Blood processing Blood samples were collected daily in 10 ml heparinized vacutainer tubes and temporarily stored on ice. Upon arrival at the lab, all blood samples were centrifuged at 4oC. at 3000 rpm for 15 minutes. Plasma was d ecanted and stored in plastic vials labeled with the date and mare number at -20oC. until being assayed for E2 and P4 via radioimmunoassay. Follicular fluid processing Follicular fluid samples were collected at the appropriate times according to experimental protocols via tr ansvaginal ultrasound guided needle aspiration. This was accomplished with a forward facing transvaginal probe fitted with a 16 Ga needle guide. Mares were tranquilized with Xylazine ( 1.5mg/kg). Their tails were wrapped and the genital area cleansed and scr ubbed with betadine solution and rinsed clean. The probe was inserted vaginally while the appropriate ova ry was held rectally to guide the needle puncture into the follicle of interest. Follic ular fluid was collected and placed into a conical tube. The conical tube was placed on ic e for transport and stored at -80oC. until further analysis for steroid hormones E2, P4 via radioimmunoassay, and enzyme and inhibitor protein gels were run for MMP-2 and TIMP-1. Hormone assays Commercially available radioimmunoa ssay kits from Diagnostic Products Corporation (DPC) were used to determine es trogen and progesterone concentrations in

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64 the blood samples and follicular fluid. Progest erone concentrations were determined using a DPC Coat-A-Count kit. The sensitivit y, inter-assay, and intra-assay coefficients of variation were 0.07ng/ml, 4.4% and 2.2%. Es trogen (estradiol-17b) concentrations were determined using a DPC double antibody ki t. The sensitivity, inter-assay and intraassay coefficients of variation we re 3.6pg/ml, 5.5% and less than 5%. Experiment 1: Inhibition of follicular P4 concentrations and its effects on MMP-2 and TIMP-1 Methods and Materials This experiment utilized seven cycling pony mares. Beginning in May mares were monitored daily and treated with prostaglandin-2 to achieve estrus synchronization, and to regress any corpus lutem present on the ovaries. Evaluation of mares involved rectal palpation and examination via ultrasonogra phy. Each mare was evaluated for follicle size, number of follicles on each ovary and loca tion of each follicle. Blood samples were collected daily from the jugular vein at the fi rst signs of estrus for all mares. Mares were randomly assigned to control or treatment (R u486) group at the firs t detection of a 25mm follicle. Mares were examined daily to track the growth of the dominant follicle until the enlargement to 30mm. At the first detection of a 30mm folli cle that was accompanied by estrus mares were treated with either se same oil control (10mls) IM or Ru486 (500mg) in 10 mls of sesame oil IM, followed by a second treatmen t of the same volume and concentration 48 hours later. Mares were examined daily to track the growth of the dominant follicle. Forty-eight hours after the second treatment the dominant follicle was completely aspirated by transvaginal u ltrasound folliculocentesis. Blood samples were collected on all mares daily, from the first day of treatme nt to five days post aspiration. Follicular

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65 fluid was evaluated for steroids via RIA and MMP-2 and TIMP-1 leve ls were determined by gelatin zymography and reverse zymography All blood plasma samples were assayed for steroid hormones via RIA. Statistical Analysis Data from this experiment were analyzed using Statistical Analysis System (SAS) by least squares analys is of variance (ANOVA), with th e main effect of group (control vs. Ru486). Estrogen concentrations, progest erone concentrations, MMP-2 Adjusted densitometric values, and TIMP-1 adjusted densitometric values were the dependent variables examined in this experiment. Leas t squares means were calculated for groups and tested for significance. Experiment 2: The Effects of MMP-2/9 cyclic inhibitor III on Follicular Steriodogenesis and Tissue Remodeling in the Preovulato ry Follicle of Cycling Pony Mares. Methods and Materials This experiment utilized eight cycling pony mares. Beginning in May mares were monitored daily and treated with prostaglandin-2 to achieve estrus synchronization, and to lyse any corpus lutem present on the ova ries. Evaluation of mares involved rectal palpation and examination via ultrasonogra phy. Each mare was evaluated for follicle size, number of follicles on each ovary and loca tion of each follicle. Blood samples were collected daily from the jugular vein at the fi rst signs of estrus for all mares. Mares were randomly assigned at the first detection of a 25mm follicle to receive control vehicle (100l) or treatment (100l i nhibitor 100M). Mares were ex amined daily to track the growth of the dominant follicle until the follicle reached 30mm. Mares were scanned daily by ultrasound to monitor diameter of the largest follicle, and treatment was administered by follicu locentesis, (using a custom made 25 Ga.

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66 injection needle to be placed within the 16Ga transvaginal aspiration needle) on the day that the largest follicle achieves a di ameter of 30mm. Forty-eight hours after folliculocentesis, the dominant follicle was completely aspirated by transvaginal ultrasound needle guided aspira tion. Blood samples were collect ed daily from the day of treatment to 5 days post aspiration. Follicula r fluid was evaluated for steroids via RIA and MMP-2 and TIMP-1 levels were dete rmined by gelatin zymography and reverse zymography. All serum samples were a ssayed for steroid hormones via RIA. Statistical Analysis Data from this experiment were analyzed using Statistical Analysis System (SAS) by least squares analys is of variance (ANOVA) with ma in effect of group (control vs. inhibitor). Estrogen concentrations, proge sterone concentrations, MMP-2 Adjusted densitometric values, and TIMP-1 adjusted densitometric values were the dependent variables examined in this experiment. L east squares means were calculated for groups and tested for significance. Experiment1: Results and Discussion There was a significant effect of group (p>0.0007*: see appendix table F.22) that was characterized by a decrea se in intrafollicular P4 compared with controls (Figure 4.1). There was no significant di fferences in peripheral E2 or P4 or intrafollicular E2 (see appendix table F.23,F.24, F.25).

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67 Mean Follicular P4 Ru486 P4 ng/ml 0 200 400 600 800 1000 1200 Control Ru486 p<0.0007* Figure 4.1: Mean Follicular P4 concentrations by group Experiment 1 There was a significant group effect on follicular MMP-2 (p>0.0006*: see appendix table F.26), that was characterized by a significant decrease in follicular MMP2 levels compared with controls (Figure 4.2). Mean MMP-2 Levels Ru486 IDV/CV Densitometric Units 0 1 2 3 4 5 Control Ru486 p<0.0006* Figure 4.2: Mean MMP-2 activity by group, Experiment 1

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68 There was also a significant group effect of follicular TIMP-1 (p<0.05*: see appendix table F.27), that was decreased co mpared with controls (Figure 4.3). The overall decrease in TIMP-1 was not as dram atic as the decrease in MMP-2. The overall levels of TIMP-1 were significant yet they do not represent such a dramatic decrease as seen in MMP-2 and P4. Mean TIMP-1 Levels Ru486 IDV/CV Densitometric Units 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Control Ru486 p<0.05* Figure 4.3: Mean follicular TIMP-1 levels by group, Experiment 1 The large decrease in P4 was expected due to the actio ns of Ru486 through either a mechanism of blocking the P4 receptor or more likely through it’s actions on the 3 HSD enzyme. Based upon the results from this ex periment it would explain the overall decrease in intrafollicular P4 concentrations. The decrease in MMP-2 was an interesting result from this experiment; and strongl y suggests that MMP-2 may be functionally associated with changes in P4.Yet interestingly the reducti on in TIMP-1 in response to Ru486 administration, was less dramatic. If MMP-2 and TIMP1 function on an eqimolar

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69 association (Shapiro, et. al 1995), then the lesser reducti on in TIMP-1 might reflect alternative regulatory mechanisms. As these data only represent one point in time reduction in P4 could affect MMP-2 and TIMP-1 with different timing. Overall these data show that there is a close and interwoven re lationship between these factors. This was demonstrated by administering Ru486 and examin ing the overall decreas es in these three intrafollicular factors. This experiment also sheds light on the role of P4 in this system, and that it may be a stimulating factor fo r MMP-2 activity, but may overall be enhanced somehow through TIMP-1’s actions. These 3 f actors and their relationship within the preovulatory follicle of the mare need to be clarified. Experiment 2: Results and Discussion There was a significant eff ect of group (p>0.006*: see ap pendix table F.28) that was characterized by a decrea se in intrafollicular P4 concentrations in treated mares compared with controls (Figure 4.4). There was no significant difference in peripheral E2 or P4 or in intrafollicular E2 concentrations between gr oups (see appendix table F.29, F.30, F.31). There was a significant effect of group on MMP-2 follicular levels (p>0.0008*: see appendix table F.33) compared with contro l animals characterized by a decrease in intrafollicular MMP-2. The data suggests that inhibitor treatment blocked the activation of MMP-2 resulting in an overall mean decr ease in the activity levels compared with controls (Figure 4.5). This was expected, due to the nature of the inhibitor used and its capability to block the activation of the enzyme within the follicle.

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70 Mean Follicular P4 MMP-Inhibitor P4 ng/ml 0 200 400 600 800 1000 1200 1400 Control MMP-Inhibitor p<0.006* Figure 4.4: Mean follicular P4 concen trations by group, Experiment 2 Mean MMP-2 Levels for MMP-Inhibitor IDV/CV Densitometric Units 0 1 2 3 4 5 6 Control MMP-Inhibitor p<0.0008* Figure 4.5: Mean follicular MMP-2 levels by group, Experiment 2

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71 There was also a significant effect of group on TIMP-1 follicular levels (p>0.02*: see appendix table F.34) compared with cont rols (Figure 4.6) characterized by a decrease in TIMP-1 in response to inhibitor administration. Mean TIMP-1 Levels MMP-Inhibitor IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 Control MMP-Inhibitor p<0.02* Figure 4.6: Mean follicular TIMP-1 levels by group, Experiment 2 This experiment allowed us to examine th e interrelationship of these three factors in yet another way. By examining the effect s of inhibiting the MMP-2 enzyme activation we could see its effects on follicular P4 concentrations and TIMP1. It also allowed us to test our intrafollicular injection system to see if it was a viable tool for future research in this area. Administration of the MMP-2/9 cyc lic inhibitor III as e xpected decreased the overall MMP-2 levels in the follicular fluid of the preovulatory follicle. In turn there was also a significant decrease in follicular P4 concentrations this agai n suggests that there is some relationship between MMP-2 and P4. Using this inhibitor the data indicated a significant decrease in TIMP-1 levels. This is not surprising, as TIMP-1 functions in an equimolar relationship to i nhibit MMP-2 (Shapiro et al 1995) and by using a cyclic

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72 specific inhibitor to reduce MMP-2 we observe d a similar decrease in TIMP-1.Yet it is still perplexing as to the interr elationships of TIMP-1 and P 4. It may be that due to the lower levels it cannot promote P4 steroidogenesis beneath a ce rtain threshold, which is why we do not see the large increase in P4 concentrations. Yet the data still point to the fact that these three factors are functionally related and may act in concerted fashion to promot e ovulation in the mare. The data overall suggest a strong relationship among these factor s. This experiment suggests that further research in this area is necessary to examin e these factors and their relationships to each other. Conclusions These experiments examined inhibition of both follicular P4 and MMP-2. Blockade of either P4 or MMP-2 resulted in reduction in MMP-2, suggesting a functional relationship between P4 and MMP-2, within the preovulatory follicle. Experiment 1 emphasized the important relationship of follicular P4 concentrations and the tissue remodeling system, through the use of Ru486. Data would suggest that RU486 exhibited reduction in follicular P4 through possibly decreasing 3 -HSD enzyme activity which reduced P4 synthesis within the follicle. We di d not do any binding studies to examine whether or not Ru486 binds to the P4 receptor in the horse follicle. Yet interestingly there was no effect of Ru486 on follicular E2, or peripheral E2 and P4. Data would suggest that follicular P4 exhibits some effect on MMP-2 activity and that these factors may possible regulate each other. In experiment 2 we examined the opposite effect of inhibi ting MMP-2 activity through the use of a cyclic inhib itor. Data from this experime nt again suggested that there is an interrelationship between P4 and MMP-2 activity. When the enzyme was inhibited

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73 there was a significant decrease in P4 concentrations within the follicle. Overall takes together these studies suggest that P4 and MMP-2 are intimately involved likely through a positive feedback relationship leading to tissue remodeling in the preovulatory follicle which is necessary for ovulation.

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74 CHAPTER 5 INTRAFOLLICULAR ADMINISTRATION OF GNRH, P4 AND MELATONIN AND THEIR EFFECTS ON TISSUE REMODELI NG AND STEROIDOGENESIS IN THE PREOVULATORY FOLLICLE. Introduction Many studied have shown that gonadotropins play a major role in the remodeling process (Hagglund et al. 1999; Smith. et al. 2002). Data fr om previous studies have shown that MMP-2 and TIMP-1 are presen t in the follicular fluid of pony mares (Desvousges et al. 2002, 2001; Popoli et al 2000, 1999). Furthermore hCG increased MMP-2, TIMP-1 and follicular P4 24 hours after administration to mares during the breeding season. Recent studies in human granulosa cell cultures suggest that melatonin plays a role in ovarian steroidogenesis (Woo et al. 2001) Melatonin has also been shown to up regulate the LH-receptor (LH-RC) and down re gulate the GnRH receptor mRNA in the human ovary (Woo et al. 2001). Addition of GnRH to human stromal cells resulted in a decrease in TIMP-1 in the medium (Chou et al. 2003). Addition of progesterone to cultured human endometrial cells resulted in a significant reduction of the active form of MMP-2 (Zhang et al. 2000). In an experiment by Nothnick et al. (2003) utilizing TIMP-1 null mice, data from this study suggested that there was a signifi cant reduction in the circulating levels of progesterone, following administration of hCG. Based upon our results that in trafollicular injection does not seriously disrupt the microenvironment of the dominant follicle a nd our ability to utilize this methodology to sample small volumes of follicular fluid from the same follicle, we feel that this

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75 technique is a viable research tool for this experiment. Our objective with experiment seven was to examine the effects of in trafollicular administration of GnRH, P4 or Melatonin on MMP-2, TIMP-1 and steroidogene sis within the preovulatory follicle of cycling pony mares during the breeding season. The hypothesis to be tested in this experiment is that GnRH will stimulate MMP-2 and TIMP-1, and P4; P4 will not stimulate MMP-2, TIMP-1 and P4 significantly above control leve ls, and Melatoni n will stimulate MMP-2, TIMP-1 and P4 within the preovulatory follicle. Methods and Materials General Procedures Monitoring of the mares All mares were maintained on pastur e with ample water provided and supplementation of hay when necessary. Daily monitoring of the mares involved collection of a jugular vein blood sample and a teaser stallion was used to detect signs of estrus. Teasing responses were recorded to k eep track of the onset and duration of estrus for each individual mare. Utilizing rectal palpation and ultrasonography the number, location and size of ovarian follicles were measured and tracked and recorded daily. Blood processing Blood samples were collected daily in 10 ml heparinzed vacutainer tubes and temporarily stored on ice. Upon arrival at the lab, all blood samples were centrifuged at 4oC. at 3000 rpm for 15 minutes. Plasma was d ecanted and stored in plastic vials labeled with the date and mare number at -20oC. until being assayed for E2 and P4 via radioimmunoassay.

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76 Follicular fluid processing Follicular fluid samples were collected at the appropriate times according to experimental protocols via tr ansvaginal ultrasound guided needle aspiration. This was accomplished with a special forward facing tran svaginal probe fitted with a 19 Ga needle guide. Mares were tranquilized with Xylazine (1.5mg/kg IV). Mares were administered the tranquilizer via jugular ve in injection. Their tails were wrapped and the genital area cleansed and scrubbed with betadine solution and rinsed clean. The probe was inserted vaginally while the appropriate ovary was held rectally to guide the needle puncture into the follicle of interest. Follicular fluid, was, (total volume of 200500l) was collected and placed on ice for transport and stored at -80oC. until further analysis for steroid hormones E2, P4 via radioimmunoassay, and enzyme and inhibitor protein gels were run for MMP-2 and TIMP-1. Hormone assays Commercially available radioimmunoa ssay kits from Diagnostic Products Corporation (DPC) were used to determine es trogen and progesterone concentrations in the blood samples and follicular fluid. Progest erone concentrations were determined using a DPC Coat-A-Count kit. The sensitivit y, inter-assay, and intra-assay coefficients of variation were 0.06ng/ml, 3.0% and 1.8%. Es trogen (estradiol-17b) concentrations were determined using a DPC double antibody ki t. The sensitivity, inter-assay and intraassay coefficients of variation we re 1.2pg/ml, 2.7% and less than 5%.

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77 Experiment 1: Intrafollicular Administra tion of GnRH, P4 or Melatonin and Their Effects on MMP-2, TIMP-1, and Steroidogenesis in the Preovulatory Follicle of Cycling Pony Mares. Materials and Methods This experiment utilized 30 intact p ony mares. Beginning in May mares were monitored daily and estrous synchronizie d by administration of prostaglandin-2 Evaluation of mares involved rectal palpati on and examination via ultrasound daily. Each mare was evaluated for follicle size, number of follicles on each ovary, and location of each follicle. Blood samples were collected da ily from the jugular vein at the first detection of estrus for all mares. Mares were randomly assigned to a control or treatment group at first detection of a 25 mm follicle. Mares were examined daily to monitor the growth of the dominant follicle until it ach ieved a diameter of 30 mm. At the first detection of a 30mm follicle accompanied by es trus, mares were treated with one of 4 treatments as follows: Group 1 saline control vehicle (100l); Group 2 GnRH (10g/100l); Group 3 P4 (1g/100l); Group 4 Melatonin (10g/100l), all were physiologically relevant doses established by our lab in previous work beyond the scope of this thesis. Treatment wa s given intrafollicularly with a 25 GA needle via transvaginal follicular injection into the dominant follic le. At 24 and 48 hours after injection the dominant follicle was sampled with the sa me 25 Ga and a small volume 200-500l of follicular fluid was collected for further analysis. Follicular fluid was evaluated for steroids, via RIA, and MMP-2 and TIMP-1 were determined by gelatin zymography and reverse zymography. Quantifica tion of MMP-2 and TIMP-1 wa s done with densitometric analysis by the Alpha Imaging 3.0 software system.

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78 Statistical Analysis Data from this experiment were analyzed using Statistical Analysis System (SAS) by least squares analys is of Variance (ANOVA). Experi mental model was a 2*4 factorial with main effects of treatment (control, Gn RH, P4, or Melatonin) and time (24 or 48 h) and the interaction of treatment by time. E2, P4 concentrations, MMP-2 and TIMP-1 adjusted densitometric units were the depende nt variables examined in this experiment. Orthogonal contrasts were used to exam ine the differences between groups. Experiment 1: Results and Discussion There was a significant effect of group (control; GnRH; P4; or Melatonin) (p<0.01* see appendix table F.35) for follicular E2 concentrations that was characterized by an increase in follicular E2 in the GnRH and P4 groups compared with control animals (Figure 5.1). E2 Follicular Concentrations by Gr oup and Time E2 pg/ml 0 100000 200000 300000 400000 24 hours 48 hours ControlGnRH P4 Melatonin a a a b a b a aGroup p<0.05* Group by time interaction p<0.05* Individual group comparisons by pdiffanalysis Bars with different superscript letters are different p<0.05* Figure 5.1: Mean follicular E2 conc entrations by group, Experiment 1

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79 There was also a significant effect of time in all groups except the melatonin group (p<0.05* see appendix table F.35) that was characterized by an increase over time from 24 to 48 h (Figure 5.1). There was no signifi cant group by time interaction for follicular E2 concentrations (p<0.53* see appendi x table F.35). The increase in E2 concentrations in the control group over time was expected, yet interestingly E2 was elevated compared with controls overall in response to follicular GnRH, and P4 administration. Further studies are indicated to examin e this response in increased E2 yet; at this point interpretation of this data becomes difficult. There was a significant effect of group (control; GnRH; P4; Melatonin) (p<0.01* see appendix table F.36) for follicular P4 concentrations that was characterized by a significant decrease in the P4 group as compared to controls. Overall group means were significantly reduced in the GnRH and P4 treatment groups compar ed with controls (p<0.01* see appendix table F.36) (Figure 5.2). There was also a significant effect of time in all groups with varied re sponses to treatments (p< 0.0001* see appendix table F.36), that was characterized by an increase in all groups except P4 treatment, (Figure 5.2). There was also a significant group by time in teraction (p<0.01* see appendix table F.36) Results suggest that the follicular injection of P4 did not stimulate any further production of P4, but may have acted in a ne gative feedback manner and blocked any increase above the 24 h control values. Results also suggest that GnRH somehow reduced the amount of follicular P4 overall compared with controls over time, while melatonin showed a marked increase in P4 concentrations overall and esp ecially by 48 h after injection.

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80 P4 Follicular Concentrations Group by Time P4 ng/ml 0 1000 2000 3000 4000 5000 24 hours 48 hours ControlGnRH P4 Melatonin * * a b a b a b a a Overall group means were significantly decreased p<0.05* in GnRH and P4 treatment grou p Group p<0.01 signified by Group by time interaction p<0.01* Individual group comparisons by pdiffanalysis Bars with different superscript letters are different p<0.01* Figure 5.2: Mean follicular P4 concentrati ons by treatment and time, Experiment 1 There was no significant effect of group (control; GnRH; P4; or Melatonin) on peripheral E2 or P4 concentrations (see a ppendix table F.37, F.38). There was a significant effect of group (control; GnRH; P4; Melatonin) on MMP-2 activity within the follic ular fluid (p<0.001* see appe ndix table F.39) that was characterized by an increase in MMP-2 in the P4 treatment group, while there was a significant decline in the MMP-2 activity in the GnRH and Melatonin treated animals as compared with controls (Figure 5.3). There wa s no significant effect of time (p<0.31* see appendix table F.39). There was an overa ll group by time interaction (p<0.0002* see appendix table F.39). This was most dramatic ally characterized by the decrease in the GnRH and Melatonin treatment groups compared with controls, (Figure 5.3). Results also suggest that there was an inve rsion in the response over time from the control animals to the P4 treated animals, showing and increase in MMP-2 from 24-48 h in the control group

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81 while there was a decrease in the P4 group from 24-48 h. The interp retation of this data is interesting yet puzzling and further work needs to be done in this area to fully understand the treatment responses. MMP-2 Follicular Levels by Group and Time IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 24 hours 48 Hours Control GnRHP4 Melatonin * * a b a a b a a a Overall group means were decr eased in GnRH and Melatonin p>0.03*Group p<0.001* Group by time interaction p<0.01* Individual group comparisons by pdiffanalysis Bars with different superscript letters are different p<0.001* Figure 5.3: Mean MMP-2 activity by group and time, Experiment 1 There was a significant effect of group for TIMP-1 activity (p<0.0001* see appendix table F.40). There was a significant effect of time in (p<0.0001* see appendix table F.40). There was also a significa nt group by time interaction (p<0.0001* see appendix table F.40) (Figure5.4). Indicati ng that TIMP-1 activity responded very differently to treatments. These data are complex and further work needs to be done elucidate the intricacies of the responses of follicular E2, P4, MMP-2 and TIMP-1 responses to follicular treatment.

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82 TIMP-1 Follicular Levels by Group and Time IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 24 hours 48 hours Control GnRHP4 Melatonin b d e a c e e c * * Overall group means were ranked by treatment effect as follows P4>Mel>GnRH>ControlGroup p<0.0001* Group by TIme interaction p<0.0001* Individual group comparisons by pdiffanalysis Bars with different superscript letters are different p<0.0001* Figure 5.4: Mean TIMP-1 activity by group and time, Experiment 1 Conclusions MMP-2, TIMP-1 and P4 all increase from 24 to 48 h af ter first detection of a 30mm follicle and intrafollicular saline admini stration. Interestingly intrafollicular P4 concentrations in experiment 1 disagree w ith our previous data from experiment 3 chapter one. This may be due to different e xperimental design and the use of different animals and animal numbers for each experime nt. Intrafollicular administration of GnRH appeared to block the increase in MMP-2 activity, and markedly increased TIMP-1 activity at 24 h compared with controls. Despite the increase in TIMP-1 at 24 h, it declined below control levels by 48 h. GnRH also decreased P4 concentrations overall compared with controls. Follicular administration of P4 did not alter overall mean P4 MMP-2 activity, however it was increased by 24 hcompared with controls, and was reduced by 48 h after

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83 treatment. TIMP-1 levels were significantly higher when compared with controls by 24 h, and continued to rise above controls by 48 h. There were no significant differences in follicular P4 concentrations at either time poi nt. Follicular Melatonin administration reduced MMP-2 activity at 24 and 48 h when compared with controls. TIMP-1 levels were increased compared with controls at 24 h, but declined below controls by 48 h after treatment. P4 follicular concentrations were below controls at 24 hours yet rose sharply by 48 h after treatment. Intrafollicular administration does not appear to adversely affect follicular development or biochemical function, indica ting it’s utility agai n as a study model. Furthermore, intrafollicular administration of hormones affects both tissue remodeling and steroidogenesis within the preovulatory follicle. Control animals followed similar trends from previous experiments with increasing MMP-2, and TIMP-1 over time. Overall these data suggest that there is ag ain an intimate relationship of MMP-2, TIMP-1 and P4. Hormone administration affects both the balance in the remodeling system and steroidogenesis. However more work needs to be done in this ar ea to elucidate the relationship between th ese three factors.

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84 CHAPTER 6 BLOCKADE OF LH AND/OR FSH AND THE EFFECTS ON TISSUE REMODELING AND STEROIDOGENESIS IN THE PREOVU LATORY FOLLICLE OF CYCLING PONY MARES Introduction Though the structure and secretory patterns of LH secretion are similar in many species, there are some animals that do have unique secretory patterns. In some species such as cows, sheep and humans, LH secreti on is low in amplitude during estrus and is secreted infrequently in high amplitude duri ng diestrus (Hauger et al. 1977, Barid 1978, Ellinwood and Norman 1983). These differing pa tterns have brought about the idea that P4 suppresses LH pulse frequency, while E2 inhibits pulse amplitude; this was best shown by the work of Goodman and Karsch in sheep in 1980. In contrast to sheep the s ecretion of equine LH has been shown to be episodic in nature (Evans et al. 1979, Po rter et al. 2001). The episodic release of LH and FSH is induced by the secretion of GnRH from the hypothalamus (Ginther 1992). Endocrine feedback and the overall inte ractions between levels of the hypothalamus and pituitary are important when studying the estrous cycl e of the mare. Data suggest that in many species that the follicle secretes E2 due to FSH secretion from the pituitary gland (Knobil 1980). The E2 then feeds back to the hypothalamus and pituitary gland to increase LH and GnRH (Tonnetta and diZerga 1989, Ginthe r 1992). The LH in turn causes ovulation of the dominant follicle. Once ovulation has o ccurred and the large lu teal cells of the follicle begin to produce P4. The P4 levels increase to averag es greater than 1ng/ml at

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85 about the time of the declin e in LH (Plotka et al. 1975, Gi nther 1992) data suggest that there is a negative f eedback of ovarian P4. E2 has also been shown to have a negativ e feedback on the LH secretion which causes an initial decline prior to the onset of the LH surge (Yamaji et al. 1972). In rats it has been shown that LH can be suppressed by either E2 or P4 (Goodman and Daniel 1985). In sheep data indicate that there is a synergy between E2 and P4 when they are administered to sheep at low doses and it cau ses a greater inhibiti on of LH (Goodman et al. 1981). E2 can have both positive and negati ve feedback depending on the species examined. In primates (Knobil 1974) it has been shown to have a negative feedback on gonadotropins, and also in sheep (Coppings and Malven 1976). In the mare positive feedback effects of E2 were originally sugge sted to be involved in the ovulatory surge of LH due to the timing of the decline in E2 and LH simultaneously and the peak in E2 which precedes the maximum LH concentrations (Pattison et al. 1972, 1974). During vernal transition in th e mare data indicate that E2 levels increase prior to the LH serum increase (Davies et al. 1987). Admini stration of E2 to ovariectomized mares during vernal transition increased LH secretion and pituitary content (Sharp et al. 1991). In another study by Garza et al. 1986b, da ta suggested that injections of E2 benzoate to ovariectomized mares during the breeding season resulted in an increase in LH secretion over the course of treatment. P4 has also been shown to have both s timulatory and inhib itory effects on gonadotropin secretion depending on the time of administration in relation to the estrous cycle. In E2 treated menopausal women progesterone inhibited the effects of the E2 treatment (Nillius and Wilde 1971, Wise et al. 1973). In anot her study by Leyendecker et

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86 al. 1972, it was shown that administration of large doses of P4 reduced the length of the LH surge in humans. Yet there is much inform ation in the literature that suggest that E2 priming is required in ovar iectomized animals for P4 to exert it’s effects (Mcpherson et al. 1975). In mares, LH remains elevated for 2-3 days post ovulation be fore declining over the next few days to diestrus levels (Evans and Irvine 1975, Ginther 1992). This delay in the decline of LH may be due to the lack of P4 feedback following ovulation. This information sets the backdrop to use P4 and E2+P4 in combination to examine the effects of the negative feedback on LH and /or FSH and the effects upon the tissue remodeling system and steroidogenesis within the pre ovulatory follicle of the cycling pony mare. Materials and Methods General Procedures Monitoring of the mares All mares were maintained on pastur e with ample water provided and supplementation of hay when necessary. Daily monitoring of the mares involved estrus detection with a teaser stalli on. Teasing responses were recorded to keep track of the onset and duration of estrus for each indivi dual mare. Utilizing rectal palpation and ultrasonography the number, location and size of ovarian follicles were measured and tracked and recorded daily. Follicular fluid processing Follicular fluid samples were collected at the appropriate times according to experimental protocols. Follicular fluid was completely evacuated with a large 35ml syringe and placed into a 50ml conical t ube. The conical tube was placed on ice for transport and stored at -80oC. until further analysis for steroid hormones E2, P4 via

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87 radioimmunoassay, and enzyme and inhibito r protein gels were run for MMP-2 and TIMP-1. Hormone assays Commercially available radioimmunoa ssay kits from Diagnostic Products Corporation (DPC) were used to determine estrogen and progesterone concentrations follicular fluid. Progesterone concentrations were determined using DPC Coat-A-Count kit. The sensitivity, inter-assay, and intra-a ssay coefficients of variation were 0.04ng/ml, 3.0% and 2.1%. Estrogen (estradiol-17b) con centrations were determined using DPC double antibody kit. The sensitivity, inter-assay and intra-assay coeffi cients of variation were 2.0pg/ml, 5.3% and less than 5%. Experiment 1: The effect of Exogenous P4 and E2+P4 Administration on Follicular P4 Concentrations, and Matrix Metallopr oteinase-2 and Tissue Inhibitor of Metalloproteinase-1 Activity within the Pre ovulatory Follicle of the Cycling Pony Mare. Methods and Materials This experiment utilized 8 intact cy cling pony mares. Beginning in May, mares were monitored daily and the luteal pha se was shortened by administration of prostagalindin-F2 to achieve estrus synchronization. Evaluation of mares involved rectal palpation and examination via ultrasonogra phy daily. Each mare was evaluated for follicle size, number of follicles on each ova ry, and location of each follicle. Mares were randomly assigned to group and treated from the day of prostaglandin injection. Mares were administered control vehicle, Group 1 (sesame oil; n=3), Group 2 P4 (150mg/day; n=3), and Group 3 E2+P4 (10mg+150mg/day; n=2) respectively until detection of a 30mm follicle. Mares were examined daily to monitor the growth of the dominant follicle until it achieved a diameter of 30mm. Mares were then taken to surgery the day of presentation of a 30mm follicle and via a la teral flank incision the dominant ovary was

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88 removed for further analysis. Ovaries were th en taken to a sterile culture hood, follicular fluid was aspirated from the dominant follicle and follicular wall and thecal tissue were collected for culture and some was snap fro zen on liquid nitrogen for mRNA extraction. Follicular wall tissue was then placed into 12 well culture plates in triplicate and cultured in the following media types 1) Control media MEM-alpha alone, 2). MEMAlpha with .01IU/ml hCG added to the media, and 3). MEM-alpha with MMP-2/9 Inhibitor at a concentration of 10M/ml. Tissue was cultured under standard conditions (370 C, N:O2:CO2, 45, 50 and 5% respectively) for 24 h. The media was then collected at 4 time points from the start of culture at 4, 9, 12 or 24 h and was evaluated for the production of P4, MMP-2 and TIMP-1. Follicular Fluid was also evaluated for P4 concentrations, MMP-2 and TI MP-1 activity. Follicular fl uid and culture media were evaluated for P4 concentrations via RIA and MMP-2 and TIMP-1 activity were determined by gelatin zymography and reve rse zymography. Quantification of MMP-2 and TIMP-1 was done with densitometric analysis by the Alpha Imaging 3.0 software system. All tissues that were snap frozen on liquid nitrogen and stored at -800 C for later RNA extraction. RNA was then dot blotted at a concentration of 5 g per sample. RNA was then probed with equine MMP-2 or TIMP -1 radiolabled with P32. Blots were then exposed to X-Ray film to quantify mRNA leve ls. All blots were then normalized to the 18s RNA Subunit for the bovine, this was acceptable due to species homology. Quantification of MMP-2 and TIMP-1 was done with densitometric analysis by the Alpha Imaging 3.0 software system.

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89 Statistical Analysis Data from this experiment were analyzed using Statistical Analysis System (SAS) by least squares analysis of variance (ANOVA). Experiment al model was a double nested model with main effects of in vivo treatment (control, P4, or P4+E2) and in vitro culture (control media, hCG media or inhibitor me dia), and time (4, 9, 12 or 24 h) and their respective interactions. P4 concentrations, MMP-2 Adjusted densitometric values and TIMP-1 adjusted densitometric values were the dependent variable s examined in this experiment. Least squares means were cal culated for all factors and tested for significance. Experiment 1: Results and Discussion There was no significant effect of in vivo treatment on follicular P4 concentrations (p<0.13* see appendix table F.41) (Figure 6.1). Yet P diff analysis suggested that P+E treatment may have decreased th e amount of intr afollicular P4 (p<0.06* see appendix table F.41), however P diff analysis showed no difference following exogenous P4 treatment as compared with controls (Figure 6.1). There was a significant effect of in vi vo treatment on MMP-2 activity within the follicular fluid collected from the dominant follicle (p<0.001* see appendix table F.42) (Figure 6.2). Data suggest that administrati on of P+E in combination reduced the amount of active MMP-2 within the follicular flui d (p<0.001* see appendix table F.42). Data indicated that there was a blockade of MMP-2 activity when the normal gonadotropin environment was disturbed by an overall lack of gonadotropins following P+E administration in vivo. Yet there was no signi ficant reduction in MMP-2 activity when the gonadotropins were reduced with P4 administration.

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90 Mean Follicular P4 Concentrations by Treatment P4 ng/ml 0 200 400 600 800 1000 1200 Control P4 P+E Group=p<0.13 a a b Individual group comparisons by pdiff analysis Bars with different superscript letter are different p<0.06* Figure 6.1: Mean follicular P4 concentr ations by treatment, Experiment 1 Mean Follicular MMP-2 By Treatment IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Control P4 P+E b Group=p<0.001* Individual group comparisons by pdiff analysis Bars with different superscript letters are different p<0. 001* a a Figure 6.2: Mean MMP-2 follicular ac tivity by treatment, Experiment 1 There was a significant effect of in vivo treatment on TIMP-1 follicular activity (p<0.01* see appendix table F.43) (Figure 6.3). P diff analysis indicated that there was no significant difference with P4 administration compared with controls, yet interestingly there was an increase in the TIMP-1 ac tivity following P+E administration compared with controls (p<0.01* see appendi x table F.43). Data suggest that there may have been a

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91 disturbance of the overall follicular homeos tasis of normal follicular development, and the increased levels of TIMP-1 could be a re flection of this disruption of the remodeling system. Yet during ultrasonic examination we could see a marked decrease in overall follicle number and overall follicle size. Mean Follicular TIMP-1 by Treatment IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Control P4 P+E b a a Group=p<0.01* Individual group comparisons by pdiff analysis Bars with different superscript letters are different p<0.01* Figure 6.3: Mean follicular TIMP-1 activity by treatment, Experiment 1 There was no significant effect of treatme nt (in vivo) on the overall expression of MMP-2 mRNA levels in either follicular wa ll or thecal tissue (p<0.8928* see appendix table F.44) (Figure 8.4). Data indicated that there were no differences in the overall amount of MMP-2 message present in either tissue type in response to any of the exogenous (in vivo) treatments. Data also would suggest that exogenous treatment had an effect on the amount of active enzyme prot ein that was already present within the follicular fluid, in the case of P+E treatment, yet interestingly there was no effect on the translation of the mRNA message that encodes the enzyme itself for any of the treatments given.

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92 MMP-2 mRNA Expression by Tissue and TX MMP-2 mRNA relative units 0 2 4 6 8 10 Follicle Wall Theca Control P4 P+E Group p<0.8928* Figure 6.4: Mean MMP-2 mRNA expression by tissue and Treatment (In Vivo), Experiment 1 There was no significant effect of treatme nt (In Vivo) on follicular TIMP-1 mRNA expression (p<0.5667* see appendix table F.45) (Figure 6.5). Data indicated that there were no differences in the overall am ount of TIMP-1 message present in either tissue type in response to any of the exoge nous (in vivo) treatment s. Data also would suggest that exogenous treatment had an effect on the amount of active enzyme protein that was already present within the follicula r fluid, in the case of P+E treatment, yet interestingly there was no effect on the tr anslation of the mRNA message that encodes the enzyme itself for any of the treatments given.

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93 TIMP-1 mRNA Expression by Tissue and Tx TIMP-1 mRNA relative units 0 5 10 15 20 Follicle Wall Theca ControlP4P+E Group p<0.5667* Figure 6.5: Mean Follicular TIMP-1 mRNA expression by ti ssue type and treatment (In Vivo), Experiment 1 There was a significant effect of treatment (In Vivo) on the overall production of P4 in culture (p<0.02* see appendix table F.46) (Figure 6.6). There was a significant effect of media type (In Vitro) in the overall production of P4 in culture (p<0.003* see appendix table F.46). There was a significant e ffect of time on the production of P4 in culture (p<0.01* see appendix table F.46) .There was no significant interacti on of treatment and culture media type (In Vivo*In Vitro) on the effect of P4 production in culture (p<0.07* see appendix table F.46) (Figure 6.6). P di ff analysis indicated that there were no differences within the control or P4 treatment groups (In Vivo) based upon media type on the production of P4 in culture. Yet interestingly P+E treatment (In Vivo) demonstrated a difference in the amount of P4 produced in culture depending on media type, with media types ranked as follows: Inhibitor>Cont rol>hCG by P diff analysis (p<0.001* see appendix table F.46) (Figure 6.6).

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94 P4 Concentrations In Vitro by TX P4 ng/ml 0 5 10 15 20 25 30 35 Control media hCG media Inhibitor media Control P4 P+E a a a a a a b a cIn Vivo p<0.02* In Vitro p<0.003* In Vivo*In Vitro p<0.07* Individual group comparisons by pdiffanalysis Bars with different superscript letters are different p<0.001* Figure 6.6: Mean P4 concentration in culture by treatment, Experiment 1 There was a significant effect of treatment (In Vivo) on the overall production of active MMP-2 in culture (p< 0.0001* see appendix table F.47 ) (Figure 6.7). There was also a significant effect of culture media (In Vitro), time treatment*culture media (In Vivo*InVitro) and all of the associated inter actions of these 3 f actors (p<0.0001* for all factors see appendix table F.47). Data s uggest that MMP-2 production in culture was different within each treatment group by P diff analysis (p<0.01* see appendix table F.45). Data indicate that in al l 3 treatment groups (In Vivo) addition of hCG to the media increased the amount of active MMP-2 produced compared to either control or inhibitor media types. This would suggest that addition of hCG to th e culture media was able to overcome the lack of gonadotropin stimulati on prior to culture, on the production of active MMP-2 (p<0.01* see appendix table F.47) (Figure 6.7).

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95 a Mean MMP-2 In Vitro by Treatment IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Control Media hCG Media Inhibitor Media Control P4 P+E e c a a a a b b dIndividual group comparisons by pdiff analysis Bars with different superscript letters are different p<0.01* In Vivo=p<0.0001* In Vitro=p<0.0001* In Vitro by Treatment=p<0.0001* Figure 6.7: Mean MMP-2 production In Vitro by Treatment, Experiment 1 There was no significant effect of treatme nt (In Vivo) on the production of TIMP-1 in culture (p<0.393* see appendix table F.48) (F igure 6.8). There was a significant effect of culture media (In Vitro) on the production of TIMP-1 in culture (p<0.01* see appendix table F.46). There was also a significant e ffect of time on the production of TIMP-1 in culture (p<0.001* see appendix table F.48). Ther e was also a significant interaction of treatment*culture media (In Vivo*In Vitro) (p<0.01*) and culture media*time (In Vitro*time) (p<0.02* see appendix table F.48). However there was no significant interaction of treatment*time (In Vivo* In Vitro) (p<0.15*) or treatment*culture media*time (In Vivo*In Vitro*time) (p<0.56* see appendix table F.48). Data would suggest that when there is lit tle or no gonadotropin stimulatio n there is no difference in the amount of TIMP-1 produced in culture, or there in no response to In Vitro treatment. Data would suggest that follicular tiss ue may be hurt by a lack of gonadotropin stimulation. The response of P+E treatmen t on TIMP-1 may be outside the normal

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96 bounds of the follicular wall, and there may be some other mechanism of inflammation or apoptosis involved, which will require further work in this area. Mean TIMP-1 Levels In Vitro by Treatment IDV/CV Densitometric Units 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Control Media hCG Media Inhibitor Media In Vitro p<0.01* In Vitro by Tx p<0.01* a a a b c c c c aIn Vivo p<0.393* ControlP4P+EIndividual group comparisons by pdiffanalysis Bars with different superscript letters are different p<0.01* Figure 6.8: Mean TIMP-1 production In Vitro by treatment, Experiment 1 Conclusions This experiment demonstrated the eff ects of reduced LH and/or FSH on the follicular production of P4, MMP-2 and TIMP-1. It appear s that P+E treatment had the most effect on all 3 factors within the follicular fluid; there was a decrease that approached significance in P4 concentrations, a significan t decrease in MMP-2 activity and a significant increase in TIMP-1 activ ity. The overall decrease in MMP-2 activity in the follicular fluid may be a re sult of significantly high TIMP-1 levels in response to P+E administration. The overall increase in TIMP-1 activity may be a re sult of a disturbance of the normal follicular homeostatic environment, or may be a normal response to decreased levels of MMP-2 activity, or possi bly may have been caused by some other unknown inflammatory or apoptotic signal. Furthe r work in this area needs to be done to

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97 elucidate this effect on TIMP -1. Data indicated that all 3 exogenous treatments had no effect on either MMP-1 or TI MP-1 mRNA expression levels in the follicular wall or thecal tissue. This is interesting in that bloc kade of LH and/or FSH appears to have no effect on the translation of the protein me ssage but does affect the overall amount of active enzyme or inhibitor within the follicular fluid. Data would again suggest that there may be a positive MMP-2-gonadotropin interaction going on and due to the overall lack of gonadotropin in the P+E treatment group ma y be an explanation for the decreased MMP-2 activity in the follicular fluid. Data woul d also suggest that P4 treatment alone is not enough of a LH and/or FSH block to significantly reduce follicular P4, MMP-2 or TIMP-1. The in vitro production of P4 appeared to be effected by the overall lack of gonadotropins in the P+E treatment group. Yet da ta would suggest that both the control media and the inhibitor media had a strong effect on the increase in the overall P4 production in vitro. It is intere sting to speculate that the overall increase in TIMP-1 production in culture may be stimulating th e increase in P4 steroidogenesis or some other unknown regulatory change may be i nvolved, which is beyond the scope of this thesis. The production of MMP-2 in vitro appear s to be effected by the overall blockade of LH and/or FSH via P4, or P+E administration in vivo. Da ta from culture indicated that addition of 0.1IU/ml hCG was able to overc ome some of this lack of gonadotropin stimulation and increase the pr oduction of MMP-2 in culture in all 3 in vivo treatment groups. Results would again suggest that there is a positive MMP-2-gonadotropin interaction demonstrated here in vitro. Data also indicated that P+ E treatment in vivo had a marked effect on the amount of MMP-2 produced in culture even more so than P4 alone

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98 compared with controls. Data suggest that overa ll culture media (in vitro) had an effect in the production of TIMP-1. It appears however that P4 administration in vivo reduced TIMP-1 levels the most, while P+E treatment increased TIMP-1 production in culture. This is an interesting yet unexp ected effect of P+E treatment, but is may be a function of the low levels of MMP-2 produced in culture in response to P+E administration. It could simply be that this is a part of the norm al regulatory balance of the remodeling system within the preovulatory follicle, yet further work in this area needs to be done to examine this relationship further. Overall from this experiment data would suggest that ovarian tissue remodeling for follicle development and ovulation depends on a positive MMP-2gonadotropin interaction.

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99 CHAPTER 7 CONCLUSION The primary focus of this lab’s research has been to gain a better understanding of the reproductive cycle of the mare. Horses are a unique and different animal when compared to other domestic farm species. Some unusual distinctions such as the 5-7 day estrus period, the inside out nature of the ovaries and their long day seasonality have earned mares the coined term “unique”. It is debatable whether or not the mare is truly unique among other domestic animals, I would prefer to say she is “quirky”. These facts have helped foster the reminder to be open minded, novel, and forces us to look for new answers to things that have not been discovered yet by other researchers. The anatomical differences in the ovary alone make one wonder how this could have become an adaptive advantage for the ma re. Over the course of the last 3 years our lab has postulated that the prolonged estrus peri od may be related to the inside out nature of the mare’s ovary, and the restriction of ovulation to only one point on the ovary, the ovulation fossa. So how does a mare ovulate a large 40mm follicle, how does it migrate to the fossa, and what drives this process? We have postulated a possible model for some of the mechanisms that lead to ovulation in the horse. In this model we hypothesize that there is an interaction between MMP-2, P4 and TIMP-1. This however is unlike the cow or sheep model where the activ ation of MMP-2 and TIMP-1 is predominately driven by estrogens (Smith et al. 2002). It is our hypothesis that the tissue remodeling system within the ovary and the steroidogenic capacity of the follicle are somehow linked together. We hypothesize that there is an in timate relationship between 3 intraovarian

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100 factors, the steroid P4, MMP-2 and TIMP1. Through these studies we have concluded that ovarian tissue remodeling for both follic le development and ovulation depends on a positive gonadotropin-MMP-2 interaction. However based upon the experiments in chapter 6 we concluded that TIMP-1 is ve ry involved in the re modeling cascade for follicular growth and ovulation. We speculate that TIMP-1 may be having an effect on the thecal-granulosa cell connections and in the absence of gonadotropins it appears that TIMP-1 increases. This is an interesting point to sp eculate on, seeing as how gona dotropin stimulation also increases TIMP-1; this may be regulating the sy stem more than we previously thought. It could be that TIMP-1 acts as the balan ce for enzymatic activation and activity and physiologically it is the inhib itor of MMP-2 in most other ti ssue types. It could be that TIMP-1 is the gate keeper of the ECM turnover within the ov ary of the mare. We could speculate that TIMP-1 must be activated first in response to stimulation and the amount of TIMP-1 produced with in the follicle sets the stage for the activation of MMP-2 and the ECM breakdown that accompan ies follicular growth and the ovulatory process. Interestingly in ot her species it has been shown that TIMP-1 has steroidogenic capacity to differentiate granulosa cells into large luteal cells and stimulate the production of progesterone, in sheep and primates (Smith et al.2000, Chaffin et al. 1999). This could be the situation in the mare and might possi bly explain why we see a large increase in progesterone following gonadotropin stimulation. It could be that TIMP-1 somehow acts in a paracrine fashion on the granulosa cells to stimulate luteinization and increase progesterone output prior to ovulatio n and the formation of the CL.

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101 In chapter 3 we examined the relationship of gonadotropin stimulation and its effects on the remodeling system and steroi dogenesis. Data from experiment one, and three suggest that gonadotropin stimulation hastens ovulation by stimulating the tissue remodeling enzymes. It also suggested to us that there was a progesterone regulatory element involved in this system which is cont radictory to most other farm species, with it being linked to estrogen. In the cow and sheep it is the preovulatory rise in estrogens that stimulate MMP-2 activity within the follicle and hasten ovulation (McIntush et al. 1996). However in the primate it appears that proge sterone may be the regulatory steroid to stimulate MMP-2 and TIMP-1 and hasten ovulation (Chaffin et al. 1999, 2000). Based upon our data we might speculate that the horse my in fact be more similar to the primate in the regulation of ECM remodeling and ovulation. The increase in P4 at 24 hours suggests a causal relationship between MMP-2 and TIMP-1 and hCG, suggesting to us that luteinization was likely initiated. In experiment 2 we examine the ability to take micro samples of the follicular environment over time and discovered that our technique had no adverse effects on follicular development when done over the cour se of 4 consecutive days. This experiment also indicated that both the single sampled animals and the serially sampled animals showed the same basic profiles with respec t to all 3 intrafollicular factors. In experiment 4 we examined the eff ects of gonadotropin administration of mRNA message expression in follicular tissues over time. Data from experiment 4 indicated that MMP-2 message translation was likel y pushed through faster following hCG administration at 9 hour after treatment. Yet there was no difference at 24 hour post treatment which suggests to us that the message was translated faster earlier in response

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102 to gonadotropins and peaked before 24 hours. This may explain why we see more active MMP-2 and TIMP-1 at 24 hours than at 9 hours after hCG administration in the follicular fluid. We can speculate that there is a time lag for message to protein translation and activation and that it occu rs somewhere between 9 a nd 24 hours after gonadotropin administration. We could also speculate that possible the effect of the increase in TIMP1 mRNA may somehow regulate the expression of it enzymatic partner MMP-2 and that both are time dependent and ma y be gonadotropin dependent. Our next step was to examine the relations hip of these 3 factors further through the inhibition of P4 and MMP-2. Data from this series of experiments would again indicate that there is a strong relati onship between P4, MMP-2 and TIMP-1 and if you block P4 or MMP-2 you reduce the amount of the other 2 factors. This again strengthens our hypothesis of a causal relationship between P 4, MMP-2 and TIMP-1 both in the normal animal and in response to gonadotropin stimul ation. We still do not fully have a complete understanding of how the system works in response to intrafollicular hormone administration yet data suggest that intraf ollicular administration of hormones affects both the tissue remodeling system and steroidogenesis. Furthermore in the last experiment 8, we again demonstrated th at by reduction or blockade of LH and/or FSH that there is an intimate relationship between follicular P4, MMP-2 and TIMP-1. Upon examining the data from the follicular fluid aspiration, data indicated that P+E decreased the amount of follicular P4 and MMP-2 while increasing the amount of TIMP-1. We can speculate that this may be due to the effect of P+E blocking the gonadotropins and therefore decrea sing the follicular P4 and MMP-2 due to their strong relationship. Yet while MMP-2 decreased TIMP-1 increased we can

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103 speculate that this may be sue to the low levels of MMP-2 and TIMP-1 is helping to inhibit the enzyme in a poor gonadotropin e nvironment or that there may be another factor that is acting to stimulate TIMP-1 vi a an inflammatory cascade of some sort or apoptotic mechanisms. Data from the mRNA experiment suggest that even in a reduced or blocked gonadotropic environment that it has no eff ect on the mRNA expression of MMP-2 or TIMP-1. Results from the culture of the follic ular tissue suggest that there is a strong relationship between MMP-2 and gonadotropi ns, demonstrated by the reduction in MMP2 production in tissue treated with P4 or P+E. Yet interestingly hCG addition to the media overcame some of this poor gonadotropin environment. So overall these series of experiments have lead us to develop a model for the horse that suggests that there is a causal relationship between follicular P4, MMP-2 and TIMP-1 in response to gonadotropin stimulation. It also leads us to believe that follicle development and ovulation in the horse depend on a positiv e gonadotropin-MMP-2 interaction, which leads to some of the associated tissue re modeling and steroidogenic changes that are associated with these processes. We hope that these experiments will help shed some light on the ovulation process in the mare, and may eventually lead to more economical methods for ovulation induction and timed breeding or AI.

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104 APPENDIX A PICTURES OF GELS AND BLOTS Tissue Remodeling SystemECM Follicle Wall Basement Membrane Theca interna cells Granulosa cells P4 MMP-2 TIMP-1 Gonadotropins Gonadotropins (-) (+) (+) (+) (+) (+) (+) ? ? ? ? Figure A.1: Hypothetical model of the remodeli ng system in the preovulatory follicle of the mare based upon this series of experiments

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105 1 2 3 4 5 67 89 10 Representative 7.5%Gelatin Zymography.Lanes2-9 follicular fluid samples, all loaded at 100micrograms of protein. Lane 10 Human MMP-2/9 recombinant standard Figure A.2: Sample Gelatin Zymography gel

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106 DOT BLOT TIMP-1BY TISSUE AND TREATMENT TIME TRENDS PROJECT 1=CL+ 2=C24FW 3=C24FW 4=C24T 5=C24T 6=H24FW 7=H24FW 8=H24T 9=H24FW 10=H24CH 11=C9FW 12=C9FW 13=C9T 14=H9FW 15=H9FW 16=H9FW 17=H9T 18=H9T 19=H9T Figure A.3: Messenger RNA Dot Blot probed with TIMP-1 in Control and hCG treated mares

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107 DOT BLOT mRNA MMP-2 BY TISSUE AND TREATMENT TIME TRENDS PROJECT 1=CL+ 2=C24FW 3=C24FW 4=C24T 5=C24T 6=H24FW 7=H24FW 8=H24T 9=H24FW 10=H24CH 11=C9FW 12=C9FW 13=C9T 14=H9FW 15=H9FW 16=H9FW 17=H9T 18=H9T 19=H9T Figure A.4: Messgenger RNA Do t Blot probed with MMP-2 in control and hCG treated mares

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108 DOT BLOT mRNA TIMP-1 BY TISSUE AND TREATMENT SURGERYPROJECT 20=CFW 21=CT 22=CFW 23=P4FW 24=P4T 25=P4FW 26=P4T 27=P4CH 28=P4T 29=P4FW 30=P+EFW 31=P+ET 32=P+EFW 33=P+ET Figure A.5: Messenger RNA Dot Bl ot probed with TIMP-1 in control P4 and P+E treated mares

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109 DOT BLOT mRNA MMP-2 BY TISSUE AND TREATMENT SURGERY PROJECT 20=CFW 21=CT 22=CFW 23=P4FW 24=P4T 25=P4FW 26=P4T 27=P4CH 28=P4T 29=P4FW 30=P+EFW 31=P+ET 32=P+EFW 33=P+ET Figure A.6: Messenger RNA Do t Blot probed with MMP-2 in control, P4 and P+E treated mares

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110 DOT BLOT mRNA 18s STANDARDIZATION Figure A.7: Messenger RNA Dot Blot probed with 18s Bovine mRNA for standardization

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111 APPENDIX B GELATIN ZYMOGRAPHY AND REVERSE ZYMOGRAPHY This specific protocol was designed by Dr Michael Smith from the University of Missouri at Columbia. This procedure was valid ated with follicular fluid samples in his lab in Missouri Fall 2000. This procedure was th en validated in our lab at the University of Florida and was utilized fo r all experiments. This methodology is designed specifically to examine the activity of matrix metallopr oteinase-2 and 9. The gelatin substrate is specific for these 2 enzymes alone. It is a ba sic SDS-PAGE non reduc ing protein gel with gelatin added to the matrix. The enzyme activ ity will digest the gelatin substrate within the gels and can be visualized as clear spots in a dark field. This allows for identification of the enzymatic form, as active, latent or pro-form depending on migration and digestion of the matrix. It also allows for quantification of enzyme amount by densitometric analysis. Procedure Casting gels The following components are mixed together to cast the gelatin zymography minigels To make a non-reducing protein zymography gel 7.5% bis acrylamide SDS page gels loaded with gelatin substrate Materials required A) ddh2o-8.5 mls B) 5mg/ml gelatin-6mls C) #3 tris hcl buffer pH 8.8-7.5 mls D) 10% SDS300 l E) 30% bis acrylaimde-7.5mls F) 10%ammonium persulfate-150 l G) TEMED-15 l

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112 Recipe for 2 minigels from Dr. Mi ke Smith University of Missouri -Mix components together in 50ml conical tube pour gel in-between glass minigel plates -Cover top of gel with wa ter solubalized butanol -Allow to polymerize for 40 minutes per gel -Wash off butanol with ddh20 -Dry top of running gel w ith watmans filter paper Cast stacking gel on top of running gel 5% SDS PAGE recipe Mix components up as follows A)Ddh2o-3 mls B) #4 buffer-tris hcl-pH-6.8-1.25 mls C) 10%SDS-50 l D) 30% bis acrylaimde-670 l E) 10%ammonium persulfate-25 l F) TEMED-4 l -Mix components together, pour over top of runn ing gel between glass plates, fill to top of plates, add 10 well-30 l per well comb and allow to polymerize for 30 minutes -Recipe enough to make 2 stacking gelsfor minigels Sample preparation and Loading of gels 1) Take samples, run Bio Rad protein assay to quantify protein conc entration per sample. Standardize all samples to the same amount of protein (100g protein per sample for follicular fluid) or if your concentra tion of protein is low standardize to 10 l per sample. 2) Take 100g or10 l of sample out of stock (ex. follicular fluid) add to 500 l minitube. 3) Label tubes 1-10 for lane designation 4) Add 10 l of 1X zymography loading dye to samples. 50mM Tris pH 6.8 2% SDS 1% Bromophenol Blue 10% glycerol Bring up to 50ml volume with ddh20 Aliquot 1 ml to tubes and store at -20 degrees Celsius Use each 1 ml tube only once never reuse dye.

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113 5)Vortex and mix well 6) Let samples sit at 4 degrees Celsius until ready to load on gels. 7) Make up fresh tank buffer for gels boxes. Recipe 250mls of 1M-glycine stock 6.25 mls of 2M tris hcl-pH7.5 1g SDS Bring up to volume in 1L ddH20. PH buffer to 8.3 8) Pour buffer over top of gels in running apparatus. 9) Load samples in lanes 1-9 10) Load 2l of human recombinant active MMP -2/9 enzyme as a standard in lane 10 11) Run gels at 60mV to stack on bench top for 30 minutes-1 hour then up voltage to 80mV and run gels for 2-3 hours until dye front reaches the bottom edge of the gel 12) Remove gels from glass plates place in to Tupperware containers with zymography wash buffer(BIORAD) 13) Place gels on Rocker Plate at Room Te mperature, let wash for 30 minutes 14) Change Buffer 3X and repeat washing for 30 minutes each time. 15) Rinse gels well with ddh20 and refill container with ddh2o, place back on rocker plate for 10 minutes repeat at least 3 tim es then fill container 1/2 way with ddh20 place back on rocker plate for 20 minutes. 16) After washing is complete add Zymogra phy incubation buffer (BIORAD) to samples and place into 37 degree Celsius incubator (horse samples bring temperature up to 39 degrees Celsius) on rock er plate for 24-30 hours. Day Two 1) Remove gels from incubator, rinse 5 X with ddh20 thoroughly. 2) Make up fresh stain for each gel: Coomassie R250 1% stock4mls Methanol4mls Acetic Acid-4 mls 3) Allow to stain gels 2-4 hours to visu alize MMP2/9 enzyme digestion bands.

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114 4) Rinse stain with ddh20 a few times until majority that is unbound is gone. 5) Make up fresh destain: 30% Methanol 30mls 1% Formic Acid 1 ml Bring up to 100ml volume with ddh2o. 6) Allow to destain for 24 hours on rocker plate. 7) Bands should appear white in blue background. 8) Analyze gels with alpha imaging denistometric software program. 9) Then if so desired dry gels between 2 sheets of cellophane (BIORAD) overnight.

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115 Reverse Gelatin Zymography This specific protocol was designed by Dr Dylan Edwards at the University of Cambridge in England and is available as a kit from his lab. Minor modifications to his protocol were made with the assistance of Dr. Michael Smith from the University of Missouri. This procedure was validated with follicular fluid samples in his lab in Missouri Fall 2000. This procedure was then va lidated in our lab at the University of Florida and was utilized for all experiments. This methodology is desi gned specifically to examine the activity of tissue inhibitors to metalloproteinase-1. The substrate of the gels is loaded with active MMP-2/9 enzyme and gelatin and is specific for th is inhibitor. It is also a basic SDS-PAGE non re ducing protein gel with act ive MMP-2/9 added to the matrix. The inhibitor activity will prevent th e activity of the enzyme within the gel and can be visualized as dark s pots on a light field. This allo ws for identification of the inhibitor and quantification by densitometric analysis. Procedure Casting gels The following components are mixed toge ther to cast the reverse gelatin zymography minigels To make a non-reducing protein reverse zymography gel 12% bis acrylamide SDS page gels loaded with gelatin substrate Materials required A) 5mg/ml of gelatin in 3mls of ddh20 B) 3.75 mls of 1.5M Tris-CL pH8.8 C) 6mls of 30% bis acryl amide D) 1 ml of solution A (active enzyme)

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116 E) 150 l 10%SDS F) 150 l of 10%APS G) 7.5 l of TEMED Solution A and standards provided by Dr. Dyla n Edwards at the University of Cambridge England Recipe for 2 minigels from Dr. Mi ke Smith University of Missouri Mix components together in 5-ml conical tube pour gel in-between glass minigel plates Cover top of gel with water solubalized butanol Allow to polymerize for 40 minutes per gel. Wash off butanol with ddh20 Dry top of running gel with watmans filter paper Cast stacking gel on top of running gel 5% SDS PAGE recipe Mix components up as follows A) Ddh2o-3 mls B) #4 buffer-Tris-Cl-pH-6.8-1.25 mls C) 10%SDS-50 l D) 30% bis acrylaimde-670 l E) 10% APS-100 l F) TEMED-10 l Recipe for 2 minigels from Dr. Mi ke Smith University of Missouri Mix components together in 50ml conical tube pour gel in-between glass minigel plates Cover top of gel with water solubalized butanol Allow to polymerize for 40 minutes per gel Wash off butanol with ddh20 Dry top of running gel with watmans filter paper Sample preparation and Loading of gels 1) Take samples, run Bio Rad protein assay to quantify protein conc entration per sample. Standardize all samples to the same amount of protein (100g protein per sample for follicular fluid) or if your concentra tion of protein is low standardize to 10 l per sample. 2) Take 100g or10 l of sample out of stock (ex. follicular fluid) add to 500 l minitube

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117 3) Label tubes 1-10 for lane designation 4) Add 10 l of 2X zymography loading dye to samples. 50mM Tris pH 6.8 2% SDS 1% Bromophenol Blue 10% glycerol Bring up to 50ml volume with ddh20 Aliquot 1 ml to tubes and store at -20 degrees Celsius Use each 1 ml tube only once never reuse dye 5)Vortex and mix well 6) Let samples sit at 4 degrees Celsius until ready to load on gels 7) Make up fresh tank buffer for gels boxes Recipe 250mls of 1M-glycine stock 6.25 mls of 2M tris hcl-pH7.5 1g SDS Bring up to volume in 1L ddH20. PH buffer to 8.3 8) Pour buffer over top of gels in running apparatus 9) Load samples in lanes 1-9 10) Load 2l of TIMP-1 as a standard in lane 10 11) Run gels at 60mV to stack on bench top fo r 30 minutes-1 hour then up voltage to 80mV and run gels for 2-3 hours until dye front reaches the bottom edge of the gel 12) Remove gels from glass plates place into Tupperware containers with zymography wash buffer(BIORAD) 13) Place gels on Rocker Plate at Room Te mperature, let wash for 30 minutes 14) Change Buffer 3X and repeat washing for 30 minutes each time 15) Leave in Wash buffer overnight on rocker plate Day Two

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118 1) Rinse gels well with ddh20 and refill contai ner with ddh2o, place back on rocker plate for 10 minutes repeat at least 3 times th en fill container 1/2 way with ddh20 place back on rocker plate for 20 minutes 2) Put into incubation buffer for 24-30 hours in 37degree incubator (39 degrees for horse samples) with no rocking Day Three 1) Remove gels from incubator, rinse 5 X with ddh20 thoroughly 2) Make up fresh stain for each gel Coomassie R250 1% stock4mls Methanol4mls Acetic Acid-4 mls 3) Allow to stain gels 2-4 hours to visu alize MMP2/9 enzyme digestion bands 4) Rinse stain with ddh20 a few times until majority that is unbound is gone 5) Make up fresh destain: 30% Methanol 30mls 1% Formic Acid 1 ml Bring up to 100ml volume with ddh2o 6) Allow to destain for 24 hours on rocker plate 7) Bands should appear white in blue background 8) Analyze gels with alpha imagi ng densitometric software program 9) Then if so desired dry gels between 2 sheets of cellophane (BIORAD) overnight

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119 APPENDIX C DOT (NORTHERN) BLOT PROCEDURE RNA Isolation The isolation of high quality RNA from cells and/or tissues is of paramount importance for most downstream applications. The isolation of total cellular RNA is the initial step in many molecular processes incl uding Northern and Dot Blot analysis, RNase protection assays, in vitro translation, cDNA library constr uction and certain polymerase chain reaction (PCR) based techniques (RTPCR and differential display, for example). Therefore, during RNA isolation procedures it is imperative to inhibit endogenous ribonucleases (RNases) which are found in al l living tissues. The extraction should be preformed quickly and efficiently. Wear latex gloves at all time during the procedure to prevent ribonuclease contamination of the sample s. All materials that are plastic in nature should be autoclaved prior to the start of the procedure. RNA EXTRACTION Solutions Trizol reagent (Invitrogen Technologies) Chloroform Isopropanol 75% Ethanol (diluted in dd h2o) Biological grade water A) Label one 15ml conical tube for each sample and add 3mls of trizol solution B) Allow frozen tissue samples to partially thaw at room temp erature 2-3 minutes C) Using a clean razor blade, mince 250-300m g of tissue and place in appropriate tubes D) Homogenize tissue with a TISSU MIZER using 2-5 second bursts

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120 E) Transfer 1ml aliquots to 1.5 microfuge t ubes and incubate for 5 minutes at room temperature F) Add 200l of chloroform, cap tubes and shake G) Incubate at room te mperature for 3 minutes H) Centrifuge at 13,000 x g for 15 minutes at 4 degrees I) After centrifugation, mixtur e will have 2 phases, lower organic phase and upper aqueous phase. RNA is in upper aqueous pha se. Transfer aqueous phase to fresh 1.5ml microfuge tube. Volume should be no more than 700l J) Add 500l of isopropanol to each tube, mix by gentle inversion K) Incubate at room te mperature for 10 minutes L) Precipitate RNA by centrifugation at 13,000 x g for 10 minutes at 4 degrees M) Decant supernatant N) Wash RNA pellet with 1ml of 75% ethanol. Mix by vortexing O)Centrifuge for 3 minutes at 12,000 x g at 4 degrees P) Decant supernatant and let tubes dry upside down on paper towel for 5 minutes Q) Resuspend RNA pellet in 25 of pure molecular biology grade water R) Quantify RNA by reading absorbance at 260nm and 280nm. To quantify place 5l of RNA into 995l of sterile water T)Calculate the ratio of absorbance 260 to 280 and RNA with the following equation A260*dilution factor*33g/ml/1000g/ml=g/ml RNA or Multiply A260 by 6.6 Dot Blot Membrane Transfer Solutions Ddh20 20X SCC (prepare 100mls) NaCL 175.3g Na-Citrate 88.2g Ddh20 80ml Adjust pH to 7 Add ddh20 to one liter

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121 10X SCC (prepare 100mls) Dilute 1:2 20 X SCC with ddh20 Deanturation Buffer 20mM Tris 200l 50% Formamie 5ml 6% Formaldehyde 1.62ml (37%) ddh20 3.18ml A) Pipette 10g of sample in to numbered 1ml microfuge tubes B) Wet a piece of nylon membrane in ddh20 and soak in 10X SCC for 10 minutes at room temperature C) Place a sheet of absorbent paper, wetted in 10 X SCC on top of the vacuum unit of the apparatus. Place the wet nylon membrane on the bottom of the sample wells cut into the upper section of the manifold. Clamp the 2 parts together, and connect vacuum line D) Fill the well with 10X SCC (500l) and appl y gentle suction until all fluid has passed through the nylon membrane in 5 minutes E) dilute RNA with 250l of sample denaturation buffer. Incubate for 5 minutes at 65 degrees C then cool samples on ice F) Add 250l of 20X SCC to each sample and mix G) Load samples into wells and apply gent le suction. After all samples have passed through to membrane, rinse each well with 500l of 20X SCC. When wash has passed through turn off vacuum H) Remove nylon membrane, wrap in plastic and expose to UV light for 90 seconds to crosslink RNA I) Remove from plastic, mark for orientation and put membrane into absorbent paper, incubate for 1 hour at 85 degrees C K) Put membrane in plastic bag and refrigerate until hybridization HYBRIDIZATION Solutions UltraHyb Buffer (Ambion) Probe of interest

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122 Random Primers DNA labeling system (Invitrogen) P32 radionucleotide A) Remove P32 from freezer B)Warm UltraHyb buffer to 42 degrees C C) Turn on hybridization oven to 42 degrees C D) Turn heat block on to 100 degrees C E) After Buffer warm up put membrane into hybridization tube and add 20l of buffer, place into oven for 1-2 h F) In a 500l microfuge tube, add 1l of probe plus 22l of ddh20, boil mixture for 3 minutes in heat block G) Spot centrifuge and place on ice H) Add 15l of random primer kit buffer I) Make 6l of dNTP mixture (2l of each from kit dGTP, dATP, and dTTP) K) Add 6l of dNTP to probe J) Add 5l pf P32 O) Add 1l of Klenow from kit P) Spot centrifuge for 5 seconds and in cubate at room temperature for 1 h Q)Fractionate using G50 Sephadex column I) Check fractions and pool 3 highest add 30l of probe to membrane and hybridize overnight WASH Solutions High (2X SCC/0.1%SDS) Ddh20 2685ml 20X SCC 300ml 20% SDS 15ml Low (0.1X SCC/0.1% SDS)

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123 Ddh20 2970ml 20X SCC 15ml 20% SDS 15ml A)Remove “hot hybridizati on fluid into radio waste” B) 1st wash low for 20 minutes in tube C) Drain and do 2nd wash for 10 minutes with High wash at 50 degrees C D) Drain second wash and remove membrane from tube. Dry with absorbent paper, and check signal intensity with Geiger counter Put down onto film for number of days appropriate for your pr obe at -80 degrees C INCUBATION AND FILM DEVELOPING A) Put membrane into plastic bag and label B) Adjust position of membrane to center into X-Ray cassette C) Go to dark room and put film inside cassette and label D) Incubate at the appropriate temperat ure and appropriate days for your probe. E) Film development. Close water switch on developer machine. Fill the water compartment with 2 L of water. Turn machine on, wait to warm up F) When machine is warmed up feed X-Ray film into machine G) After development, turn off machine a nd perform densitometric analysis on X-Ray film

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124 APPENDIX D ESTROGEN RADIOIMMUNOASSAY The Diagnostic Products Corporation (D PC, Los Angeles CA) Double Antibody kit is a sequential radioimmunoassay in which th e unknown sample is pre-incubated with anti-estrogen antiserum I-125-labled estradio l is then added and competes with the estradiol present in the unknown sample fo r antibody sites. The samples are then incubated for a fixed time and separati on of bound from few is achieved by PEGaccelerated double antibody method. The anti body-bound fraction is then precipitated and counted on a gamma counter. The concentr ation of estrogen in the unknown sample is determined by the calibration curve. Materials Supplied in the Kit 1. Estradiol Antiserum (E2D2, 5 E2D1) 2. I-125 labeled Estradiol (E2D2) 3. Estradiol Calibrators (E2D3-9); 0, 5 20, 20, 150, 500 pg/ml 4. Precipitating Solution (N6, 5N6) Materials Not Provided but Required 1. Plain, uncoated 12x75mm glass tubes 2. Plasma from control mare with known concentration of estrogen Procedure 1. Label 18 tubes in duplicate: T (Total Tube s), NBS (Non-specific binding), a nd calibrators A-G. Labe l additional tubes in duplicate for all unknown plasma and follicular fluid samples.

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125 125 2. Pipette 200l of zero calibrator in to A and NBS tubes. Pipette 200l of each calibrator B-G into tubes. Pipette 200l of each unknown into tubes. 3. Add 100l of Estradiol Antiserum to all tubes except T and NBS and vortex. 4. Incubate for 2 hours. 5. Add 100l of I-125 Estradiol to all tubes and vortex 6. Incubate for 1 hour at room temperature. 7. Add 1ml of cold precipitating soluti on to all tubes except T and vortex 8. Incubate 10 minutes at room temperature 9. Centrifuge fro 15 minutes at 3000 x g 10. Decant supernatant and retain precipitate for counting Calculation of Results Estradiol concentrations of the unknown samples are determined from a logit-log representation of the calibration curve. The average NBS-corrected counts per minute is calculated first or each pair of tubes. Net Counts=Average CPM-Average NBS CPM The Binding of each pair of tubes as a per cent of the maximum binding determined with the NBS-corrected counts for tube A taken as 100%. Percent bound=(Net counts /Net MB counts) x 100 Validation of Assay The DPC estradiol double antibody kit is desi gned for human serum, but it has been validated previously in our lab fo r use in equine plasma samples.

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126 APPENDIX E PROGESTERONE RADIOIMMUNOASSAY The Diagnostic Products Corporation (DPC Lo s Angeles, CA) Coat a Count Progesterone kit is a solid phase radioimmunoassay. In th is kit I-125 labeled progesterone competes with progesterone in the unknown samples over a fixed time for antibody sites. The antibody is immobilized to the wall of the polypropylene tube so decanting the supernatant is sufficient to terminate th e competition and to isolate the antibody bound fraction of the radiolabel progesterone. The tubes are counted using a gamma counter to yield a number which is converted, by way of a calibration curve to the concentration of progesterone in the unknown sample. Materials Supplied in the Kit 1. Progesterone Antibody Coated Tubes (TPG1) 2. I-125 Progesterone (TPG2) 3. Progesterone Calibrators (PGC-G); 0, 0.1, 0.5, 2 10, 20 and 40 ng/ml Materials Not Provided but Required 1. Plain 12x75 mm polypropylene tubes 2. Plasma from a control mare containing a known amount of progesterone Procedure 1. Label 4 plain tubes T (Total), and NBS (Non Specific Binding) in duplicate. Label 14 Progesterone-Ab Coated tubes A-G in duplicate.

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127 Label additional coated tubes in duplicate for unknown and control samples. 2. Pipette 100l of zero calibrator A in to NBS and A tubes, and 100l of each calibrator into their respec tive tubes. Add 100l of each unknown sample and control sample into their respective tubes. 3. Add 1.0ml of I-125 Progesteron e to every tube and vortex 4. Incubate at room temperature for 3 hours 5. Decant thoroughly 6. Count on gamma counter Calculation of Results Progesterone concentrations for each unknown sa mple are determined from a logit-log representation of the calibration curve. The average NBS-corrected counts per minute is calculated first for each pair of tubes. Net Counts=Average CPM-Average NBS CPM The binding of each pair of tubes as a percen t of the maximum binding MB is determined with the NBS-corrected counts of the A tube taken as 100%. Percent Bound= (Net Counts/ Net MB) x 100 Validation of Assay The DPC Progesterone kit is designed for human serum, but it has been validates previously in our lab for use with equine samples.

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128 APPENDIX F ANOVA TABLES Table F.1 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 1 1255284.900 1255284.900 52.63 <.0001 Error 8 190815.600 23851.950 Corrected Total 9 1446100.500 R-Square Coeff Var Root MSE p4 Mean 0.868048 20.77213 154.4408 743.5000 Source DF Type I SS Mean Square F Value Pr > F g 1 1255284.900 1255284.900 52.63 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 1255284.900 1255284.900 52.63 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 1255284.900 1255284.900 52.63 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 1 1255284.900 1255284.900 52.63 <.0001

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129 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g p4 LSMEAN Error Pr > |t| Pr > |t| 1 389.20000 69.06801 0.0005 <.0001 2 1097.80000 69.06801 <.0001 Table F.2 Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 12 417.2855849 34.7737987 66.02 <.0001 Error 32 16.8539224 0.5266851 Corrected Total 44 434.1395073 R-Square Coeff Var Root MSE p4 Mean 0.961179 15.65924 0.725731 4.634520 Source DF Type I SS Mean Square F Value Pr > F g 1 1.8576600 1.8576600 3.53 0.0695 m(g) 7 15.8176346 2.2596621 4.29 0.0019 d 4 399.6102904 99.9025726 189.68 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 1.8576600 1.8576600 3.53 0.0695 m(g) 7 15.8176346 2.2596621 4.29 0.0019 d 4 399.6102904 99.9025726 189.68 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 1.8576600 1.8576600 3.53 0.0695 m(g) 7 15.8176346 2.2596621 4.29 0.0019 d 4 399.6102904 99.9025726 189.68 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 1 1.8576600 1.8576600 3.53 0.0695 m(g) 7 15.8176346 2.2596621 4.29 0.0019 d 4 399.6102904 99.9025726 189.68 <.0001

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130 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g p4 LSMEAN Error Pr > |t| Pr > |t| 1 4.81624800 0.14514614 <.0001 0.0695 2 4.40736000 0.16227832 <.0001 Table F.3 Dependent Variable: e2 Sum of Source DF Squares Mean Square F Value Pr > F Model 12 1131.008504 94.250709 36.67 <.0001 Error 32 82.257945 2.570561 Corrected Total 44 1213.266449 R-Square Coeff Var Root MSE e2 Mean 0.932201 15.19914 1.603297 10.54860 Source DF Type I SS Mean Square F Value Pr > F g 1 0.241474 0.241474 0.09 0.7612 m(g) 7 78.194121 11.170589 4.35 0.0018 d 4 1052.572909 263.143227 102.37 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 0.241474 0.241474 0.09 0.7612 m(g) 7 78.194121 11.170589 4.35 0.0018 d 4 1052.572909 263.143227 102.37 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 0.241474 0.241474 0.09 0.7612 m(g) 7 78.194121 11.170589 4.35 0.0018 d 4 1052.572909 263.143227 102.37 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.241474 0.241474 0.09 0.7612 m(g) 7 78.194121 11.170589 4.35 0.0018 d 4 1052.572909 263.143227 102.37 <.0001 The GLM Procedure Least Squares Means

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131 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g e2 LSMEAN Erro r Pr > |t| Pr > |t| 1 10.4830800 0.3206594 <.0001 0.7612 2 10.6305000 0.3585081 <.0001 Table F.4 The GLM Procedure Dependent Variable: e2 Sum of Source DF Squares Mean Square F Value Pr > F Model 1 117288.900 117288.900 0.13 0.7282 Error 8 7241444.000 905180.500 Corrected Total 9 7358732.900 R-Square Coeff Var Root MSE e2 Mean 0.015939 25.24370 951.4097 3768.900 Source DF Type I SS Mean Square F Value Pr > F g 1 117288.9000 117288.9000 0.13 0.7282 Source DF Type II SS Mean Square F Value Pr > F g 1 117288.9000 117288.9000 0.13 0.7282 Source DF Type III SS Mean Square F Value Pr > F g 1 117288.9000 117288.9000 0.13 0.7282 Source DF Type IV SS Mean Square F Value Pr > F g 1 117288.9000 117288.9000 0.13 0.7282 The GLM Procedure

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132 Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g e2 LSMEAN Erro r Pr > |t| Pr > |t| 1 3660.60000 425.48337 <.0001 0.7282 2 3877.20000 425.48337 <.0001 Table F.5 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 1 2.60100000 2.60100000 373.98 <.0001 Error 8 0.05564000 0.00695500 Corrected Total 9 2.65664000 R-Square Coeff Var Root MSE adj Mean 0.979056 6.337131 0.083397 1.316000 Source DF Type I SS Mean Square F Value Pr > F g 1 2.60100000 2.60100000 373.98 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 2.60100000 2.60100000 373.98 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 2.60100000 2.60100000 373.98 <.0001 Source DF Type IV SS Mean Square F Value Pr > F

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133 g 1 2.60100000 2.60100000 373.98 <.0001 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 0.80600000 0.03729611 <.0001 <.0001 2 1.82600000 0.03729611 <.0001 Table F.6 The GLM Procedure Dependent Variable: od Sum of Source DF Squares Mean Square F Value Pr > F Model 1 0.43226402 0.43226402 130.75 <.0001 Error 6 0.01983663 0.00330610 Corrected Total 7 0.45210065 R-Square Coeff Var Root MSE od Mean 0.956123 6.305913 0.057499 0.911823 Source DF Type I SS Mean Square F Value Pr > F g 1 0.43226402 0.43226402 130.75 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 0.43226402 0.43226402 130.75 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 0.43226402 0.43226402 130.75 <.0001

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134 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.43226402 0.43226402 130.75 <.0001 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g od LSMEAN Error Pr > |t| Pr > |t| 1 0.67937250 0.02874937 <.0001 <.0001 2 1.14427250 0.02874937 <.0001 Table F.7 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 2 502532.0803 251266.0401 3.02 0.1380 Error 5 416019.5493 83203.9099 Corrected Total 7 918551.6296 R-Square Coeff Var Root MSE p4 Mean 0.547092 41.69246 288.4509 691.8538 Source DF Type I SS Mean Square F Value Pr > F g 2 502532.0803 251266.0401 3.02 0.1380 Source DF Type II SS Mean Square F Value Pr > F g 2 502532.0803 251266.0401 3.02 0.1380 Source DF Type III SS Mean Square F Value Pr > F g 2 502532.0803 251266.0401 3.02 0.1380

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135 Source DF Type IV SS Mean Square F Value Pr > F g 2 502532.0803 251266.0401 3.02 0.1380 The GLM Procedure Least Squares Means Standard LSMEAN g p4 LSMEAN Error Pr > |t| Number 1 972.566667 166.537193 0.0021 1 2 652.160000 166.537193 0.0112 2 3 330.325000 203.965573 0.1663 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 1 0.2318 0.0587 2 0.2318 0.2761 3 0.0587 0.276 Table F.8 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 3 1478351.270 492783.757 12.85 <.0001 Error 20 767076.416 38353.821 Corrected Total 23 2245427.686 R-Square Coeff Var Root MSE p4 Mean 0.658383 28.97207 195.8413 675.9658

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136 Source DF Type I SS Mean Square F Value Pr > F g 3 1478351.270 492783.757 12.85 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 3 1478351.270 492783.757 12.85 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 3 1478351.270 492783.757 12.85 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 3 1478351.270 492783.757 12.85 <.0001 The GLM Procedure Least Squares Means Standard LSMEAN g p4 LSMEAN Error Pr > |t| Number 0 973.671250 69.240361 <.0001 1 24 682.567500 69.240361 <.0001 2 48 430.980000 97.920658 0.0003 3 72 312.337500 97.920658 0.0046 4 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4 1 0.0075 0.0002 <.0001 2 0.0075 0.0488 0.0058 3 0.0002 0.0488 0.4017 4 <.0001 0.0058 0.4017

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137 Table F.9 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 2 4.93710740 2.46855370 22.07 <.0001 Error 13 1.45397253 0.11184404 Corrected Total 15 6.39107994 R-Square Coeff Var Root MSE adj Mean 0.772500 23.89539 0.334431 1.399563 Source DF Type I SS Mean Square F Value Pr > F g 2 4.93710740 2.46855370 22.07 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 2 4.93710740 2.46855370 22.07 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 2 4.93710740 2.46855370 22.07 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 2 4.93710740 2.46855370 22.07 <.0001 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1 0.90766667 0.13653085 <.0001 1

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138 3 1.18340000 0.14956205 <.0001 2 4 2.20600000 0.14956205 <.0001 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 1 0.1965 <.0001 2 0.1965 0.0003 3 <.0001 0.0003 Table F.10 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 3 4.23690133 1.41230044 9.34 0.0005 Error 20 3.02486650 0.15124333 Corrected Total 23 7.26176783 R-Square Coeff Var Root MSE adj Mean 0.583453 31.53884 0.388900 1.233083 Source DF Type I SS Mean Square F Value Pr > F t 3 4.23690133 1.41230044 9.34 0.0005 Source DF Type II SS Mean Square F Value Pr > F t 3 4.23690133 1.41230044 9.34 0.0005 Source DF Type III SS Mean Square F Value Pr > F t 3 4.23690133 1.41230044 9.34 0.0005

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139 Source DF Type IV SS Mean Square F Value Pr > F t 3 4.23690133 1.41230044 9.34 0.0005 The GLM Procedure Least Squares Means Standard LSMEAN t adj LSMEAN Error Pr > |t| Number 0 0.96875000 0.13749697 <.0001 1 24 1.03875000 0.13749697 <.0001 2 48 1.23175000 0.19445007 <.0001 3 72 2.15175000 0.19445007 <.0001 4 Least Squares Means for effect t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 1 0.7226 0.2826 <.0001 2 0.7226 0.4272 0.0001 3 0.2826 0.4272 0.0032 4 <.0001 0.0001 0.0032 Table F.11 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F

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140 Model 2 0.61975996 0.30987998 58.08 <.0001 Error 13 0.06936281 0.00533560 Corrected Total 15 0.68912277 R-Square Coeff Var Root MSE adj Mean 0.899346 8.206116 0.073045 0.890131 Source DF Type I SS Mean Square F Value Pr > F g 2 0.61975996 0.30987998 58.08 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 2 0.61975996 0.30987998 58.08 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 2 0.61975996 0.30987998 58.08 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 2 0.61975996 0.30987998 58.08 <.0001 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1 0.66266667 0.02982058 <.0001 1 3 0.91568000 0.03266681 <.0001 2 4 1.13754000 0.03266681 <.0001 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj

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141 i/j 1 2 3 1 <.0001 <.0001 2 <.0001 0.0003 3 <.0001 0.0003 Table F.12 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 3 0.64115887 0.21371962 31.21 <.0001 Error 20 0.13696163 0.00684808 Corrected Total 23 0.77812050 R-Square Coeff Var Root MSE adj Mean 0.823984 10.18187 0.082753 0.812750 Source DF Type I SS Mean Square F Value Pr > F t 3 0.64115887 0.21371962 31.21 <.0001 Source DF Type II SS Mean Square F Value Pr > F t 3 0.64115888 0.21371963 31.21 <.0001 Source DF Type III SS Mean Square F Value Pr > F t 3 0.64115887 0.21371962 31.21 <.0001 Source DF Type IV SS Mean Square F Value Pr > F t 3 0.64115887 0.21371962 31.21 <.0001 The GLM Procedure Least Squares Means

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142 Standard LSMEAN t adj LSMEAN Error Pr > |t| Number 0 0.63912500 0.02925765 <.0001 1 24 0.77450000 0.02925765 <.0001 2 48 0.96425000 0.04137657 <.0001 3 72 1.08500000 0.04137657 <.0001 4 Least Squares Means for effect t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 1 0.0038 <.0001 <.0001 2 0.0038 0.0013 <.0001 3 <.0001 0.0013 0.0523 4 <.0001 <.0001 0.0523 Table F.13 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 5 10073589.28 2014717.86 68.42 <.0001 Error 17 500612.05 29447.77 Corrected Total 22 10574201.33 R-Square Coeff Var Root MSE p4 Mean 0.952657 30.77608 171.6035 557.5874 Source DF Type I SS Mean Square F Value Pr > F g 1 1776068.051 1776068.051 60.31 <.0001 t 2 4893826.874 2446913.437 83.09 <.0001 g*t 2 3403694.356 1701847.178 57.79 <.0001

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143 Source DF Type II SS Mean Square F Value Pr > F g 1 2020013.353 2020013.353 68.60 <.0001 t 2 4893826.874 2446913.437 83.09 <.0001 g*t 2 3403694.356 1701847.178 57.79 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 1815089.899 1815089.899 61.64 <.0001 t 2 4615910.779 2307955.390 78.37 <.0001 g*t 2 3403694.356 1701847.178 57.79 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 1 1815089.899 1815089.899 61.64 <.0001 t 2 4615910.779 2307955.390 78.37 <.0001 g*t 2 3403694.356 1701847.178 57.79 <.0001 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g p4 LSMEAN Error Pr > |t| Pr > |t| 1 258.557222 52.217288 0.0001 <.0001 2 823.642500 49.537669 <.0001 Standard LSMEAN t p4 LSMEAN Error Pr > |t| Number 4 149.67583 65.53218 0.0355 1 9 306.60750 60.67101 <.0001 2 24 1167.01625 60.67101 <.0001 3 Least Squares Means for effect t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 1 0.0969 <.0001 2 0.0969 <.0001 3 <.0001 <.0001

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144 Standard LSMEAN g t p4 LSMEAN Error Pr > |t| Number 1 4 161.88667 99.07534 0.1206 1 1 9 269.22750 85.80176 0.0060 2 1 24 344.55750 85.80176 0.0009 3 2 4 137.46500 85.80176 0.1275 4 2 9 343.98750 85.80176 0.0009 5 2 24 1989.47500 85.80176 <.0001 6 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4 5 6 1 0.4241 0.1813 0.8544 0.1826 <.0001 2 0.4241 0.5430 0.2927 0.5460 <.0001 3 0.1813 0.5430 0.1061 0.9963 <.0001 4 0.8544 0.2927 0.1061 0.1070 <.0001 5 0.1826 0.5460 0.9963 0.1070 <.0001 6 <.0001 <.0001 <.0001 <.0001 <.0001 Table F.14 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 3 6669894.92 2223298.31 10.82 0.0002 Error 19 3904306.40 205489.81 Corrected Total 22 10574201.33 R-Square Coeff Var Root MSE p4 Mean 0.630771 81.29844 453.3098 557.5874 Source DF Type I SS Mean Square F Value Pr > F

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145 g 1 1776068.051 1776068.051 8.64 0.0084 t 1 4874511.045 4874511.045 23.72 0.0001 t*t 1 19315.829 19315.829 0.09 0.7625 Source DF Type II SS Mean Square F Value Pr > F g 1 2020013.353 2020013.353 9.83 0.0054 t 1 25409.502 25409.502 0.12 0.7290 t*t 1 19315.829 19315.829 0.09 0.7625 Source DF Type III SS Mean Square F Value Pr > F g 1 2020013.353 2020013.353 9.83 0.0054 t 1 25409.502 25409.502 0.12 0.7290 t*t 1 19315.829 19315.829 0.09 0.7625 Source DF Type IV SS Mean Square F Value Pr > F g 1 2020013.353 2020013.353 9.83 0.0054 t 1 25409.502 25409.502 0.12 0.7290 t*t 1 19315.829 19315.829 0.09 0.7625 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g p4 LSMEAN Error Pr > |t| Pr > |t| 1 247.381848 136.832994 0.0865 0.0054 2 841.942473 130.995262 <.0001 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 5 10073589.28 2014717.86 68.42 <.0001 Error 17 500612.05 29447.77 Corrected Total 22 10574201.33 R-Square Coeff Var Root MSE p4 Mean 0.952657 30.77608 171.6035 557.5874

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146 Source DF Type I SS Mean Square F Value Pr > F g 1 1776068.051 1776068.051 60.31 <.0001 t 1 4874511.045 4874511.045 165.53 <.0001 t*g 1 3252691.191 3252691.191 110.46 <.0001 t*t*g 2 170318.995 85159.497 2.89 0.0830 Source DF Type II SS Mean Square F Value Pr > F g 1 675.2424 675.2424 0.02 0.8814 t 1 5305.5139 5305.5139 0.18 0.6766 t*g 1 9293.9620 9293.9620 0.32 0.5816 t*t*g 2 170318.9946 85159.4973 2.89 0.0830 Source DF Type III SS Mean Square F Value Pr > F g 1 675.2424 675.2424 0.02 0.8814 t 1 6271.6275 6271.6275 0.21 0.6503 t*g 1 9293.9620 9293.9620 0.32 0.5816 t*t*g 2 170318.9946 85159.4973 2.89 0.0830 Source DF Type IV SS Mean Square F Value Pr > F g 1 675.2424 675.2424 0.02 0.8814 t 1 7969.0327 7969.0327 0.27 0.6096 t*g 1 9293.9620 9293.9620 0.32 0.5816 t*t*g 2 170318.9946 85159.4973 2.89 0.0830 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g p4 LSMEAN Error Pr > |t| Pr > |t| 1 262.760290 51.870654 <.0001 <.0001 2 853.476304 49.631224 <.0001 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 5 10073589.28 2014717.86 68.42 <.0001 Error 17 500612.05 29447.77

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147 Corrected Total 22 10574201.33 R-Square Coeff Var Root MSE p4 Mean 0.952657 30.77608 171.6035 557.5874 Source DF Type I SS Mean Square F Value Pr > F g 1 1776068.051 1776068.051 60.31 <.0001 t 1 4874511.045 4874511.045 165.53 <.0001 t*g 1 3252691.191 3252691.191 110.46 <.0001 t*t*g 2 170318.995 85159.497 2.89 0.0830 Source DF Type III SS Mean Square F Value Pr > F g 1 675.2424 675.2424 0.02 0.8814 t 1 6271.6275 6271.6275 0.21 0.6503 t*g 1 9293.9620 9293.9620 0.32 0.5816 t*t*g 2 170318.9946 85159.4973 2.89 0.0830 Standard Parameter Estimate Error t Value Pr > |t| Intercept 95.35740000 B 215.6749722 0.44 0.6640 g 1 -48.94650000 B 323.2347125 -0.15 0.8814 g 2 0.00000000 B . t -3.15203333 B 42.9142245 -0.07 0.9423 t*g 1 35.31020833 B 62.8530017 0.56 0.5816 t*g 2 0.00000000 B . t*t*g 1 -0.82230833 1.5401887 -0.53 0.6003 t*t*g 2 3.41973333 1.4583495 2.34 0.0314 Table F.15 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 5 16.68977855 3.33795571 25.77 <.0001 Error 17 2.20185875 0.12952110 Corrected Total 22 18.89163730

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148 R-Square Coeff Var Root MSE adj Mean 0.883448 25.19934 0.359890 1.428174 Source DF Type I SS Mean Square F Value Pr > F g 1 7.27121940 7.27121940 56.14 <.0001 t 2 7.06266488 3.53133244 27.26 <.0001 g*t 2 2.35589428 1.17794714 9.09 0.0021 Source DF Type II SS Mean Square F Value Pr > F g 1 7.84654020 7.84654020 60.58 <.0001 t 2 7.06266488 3.53133244 27.26 <.0001 g*t 2 2.35589428 1.17794714 9.09 0.0021 Source DF Type III SS Mean Square F Value Pr > F g 1 7.49450688 7.49450688 57.86 <.0001 t 2 6.78169073 3.39084537 26.18 <.0001 g*t 2 2.35589428 1.17794714 9.09 0.0021 Source DF Type IV SS Mean Square F Value Pr > F g 1 7.49450688 7.49450688 57.86 <.0001 t 2 6.78169073 3.39084537 26.18 <.0001 g*t 2 2.35589428 1.17794714 9.09 0.0021 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 0.81825000 0.10951116 <.0001 <.0001 2 1.96650000 0.10389141 <.0001 Standard LSMEAN

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149 t adj LSMEAN Error Pr > |t| Number 4 0.90750000 0.13743542 <.0001 1 9 1.11987500 0.12724047 <.0001 2 24 2.14975000 0.12724047 <.0001 3 Least Squares Means for effect t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 1 0.2726 <.0001 2 0.2726 <.0001 3 <.0001 <.0001 Standard LSMEAN g t adj LSMEAN Error Pr > |t| Number 1 4 0.56900000 0.20778282 0.0140 1 1 9 0.75925000 0.17994520 0.0006 2 1 24 1.12650000 0.17994520 <.0001 3 2 4 1.24600000 0.17994520 <.0001 4 2 9 1.48050000 0.17994520 <.0001 5 2 24 3.17300000 0.17994520 <.0001 6 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 5 6 1 0.4982 0.0585 0.0248 0.0041 <.0001 2 0.4982 0.1672 0.0728 0.0114 <.0001 3 0.0585 0.1672 0.6446 0.1821 <.0001 4 0.0248 0.0728 0.6446 0.3697 <.0001 5 0.0041 0.0114 0.1821 0.3697 <.0001 6 <.0001 <.0001 <.0001 <.0001 <.0001 Table F.16 The GLM Procedure

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150 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 3 14.33388427 4.77796142 19.92 <.0001 Error 19 4.55785503 0.23988711 Corrected Total 22 18.89173930 R-Square Coeff Var Root MSE adj Mean 0.758738 34.29433 0.489783 1.428174 Source DF Type I SS Mean Square F Value Pr > F g 1 7.27121940 7.27121940 30.31 <.0001 t 1 7.03861877 7.03861877 29.34 <.0001 t*t 1 0.02404611 0.02404611 0.10 0.7550 Source DF Type II SS Mean Square F Value Pr > F g 1 7.84654020 7.84654020 32.71 <.0001 t 1 0.04136252 0.04136252 0.17 0.6826 t*t 1 0.02404611 0.02404611 0.10 0.7550 Source DF Type III SS Mean Square F Value Pr > F g 1 7.84654020 7.84654020 32.71 <.0001 t 1 0.04136252 0.04136252 0.17 0.6826 t*t 1 0.02404611 0.02404611 0.10 0.7550 Source DF Type IV SS Mean Square F Value Pr > F g 1 7.84654020 7.84654020 32.71 <.0001 t 1 0.04136252 0.04136252 0.17 0.6826 t*t 1 0.02404611 0.02404611 0.10 0.7550 The GLM Procedure Least Squares Means

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151 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 0.81679348 0.14784245 <.0001 <.0001 2 1.98860598 0.14153502 <.0001 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 5 16.68977855 3.33795571 25.77 <.0001 Error 17 2.20196075 0.12952710 Corrected Total 22 18.89173930 R-Square Coeff Var Root MSE adj Mean 0.883443 25.19992 0.359899 1.428174 Source DF Type I SS Mean Square F Value Pr > F g 1 7.27121940 7.27121940 56.14 <.0001 t 1 7.03861877 7.03861877 54.34 <.0001 t*g 1 2.22374820 2.22374820 17.17 0.0007 t*t*g 2 0.15619219 0.07809609 0.60 0.5585 Source DF Type II SS Mean Square F Value Pr > F g 1 0.17354963 0.17354963 1.34 0.2631 t 1 0.01726397 0.01726397 0.13 0.7196 t*g 1 0.01367084 0.01367084 0.11 0.7492 t*t*g 2 0.15619219 0.07809609 0.60 0.5585 Source DF Type III SS Mean Square F Value Pr > F g 1 0.17354963 0.17354963 1.34 0.2631 t 1 0.01932124 0.01932124 0.15 0.7041 t*g 1 0.01367084 0.01367084 0.11 0.7492 t*t*g 2 0.15619219 0.07809609 0.60 0.5585 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.17354963 0.17354963 1.34 0.2631 t 1 0.02286272 0.02286272 0.18 0.6797 t*g 1 0.01367084 0.01367084 0.11 0.7492

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152 t*t*g 2 0.15619219 0.07809609 0.60 0.5585 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 0.82908696 0.10878672 <.0001 <.0001 2 1.99782609 0.10409003 <.0001 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 5 16.68977855 3.33795571 25.77 <.0001 Error 17 2.20196075 0.12952710 Corrected Total 22 18.89173930 R-Square Coeff Var Root MSE adj Mean 0.883443 25.19992 0.359899 1.428174 Source DF Type I SS Mean Square F Value Pr > F g 1 7.27121940 7.27121940 56.14 <.0001 t 1 7.03861877 7.03861877 54.34 <.0001 t*g 1 2.22374820 2.22374820 17.17 0.0007 t*t*g 2 0.15619219 0.07809609 0.60 0.5585 Source DF Type III SS Mean Square F Value Pr > F g 1 0.17354963 0.17354963 1.34 0.2631 t 1 0.01932124 0.01932124 0.15 0.7041 t*g 1 0.01367084 0.01367084 0.11 0.7492 t*t*g 2 0.15619219 0.07809609 0.60 0.5585 Standard Parameter Estimate Error t Value Pr > |t| Intercept 1.177080000 B 0.45232843 2.60 0.0186 g 1 -0.784700000 B 0.67791014 -1.16 0.2631 g 2 0.000000000 B . t 0.004043333 B 0.09000267 0.04 0.9647 t*g 1 0.042825000 B 0.13181965 0.32 0.7492

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153 t*g 2 0.000000000 B . t*t*g 1 -0.000678333 0.00323019 -0.21 0.8362 t*t*g 2 0.003296667 0.00305855 1.08 0.2962 Table F.17 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 5 1.65075615 0.33015123 431.70 <.0001 Error 17 0.01300113 0.00076477 Corrected Total 22 1.66375729 R-Square Coeff Var Root MSE adj Mean 0.992186 4.506464 0.027655 0.613663 Source DF Type I SS Mean Square F Value Pr > F g 1 0.35705536 0.35705536 466.88 <.0001 t 2 1.25601441 0.62800720 821.17 <.0001 g*t 2 0.03768639 0.01884319 24.64 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 0.42456905 0.42456905 555.16 <.0001 t 2 1.25601441 0.62800720 821.17 <.0001 g*t 2 0.03768639 0.01884319 24.64 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 0.41363579 0.41363579 540.86 <.0001 t 2 1.23350323 0.61675162 806.45 <.0001 g*t 2 0.03768639 0.01884319 24.64 <.0001

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154 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.41363579 0.41363579 540.86 <.0001 t 2 1.23350323 0.61675162 806.45 <.0001 g*t 2 0.03768639 0.01884319 24.64 <.0001 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 0.46319722 0.00841500 <.0001 <.0001 2 0.73295500 0.00798317 <.0001 Standard LSMEAN t adj LSMEAN Error Pr > |t| Number 4 0.34622833 0.01056075 <.0001 1 9 0.53885000 0.00977735 <.0001 2 24 0.90915000 0.00977735 <.0001 3 Least Squares Means for effect t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 1 <.0001 <.0001 2 <.0001 <.0001 3 <.0001 <.0001 Standard LSMEAN g t adj LSMEAN Error Pr > |t| Number 1 4 0.23956667 0.01596635 <.0001 1 1 9 0.34722500 0.01382726 <.0001 2 1 24 0.80280000 0.01382726 <.0001 3 2 4 0.45289000 0.01382726 <.0001 4

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155 2 9 0.73047500 0.01382726 <.0001 5 2 24 1.01550000 0.01382726 <.0001 6 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 5 6 1 <.0001 <.0001 <.0001 <.0001 <.0001 2 <.0001 <.0001 <.0001 <.0001 <.0001 3 <.0001 <.0001 <.0001 0.0018 <.0001 4 <.0001 <.0001 <.0001 <.0001 <.0001 5 <.0001 <.0001 0.0018 <.0001 <.0001 6 <.0001 <.0001 <.0001 <.0001 <.0001 Table F.18 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 3 1.61306976 0.53768992 201.55 <.0001 Error 19 0.05068752 0.00266776 Corrected Total 22 1.66375729 R-Square Coeff Var Root MSE adj Mean 0.969534 8.416731 0.051650 0.613663 Source DF Type I SS Mean Square F Value Pr > F g 1 0.35705536 0.35705536 133.84 <.0001 t 1 1.24182335 1.24182335 465.49 <.0001 t*t 1 0.01419106 0.01419106 5.32 0.0325

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156 Source DF Type II SS Mean Square F Value Pr > F g 1 0.42456905 0.42456905 159.15 <.0001 t 1 0.07182942 0.07182942 26.92 <.0001 t*t 1 0.01419106 0.01419106 5.32 0.0325 Source DF Type III SS Mean Square F Value Pr > F g 1 0.42456905 0.42456905 159.15 <.0001 t 1 0.07182942 0.07182942 26.92 <.0001 t*t 1 0.01419106 0.01419106 5.32 0.0325 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.42456905 0.42456905 159.15 <.0001 t 1 0.07182942 0.07182942 26.92 <.0001 t*t 1 0.01419106 0.01419106 5.32 0.0325

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157 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 0.47144809 0.01559084 <.0001 <.0001 2 0.74402759 0.01492568 <.0001 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 5 1.65075615 0.33015123 431.70 <.0001 Error 17 0.01300113 0.00076477 Corrected Total 22 1.66375729 R-Square Coeff Var Root MSE adj Mean 0.992186 4.506464 0.027655 0.613663 Source DF Type I SS Mean Square F Value Pr > F g 1 0.35705536 0.35705536 466.88 <.0001 t 1 1.24182335 1.24182335 1623.78 <.0001 t*g 1 0.00329714 0.00329714 4.31 0.0533 t*t*g 2 0.04858031 0.02429015 31.76 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 0.00000511 0.00000511 0.01 0.9358 t 1 0.07388584 0.07388584 96.61 <.0001 t*g 1 0.03002525 0.03002525 39.26 <.0001 t*t*g 2 0.04858031 0.02429015 31.76 <.0001 Source DF Type III SS Mean Square F Value Pr > F

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158 g 1 0.00000511 0.00000511 0.01 0.9358 t 1 0.06732736 0.06732736 88.04 <.0001 t*g 1 0.03002525 0.03002525 39.26 <.0001 t*t*g 2 0.04858031 0.02429015 31.76 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.00000511 0.00000511 0.01 0.9358 t 1 0.06998176 0.06998176 91.51 <.0001 t*g 1 0.03002525 0.03002525 39.26 <.0001 t*t*g 2 0.04858031 0.02429015 31.76 <.0001 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 0.47292029 0.00835914 <.0001 <.0001 2 0.74513174 0.00799825 <.0001 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 5 1.65075615 0.33015123 431.70 <.0001 Error 17 0.01300113 0.00076477 Corrected Total 22 1.66375729 R-Square Coeff Var Root MSE adj Mean 0.992186 4.506464 0.027655 0.613663 Source DF Type I SS Mean Square F Value Pr > F g 1 0.35705536 0.35705536 466.88 <.0001 t 1 1.24182335 1.24182335 1623.78 <.0001

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159 t*g 1 0.00329714 0.00329714 4.31 0.0533 t*t*g 2 0.04858031 0.02429015 31.76 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 0.00000511 0.00000511 0.01 0.9358 t 1 0.06732736 0.06732736 88.04 <.0001 t*g 1 0.03002525 0.03002525 39.26 <.0001 t*t*g 2 0.04858031 0.02429015 31.76 <.0001 Standard Parameter Estimate Error t Value Pr > |t| Intercept 0.1650944000 B 0.03475680 4.75 0.0002 g 1 0.0042576000 B 0.05209043 0.08 0.9358 g 2 0.0000000000 B . t 0.0792519667 B 0.00691578 11.46 <.0001 t*g 1 -.0634663000 B 0.01012899 -6.27 <.0001 t*g 2 0.0000000000 B . t*t*g 1 0.0004420000 0.00024821 1.78 0.0928 t*t*g 2 -.0018257667 0.00023502 -7.77 <.0001 Table F.19 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 3 25.91407313 8.63802438 5.80 0.0260 Error 7 10.42806391 1.48972342 Corrected Total 10 36.34213705 R-Square Coeff Var Root MSE adj Mean 0.713059 27.99502 1.220542 4.359855 Source DF Type I SS Mean Square F Value Pr > F g 1 3.85370056 3.85370056 2.59 0.1518 t 1 18.70783507 18.70783507 12.56 0.0094

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160 g*t 1 3.35253750 3.35253750 2.25 0.1773 Source DF Type II SS Mean Square F Value Pr > F g 1 5.69189809 5.69189809 3.82 0.0915 t 1 18.70783507 18.70783507 12.56 0.0094 g*t 1 3.35253750 3.35253750 2.25 0.1773 Source DF Type III SS Mean Square F Value Pr > F g 1 6.62774613 6.62774613 4.45 0.0729 t 1 20.26725846 20.26725846 13.60 0.0078 g*t 1 3.35253750 3.35253750 2.25 0.1773 Source DF Type IV SS Mean Square F Value Pr > F g 1 6.62774613 6.62774613 4.45 0.0729 t 1 20.26725846 20.26725846 13.60 0.0078 g*t 1 3.35253750 3.35253750 2.25 0.1773

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161 The SAS System 05:43 Friday, July 2, 2004 22 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 5.39605000 0.55709877 <.0001 0.0729 2 3.81953333 0.49828429 0.0001 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 t adj LSMEAN Error Pr > |t| Pr > |t| 9 5.98621667 0.55709877 <.0001 0.0078 24 3.22936667 0.49828429 0.0003 Standard LSMEAN g t adj LSMEAN Error Pr > |t| Number 1 9 7.33510000 0.86305371 <.0001 1 1 24 3.45700000 0.70468040 0.0017 2 2 9 4.63733333 0.70468040 0.0003 3 2 24 3.00173333 0.70468040 0.0037 4 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 1 0.0103 0.0460 0.0060 2 0.0103 0.2749 0.6616 3 0.0460 0.2749 0.1448 4 0.0060 0.6616 0.1448

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162 Table F.20 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 3 13.45207550 4.48402517 188.70 0.0053 Error 2 0.04752600 0.02376300 Corrected Total 5 13.49960150 R-Square Coeff Var Root MSE adj Mean 0.996479 4.999271 0.154153 3.083500 Source DF Type I SS Mean Square F Value Pr > F g 1 2.84310675 2.84310675 119.64 0.0083 t 1 3.82025405 3.82025405 160.76 0.0062 g*t 1 6.78871470 6.78871470 285.68 0.0035 Source DF Type II SS Mean Square F Value Pr > F g 1 1.30867280 1.30867280 55.07 0.0177 t 1 3.82025405 3.82025405 160.76 0.0062 g*t 1 6.78871470 6.78871470 285.68 0.0035 Source DF Type III SS Mean Square F Value Pr > F g 1 0.35970750 0.35970750 15.14 0.0602 t 1 1.94310750 1.94310750 81.77 0.0120 g*t 1 6.78871470 6.78871470 285.68 0.0035 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.35970750 0.35970750 15.14 0.0602 t 1 1.94310750 1.94310750 81.77 0.0120

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163 g*t 1 6.78871470 6.78871470 285.68 0.0035 The SAS System 05:43 Friday, July 2, 2004 18 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1t 4.05700000 0.10900229 0.0007 0.0602 2t 3.50950000 0.08900000 0.0006 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 t adj LSMEAN Error Pr > |t| Pr > |t| 9 3.14700000 0.08900000 0.0008 0.0120 24 4.41950000 0.10900229 0.0006 Standard LSMEAN g t adj LSMEAN Error Pr > |t| Number 1t 9 4.61000000 0.15415252 0.0011 1 1t 24 3.50400000 0.15415252 0.0019 2 2t 9 1.68400000 0.08900000 0.0028 3 2t 24 5.33500000 0.15415252 0.0008 4 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 1 0.0367 0.0037 0.0798 2 0.0367 0.0094 0.0139 3 0.0037 0.0094 0.0024 4 0.0798 0.0139 0.0024

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164 Table F.21 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 3 143.2911706 47.7637235 1.24 0.3876 Error 5 192.5233190 38.5046638 Corrected Total 8 335.8144896 R-Square Coeff Var Root MSE adj Mean 0.426697 54.47098 6.205213 11.39178 Source DF Type I SS Mean Square F Value Pr > F g 1 77.10795401 77.10795401 2.00 0.2162 t 1 63.21691255 63.21691255 1.64 0.2563 g*t 1 2.96630400 2.96630400 0.08 0.7925 Source DF Type II SS Mean Square F Value Pr > F g 1 63.07547455 63.07547455 1.64 0.2567 t 1 63.21691255 63.21691255 1.64 0.2563 g*t 1 2.96630400 2.96630400 0.08 0.7925 Source DF Type III SS Mean Square F Value Pr > F g 1 60.10200491 60.10200491 1.56 0.2668 t 1 60.23949873 60.23949873 1.56 0.2664 g*t 1 2.96630400 2.96630400 0.08 0.7925 Source DF Type IV SS Mean Square F Value Pr > F g 1 60.10200491 60.10200491 1.56 0.2668 t 1 60.23949873 60.23949873 1.56 0.2664 g*t 1 2.96630400 2.96630400 0.08 0.7925

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165 The SAS System 05:43 Friday, July 2, 2004 26 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 13.3677500 2.8322791 0.0052 0.2668 2 8.1192500 3.1026063 0.0473 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 t adj LSMEAN Error Pr > |t| Pr > |t| 9 13.3707500 2.8322791 0.0052 0.2664 24 8.1162500 3.1026063 0.0473 Standard LSMEAN g t adj LSMEAN Error Pr > |t| Number 1 9 16.5780000 3.5825812 0.0057 1 1 24 10.1575000 4.3877479 0.0685 2 2 9 10.1635000 4.3877479 0.0684 3 2 24 6.0750000 4.3877479 0.2248 4 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 1 0.3084 0.3088 0.1229 2 0.3084 0.9993 0.5397 3 0.3088 0.9993 0.5391 4 0.1229 0.5397 0.5391

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166 Table F.22 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 3 31.42564971 10.47521657 3.79 0.1152 Error 4 11.04448517 2.76112129 Corrected Total 7 42.47013487 R-Square Coeff Var Root MSE adj Mean 0.739947 21.10014 1.661662 7.875125 Source DF Type I SS Mean Square F Value Pr > F g 1 4.51515608 4.51515608 1.64 0.2701 t 1 0.05602905 0.05602905 0.02 0.8936 g*t 1 26.85446458 26.85446458 9.73 0.0356 Source DF Type II SS Mean Square F Value Pr > F g 1 4.46880700 4.46880700 1.62 0.2722 t 1 0.05602905 0.05602905 0.02 0.8936 g*t 1 26.85446458 26.85446458 9.73 0.0356 Source DF Type III SS Mean Square F Value Pr > F g 1 8.02349430 8.02349430 2.91 0.1635 t 1 2.91537144 2.91537144 1.06 0.3622 g*t 1 26.85446458 26.85446458 9.73 0.0356 Source DF Type IV SS Mean Square F Value Pr > F g 1 8.02349430 8.02349430 2.91 0.1635 t 1 2.91537144 2.91537144 1.06 0.3622 g*t 1 26.85446458 26.85446458 9.73 0.0356

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167 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1t 9.72200000 1.01755613 0.0007 0.1635 2t 7.55858333 0.75844156 0.0006 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 t adj LSMEAN Error Pr > |t| Pr > |t| 9 9.29233333 0.95936112 0.0006 0.3622 24 7.98825000 0.83083110 0.0007 Standard LSMEAN g t adj LSMEAN Error Pr > |t| Number 1t 9 12.3530000 1.6616622 0.0017 1 1t 24 7.0910000 1.1749726 0.0038 2 2t 9 6.2316667 0.9593611 0.0029 3 2t 24 8.8855000 1.1749726 0.0016 4 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 1 0.0610 0.0332 0.1636 2 0.0610 0.6013 0.3409 3 0.0332 0.6013 0.1551 4 0.1636 0.3409 0.1551

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168 Table F.23 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 1 743730.2715 743730.2715 92.42 0.0007 Error 4 32190.8450 8047.7113 Corrected Total 5 775921.1166 R-Square Coeff Var Root MSE p4 Mean 0.958513 18.02747 89.70904 497.6242 Source DF Type I SS Mean Square F Value Pr > F g 1 743730.2715 743730.2715 92.42 0.0007 Source DF Type II SS Mean Square F Value Pr > F g 1 743730.2715 743730.2715 92.42 0.0007 Source DF Type III SS Mean Square F Value Pr > F g 1 743730.2715 743730.2715 92.42 0.0007 Source DF Type IV SS Mean Square F Value Pr > F g 1 743730.2715 743730.2715 92.42 0.0007 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g p4 LSMEAN Error Pr > |t| Pr > |t| 1 849.696667 51.793536 <.0001 0.0007

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169 2 145.551667 51.793536 0.0483 Table F.24 The GLM Procedure Dependent Variable: e2 Sum of Source DF Squares Mean Square F Value Pr > F Model 9 333.5408622 37.0600958 6.11 0.0005 Error 19 115.3032463 6.0685919 Corrected Total 28 448.8441086 R-Square Coeff Var Root MSE e2 Mean 0.743111 27.45299 2.463451 8.973345 Source DF Type I SS Mean Square F Value Pr > F g 1 11.2076679 11.2076679 1.85 0.1901 m(g) 4 46.1652758 11.5413190 1.90 0.1516 d 4 276.1679185 69.0419796 11.38 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 5.0357303 5.0357303 0.83 0.3737 m(g) 4 47.9458752 11.9864688 1.98 0.1393 d 4 276.1679185 69.0419796 11.38 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 4.3852495 4.3852495 0.72 0.4059 m(g) 4 47.9458752 11.9864688 1.98 0.1393 d 4 276.1679185 69.0419796 11.38 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 1 4.3852495 4.3852495 0.72 0.4059 m(g) 4 47.9458752 11.9864688 1.98 0.1393 d 4 276.1679185 69.0419796 11.38 <.0001 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g e2 LSMEAN Erro r Pr > |t| Pr > |t| 1 9.57393333 0.63606037 <.0001 0.4059 2 8.79039333 0.66710574 <.0001

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170 Table F.25 Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 9 127.5142307 14.1682479 20.73 <.0001 Error 20 13.6704534 0.6835227 Corrected Total 29 141.1846841 R-Square Coeff Var Root MSE p4 Mean 0.903173 34.96091 0.826754 2.364796 Source DF Type I SS Mean Square F Value Pr > F g 1 1.9818195 1.9818195 2.90 0.1041 m(g) 4 23.3452845 5.8363211 8.54 0.0003 d 4 102.1871267 25.5467817 37.38 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 1.9818195 1.9818195 2.90 0.1041 m(g) 4 23.3452845 5.8363211 8.54 0.0003 d 4 102.1871267 25.5467817 37.38 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 1.9818195 1.9818195 2.90 0.1041 m(g) 4 23.3452845 5.8363211 8.54 0.0003 d 4 102.1871267 25.5467817 37.38 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 1 1.9818195 1.9818195 2.90 0.1041 m(g) 4 23.3452845 5.8363211 8.54 0.0003 d 4 102.1871267 25.5467817 37.38 <.0001 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g p4 LSMEAN Error Pr > |t| Pr > |t| 1 2.62181867 0.21346704 <.0001 0.1041 2 2.10777333 0.21346704 <.0001

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171 Table F.26 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 1 3.08195334 3.08195334 101.00 0.0006 Error 4 0.12205216 0.03051304 Corrected Total 5 3.20400550 R-Square Coeff Var Root MSE adj Mean 0.961906 6.016596 0.174680 2.903300 Source DF Type I SS Mean Square F Value Pr > F g 1 3.08195334 3.08195334 101.00 0.0006 Source DF Type II SS Mean Square F Value Pr > F g 1 3.08195334 3.08195334 101.00 0.0006 Source DF Type III SS Mean Square F Value Pr > F g 1 3.08195334 3.08195334 101.00 0.0006 Source DF Type IV SS Mean Square F Value Pr > F g 1 3.08195334 3.08195334 101.00 0.0006 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 3.62000000 0.10085144 <.0001 0.0006

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172 2 2.18660000 0.10085144 <.0001 Table F.27 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 1 0.04893275 0.04893275 7.84 0.0380 Error 5 0.03122483 0.00624497 Corrected Total 6 0.08015758 R-Square Coeff Var Root MSE adj Mean 0.610457 15.44838 0.079025 0.511543 Source DF Type I SS Mean Square F Value Pr > F g 1 0.04893275 0.04893275 7.84 0.0380 Source DF Type II SS Mean Square F Value Pr > F g 1 0.04893275 0.04893275 7.84 0.0380 Source DF Type III SS Mean Square F Value Pr > F g 1 0.04893275 0.04893275 7.84 0.0380 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.04893275 0.04893275 7.84 0.0380 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t|

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173 1 0.58395000 0.03951255 <.0001 0.0380 2 0.41500000 0.04562516 0.0003 Table F.28 Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 1 1208425.559 1208425.559 17.45 0.0058 Error 6 415392.240 69232.040 Corrected Total 7 1623817.799 R-Square Coeff Var Root MSE p4 Mean 0.744188 41.85545 263.1198 628.6394 Source DF Type I SS Mean Square F Value Pr > F g 1 1208425.559 1208425.559 17.45 0.0058 Source DF Type II SS Mean Square F Value Pr > F g 1 1208425.559 1208425.559 17.45 0.0058 Source DF Type III SS Mean Square F Value Pr > F g 1 1208425.559 1208425.559 17.45 0.0058 Source DF Type IV SS Mean Square F Value Pr > F g 1 1208425.559 1208425.559 17.45 0.0058 The GLM ProcedureLeast Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g p4 LSMEAN Error Pr > |t| Pr > |t|

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174 1 1017.29500 131.55991 0.0002 0.0058 2 239.98375 131.55991 0.1179 Table F.29 Dependent Variable: e2 Sum of Source DF Squares Mean Square F Value Pr > F Model 11 1339.088271 121.735297 67.14 <.0001 Error 28 50.764834 1.813030 Corrected Total 39 1389.853105 R-Square Coeff Var Root MSE e2 Mean 0.963475 11.42429 1.346488 11.78618 Source DF Type I SS Mean Square F Value Pr > F g 1 0.272696 0.272696 0.15 0.7011 m(g) 6 99.304362 16.550727 9.13 <.0001 d 4 1239.511213 309.877803 170.92 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 0.272696 0.272696 0.15 0.7011 m(g) 6 99.304362 16.550727 9.13 <.0001 d 4 1239.511213 309.877803 170.92 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 0.272696 0.272696 0.15 0.7011 m(g) 6 99.304362 16.550727 9.13 <.0001 d 4 1239.511213 309.877803 170.92 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.272696 0.272696 0.15 0.7011 m(g) 6 99.304362 16.550727 9.13 <.0001 d 4 1239.511213 309.877803 170.92 <.0001 The GLM Procedure Least Squares Means

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175 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g e2 LSMEAN Erro r Pr > |t| Pr > |t| 1 11.7036150 0.3010839 <.0001 0.7011 2 11.8687500 0.3010839 <.0001 Table F.30 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 11 351.9398755 31.9945341 45.09 <.0001 Error 28 19.8670376 0.7095371 Corrected Total 39 371.8069131 R-Square Coeff Var Root MSE p4 Mean 0.946566 22.45130 0.842340 3.751855 Source DF Type I SS Mean Square F Value Pr > F g 1 0.8496642 0.8496642 1.20 0.2831 m(g) 6 28.2527350 4.7087892 6.64 0.0002 d 4 322.8374763 80.7093691 113.75 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 1 0.8496642 0.8496642 1.20 0.2831 m(g) 6 28.2527350 4.7087892 6.64 0.0002 d 4 322.8374763 80.7093691 113.75 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 1 0.8496642 0.8496642 1.20 0.2831 m(g) 6 28.2527350 4.7087892 6.64 0.0002 d 4 322.8374763 80.7093691 113.75 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.8496642 0.8496642 1.20 0.2831 m(g) 6 28.2527350 4.7087892 6.64 0.0002 d 4 322.8374763 80.7093691 113.75 <.0001 The GLM Procedure Least Squares Means

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176 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g p4 LSMEAN Error Pr > |t| Pr > |t| 1 3.89760000 0.18835300 <.0001 0.2831 2 3.60611000 0.18835300 <.0001 Table F.31 Dependent Variable: e2 Sum of Source DF Squares Mean Square F Value Pr > F Model 1 8173753.34 8173753.34 1.38 0.2850 Error 6 35596363.19 5932727.20 Corrected Total 7 43770116.53 R-Square Coeff Var Root MSE e2 Mean 0.186743 110.4428 2435.719 2205.411 Source DF Type I SS Mean Square F Value Pr > F g 1 8173753.336 8173753.336 1.38 0.2850 Source DF Type II SS Mean Square F Value Pr > F g 1 8173753.336 8173753.336 1.38 0.2850 Source DF Type III SS Mean Square F Value Pr > F g 1 8173753.336 8173753.336 1.38 0.2850 Source DF Type IV SS Mean Square F Value Pr > F g 1 8173753.336 8173753.336 1.38 0.2850 The GLM Procedure

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177 Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g e2 LSMEAN Erro r Pr > |t| Pr > |t| 1 3216.21250 1217.85952 0.0385 0.2850 2 1194.61000 1217.85952 0.3645 Table F.32 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 1 16.08295613 16.08295613 38.47 0.0008 Error 6 2.50808915 0.41801486 Corrected Total 7 18.59104527 R-Square Coeff Var Root MSE adj Mean 0.865092 16.94878 0.646541 3.814675 Source DF Type I SS Mean Square F Value Pr > F g 1 16.08295613 16.08295613 38.47 0.0008 Source DF Type II SS Mean Square F Value Pr > F g 1 16.08295613 16.08295613 38.47 0.0008 Source DF Type III SS Mean Square F Value Pr > F g 1 16.08295613 16.08295613 38.47 0.0008

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178 Source DF Type IV SS Mean Square F Value Pr > F g 1 16.08295613 16.08295613 38.47 0.0008 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 5.23255000 0.32327034 <.0001 0.0008 2 2.39680000 0.32327034 0.0003 Table F.33 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 1 0.25190003 0.25190003 10.81 0.0167 Error 6 0.13984247 0.02330708 Corrected Total 7 0.39174250 R-Square Coeff Var Root MSE adj Mean 0.643025 25.07458 0.152667 0.608850 Source DF Type I SS Mean Square F Value Pr > F g 1 0.25190003 0.25190003 10.81 0.0167 Source DF Type II SS Mean Square F Value Pr > F g 1 0.25190003 0.25190003 10.81 0.0167 Source DF Type III SS Mean Square F Value Pr > F g 1 0.25190003 0.25190003 10.81 0.0167

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179 Source DF Type IV SS Mean Square F Value Pr > F g 1 0.25190003 0.25190003 10.81 0.0167 The GLM Procedure Least Squares Means H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 g adj LSMEAN Erro r Pr > |t| Pr > |t| 1 0.83793333 0.08814208 <.0001 0.0167 2 0.47140000 0.06827456 0.0005 Table F.34 The GLM Procedure Dependent Variable: e2 Sum of Source DF Squares Mean Square F Value Pr > F Model 23 731467316899 31802926822 2.83 0.0180 Error 16 179545035506 11221564719 Corrected Total 39 911012352405 R-Square Coeff Var Root MSE e2 Mean 0.802917 76.39974 105931.9 138654.8 Source DF Type I SS Mean Square F Value Pr > F g 3 176945641932 58981880644 5.26 0.0103 m(g) 16 449031779909 28064486244 2.50 0.0379 t 1 79886885849 79886885849 7.12 0.0168 t*g 3 25603009209 8534336403 0.76 0.5325 Source DF Type II SS Mean Square F Value Pr > F g 3 176945641932 58981880644 5.26 0.0103 m(g) 16 449031779909 28064486244 2.50 0.0379 t 1 79886885849 79886885849 7.12 0.0168 t*g 3 25603009209 8534336403 0.76 0.5325 Source DF Type III SS Mean Square F Value Pr > F

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180 g 3 176945641932 58981880644 5.26 0.0103 m(g) 16 449031779909 28064486244 2.50 0.0379 t 1 55645696498 55645696498 4.96 0.0407 t*g 3 25603009209 8534336403 0.76 0.5325 Source DF Type IV SS Mean Square F Value Pr > F g 3 176945641932 58981880644 5.26 0.0103 m(g) 16 449031779909 28064486244 2.50 0.0379 t 1 55645696498 55645696498 4.96 0.0407 t*g 3 25603009209 8534336403 0.76 0.5325 The GLM Procedure Least Squares Means Standard LSMEAN g e2 LSMEAN Error Pr > |t| Number 1 70283.417 30579.902 0.0354 1 2 230767.250 30579.902 <.0001 2 3 143729.600 33498.604 0.0006 3 4 82714.487 43246.512 0.0739 4 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: e2 i/j 1 2 3 4 1 0.0019 0.1249 0.8174 2 0.0019 0.0730 0.0130 3 0.1249 0.0730 0.2812 4 0.8174 0.0130 0.2812 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 t e2 LSMEAN E rror Pr > |t| Pr > |t| 24 93052.677 24654.305 0.0017 0.0407 48 170694.700 24654.305 <.0001

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181 Standard LSMEAN t g e2 LSMEAN Error Pr > |t| Number 24 1 33899.167 43246.512 0.4446 1 24 2 166344.000 43246.512 0.0014 2 24 3 80969.600 47374.180 0.1067 3 24 4 90997.940 61159.804 0.1562 4 48 1 106667.667 43246.512 0.0253 5 48 2 295190.500 43246.512 <.0001 6 48 3 206489.600 47374.180 0.0005 7 48 4 74431.033 61159.804 0.2413 8 Least Squares Means for effect t*g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: e2 i/j 1 2 3 4 5 6 7 8 1 0.0458 0.4737 0.4570 0.2515 0.0006 0.0161 0.5959 2 0.0458 0.2019 0.3294 0.3437 0.0513 0.5402 0.2376 3 0.4737 0.2019 0.8985 0.6940 0.0042 0.0794 0.9337 4 0.4570 0.3294 0.8985 0.8369 0.0150 0.1549 0.8505 5 0.2515 0.3437 0.6940 0.8369 0.0071 0.1392 0.6727 6 0.0006 0.0513 0.0042 0.0150 0.0071 0.1857 0.0095 7 0.0161 0.5402 0.0794 0.1549 0.1392 0.1857 0.1071 8 0.5959 0.2376 0.9337 0.8505 0.6727 0.0095 0.1071 The GLM Procedure Dependent Variable: e2 Contrast DF C ontrast SS Mean Square F Value Pr > F 1 vs 2,3,4 1 55176168203 55176168203 4.92 0.0414 1 vs 2 1 154530364568 154530364568 13.77 0.0019 2 vs 3 1 41321195550 41321195550 3.68 0.0730 Table F.35 The GLM Procedure Dependent Variable: p4 Sum of

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182 Source DF Squares Mean Square F Value Pr > F Model 22 64504310.68 2932014.12 5.23 0.0009 Error 15 8413678.61 560911.91 Corrected Total 37 72917989.29 R-Square Coeff Var Root MSE p4 Mean 0.884615 50.35117 748.9405 1487.434 Source DF Type I SS Mean Square F Value Pr > F g 3 8258675.30 2752891.77 4.91 0.0143 m(g) 15 11834288.04 788952.54 1.41 0.2584 t 1 35623369.90 35623369.90 63.51 <.0001 g*t 3 8787977.44 2929325.81 5.22 0.0114 Source DF Type II SS Mean Square F Value Pr > F g 3 8258675.30 2752891.77 4.91 0.0143 m(g) 15 11834288.04 788952.54 1.41 0.2584 t 1 35623369.90 35623369.90 63.51 <.0001 g*t 3 8787977.44 2929325.81 5.22 0.0114 Source DF Type III SS Mean Square F Value Pr > F g 3 8258675.30 2752891.77 4.91 0.0143 m(g) 15 11834288.04 788952.54 1.41 0.2584 t 1 27674418.01 27674418.01 49.34 <.0001 g*t 3 8787977.44 2929325.81 5.22 0.0114 Source DF Type IV SS Mean Square F Value Pr > F g 3 8258675.30 2752891.77 4.91 0.0143 m(g) 15 11834288.04 788952.54 1.41 0.2584 t 1 27674418.01 27674418.01 49.34 <.0001 g*t 3 8787977.44 2929325.81 5.22 0.0114 The GLM Procedure Least Squares Means Standard LSMEAN g p4 LSMEAN Error Pr > |t| Number

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183 1 1954.28000 236.83579 <.0001 1 2 1164.50000 216.20051 <.0001 2 3 1861.61000 236.83579 <.0001 3 4 731.60000 305.75369 0.0302 4 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4 1 0.0264 0.7858 0.0065 2 0.0264 0.0461 0.2657 3 0.7858 0.0461 0.0105 4 0.0065 0.2657 0.0105 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 t p4 LSMEAN Erro r Pr > |t| Pr > |t| 24 545.76000 177.62684 0.0077 <.0001 48 2310.23500 177.62684 <.0001 Standard LSMEAN g t p4 LSMEAN Error Pr > |t| Number 1 24 772.84000 334.93638 0.0357 1 1 48 3135.72000 334.93638 <.0001 2 2 24 347.66667 305.75369 0.2733 3 2 48 1981.33333 305.75369 <.0001 4 3 24 363.60000 334.93638 0.2948 5 3 48 3359.62000 334.93638 <.0001 6 4 24 698.93333 432.40101 0.1268 7 4 48 764.26667 432.40101 0.0975 8 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4 5 6 7 8

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184 1 0.0002 0.3633 0.0177 0.4012 <.0001 0.8943 0.9877 2 0.0002 <.0001 0.0224 <.0001 0.6432 0.0005 0.0006 3 0.3633 <.0001 0.0018 0.9724 <.0001 0.5172 0.4437 4 0.0177 0.0224 0.0018 0.0028 0.0083 0.0286 0.0364 5 0.4012 <.0001 0.9724 0.0028 <.0001 0.5490 0.4751 6 <.0001 0.6432 <.0001 0.0083 <.0001 0.0002 0.0003 7 0.8943 0.0005 0.5172 0.0286 0.5490 0.0002 0.9163 8 0.9877 0.0006 0.4437 0.0364 0.4751 0.0003 0.9163 The GLM Procedure Dependent Variable: p4 Contrast DF C ontrast SS Mean Square F Value Pr > F 1 vs 2,3,4 1 3545257.854 3545257.854 6.32 0.0238 1 vs 2 1 3402286.082 3402286.082 6.07 0.0264 2 vs 3 1 2650703.739 2650703.739 4.73 0.0461 Table F.36 The GLM Procedure Dependent Variable: e2 Sum of Source DF Squares Mean Square F Value Pr > F Model 22 2605.626351 118.437561 46.15 <.0001 Error 72 184.769087 2.566237 Corrected Total 94 2790.395438 R-Square Coeff Var Root MSE e2 Mean 0.933784 13.64485 1.601948 11.74031 Source DF Type I SS Mean Square F Value Pr > F g 3 23.076253 7.692084 3.00 0.0762 m(g) 15 283.692405 18.912827 7.37 <.0001 d 4 2298.857693 574.714423 223.95 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 3 23.076253 7.692084 3.00 0.0762 m(g) 15 283.692405 18.912827 7.37 <.0001

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185 d 4 2298.857693 574.714423 223.95 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 3 23.076253 7.692084 3.00 0.0762 m(g) 15 283.692405 18.912827 7.37 <.0001 d 4 2298.857693 574.714423 223.95 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 3 23.076253 7.692084 3.00 0.0762 m(g) 15 283.692405 18.912827 7.37 <.0001 d 4 2298.857693 574.714423 223.95 <.0001 The GLM Procedure Least Squares Means Standard LSMEAN g e2 LSMEAN Error Pr > |t| Number 1 12.4288800 0.3582065 <.0001 1 2 11.6669967 0.2924743 <.0001 2 3 11.0520000 0.3203896 <.0001 3 4 12.0221050 0.3582065 <.0001 4 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: e2 i/j 1 2 3 4 1 0.1038 0.0755 0.4246 2 0.1038 0.1606 0.4451 3 0.0755 0.1606 0.0473 4 0.4246 0.4451 0.0473 Table F.37 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F

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186 Model 22 479.3335212 21.7878873 32.06 <.0001 Error 72 48.9327062 0.6796209 Corrected Total 94 528.2662275 R-Square Coeff Var Root MSE p4 Mean 0.907371 27.19384 0.824391 3.031537 Source DF Type I SS Mean Square F Value Pr > F g 3 0.7750749 0.2583583 0.38 0.7676 m(g) 15 30.7861947 2.0524130 3.02 0.0009 d 4 447.7722516 111.9430629 164.71 <.0001 Source DF Type II SS Mean Square F Value Pr > F g 3 0.7750749 0.2583583 0.38 0.7676 m(g) 15 30.7861947 2.0524130 3.02 0.0009 d 4 447.7722516 111.9430629 164.71 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 3 0.7750749 0.2583583 0.38 0.7676 m(g) 15 30.7861947 2.0524130 3.02 0.0009 d 4 447.7722516 111.9430629 164.71 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 3 0.7750749 0.2583583 0.38 0.7676 m(g) 15 30.7861947 2.0524130 3.02 0.0009 d 4 447.7722516 111.9430629 164.71 <.0001

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187 The GLM Procedure Least Squares Means Standard LSMEAN g p4 LSMEAN Error Pr > |t| Number 1 3.02049000 0.18433949 <.0001 1 2 3.05810667 0.15051256 <.0001 2 3 2.90372000 0.16487825 <.0001 3 4 3.16250000 0.18433949 <.0001 4 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4 1 0.8748 0.6383 0.5876 2 0.8748 0.4914 0.6622 3 0.6383 0.4914 0.2989 4 0.5876 0.6622 0.2989 Table F.38 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 23 4.34131895 0.18875300 5.15 0.0007 Error 16 0.58589380 0.03661836 Corrected Total 39 4.92721275 R-Square Coeff Var Root MSE adj Mean 0.881090 25.11886 0.191359 0.761815 Source DF Type I SS Mean Square F Value Pr > F g 3 1.46956753 0.48985584 13.38 0.0001 m(g) 16 1.44513473 0.09032092 2.47 0.0401 t 1 0.04024634 0.04024634 1.10 0.3100 g*t 3 1.38637035 0.46212345 12.62 0.0002

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188 Source DF Type II SS Mean Square F Value Pr > F g 3 1.46956753 0.48985584 13.38 0.0001 m(g) 16 1.44513473 0.09032092 2.47 0.0401 t 1 0.04024634 0.04024634 1.10 0.3100 g*t 3 1.38637035 0.46212345 12.62 0.0002 Source DF Type III SS Mean Square F Value Pr > F g 3 1.46956753 0.48985584 13.38 0.0001 m(g) 16 1.44513473 0.09032092 2.47 0.0401 t 1 0.00215820 0.00215820 0.06 0.8113 g*t 3 1.38637035 0.46212345 12.62 0.0002 Source DF Type IV SS Mean Square F Value Pr > F g 3 1.46956753 0.48985584 13.38 0.0001 m(g) 16 1.44513473 0.09032092 2.47 0.0401 t 1 0.00215820 0.00215820 0.06 0.8113 g*t 3 1.38637035 0.46212345 12.62 0.0002

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189 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1 0.94514167 0.05524066 <.0001 1 2 0.62883333 0.05524066 <.0001 2 3 0.95198750 0.06765571 <.0001 3 4 0.49612500 0.06765571 <.0001 4 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 1 0.0009 0.9385 <.0001 2 0.0009 0.0019 0.1482 3 0.9385 0.0019 0.0002 4 <.0001 0.1482 0.0002 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 t adj LSMEAN Error Pr > |t| Pr > |t| 24 0.74802500 0.04367157 <.0001 0.8113 48 0.76301875 0.04367157 <.0001 Standard LSMEAN g t adj LSMEAN Error Pr > |t| Number 1 24 0.70950000 0.07812209 <.0001 1 1 48 1.18078333 0.07812209 <.0001 2 2 24 0.60725000 0.07812209 <.0001 3 2 48 0.65041667 0.07812209 <.0001 4 3 24 1.25070000 0.09567962 <.0001 5 3 48 0.65327500 0.09567962 <.0001 6 4 24 0.42465000 0.09567962 0.0004 7

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190 4 48 0.56760000 0.09567962 <.0001 8 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 5 6 7 8 1 0.0006 0.3685 0.6002 0.0005 0.6551 0.0348 0.2675 2 0.0006 <.0001 0.0002 0.5792 0.0006 <.0001 0.0001 3 0.3685 <.0001 0.7012 <.0001 0.7143 0.1587 0.7524 4 0.6002 0.0002 0.7012 0.0002 0.9818 0.0863 0.5121 5 0.0005 0.5792 <.0001 0.0002 0.0004 <.0001 0.0001 6 0.6551 0.0006 0.7143 0.9818 0.0004 0.1105 0.5356 7 0.0348 <.0001 0.1587 0.0863 <.0001 0.1105 0.3065 8 0.2675 0.0001 0.7524 0.5121 0.0001 0.5356 0.3065 The GLM Procedure Dependent Variable: adj Contrast DF C ontrast SS Mean Square F Value Pr > F 1 vs 2,3,4 1 0.53103752 0.53103752 14.50 0.0015 1 vs 2 1 0.60030577 0.60030577 16.39 0.0009 2 vs 3 1 0.50125735 0.50125735 13.69 0.0019 Table F.39 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 23 1.90556871 0.08285081 44.44 <.0001 Error 16 0.02983242 0.00186453 Corrected Total 39 1.93540113 R-Square Coeff Var Root MSE adj Mean 0.984586 5.217610 0.043180 0.827585 Source DF Type I SS Mean Square F Value Pr > F g 3 0.14641984 0.04880661 26.18 <.0001 m(g) 16 0.53773160 0.03360822 18.03 <.0001 t 1 0.08884948 0.08884948 47.65 <.0001 g*t 3 1.13256780 0.37752260 202.48 <.0001

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191 Source DF Type II SS Mean Square F Value Pr > F g 3 0.14641984 0.04880661 26.18 <.0001 m(g) 16 0.53773160 0.03360822 18.03 <.0001 t 1 0.08884948 0.08884948 47.65 <.0001 g*t 3 1.13256780 0.37752260 202.48 <.0001 Source DF Type III SS Mean Square F Value Pr > F g 3 0.14641984 0.04880661 26.18 <.0001 m(g) 16 0.53773160 0.03360822 18.03 <.0001 t 1 0.05507328 0.05507328 29.54 <.0001 g*t 3 1.13256780 0.37752260 202.48 <.0001 Source DF Type IV SS Mean Square F Value Pr > F g 3 0.14641984 0.04880661 26.18 <.0001 m(g) 16 0.53773160 0.03360822 18.03 <.0001 t 1 0.05507328 0.05507328 29.54 <.0001 g*t 3 1.13256780 0.37752260 202.48 <.0001

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192 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1 0.74785000 0.01246504 <.0001 1 2 0.82246667 0.01246504 <.0001 2 3 0.90892500 0.01526649 <.0001 3 4 0.87352500 0.01526649 <.0001 4 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 1 0.0006 <.0001 <.0001 2 0.0006 0.0005 0.0197 3 <.0001 0.0005 0.1206 4 <.0001 0.0197 0.1206 H0:LSMean1= Standard H0:LSMEAN=0 LSMean2 t adj LSMEAN Error Pr > |t| Pr > |t| 24 0.87606250 0.00985448 <.0001 <.0001 48 0.80032083 0.00985448 <.0001 Standard LSMEAN g t adj LSMEAN Error Pr > |t| Number 1 24 0.66568333 0.01762823 <.0001 1 1 48 0.83001667 0.01762823 <.0001 2 2 24 1.07296667 0.01762823 <.0001 3 2 48 0.57196667 0.01762823 <.0001 4 3 24 0.74512500 0.02159008 <.0001 5 3 48 1.07272500 0.02159008 <.0001 6 4 24 1.02047500 0.02159008 <.0001 7 4 48 0.72657500 0.02159008 <.0001 8

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193 Least Squares Means for effect g*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 4 5 6 7 8 1 <.0001 <.0001 0.0017 0.0116 <.0001 <.0001 0.0441 2 <.0001 <.0001 <.0001 0.0077 <.0001 <.0001 0.0019 3 <.0001 <.0001 <.0001 <.0001 0.9932 0.0780 <.0001 4 0.0017 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 5 0.0116 0.0077 <.0001 <.0001 <.0001 <.0001 0.5520 6 <.0001 <.0001 0.9932 <.0001 <.0001 0.1063 <.0001 7 <.0001 <.0001 0.0780 <.0001 <.0001 0.1063 <.0001 8 0.0441 0.0019 <.0001 <.0001 0.5520 <.0001 <.0001 The GLM Procedure Dependent Variable: adj Contrast DF C ontrast SS Mean Square F Value Pr > F 1 vs 2,3,4 1 0.12054080 0.12054080 64.65 <.0001 1 vs 2 1 0.03340588 0.03340588 17.92 0.0006 2 vs 3 1 0.03588021 0.03588021 19.24 0.0005 Table F.40 Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 2 502532.0803 251266.0401 3.02 0.1380 Error 5 416019.5493 83203.9099 Corrected Total 7 918551.6296 R-Square Coeff Var Root MSE p4 Mean 0.547092 41.69246 288.4509 691.8538 Source DF Type I SS Mean Square F Value Pr > F g 2 502532.0803 251266.0401 3.02 0.1380 Source DF Type II SS Mean Square F Value Pr > F g 2 502532.0803 251266.0401 3.02 0.1380

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194 Source DF Type III SS Mean Square F Value Pr > F g 2 502532.0803 251266.0401 3.02 0.1380 Source DF Type IV SS Mean Square F Value Pr > F g 2 502532.0803 251266.0401 3.02 0.1380 The GLM Procedure Least Squares Means Standard LSMEAN g p4 LSMEAN Error Pr > |t| Number 1 972.566667 166.537193 0.0021 1 2 652.160000 166.537193 0.0112 2 3 330.325000 203.965573 0.1663 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 1 0.2318 0.0587 2 0.2318 0.2761 3 0.0587 0.2761 Table F.41 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 2 0.49252121 0.24626060 35.26 0.0011 Error 5 0.03491867 0.00698373 Corrected Total 7 0.52743988 R-Square Coeff Var Root MSE adj Mean 0.933796 7.963667 0.083569 1.049375 Source DF Type I SS Mean Square F Value Pr > F g 2 0.49252121 0.24626060 35.26 0.0011 Source DF Type II SS Mean Square F Value Pr > F

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195 g 2 0.49252121 0.24626060 35.26 0.0011 Source DF Type III SS Mean Square F Value Pr > F g 2 0.49252121 0.24626060 35.26 0.0011 Source DF Type IV SS Mean Square F Value Pr > F g 2 0.49252121 0.24626060 35.26 0.0011 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1 1.23200000 0.04824843 <.0001 1 2 1.15033333 0.04824843 <.0001 2 3 0.62400000 0.05909202 0.0001 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 1 0.2850 0.0005 2 0.2850 0.0010 3 0.0005 0.0010 Table F.42 The GLM Procedure Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 2 0.49252121 0.24626060 35.26 0.0011 Error 5 0.03491867 0.00698373 Corrected Total 7 0.52743988 R-Square Coeff Var Root MSE adj Mean 0.933796 7.963667 0.083569 1.049375 Source DF Type I SS Mean Square F Value Pr > F

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196 g 2 0.49252121 0.24626060 35.26 0.0011 Source DF Type II SS Mean Square F Value Pr > F g 2 0.49252121 0.24626060 35.26 0.0011 Source DF Type III SS Mean Square F Value Pr > F g 2 0.49252121 0.24626060 35.26 0.0011 Source DF Type IV SS Mean Square F Value Pr > F g 2 0.49252121 0.24626060 35.26 0.0011 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1 1.23200000 0.04824843 <.0001 1 2 1.15033333 0.04824843 <.0001 2 3 0.62400000 0.05909202 0.0001 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 1 0.2850 0.0005 2 0.2850 0.0010 3 0.0005 0.0010 Table F.43 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 2 4.69522838 2.34761419 0.21 0.8164 Error 5 55.53695450 11.10739090 Corrected Total 7 60.23218288 R-Square Coeff Var Root MSE adj Mean 0.077952 66.84099 3.332775 4.986125 Source DF Type I SS Mean Square F Value Pr > F

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197 g 2 4.69522838 2.34761419 0.21 0.8164 Source DF Type II SS Mean Square F Value Pr > F g 2 4.69522838 2.34761419 0.21 0.8164 Source DF Type III SS Mean Square F Value Pr > F g 2 4.69522837 2.34761419 0.21 0.8164 Source DF Type IV SS Mean Square F Value Pr > F g 2 4.69522837 2.34761419 0.21 0.8164 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1 6.27450000 2.35662798 0.0447 1 2 4.40700000 1.66638763 0.0457 2 3 4.85600000 2.35662798 0.0944 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 1 0.5461 0.6881 2 0.5461 0.8825 3 0.6881 0.8825 Table F.44 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 2 0.48820967 0.24410483 0.08 0.9242 Error 3 9.04372917 3.01457639 Corrected Total 5 9.53193883 R-Square Coeff Var Root MSE adj Mean 0.051218 47.84605 1.736254 3.628833

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198 Source DF Type I SS Mean Square F Value Pr > F g 2 0.48820967 0.24410483 0.08 0.9242 Source DF Type II SS Mean Square F Value Pr > F g 2 0.48820967 0.24410483 0.08 0.9242 Source DF Type III SS Mean Square F Value Pr > F g 2 0.48820967 0.24410483 0.08 0.9242 Source DF Type IV SS Mean Square F Value Pr > F g 2 0.48820967 0.24410483 0.08 0.9242 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1 3.84200000 1.73625355 0.1138 1 2 3.82666667 1.00242645 0.0316 2 3 3.22550000 1.22771666 0.0785 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 1 0.9944 0.7908 2 0.9944 0.7297 3 0.7908 0.7297 Table F.45 Dependent Variable: adj Sum of Source DF Squares Mean Square F Value Pr > F Model 2 29.9217022 14.9608511 0.55 0.6073 Error 5 135.5273013 27.1054603 Corrected Total 7 165.4490035 R-Square Coeff Var Root MSE adj Mean

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199 0.180852 55.78517 5.206290 9.332750 Source DF Type I SS Mean Square F Value Pr > F g 2 29.92170225 14.96085112 0.55 0.6073 Source DF Type II SS Mean Square F Value Pr > F g 2 29.92170225 14.96085112 0.55 0.6073 Source DF Type III SS Mean Square F Value Pr > F g 2 29.92170225 14.96085112 0.55 0.6073 Source DF Type IV SS Mean Square F Value Pr > F g 2 29.92170225 14.96085112 0.55 0.6073 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1 12.0395000 3.6814033 0.0222 1 2 7.5002500 2.6031452 0.0345 2 3 10.2910000 3.6814033 0.0382 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 1 0.3603 0.7506 2 0.3603 0.5631 3 0.7506 0.5631 Table F.46 Sum of Source DF Squares Mean Square F Value Pr > F Model 2 23.00968417 11.50484208 49.28 0.0051 Error 3 0.70037517 0.23345839 Corrected Total 5 23.71005933 R-Square Coeff Var Root MSE adj Mean

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200 0.970461 6.805606 0.483175 7.099667 Source DF Type I SS Mean Square F Value Pr > F g 2 23.00968417 11.50484208 49.28 0.0051 Source DF Type II SS Mean Square F Value Pr > F g 2 23.00968417 11.50484208 49.28 0.0051 Source DF Type III SS Mean Square F Value Pr > F g 2 23.00968417 11.50484208 49.28 0.0051 Source DF Type IV SS Mean Square F Value Pr > F g 2 23.00968417 11.50484208 49.28 0.0051 The GLM Procedure Least Squares Means Standard LSMEAN g adj LSMEAN Error Pr > |t| Number 1t 4.02800000 0.48317532 0.0036 1 2t 6.46566667 0.27896140 0.0002 2 3t 9.58650000 0.34165654 <.0001 3 Least Squares Means for effect g Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: adj i/j 1 2 3 1 0.0222 0.0026 2 0.0222 0.0058 3 0.0026 0.0058 Table F.47 The GLM Procedure Dependent Variable: p4 Sum of Source DF Squares Mean Square F Value Pr > F Model 65 6947.138566 106.879055 6.28 <.0001

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201 Error 30 510.561622 17.018721 Corrected Total 95 7457.700188 R-Square Coeff Var Root MSE p4 Mean 0.931539 35.00267 4.125375 11.78589 Source DF Type I SS Mean Square F Value Pr > F a 2 2856.161475 1428.080737 83.91 <.0001 m(a) 5 793.821607 158.764321 9.33 <.0001 b 2 813.698236 406.849118 23.91 <.0001 t 3 245.000292 81.666764 4.80 0.0076 a*b 4 536.606169 134.151542 7.88 0.0002 m*b(a) 10 453.385168 45.338517 2.66 0.0185 a*t 6 329.217091 54.869515 3.22 0.0145 m*t(a) 15 320.515580 21.367705 1.26 0.2881 b*t 6 301.926043 50.321007 2.96 0.0218 a*b*t 12 296.806905 24.733909 1.45 0.1970 Source DF Type III SS Mean Square F Value Pr > F a 2 2856.161475 1428.080737 83.91 <.0001 m(a) 5 793.821607 158.764321 9.33 <.0001 b 2 969.972971 484.986485 28.50 <.0001 t 3 311.346314 103.782105 6.10 0.0023 a*b 4 536.606169 134.151542 7.88 0.0002 m*b(a) 10 453.385168 45.338517 2.66 0.0185 a*t 6 329.217091 54.869515 3.22 0.0145 m*t(a) 15 320.515580 21.367705 1.26 0.2881 b*t 6 309.978278 51.663046 3.04 0.0193 a*b*t 12 296.806905 24.733909 1.45 0.1970 The GLM Procedure Source Type III Expected Mean Square a Var(Error) + 3 Var(m*t(a)) + 4 Var(m*b(a)) + 12 Var(m(a)) + Q(a,a*b,a*t,a*b*t) m(a) Var(Error) + 3 Var(m*t(a)) + 4 Var(m*b(a)) + 12 Var(m(a)) b Var(Error) + 4 Var(m*b(a )) + Q(b,a*b,b*t,a*b*t) t Var(Error) + 3 Var(m*t(a)) + Q(t,a*t,b*t,a*b*t) a*b Var(Error) + 4 Var(m*b(a)) + Q(a*b,a*b*t)

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202 m*b(a) Var(Error) + 4 Var(m*b(a)) a*t Var(Error) + 3 Var(m*t(a)) + Q(a*t,a*b*t) m*t(a) Var(Error) + 3 Var(m*t(a)) b*t Var(Error) + Q(b*t,a*b*t) a*b*t Var(Error) + Q(a*b*t) The GLM Procedure Tests of Hypotheses for Mixed Model Analysis of Variance Dependent Variable: p4 Source DF Type III SS Mean Square F Value Pr > F a 2 2856.161475 1428.080737 8.99 0.0221 Error: MS(m(a)) 5 793.821607 158.764321 This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F m(a) 5 793.821607 158.764321 3.20 0.0554 Error 10.05 499.370033 49.687501 Error: MS(m*b(a)) + MS (m*t(a)) MS(Error) Source DF Type III SS Mean Square F Value Pr > F b 2 969.972971 484.986485 10.70 0.0033 a*b 4 536.606169 134.151542 2.96 0.0747 Error: MS(m*b(a)) 10 453.385168 45.338517 This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F t 3 311.346314 103.782105 4.86 0.0148 a*t 6 329.217091 54.869515 2.57 0.0648 Error: MS(m*t(a)) 15 320.515580 21.367705 This test assumes one or more other fixed effects are zero.

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203 Source DF Type III SS Mean Square F Value Pr > F m*b(a) 10 453.385168 45.338517 2.66 0.0185 m*t(a) 15 320.515580 21.367705 1.26 0.2881 b*t 6 309.978278 51.663046 3.04 0.0193 a*b*t 12 296.806905 24.733909 1.45 0.1970 Error: MS(Error) 30 510.561622 17.018721 This test assumes one or more other fixed effects are zero. Least Squares Means Standard Errors and Probabilities Calculat ed Using the Type III MS for m(a) as an Error Term Standard LSMEAN a p4 LSMEAN Error Pr > |t| Number 1 7.3185611 2.1000286 0.0176 1 2 10.1116675 2.1000286 0.0048 2 3 20.9982000 2.5719992 0.0004 3 Least Squares Means for effect a Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 1 0.3902 0.0092 2 0.3902 0.0220 3 0.0092 0.0220 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*t(a) as an Error Term Standard LSMEAN t p4 LSMEAN Error Pr > |t| Number 4 13.5678519 0.9608829 <.0001 1 9 15.4344404 0.9608829 <.0001 2 12 11.8814807 0.9608829 <.0001 3 24 10.3541319 0.9608829 <.0001 4 Least Squares Means for effect t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4

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204 1 0.1897 0.2337 0.0319 2 0.1897 0.0195 0.0020 3 0.2337 0.0195 0.2787 4 0.0319 0.0020 0.2787 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*b(a) as an Error Term Standard LSMEAN b p4 LSMEAN Error Pr > |t| Number c 14.0757431 1.2121484 <.0001 1 h 8.2635981 1.2121484 <.0001 2 m 16.0890875 1.2121484 <.0001 3 Least Squares Means for effect b Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 1 0.0069 0.2674 2 0.0069 0.0010 3 0.2674 0.0010 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*t(a) as an Error Term Standard LSMEAN a t p4 LSMEAN Error Pr > |t| Number 1 4 7.2579000 1.5408405 0.0003 1 1 9 8.3729556 1.5408405 <.0001 2 1 12 7.8424667 1.5408405 0.0001 3 1 24 5.8009222 1.5408405 0.0019 4 2 4 11.3241222 1.5408405 <.0001 5 2 9 11.0102989 1.5408405 <.0001 6 2 12 7.5482756 1.5408405 0.0002 7 2 24 10.5639733 1.5408405 <.0001 8 3 4 22.1215333 1.8871365 <.0001 9 3 9 26.9200667 1.8871365 <.0001 10 3 12 20.2537000 1.8871365 <.0001 11 3 24 14.6975000 1.8871365 <.0001 12 Least Squares Means for effect a*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4

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205 i/j 1 2 3 4 5 6 1 0.6163 0.7922 0.5139 0.0817 0.1056 2 0.6163 0.8110 0.2562 0.1957 0.2449 3 0.7922 0.8110 0.3637 0.1309 0.1666 4 0.5139 0.2562 0.3637 0.0229 0.0304 5 0.0817 0.1957 0.1309 0.0229 0.8874 6 0.1056 0.2449 0.1666 0.0304 0.8874 7 0.8958 0.7104 0.8944 0.4351 0.1036 0.1330 8 0.1500 0.3306 0.2308 0.0451 0.7321 0.8405 9 <.0001 <.0001 <.0001 <.0001 0.0005 0.0004 10 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 11 <.0001 0.0002 0.0001 <.0001 0.0023 0.0018 12 0.0080 0.0203 0.0131 0.0024 0.1864 0.1509 Least Squares Means for effect a*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 7 8 9 10 11 12 1 0.8958 0.1500 <.0001 <.0001 <.0001 0.0080 2 0.7104 0.3306 <.0001 <.0001 0.0002 0.0203 3 0.8944 0.2308 <.0001 <.0001 0.0001 0.0131 4 0.4351 0.0451 <.0001 <.0001 <.0001 0.0024 5 0.1036 0.7321 0.0005 <.0001 0.0023 0.1864 6 0.1330 0.8405 0.0004 <.0001 0.0018 0.1509 7 0.1866 <.0001 <.0001 0.0001 0.0103 8 0.1866 0.0003 <.0001 0.0012 0.1104 9 <.0001 0.0003 0.0923 0.4947 0.0140 10 <.0001 <.0001 0.0923 0.0246 0.0004 11 0.0001 0.0012 0.4947 0.0246 0.0549 12 0.0103 0.1104 0.0140 0.0004 0.0549 Least Squares Means Standard LSMEAN b t p4 LSMEAN Error Pr > |t| Number c 4 13.5290889 1.4853048 <.0001 1 c 9 17.0759667 1.4853048 <.0001 2 c 12 15.6352444 1.4853048 <.0001 3 c 24 10.0626722 1.4853048 <.0001 4 h 4 9.6972333 1.4853048 <.0001 5 h 9 13.2192211 1.4853048 <.0001 6 h 12 5.9782533 1.4853048 0.0004 7 h 24 4.1596844 1.4853048 0.0088 8

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206 m 4 17.4772333 1.4853048 <.0001 9 m 9 16.0081333 1.4853048 <.0001 10 m 12 14.0309444 1.4853048 <.0001 11 m 24 16.8400389 1.4853048 <.0001 12 Least Squares Means for effect b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4 5 6 1 0.1017 0.3240 0.1093 0.0781 0.8837 2 0.1017 0.4981 0.0023 0.0014 0.0763 3 0.3240 0.4981 0.0126 0.0083 0.2592 4 0.1093 0.0023 0.0126 0.8631 0.1434 5 0.0781 0.0014 0.0083 0.8631 0.1040 6 0.8837 0.0763 0.2592 0.1434 0.1040 7 0.0011 <.0001 <.0001 0.0613 0.0868 0.0017 8 0.0001 <.0001 <.0001 0.0086 0.0131 0.0002 9 0.0699 0.8498 0.3875 0.0014 0.0009 0.0516 10 0.2472 0.6149 0.8603 0.0082 0.0053 0.1943 11 0.8128 0.1575 0.4510 0.0686 0.0478 0.7019 12 0.1255 0.9113 0.5705 0.0030 0.0019 0.0950 Least Squares Means for effect b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 7 8 9 10 11 12 1 0.0011 0.0001 0.0699 0.2472 0.8128 0.1255 2 <.0001 <.0001 0.8498 0.6149 0.1575 0.9113 3 <.0001 <.0001 0.3875 0.8603 0.4510 0.5705 4 0.0613 0.0086 0.0014 0.0082 0.0686 0.0030 5 0.0868 0.0131 0.0009 0.0053 0.0478 0.0019 6 0.0017 0.0002 0.0516 0.1943 0.7019 0.0950 7 0.3935 <.0001 <.0001 0.0006 <.0001 8 0.3935 <.0001 <.0001 <.0001 <.0001 9 <.0001 <.0001 0.4897 0.1113 0.7637 10 <.0001 <.0001 0.4897 0.3541 0.6949 11 0.0006 <.0001 0.1113 0.3541 0.1912 12 <.0001 <.0001 0.7637 0.6949 0.1912 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*b(a) as an Error Term Standard LSMEAN a b p4 LSMEAN Error Pr > |t| Number 1 c 8.3586917 1.9437617 0.0016 1

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207 1 h 5.9632333 1.9437617 0.0119 2 1 m 7.6337583 1.9437617 0.0028 3 2 c 11.8973250 1.9437617 0.0001 4 2 h 6.4711358 1.9437617 0.0076 5 2 m 11.9665417 1.9437617 0.0001 6 3 c 21.9712125 2.3806122 <.0001 7 3 h 12.3564250 2.3806122 0.0004 8 3 m 28.6669625 2.3806122 <.0001 9 Least Squares Means for effect a*b Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4 5 1 0.4040 0.7974 0.2270 0.5079 2 0.4040 0.5569 0.0562 0.8571 3 0.7974 0.5569 0.1519 0.6813 4 0.2270 0.0562 0.1519 0.0766 5 0.5079 0.8571 0.6813 0.0766 6 0.2187 0.0539 0.1461 0.9804 0.0735 7 0.0013 0.0004 0.0009 0.0083 0.0005 8 0.2225 0.0642 0.1554 0.8842 0.0845 9 <.0001 <.0001 <.0001 0.0003 <.0001 Least Squares Means for effect a*b Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 6 7 8 9 1 0.2187 0.0013 0.2225 <.0001 2 0.0539 0.0004 0.0642 <.0001 3 0.1461 0.0009 0.1554 <.0001 4 0.9804 0.0083 0.8842 0.0003 5 0.0735 0.0005 0.0845 <.0001 6 0.0086 0.9016 0.0003 7 0.0086 0.0171 0.0748 8 0.9016 0.0171 0.0007 9 0.0003 0.0748 0.0007 Least Squares Means Standard LSMEAN a b t p4 LSMEAN Error Pr > |t| Number 1 c 4 7.0782667 2.3817865 0.0058 1

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208 1 c 9 10.8605333 2.3817865 <.0001 2 1 c 12 9.7528000 2.3817865 0.0003 3 1 c 24 5.7431667 2.3817865 0.0222 4 1 h 4 7.5208667 2.3817865 0.0036 5 1 h 9 7.9075000 2.3817865 0.0024 6 1 h 12 5.1233333 2.3817865 0.0396 7 1 h 24 3.3012333 2.3817865 0.1760 8 1 m 4 7.1745667 2.3817865 0.0052 9 1 m 9 6.3508333 2.3817865 0.0122 10 1 m 12 8.6512667 2.3817865 0.0010 11 1 m 24 8.3583667 2.3817865 0.0014 12 2 c 4 10.7155000 2.3817865 <.0001 13 2 c 9 12.1813667 2.3817865 <.0001 14 2 c 12 11.3054333 2.3817865 <.0001 15 2 c 24 13.3870000 2.3817865 <.0001 16 2 h 4 12.1142333 2.3817865 <.0001 17 2 h 9 9.6164633 2.3817865 0.0003 18 2 h 12 2.1309267 2.3817865 0.3781 19 2 h 24 2.0229200 2.3817865 0.4024 20 2 m 4 11.1426333 2.3817865 <.0001 21 2 m 9 11.2330667 2.3817865 <.0001 22 2 m 12 9.2084667 2.3817865 0.0006 23 2 m 24 16.2820000 2.3817865 <.0001 24 3 c 4 22.7935000 2.9170808 <.0001 25 3 c 9 28.1860000 2.9170808 <.0001 26 3 c 12 25.8475000 2.9170808 <.0001 27 3 c 24 11.0578500 2.9170808 0.0007 28 3 h 4 9.4566000 2.9170808 0.0029 29 3 h 9 22.1337000 2.9170808 <.0001 30 3 h 12 10.6805000 2.9170808 0.0010 31 3 h 24 7.1549000 2.9170808 0.0202 32 3 m 4 34.1145000 2.9170808 <.0001 33 3 m 9 30.4405000 2.9170808 <.0001 34 3 m 12 24.2331000 2.9170808 <.0001 35 3 m 24 25.8797500 2.9170808 <.0001 36 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4 5 6 7 8 1 0.2704 0.4334 0.6946 0.8963 0.8072 0.5660 0.2710 2 0.2704 0.7445 0.1392 0.3294 0.3876 0.0989 0.0324 3 0.4334 0.7445 0.2432 0.5126 0.5879 0.1795 0.0650

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209 4 0.6946 0.1392 0.2432 0.6015 0.5254 0.8552 0.4741 5 0.8963 0.3294 0.5126 0.6015 0.9094 0.4821 0.2200 6 0.8072 0.3876 0.5879 0.5254 0.9094 0.4150 0.1816 7 0.5660 0.0989 0.1795 0.8552 0.4821 0.4150 0.5925 8 0.2710 0.0324 0.0650 0.4741 0.2200 0.1816 0.5925 9 0.9774 0.2825 0.4500 0.6739 0.9188 0.8292 0.5471 0.2593 10 0.8305 0.1907 0.3206 0.8580 0.7307 0.6473 0.7181 0.3725 11 0.6439 0.5169 0.7459 0.3948 0.7395 0.8267 0.3033 0.1227 12 0.7066 0.4634 0.6818 0.4436 0.8053 0.8944 0.3445 0.1437 13 0.2888 0.9659 0.7770 0.1503 0.3505 0.4111 0.1073 0.0356 14 0.1402 0.6977 0.4765 0.0656 0.1767 0.2143 0.0447 0.0131 15 0.2192 0.8958 0.6482 0.1091 0.2701 0.3211 0.0764 0.0241 16 0.0709 0.4591 0.2892 0.0306 0.0918 0.1142 0.0202 0.0055 17 0.1453 0.7124 0.4887 0.0683 0.1828 0.2214 0.0466 0.0138 18 0.4570 0.7145 0.9680 0.2593 0.5385 0.6156 0.1923 0.0706 19 0.1523 0.0146 0.0310 0.2921 0.1200 0.0967 0.3814 0.7307 20 0.1439 0.0135 0.0289 0.2782 0.1131 0.0909 0.3647 0.7070 21 0.2370 0.9338 0.6828 0.1194 0.2908 0.3445 0.0840 0.0268 22 0.2270 0.9127 0.6635 0.1136 0.2792 0.3314 0.0797 0.0253 23 0.5319 0.6274 0.8727 0.3118 0.6200 0.7021 0.2347 0.0897 24 0.0104 0.1180 0.0620 0.0039 0.0143 0.0187 0.0024 0.0006 25 0.0002 0.0035 0.0016 <.0001 0.0003 0.0004 <.0001 <.0001 26 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 27 <.0001 0.0004 0.0002 <.0001 <.0001 <.0001 <.0001 <.0001

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210 Least Squares Means Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 1 2 3 4 5 6 7 8 28 0.2991 0.9586 0.7314 0.1685 0.3551 0.4095 0.1256 0.0482 29 0.5325 0.7119 0.9378 0.3320 0.6110 0.6837 0.2590 0.1126 30 0.0004 0.0055 0.0026 0.0001 0.0005 0.0007 <.0001 <.0001 31 0.3464 0.9622 0.8071 0.1998 0.4081 0.4672 0.1505 0.0594 32 0.9839 0.3330 0.4956 0.7104 0.9232 0.8430 0.5936 0.3143 33 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 34 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 35 <.0001 0.0013 0.0006 <.0001 0.0001 0.0002 <.0001 <.0001 36 <.0001 0.0004 0.0002 <.0001 <.0001 <.0001 <.0001 <.0001 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 9 10 11 12 13 14 15 16 1 0.9774 0.8305 0.6439 0.7066 0.2888 0.1402 0.2192 0.0709 2 0.2825 0.1907 0.5169 0.4634 0.9659 0.6977 0.8958 0.4591 3 0.4500 0.3206 0.7459 0.6818 0.7770 0.4765 0.6482 0.2892 4 0.6739 0.8580 0.3948 0.4436 0.1503 0.0656 0.1091 0.0306 5 0.9188 0.7307 0.7395 0.8053 0.3505 0.1767 0.2701 0.0918 6 0.8292 0.6473 0.8267 0.8944 0.4111 0.2143 0.3211 0.1142 7 0.5471 0.7181 0.3033 0.3445 0.1073 0.0447 0.0764 0.0202 8 0.2593 0.3725 0.1227 0.1437 0.0356 0.0131 0.0241 0.0055 9 0.8085 0.6642 0.7277 0.3015 0.1476 0.2296 0.0750 10 0.8085 0.4999 0.5556 0.2049 0.0937 0.1517 0.0453 11 0.6642 0.4999 0.9313 0.5446 0.3030 0.4369 0.1700 12 0.7277 0.5556 0.9313 0.4895 0.2654 0.3886 0.1459 13 0.3015 0.2049 0.5446 0.4895 0.6665 0.8621 0.4339 14 0.1476 0.0937 0.3030 0.2654 0.6665 0.7966 0.7229 15 0.2296 0.1517 0.4369 0.3886 0.8621 0.7966 0.5413 16 0.0750 0.0453 0.1700 0.1459 0.4339 0.7229 0.5413 17 0.1529 0.0974 0.3121 0.2737 0.6809 0.9842 0.8119 0.7082

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211 18 0.4741 0.3400 0.7764 0.7114 0.7465 0.4523 0.6197 0.2719 19 0.1447 0.2200 0.0624 0.0744 0.0162 0.0056 0.0107 0.0022 20 0.1366 0.2087 0.0584 0.0697 0.0150 0.0052 0.0099 0.0021 21 0.2480 0.1652 0.4653 0.4150 0.8999 0.7599 0.9618 0.5103 22 0.2377 0.1576 0.4494 0.4002 0.8789 0.7802 0.9830 0.5274 23 0.5505 0.4029 0.8697 0.8025 0.6578 0.3845 0.5383 0.2244 24 0.0112 0.0061 0.0309 0.0254 0.1088 0.2329 0.1500 0.3969 25 0.0003 0.0001 0.0007 0.0006 0.0032 0.0085 0.0047 0.0182 26 <.0001 <.0001 <.0001 <.0001 <.0001 0.0002 <.0001 0.0005 27 <.0001 <.0001 <.0001 <.0001 0.0004 0.0010 0.0006 0.0024 28 0.3107 0.2210 0.5276 0.4790 0.9282 0.7675 0.9480 0.5409 29 0.5491 0.4161 0.8321 0.7726 0.7405 0.4750 0.6270 0.3050 30 0.0004 0.0002 0.0012 0.0010 0.0050 0.0129 0.0074 0.0272 31 0.3593 0.2594 0.5940 0.5421 0.9926 0.6931 0.8693 0.4779 32 0.9959 0.8324 0.6939 0.7515 0.3520 0.1920 0.2792 0.1084 33 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 34 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 35 <.0001 <.0001 0.0003 0.0002 0.0012 0.0032 0.0018 0.0073 36 <.0001 <.0001 <.0001 <.0001 0.0004 0.0010 0.0005 0.0024 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 17 18 19 20 21 22 23 24 1 0.1453 0.4570 0.1523 0.1439 0.2370 0.2270 0.5319 0.0104 2 0.7124 0.7145 0.0146 0.0135 0.9338 0.9127 0.6274 0.1180 3 0.4887 0.9680 0.0310 0.0289 0.6828 0.6635 0.8727 0.0620 4 0.0683 0.2593 0.2921 0.2782 0.1194 0.1136 0.3118 0.0039 5 0.1828 0.5385 0.1200 0.1131 0.2908 0.2792 0.6200 0.0143 6 0.2214 0.6156 0.0967 0.0909 0.3445 0.3314 0.7021 0.0187 7 0.0466 0.1923 0.3814 0.3647 0.0840 0.0797 0.2347 0.0024 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 17 18 19 20 21 22 23 24 8 0.0138 0.0706 0.7307 0.7070 0.0268 0.0253 0.0897 0.0006 9 0.1529 0.4741 0.1447 0.1366 0.2480 0.2377 0.5505 0.0112 10 0.0974 0.3400 0.2200 0.2087 0.1652 0.1576 0.4029 0.0061

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212 11 0.3121 0.7764 0.0624 0.0584 0.4653 0.4494 0.8697 0.0309 12 0.2737 0.7114 0.0744 0.0697 0.4150 0.4002 0.8025 0.0254 13 0.6809 0.7465 0.0162 0.0150 0.8999 0.8789 0.6578 0.1088 14 0.9842 0.4523 0.0056 0.0052 0.7599 0.7802 0.3845 0.2329 15 0.8119 0.6197 0.0107 0.0099 0.9618 0.9830 0.5383 0.1500 16 0.7082 0.2719 0.0022 0.0021 0.5103 0.5274 0.2244 0.3969 17 0.4641 0.0059 0.0054 0.7750 0.7954 0.3952 0.2256 18 0.4641 0.0339 0.0316 0.6537 0.6348 0.9044 0.0571 19 0.0059 0.0339 0.9746 0.0120 0.0112 0.0441 0.0002 20 0.0054 0.0316 0.9746 0.0111 0.0104 0.0412 0.0002 21 0.7750 0.6537 0.0120 0.0111 0.9788 0.5701 0.1375 22 0.7954 0.6348 0.0112 0.0104 0.9788 0.5523 0.1443 23 0.3952 0.9044 0.0441 0.0412 0.5701 0.5523 0.0442 24 0.2256 0.0571 0.0002 0.0002 0.1375 0.1443 0.0442 25 0.0081 0.0015 <.0001 <.0001 0.0043 0.0045 0.0011 0.0941 26 0.0002 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0036 27 0.0010 0.0002 <.0001 <.0001 0.0005 0.0005 0.0001 0.0165 28 0.7810 0.7046 0.0244 0.0229 0.9822 0.9632 0.6269 0.1756 29 0.4858 0.9664 0.0612 0.0577 0.6576 0.6405 0.9479 0.0799 30 0.0124 0.0023 <.0001 <.0001 0.0066 0.0070 0.0018 0.1307 31 0.7061 0.7795 0.0305 0.0287 0.9032 0.8843 0.6986 0.1473 32 0.1978 0.5183 0.1922 0.1831 0.2981 0.2875 0.5896 0.0216 33 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 34 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0007 35 0.0031 0.0005 <.0001 <.0001 0.0016 0.0017 0.0004 0.0432 36 0.0010 0.0002 <.0001 <.0001 0.0005 0.0005 0.0001 0.0162 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 25 26 27 28 29 30 31 32 1 0.0002 <.0001 <.0001 0.2991 0.5325 0.0004 0.3464 0.9839 2 0.0035 <.0001 0.0004 0.9586 0.7119 0.0055 0.9622 0.3330 3 0.0016 <.0001 0.0002 0.7314 0.9378 0.0026 0.8071 0.4956 4 <.0001 <.0001 <.0001 0.1685 0.3320 0.0001 0.1998 0.7104 5 0.0003 <.0001 <.0001 0.3551 0.6110 0.0005 0.4081 0.9232 6 0.0004 <.0001 <.0001 0.4095 0.6837 0.0007 0.4672 0.8430 7 <.0001 <.0001 <.0001 0.1256 0.2590 <.0001 0.1505 0.5936 8 <.0001 <.0001 <.0001 0.0482 0.1126 <.0001 0.0594 0.3143 9 0.0003 <.0001 <.0001 0.3107 0.5491 0.0004 0.3593 0.9959 10 0.0001 <.0001 <.0001 0.2210 0.4161 0.0002 0.2594 0.8324 11 0.0007 <.0001 <.0001 0.5276 0.8321 0.0012 0.5940 0.6939

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213 12 0.0006 <.0001 <.0001 0.4790 0.7726 0.0010 0.5421 0.7515 13 0.0032 <.0001 0.0004 0.9282 0.7405 0.0050 0.9926 0.3520 14 0.0085 0.0002 0.0010 0.7675 0.4750 0.0129 0.6931 0.1920 15 0.0047 <.0001 0.0006 0.9480 0.6270 0.0074 0.8693 0.2792 16 0.0182 0.0005 0.0024 0.5409 0.3050 0.0272 0.4779 0.1084 17 0.0081 0.0002 0.0010 0.7810 0.4858 0.0124 0.7061 0.1978 18 0.0015 <.0001 0.0002 0.7046 0.9664 0.0023 0.7795 0.5183 19 <.0001 <.0001 <.0001 0.0244 0.0612 <.0001 0.0305 0.1922 20 <.0001 <.0001 <.0001 0.0229 0.0577 <.0001 0.0287 0.1831 21 0.0043 <.0001 0.0005 0.9822 0.6576 0.0066 0.9032 0.2981 22 0.0045 <.0001 0.0005 0.9632 0.6405 0.0070 0.8843 0.2875 23 0.0011 <.0001 0.0001 0.6269 0.9479 0.0018 0.6986 0.5896 24 0.0941 0.0036 0.0165 0.1756 0.0799 0.1307 0.1473 0.0216 25 0.2011 0.4649 0.0079 0.0030 0.8740 0.0063 0.0007 26 0.2011 0.5750 0.0003 <.0001 0.1528 0.0002 <.0001 27 0.4649 0.5750 0.0012 0.0004 0.3752 0.0009 <.0001 28 0.0079 0.0003 0.0012 0.7006 0.0117 0.9277 0.3517 29 0.0030 <.0001 0.0004 0.7006 0.0045 0.7688 0.5810 30 0.8740 0.1528 0.3752 0.0117 0.0045 0.0094 0.0010 31 0.0063 0.0002 0.0009 0.9277 0.7688 0.0094 0.3995 Least Squares Means Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 25 26 27 28 29 30 31 32 32 0.0007 <.0001 <.0001 0.3517 0.5810 0.0010 0.3995 33 0.0101 0.1610 0.0542 <.0001 <.0001 0.0068 <.0001 <.0001 34 0.0736 0.5888 0.2744 <.0001 <.0001 0.0531 <.0001 <.0001 35 0.7296 0.3456 0.6983 0.0033 0.0012 0.6145 0.0026 0.0003 36 0.4602 0.5803 0.9938 0.0012 0.0004 0.3711 0.0009 <.0001 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: p4 i/j 33 34 35 36 1 <.0001 <.0001 <.0001 <.0001

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214 2 <.0001 <.0001 0.0013 0.0004 3 <.0001 <.0001 0.0006 0.0002 4 <.0001 <.0001 <.0001 <.0001 5 <.0001 <.0001 0.0001 <.0001 6 <.0001 <.0001 0.0002 <.0001 7 <.0001 <.0001 <.0001 <.0001 8 <.0001 <.0001 <.0001 <.0001 9 <.0001 <.0001 <.0001 <.0001 10 <.0001 <.0001 <.0001 <.0001 11 <.0001 <.0001 0.0003 <.0001 12 <.0001 <.0001 0.0002 <.0001 13 <.0001 <.0001 0.0012 0.0004 14 <.0001 <.0001 0.0032 0.0010 15 <.0001 <.0001 0.0018 0.0005 16 <.0001 <.0001 0.0073 0.0024 17 <.0001 <.0001 0.0031 0.0010 18 <.0001 <.0001 0.0005 0.0002 19 <.0001 <.0001 <.0001 <.0001 20 <.0001 <.0001 <.0001 <.0001 21 <.0001 <.0001 0.0016 0.0005 22 <.0001 <.0001 0.0017 0.0005 23 <.0001 <.0001 0.0004 0.0001 24 <.0001 0.0007 0.0432 0.0162 25 0.0101 0.0736 0.7296 0.4602 26 0.1610 0.5888 0.3456 0.5803 27 0.0542 0.2744 0.6983 0.9938 28 <.0001 <.0001 0.0033 0.0012 29 <.0001 <.0001 0.0012 0.0004 30 0.0068 0.0531 0.6145 0.3711 31 <.0001 <.0001 0.0026 0.0009 32 <.0001 <.0001 0.0003 <.0001 33 0.3802 0.0231 0.0551 34 0.3802 0.1429 0.2777 35 0.0231 0.1429 0.6926 36 0.0551 0.2777 0.6926 Table F.47 The GLM Procedure Dependent Variable: Adj Sum of Source DF Squares Mean Square F Value Pr > F Model 66 8.04502767 0.12189436 92.45 <.0001 Error 26 0.03428237 0.00131855 Corrected Total 92 8.07931003

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215 R-Square Coeff Var Root MSE Adj Mean 0.995757 8.279590 0.036312 0.438571 Source DF Type I SS Mean Square F Value Pr > F a 2 3.09078650 1.54539325 1172.04 <.0001 m(a) 5 0.05491971 0.01098394 8.33 <.0001 b 2 2.07771755 1.03885878 787.88 <.0001 t 3 1.07363772 0.35787924 271.42 <.0001 a*b 4 1.16746404 0.29186601 221.35 <.0001 m*b(a) 10 0.03869599 0.00386960 2.93 0.0133 a*t 6 0.15084168 0.02514028 19.07 <.0001 m*t(a) 15 0.01776941 0.00118463 0.90 0.5746 b*t 6 0.20244851 0.03374142 25.59 <.0001 a*b*t 12 0.16124303 0.01343692 10.19 <.0001 Prot 1 0.00950353 0.00950353 7.21 0.0125 Source DF Type III SS Mean Square F Value Pr > F a 2 2.53248870 1.26624435 960.33 <.0001 m(a) 5 0.03990393 0.00798079 6.05 0.0008 b 2 1.36568354 0.68284177 517.87 <.0001 t 3 0.80476424 0.26825475 203.45 <.0001 a*b 4 1.15413814 0.28853454 218.83 <.0001 m*b(a) 10 0.04538605 0.00453860 3.44 0.0054 a*t 6 0.14498234 0.02416372 18.33 <.0001 m*t(a) 15 0.02467881 0.00164525 1.25 0.3007 b*t 6 0.14851799 0.02475300 18.77 <.0001 a*b*t 12 0.14509572 0.01209131 9.17 <.0001 Prot 1 0.00950353 0.00950353 7.21 0.0125 The GLM Procedure Source Type III Expected Mean Square a Var(Error) + 2.3449 Var(m*t(a )) + 3.1265 Var(m*b(a)) + 9.3795 Var(m(a)) + Q(a,a*b,a*t,a*b*t) m(a) Var(Error) + 2.6499 Var(m*t(a)) + 3.5331 Var(m*b(a)) + 10.599 Var(m(a)) b Var(Error) + 3.1496 Var(m*b(a)) + Q(b,a*b,b*t,a*b*t) t Var(Error) + 2.4104 Var(m*t(a)) + Q(t,a*t,b*t,a*b*t) a*b Var(Error) + 3.471 Var(m*b(a)) + Q(a*b,a*b*t)

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216 m*b(a) Var(Error) + 3.6268 Var(m*b(a)) a*t Var(Error) + 2.5793 Var(m*t(a)) + Q(a*t,a*b*t) m*t(a) Var(Error) + 2.7224 Var(m*t(a)) b*t Var(Error) + Q(b*t,a*b*t) a*b*t Var(Error) + Q(a*b*t) Prot Var(Error) + Q(Prot) The GLM Procedure Tests of Hypotheses for Mixed Model Analysis of Variance Dependent Variable: Adj Source DF Type III SS Mean Square F Value Pr > F a 2 2.532489 1.266244 175.53 <.0001 Error 5.2167 0.037634 0.007214 Error: 0.8849*MS(m(a)) + 0.1151*MS(Error) This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F m(a) 5 0.039904 0.007981 1.67 0.2255 Error 10.424 0.049759 0.004773 Error: 0.9742*MS(m*b(a)) + 0.9733 *MS(m*t(a)) 0.9475*MS(Error) Source DF Type III SS Mean Square F Value Pr > F b 2 1.365684 0.682842 165.94 <.0001 Error 10.892 0.044818 0.004115 Error: 0.8684*MS(m*b(a)) + 0.1316*MS(Error) This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F t 3 0.804764 0.268255 166.85 <.0001

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217 Error 18.162 0.029200 0.001608 Error: 0.8854*MS(m*t(a)) + 0.1146*MS(Error) This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F a*b 4 1.154138 0.288535 65.57 <.0001 Error 10.262 0.045155 0.004400 Error: 0.957*MS(m*b(a)) + 0.043*MS(Error) This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F m*b(a) 10 0.045386 0.004539 3.44 0.0054 m*t(a) 15 0.024679 0.001645 1.25 0.3007 b*t 6 0.148518 0.024753 18.77 <.0001 a*b*t 12 0.145096 0.012091 9.17 <.0001 Prot 1 0.009504 0.009504 7.21 0.0125 Error: MS(Error) 26 0.034282 0.001319 This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F a*t 6 0.144982 0.024164 14.84 <.0001 Error 16.345 0.026611 0.001628 Error: 0.9474*MS(m*t(a)) + 0.0526*MS(Error) This test assumes one or more other fixed effects are zero. Least Squares Means Standard Errors and Probabilities Calculat ed Using the Type III MS for m(a) as an Error Term Standard LSMEAN a Adj LSMEAN Error Pr > |t| Number 1 0.66799300 0.01545141 <.0001 1 2 0.33388521 0.01550098 <.0001 2 3 0.23610229 0.02341905 0.0002 3 Least Squares Means for effect a Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj

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218 i/j 1 2 3 1 <.0001 <.0001 2 <.0001 0.0174 3 <.0001 0.0174 east Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*t(a) as an Error Term Standard LSMEAN t Adj LSMEAN Error Pr > |t| Number 4 0.28119791 0.00983973 <.0001 1 9 0.35761745 0.00986426 <.0001 2 12 0.46388288 0.00917432 <.0001 3 24 0.54794243 0.00900162 <.0001 4 Least Squares Means for effect t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 1 <.0001 <.0001 <.0001 2 <.0001 <.0001 <.0001 3 <.0001 <.0001 <.0001 4 <.0001 <.0001 <.0001 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*b(a) as an Error Term Standard LSMEAN b Adj LSMEAN Error Pr > |t| Number c 0.38858369 0.01276813 <.0001 1 h 0.59570958 0.01590022 <.0001 2 m 0.25368723 0.01235422 <.0001 3 Least Squares Means for effect b Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 1 <.0001 <.0001 2 <.0001 <.0001

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219 3 <.0001 <.0001 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*t(a) as an Error Term Standard LSMEAN a t Adj LSMEAN Error Pr > |t| Number 1 4 0.48022513 0.01363439 <.0001 1 1 9 0.60183014 0.01359892 <.0001 2 1 12 0.69664147 0.01354280 <.0001 3 1 24 0.89327527 0.01496954 <.0001 4 2 4 0.21764947 0.01409101 <.0001 5 2 9 0.29637796 0.01356794 <.0001 6 2 12 0.41675254 0.01446539 <.0001 7 2 24 0.40476087 0.01562310 <.0001 8 3 4 0.14571914 0.02191510 <.0001 9 3 9 0.17464426 0.02250895 <.0001 10 3 12 0.27825463 0.01778271 <.0001 11 3 24 0.34579115 0.01656689 <.0001 12 Least Squares Means for effect a*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 5 6 1 <.0001 <.0001 <.0001 <.0001 <.0001 2 <.0001 0.0002 <.0001 <.0001 <.0001 3 <.0001 0.0002 <.0001 <.0001 <.0001 4 <.0001 <.0001 <.0001 <.0001 <.0001 5 <.0001 <.0001 <.0001 <.0001 0.0010 6 <.0001 <.0001 <.0001 <.0001 0.0010 7 0.0070 <.0001 <.0001 <.0001 <.0001 <.0001 8 0.0025 <.0001 <.0001 <.0001 <.0001 0.0001 9 <.0001 <.0001 <.0001 <.0001 0.0149 <.0001 10 <.0001 <.0001 <.0001 <.0001 0.1363 0.0004 11 <.0001 <.0001 <.0001 <.0001 0.0223 0.4370 12 <.0001 <.0001 <.0001 <.0001 <.0001 0.0359 Least Squares Means for effect a*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj

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220 i/j 7 8 9 10 11 12 1 0.0070 0.0025 <.0001 <.0001 <.0001 <.0001 2 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 3 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 4 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 5 <.0001 <.0001 0.0149 0.1363 0.0223 <.0001 6 <.0001 0.0001 <.0001 0.0004 0.4370 0.0359 7 0.5791 <.0001 <.0001 <.0001 0.0055 8 0.5791 <.0001 <.0001 <.0001 0.0205 9 <.0001 <.0001 0.3350 0.0003 <.0001 10 <.0001 <.0001 0.3350 0.0019 <.0001 11 <.0001 <.0001 0.0003 0.0019 0.0136 12 0.0055 0.0205 <.0001 <.0001 0.0136 Least Squares Means Standard LSMEAN b t Adj LSMEAN Error Pr > |t| Number c 4 0.29204335 0.01310575 <.0001 1 c 9 0.34057353 0.01313196 <.0001 2 c 12 0.37878335 0.01430776 <.0001 3 c 24 0.54293455 0.01308968 <.0001 4 h 4 0.37590951 0.01836512 <.0001 5 h 9 0.52155998 0.01889756 <.0001 6 h 12 0.71699345 0.01311008 <.0001 7 h 24 0.76837537 0.01504910 <.0001 8 m 4 0.17564088 0.01374183 <.0001 9 m 9 0.21071884 0.01310710 <.0001 10 m 12 0.29587184 0.01339679 <.0001 11 m 24 0.33251738 0.01337724 <.0001 12 Least Squares Means for effect b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 5 6 1 0.0143 0.0001 <.0001 0.0010 <.0001 2 0.0143 0.0554 <.0001 0.1307 <.0001 3 0.0001 0.0554 <.0001 0.9040 <.0001 4 <.0001 <.0001 <.0001 <.0001 0.3583 5 0.0010 0.1307 0.9040 <.0001 <.0001 6 <.0001 <.0001 <.0001 0.3583 <.0001 7 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 8 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 9 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

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221 10 0.0002 <.0001 <.0001 <.0001 <.0001 <.0001 11 0.8385 0.0234 0.0002 <.0001 0.0017 <.0001 12 0.0414 0.6739 0.0320 <.0001 0.0655 <.0001 Least Squares Means for effect b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 7 8 9 10 11 12 1 <.0001 <.0001 <.0001 0.0002 0.8385 0.0414 2 <.0001 <.0001 <.0001 <.0001 0.0234 0.6739 3 <.0001 <.0001 <.0001 <.0001 0.0002 0.0320 4 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 5 <.0001 <.0001 <.0001 <.0001 0.0017 0.0655 6 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 7 0.0167 <.0001 <.0001 <.0001 <.0001 8 0.0167 <.0001 <.0001 <.0001 <.0001 9 <.0001 <.0001 0.0731 <.0001 <.0001 10 <.0001 <.0001 0.0731 0.0001 <.0001 11 <.0001 <.0001 <.0001 0.0001 0.0696 12 <.0001 <.0001 <.0001 <.0001 0.0696 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*b(a) as an Error Term Standard LSMEAN a b Adj LSMEAN Error Pr > |t| Number 1 c 0.72437119 0.01945320 <.0001 1 1 h 0.97021852 0.01953288 <.0001 2 1 m 0.30938930 0.02069116 <.0001 3 2 c 0.29957541 0.02002789 <.0001 4 2 h 0.39144911 0.02179733 <.0001 5 2 m 0.31063110 0.01960463 <.0001 6 3 c 0.14180448 0.02501709 0.0002 7 3 h 0.42546110 0.03810673 <.0001 8 3 m 0.14104130 0.02394700 0.0002 9 Least Squares Means for effect a*b Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 5 1 <.0001 <.0001 <.0001 <.0001 2 <.0001 <.0001 <.0001 <.0001

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222 3 <.0001 <.0001 0.7499 0.0200 4 <.0001 <.0001 0.7499 0.0117 5 <.0001 <.0001 0.0200 0.0117 6 <.0001 <.0001 0.9654 0.7057 0.0198 7 <.0001 <.0001 0.0006 0.0005 <.0001 8 <.0001 <.0001 0.0256 0.0141 0.4584 9 <.0001 <.0001 0.0004 0.0004 <.0001 Least Squares Means for effect a*b Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 6 7 8 9 1 <.0001 <.0001 <.0001 <.0001 2 <.0001 <.0001 <.0001 <.0001 3 0.9654 0.0006 0.0256 0.0004 4 0.7057 0.0005 0.0141 0.0004 5 0.0198 <.0001 0.4584 <.0001 6 0.0004 0.0239 0.0003 7 0.0004 <.0001 0.9826 8 0.0239 <.0001 <.0001 9 0.0003 0.9826 <.0001

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223 Least Squares Means Standard LSMEAN a b t Adj LSMEAN Error Pr > |t| Number 1 c 4 0.56594966 0.02104558 <.0001 1 1 c 9 0.62209555 0.02097284 <.0001 2 1 c 12 0.69796344 0.02143871 <.0001 3 1 c 24 1.01147610 0.02138371 <.0001 4 1 h 4 0.62544527 0.02114730 <.0001 5 1 h 9 0.89640871 0.02196479 <.0001 6 1 h 12 1.07073332 0.02121555 <.0001 7 1 h 24 1.28828679 0.02143226 <.0001 8 1 m 4 0.24928046 0.02096493 <.0001 9 1 m 9 0.28698615 0.02121097 <.0001 10 1 m 12 0.32122766 0.02122483 <.0001 11 1 m 24 0.38006293 0.02265645 <.0001 12 2 c 4 0.23330184 0.02098875 <.0001 13 2 c 9 0.28852197 0.02096557 <.0001 14 2 c 12 0.29289299 0.02167487 <.0001 15 2 c 24 0.38358483 0.02134354 <.0001 16 2 h 4 0.23279245 0.02141988 <.0001 17 2 h 9 0.36099367 0.02097050 <.0001 18 2 h 12 0.54912698 0.02113581 <.0001 19 2 h 24 0.42288335 0.02966907 <.0001 20 2 m 4 0.18685411 0.02218980 <.0001 21 2 m 9 0.23961822 0.02109635 <.0001 22 2 m 12 0.40823765 0.02170563 <.0001 23 2 m 24 0.40781444 0.02100707 <.0001 24 3 c 4 0.07687855 0.02592528 0.0064 25 3 c 9 0.11110308 0.02588935 0.0002 26 3 c 12 0.14549361 0.02673591 <.0001 27 3 c 24 0.23374271 0.02576577 <.0001 28 3 h 4 0.26949081 0.04641705 <.0001 29 3 h 9 0.30727757 0.04694330 <.0001 30 3 h 12 0.53112006 0.02591300 <.0001 31 3 h 24 0.59395595 0.02577204 <.0001 32 3 m 4 0.09078806 0.02622214 0.0019 33 3 m 9 0.10555214 0.02582581 0.0004 34 3 m 12 0.15815022 0.02647716 <.0001 35 3 m 24 0.20967479 0.02571588 <.0001 36 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j)

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224 Dependent Variable: Adj i/j 1 2 3 4 5 6 7 8 1 0.0703 0.0002 <.0001 0.0554 <.0001 <.0001 <.0001 2 0.0703 0.0175 <.0001 0.9115 <.0001 <.0001 <.0001 3 0.0002 0.0175 <.0001 0.0252 <.0001 <.0001 <.0001 4 <.0001 <.0001 <.0001 <.0001 0.0012 0.0563 <.0001 5 0.0554 0.9115 0.0252 <.0001 <.0001 <.0001 <.0001 6 <.0001 <.0001 <.0001 0.0012 <.0001 <.0001 <.0001 7 <.0001 <.0001 <.0001 0.0563 <.0001 <.0001 <.0001 8 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 9 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 10 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 11 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 12 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 13 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 14 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 15 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 16 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 17 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 18 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 19 0.5797 0.0211 <.0001 <.0001 0.0177 <.0001 <.0001 <.0001 20 0.0005 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 21 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 22 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 23 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 24 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 25 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 26 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 27 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 5 6 7 8 28 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 29 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 30 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 31 0.3091 0.0111 <.0001 <.0001 0.0096 <.0001 <.0001 <.0001 32 0.4059 0.4053 0.0049 <.0001 0.3509 <.0001 <.0001 <.0001 33 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 34 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 35 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 36 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

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225 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 9 10 11 12 13 14 15 16 1 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 2 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 3 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 4 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 5 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 6 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 7 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 8 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 9 0.2172 0.0232 0.0002 0.5948 0.1972 0.1603 0.0001 10 0.2172 0.2586 0.0047 0.0847 0.9593 0.8499 0.0039 11 0.0232 0.2586 0.0616 0.0069 0.2827 0.3681 0.0514 12 0.0002 0.0047 0.0616 <.0001 0.0063 0.0133 0.9138 13 0.5948 0.0847 0.0069 <.0001 0.0741 0.0575 <.0001 14 0.1972 0.9593 0.2827 0.0063 0.0741 0.8860 0.0038 15 0.1603 0.8499 0.3681 0.0133 0.0575 0.8860 0.0051 16 0.0001 0.0039 0.0514 0.9138 <.0001 0.0038 0.0051 17 0.5868 0.0793 0.0062 <.0001 0.9866 0.0741 0.0655 <.0001 18 0.0009 0.0197 0.1934 0.5404 0.0002 0.0216 0.0330 0.4580 19 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 20 <.0001 0.0009 0.0096 0.2586 <.0001 0.0010 0.0016 0.2937 21 0.0509 0.0025 0.0001 <.0001 0.1434 0.0026 0.0029 <.0001 22 0.7478 0.1224 0.0107 <.0001 0.8340 0.1120 0.0942 <.0001 23 <.0001 0.0006 0.0093 0.3993 <.0001 0.0005 0.0006 0.4139 24 <.0001 0.0004 0.0072 0.3716 <.0001 0.0004 0.0008 0.4285 25 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 26 0.0003 <.0001 <.0001 <.0001 0.0011 <.0001 <.0001 <.0001 27 0.0052 0.0004 <.0001 <.0001 0.0152 0.0003 0.0001 <.0001 28 0.6439 0.1249 0.0150 0.0003 0.9895 0.1113 0.0876 0.0001 29 0.6948 0.7356 0.3222 0.0440 0.4832 0.7118 0.6492 0.0335 30 0.2697 0.6995 0.7907 0.1845 0.1610 0.7184 0.7796 0.1463 31 <.0001 <.0001 <.0001 0.0002 <.0001 <.0001 <.0001 0.0001 32 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 33 <.0001 <.0001 <.0001 <.0001 0.0003 <.0001 <.0001 <.0001 34 0.0002 <.0001 <.0001 <.0001 0.0007 <.0001 <.0001 <.0001 35 0.0121 0.0009 <.0001 <.0001 0.0341 0.0007 0.0004 <.0001 36 0.2434 0.0291 0.0026 <.0001 0.4824 0.0252 0.0194 <.0001

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226 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 17 18 19 20 21 22 23 24 1 <.0001 <.0001 0.5797 0.0005 <.0001 <.0001 <.0001 <.0001 2 <.0001 <.0001 0.0211 <.0001 <.0001 <.0001 <.0001 <.0001 3 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 4 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 5 <.0001 <.0001 0.0177 <.0001 <.0001 <.0001 <.0001 <.0001 6 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 7 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 17 18 19 20 21 22 23 24 8 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 9 0.5868 0.0009 <.0001 <.0001 0.0509 0.7478 <.0001 <.0001 10 0.0793 0.0197 <.0001 0.0009 0.0025 0.1224 0.0006 0.0004 11 0.0062 0.1934 <.0001 0.0096 0.0001 0.0107 0.0093 0.0072 12 <.0001 0.5404 <.0001 0.2586 <.0001 <.0001 0.3993 0.3716 13 0.9866 0.0002 <.0001 <.0001 0.1434 0.8340 <.0001 <.0001 14 0.0741 0.0216 <.0001 0.0010 0.0026 0.1120 0.0005 0.0004 15 0.0655 0.0330 <.0001 0.0016 0.0029 0.0942 0.0006 0.0008 16 <.0001 0.4580 <.0001 0.2937 <.0001 <.0001 0.4139 0.4285 17 0.0002 <.0001 <.0001 0.1351 0.8201 <.0001 <.0001 18 0.0002 <.0001 0.1003 <.0001 0.0004 0.1307 0.1265 19 <.0001 <.0001 0.0019 <.0001 <.0001 <.0001 <.0001 20 <.0001 0.1003 0.0019 <.0001 <.0001 0.6949 0.6816 21 0.1351 <.0001 <.0001 <.0001 0.0909 <.0001 <.0001 22 0.8201 0.0004 <.0001 <.0001 0.0909 <.0001 <.0001 23 <.0001 0.1307 <.0001 0.6949 <.0001 <.0001 0.9890 24 <.0001 0.1265 <.0001 0.6816 <.0001 <.0001 0.9890 25 0.0001 <.0001 <.0001 <.0001 0.0040 <.0001 <.0001 <.0001 26 0.0014 <.0001 <.0001 <.0001 0.0388 0.0007 <.0001 <.0001 27 0.0199 <.0001 <.0001 <.0001 0.2645 0.0114 <.0001 <.0001 28 0.9778 0.0007 <.0001 <.0001 0.1853 0.8619 <.0001 <.0001 29 0.4817 0.0842 <.0001 0.0099 0.1237 0.5642 0.0113 0.0117 30 0.1660 0.3064 <.0001 0.0479 0.0316 0.2031 0.0578 0.0624 31 <.0001 <.0001 0.5917 0.0109 <.0001 <.0001 0.0010 0.0011 32 <.0001 <.0001 0.1925 0.0002 <.0001 <.0001 <.0001 <.0001 33 0.0002 <.0001 <.0001 <.0001 0.0076 0.0001 <.0001 <.0001

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227 34 0.0009 <.0001 <.0001 <.0001 0.0268 0.0005 <.0001 <.0001 35 0.0419 <.0001 <.0001 <.0001 0.4310 0.0252 <.0001 <.0001 36 0.4982 0.0001 <.0001 <.0001 0.5113 0.3777 <.0001 <.0001 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 25 26 27 28 29 30 31 32 1 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.3091 0.4059 2 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0111 0.4053 3 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0049 4 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 5 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0096 0.3509 6 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 7 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 8 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 9 <.0001 0.0003 0.0052 0.6439 0.6948 0.2697 <.0001 <.0001 10 <.0001 <.0001 0.0004 0.1249 0.7356 0.6995 <.0001 <.0001 11 <.0001 <.0001 <.0001 0.0150 0.3222 0.7907 <.0001 <.0001 12 <.0001 <.0001 <.0001 0.0003 0.0440 0.1845 0.0002 <.0001 13 <.0001 0.0011 0.0152 0.9895 0.4832 0.1610 <.0001 <.0001 14 <.0001 <.0001 0.0003 0.1113 0.7118 0.7184 <.0001 <.0001 15 <.0001 <.0001 0.0001 0.0876 0.6492 0.7796 <.0001 <.0001 16 <.0001 <.0001 <.0001 0.0001 0.0335 0.1463 0.0001 <.0001 17 0.0001 0.0014 0.0199 0.9778 0.4817 0.1660 <.0001 <.0001 18 <.0001 <.0001 <.0001 0.0007 0.0842 0.3064 <.0001 <.0001 19 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.5917 0.1925 20 <.0001 <.0001 <.0001 <.0001 0.0099 0.0479 0.0109 0.0002 21 0.0040 0.0388 0.2645 0.1853 0.1237 0.0316 <.0001 <.0001 22 <.0001 0.0007 0.0114 0.8619 0.5642 0.2031 <.0001 <.0001 23 <.0001 <.0001 <.0001 <.0001 0.0113 0.0578 0.0010 <.0001 24 <.0001 <.0001 <.0001 <.0001 0.0117 0.0624 0.0011 <.0001 25 0.3546 0.0715 0.0002 0.0012 0.0002 <.0001 <.0001 26 0.3546 0.3554 0.0023 0.0060 0.0010 <.0001 <.0001 27 0.0715 0.3554 0.0236 0.0275 0.0051 <.0001 <.0001 28 0.0002 0.0023 0.0236 0.5055 0.1789 <.0001 <.0001 29 0.0012 0.0060 0.0275 0.5055 0.5174 <.0001 <.0001 30 0.0002 0.0010 0.0051 0.1789 0.5174 0.0003 <.0001 31 <.0001 <.0001 <.0001 <.0001 <.0001 0.0003 0.0993

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228 Least Squares Means Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 25 26 27 28 29 30 31 32 32 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0993 33 0.7129 0.5909 0.1672 0.0007 0.0026 0.0005 <.0001 <.0001 34 0.4370 0.8797 0.2853 0.0016 0.0046 0.0008 <.0001 <.0001 35 0.0345 0.2081 0.7303 0.0488 0.0456 0.0091 <.0001 <.0001 36 0.0012 0.0117 0.0930 0.5134 0.2691 0.0786 <.0001 <.0001 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 33 34 35 36 1 <.0001 <.0001 <.0001 <.0001 2 <.0001 <.0001 <.0001 <.0001 3 <.0001 <.0001 <.0001 <.0001 4 <.0001 <.0001 <.0001 <.0001 5 <.0001 <.0001 <.0001 <.0001 6 <.0001 <.0001 <.0001 <.0001 7 <.0001 <.0001 <.0001 <.0001 8 <.0001 <.0001 <.0001 <.0001 9 <.0001 0.0002 0.0121 0.2434 10 <.0001 <.0001 0.0009 0.0291 11 <.0001 <.0001 <.0001 0.0026 12 <.0001 <.0001 <.0001 <.0001 13 0.0003 0.0007 0.0341 0.4824 14 <.0001 <.0001 0.0007 0.0252 15 <.0001 <.0001 0.0004 0.0194 16 <.0001 <.0001 <.0001 <.0001 17 0.0002 0.0009 0.0419 0.4982 18 <.0001 <.0001 <.0001 0.0001 19 <.0001 <.0001 <.0001 <.0001 20 <.0001 <.0001 <.0001 <.0001 21 0.0076 0.0268 0.4310 0.5113 22 0.0001 0.0005 0.0252 0.3777 23 <.0001 <.0001 <.0001 <.0001 24 <.0001 <.0001 <.0001 <.0001 25 0.7129 0.4370 0.0345 0.0012 26 0.5909 0.8797 0.2081 0.0117 27 0.1672 0.2853 0.7303 0.0930 28 0.0007 0.0016 0.0488 0.5134 29 0.0026 0.0046 0.0456 0.2691 30 0.0005 0.0008 0.0091 0.0786 31 <.0001 <.0001 <.0001 <.0001 32 <.0001 <.0001 <.0001 <.0001

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229 33 0.6947 0.0894 0.0034 34 0.6947 0.1615 0.0081 35 0.0894 0.1615 0.1717 36 0.0034 0.0081 0.1717 Table F.48 The GLM Procedure Dependent Variable: Adj Sum of Source DF Squares Mean Square F Value Pr > F Model 66 12.39851161 0.18785624 5.09 <.0001 Error 24 0.88585899 0.03691079 Corrected Total 90 13.28437060 R-Square Coeff Var Root MSE Adj Mean 0.933316 30.08169 0.192122 0.638667 Source DF Type I SS Mean Square F Value Pr > F a 2 1.08606861 0.54303431 14.71 <.0001 m(a) 5 2.52120267 0.50424053 13.66 <.0001 b 2 0.17285291 0.08642646 2.34 0.1178 t 3 2.80235429 0.93411810 25.31 <.0001 a*b 4 0.32630664 0.08157666 2.21 0.0982 m*b(a) 10 0.35930598 0.03593060 0.97 0.4904 a*t 6 1.46709486 0.24451581 6.62 0.0003 m*t(a) 15 2.25474467 0.15031631 4.07 0.0011 b*t 6 0.91822592 0.15303765 4.15 0.0054 a*b*t 12 0.39575032 0.03297919 0.89 0.5654 Prot 1 0.09460473 0.09460473 2.56 0.1225 Source DF Type III SS Mean Square F Value Pr > F a 2 0.79464319 0.39732159 10.76 0.0005 m(a) 5 2.09296593 0.41859319 11.34 <.0001 b 2 0.22547787 0.11273893 3.05 0.0658 t 3 3.22603196 1.07534399 29.13 <.0001 a*b 4 0.35478260 0.08869565 2.40 0.0778 m*b(a) 10 0.15707941 0.01570794 0.43 0.9198 a*t 6 1.47462892 0.24577149 6.66 0.0003 m*t(a) 15 2.13078222 0.14205215 3.85 0.0017

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230 b*t 6 0.70370239 0.11728373 3.18 0.0194 a*b*t 12 0.39843349 0.03320279 0.90 0.5602 Prot 1 0.09460473 0.09460473 2.56 0.1225 The GLM Procedure Source Type III Expected Mean Square a Var(Error) + 2.0246 Var(m*t(a )) + 2.6994 Var(m*b(a)) + 8.0983 Var(m(a)) + Q(a,a*b,a*t,a*b*t) m(a) Var(Error) + 2.4386 Var(m*t(a)) + 3.2515 Var(m*b(a)) + 9.7545 Var(m(a)) b Var(Error) + 2.943 Var(m*b(a)) + Q(b,a*b,b*t,a*b*t) t Var(Error) + 2.3327 Var(m*t(a)) + Q(t,a*t,b*t,a*b*t) a*b Var(Error) + 2.991 Var(m*b(a)) + Q(a*b,a*b*t) m*b(a) Var(Error) + 3.4501 Var(m*b(a)) a*t Var(Error) + 2.363 Var(m*t(a)) + Q(a*t,a*b*t) m*t(a) Var(Error) + 2.5969 Var(m*t(a)) b*t Var(Error) + Q(b*t,a*b*t) a*b*t Var(Error) + Q(a*b*t) Prot Var(Error) + Q(Prot) The GLM Procedure Tests of Hypotheses for Mixed Model Analysis of Variance Dependent Variable: Adj Source DF Type III SS Mean Square F Value Pr > F a 2 0.794643 0.397322 1.12 0.3934 Error 5.1816 1.833203 0.353791 Error: 0.8302*MS(m(a)) + 0.1698*MS(Error) This test assumes one or more other fixed effects are zero.

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231 Source DF Type III SS Mean Square F Value Pr > F m(a) 5 2.092966 0.418593 3.62 0.0365 Error 10.683 1.235561 0.115661 Error: 0.9424*MS(m*b(a)) + 0.939* MS(m*t(a)) 0.8815*MS(Error) Source DF Type III SS Mean Square F Value Pr > F b 2 0.225478 0.112739 5.99 0.0099 Error 18.476 0.347804 0.018825 Error: 0.853*MS(m*b(a)) + 0.147*MS(Error) This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F t 3 3.226032 1.075344 8.19 0.0016 Error 15.887 2.086871 0.131353 Error: 0.8982*MS(m*t(a)) + 0.1018*MS(Error) This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F a*b 4 0.354783 0.088696 4.79 0.0086 Error 17.563 0.325429 0.018529 Error: 0.8669*MS(m*b(a)) + 0.1331*MS(Error) This test assumes one or more other fixed effects are zero. Source DF Type III SS Mean Square F Value Pr > F m*b(a) 10 0.157079 0.015708 0.43 0.9198 m*t(a) 15 2.130782 0.142052 3.85 0.0017 b*t 6 0.703702 0.117284 3.18 0.0194 a*b*t 12 0.398433 0.033203 0.90 0.5602 Prot 1 0.094605 0.094605 2.56 0.1225 Error: MS(Error) 24 0.885859 0.036911 This test assumes one or more other fixed effects are zero.

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232 Source DF Type III SS Mean Square F Value Pr > F a*t 6 1.474629 0.245771 1.85 0.1524 Error 15.775 2.091481 0.132581 Error: 0.9099*MS(m*t(a)) + 0.0901*MS(Error) This test assumes one or more other fixed effects are zero. Least Squares Means Standard Errors and Probabilities Calculat ed Using the Type III MS for m(a) as an Error Term Standard LSMEAN a Adj LSMEAN Error Pr > |t| Number 1 0.66790785 0.11422722 0.0021 1 2 0.51007556 0.11532390 0.0069 2 3 0.79124536 0.20337386 0.0115 3 Least Squares Means for effect a Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 1 0.3734 0.6392 2 0.3734 0.2853 3 0.6392 0.2853 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*t(a) as an Error Term Standard LSMEAN t Adj LSMEAN Error Pr > |t| Number 4 0.99778796 0.09180111 <.0001 1 9 0.60231531 0.09848655 <.0001 2 12 0.62965809 0.08994747 <.0001 3 24 0.39587701 0.08367871 0.0003 4 Least Squares Means for effect t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 1 0.0090 0.0103 0.0002

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233 2 0.0090 0.8341 0.1229 3 0.0103 0.8341 0.0746 4 0.0002 0.1229 0.0746 Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*b(a) as an Error Term Standard LSMEAN b Adj LSMEAN Error Pr > |t| Number c 0.73527633 0.02516922 <.0001 1 h 0.62138311 0.03055196 <.0001 2 m 0.61256934 0.02449693 <.0001 3 Least Squares Means for effect b Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 1 0.0138 0.0064 2 0.0138 0.8240 3 0.0064 0.8240 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*t(a) as an Error Term Standard LSMEAN a t Adj LSMEAN Error Pr > |t| Number 1 4 1.03517399 0.14202903 <.0001 1 1 9 0.57696197 0.12563505 0.0004 2 1 12 0.67776680 0.13774855 0.0002 3 1 24 0.38172865 0.13060412 0.0105 4 2 4 0.56249515 0.12563689 0.0004 5 2 9 0.53021277 0.14624046 0.0025 6 2 12 0.47911862 0.12634022 0.0018 7 2 24 0.46847572 0.14587604 0.0058 8 3 4 1.39569475 0.20746779 <.0001 9 3 9 0.69977118 0.21950056 0.0061 10 3 12 0.73208885 0.22080246 0.0047 11 3 24 0.33742665 0.15545362 0.0464 12

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234 Least Squares Means for effect a*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 5 6 1 0.0287 0.0629 0.0061 0.0251 0.0284 2 0.0287 0.5962 0.2988 0.9362 0.8117 3 0.0629 0.5962 0.1598 0.5463 0.4822 4 0.0061 0.2988 0.1598 0.3338 0.4549 5 0.0251 0.9362 0.5463 0.3338 0.8692 6 0.0284 0.8117 0.4822 0.4549 0.8692 7 0.0090 0.5909 0.2944 0.6050 0.6467 0.7961 8 0.0128 0.5813 0.3073 0.6667 0.6324 0.7743 9 0.1817 0.0042 0.0128 0.0008 0.0037 0.0037 10 0.2494 0.6346 0.9373 0.2133 0.5948 0.5238 11 0.3028 0.5510 0.8481 0.1714 0.5140 0.4498 12 0.0059 0.2495 0.1320 0.8269 0.2776 0.3775 Least Squares Means for effect a*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 7 8 9 10 11 12 1 0.0090 0.0128 0.1817 0.2494 0.3028 0.0059 2 0.5909 0.5813 0.0042 0.6346 0.5510 0.2495 3 0.2944 0.3073 0.0128 0.9373 0.8481 0.1320 4 0.6050 0.6667 0.0008 0.2133 0.1714 0.8269 5 0.6467 0.6324 0.0037 0.5948 0.5140 0.2776 6 0.7961 0.7743 0.0037 0.5238 0.4498 0.3775 7 0.9566 0.0019 0.4045 0.3442 0.4933 8 0.9566 0.0024 0.3986 0.3404 0.5497 9 0.0019 0.0024 0.0216 0.0323 0.0009 10 0.4045 0.3986 0.0216 0.9076 0.1880 11 0.3442 0.3404 0.0323 0.9076 0.1537 12 0.4933 0.5497 0.0009 0.1880 0.1537 Squares Means Standard LSMEAN b t Adj LSMEAN Error Pr > |t| Number c 4 1.05092816 0.07105032 <.0001 1 c 9 0.71409036 0.07147466 <.0001 2

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235 c 12 0.65776550 0.09635969 <.0001 3 c 24 0.51832130 0.07073533 <.0001 4 h 4 1.08458966 0.09847595 <.0001 5 h 9 0.34203433 0.11210183 0.0055 6 h 12 0.69389873 0.07215962 <.0001 7 h 24 0.36500971 0.07914911 0.0001 8 m 4 0.85784608 0.06983754 <.0001 9 m 9 0.75082122 0.07339630 <.0001 10 m 12 0.53731004 0.07355075 <.0001 11 m 24 0.30430001 0.07162524 0.0003 12 Least Squares Means for effect b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 5 6 1 0.0021 0.0033 <.0001 0.7850 <.0001 2 0.0021 0.6451 0.0699 0.0058 0.0127 3 0.0033 0.6451 0.2524 0.0030 0.0320 4 <.0001 0.0699 0.2524 <.0001 0.1818 5 0.7850 0.0058 0.0030 <.0001 <.0001 6 <.0001 0.0127 0.0320 0.1818 <.0001 7 0.0013 0.8383 0.7683 0.1044 0.0040 0.0184 8 <.0001 0.0029 0.0279 0.1661 <.0001 0.8722 9 0.0606 0.1561 0.1068 0.0026 0.0733 0.0008 10 0.0092 0.7336 0.4459 0.0265 0.0115 0.0036 11 <.0001 0.1111 0.3261 0.8485 0.0002 0.1363 12 <.0001 0.0006 0.0068 0.0388 <.0001 0.7699 Least Squares Means for effect b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 7 8 9 10 11 12 1 0.0013 <.0001 0.0606 0.0092 <.0001 <.0001 2 0.8383 0.0029 0.1561 0.7336 0.1111 0.0006 3 0.7683 0.0279 0.1068 0.4459 0.3261 0.0068 4 0.1044 0.1661 0.0026 0.0265 0.8485 0.0388 5 0.0040 <.0001 0.0733 0.0115 0.0002 <.0001 6 0.0184 0.8722 0.0008 0.0036 0.1363 0.7699 7 0.0047 0.1088 0.6020 0.1597 0.0011 8 0.0047 <.0001 0.0018 0.1304 0.5800 9 0.1088 <.0001 0.3120 0.0050 <.0001

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236 10 0.6020 0.0018 0.3120 0.0391 0.0001 11 0.1597 0.1304 0.0050 0.0391 0.0258 12 0.0011 0.5800 <.0001 0.0001 0.0258 Least Squares Means Standard Errors and Probabilities Calcul ated Using the Type III MS for m*b(a) as an Error Term Standard LSMEAN a b Adj LSMEAN Error Pr > |t| Number 1 c 0.82330425 0.04423772 <.0001 1 1 h 0.66124470 0.03801100 <.0001 2 1 m 0.51917461 0.03924686 <.0001 3 2 c 0.48965105 0.03640428 <.0001 4 2 h 0.49057147 0.04324371 <.0001 5 2 m 0.55000417 0.03625139 <.0001 6 3 c 0.89287368 0.05950226 <.0001 7 3 h 0.71233315 0.07430013 <.0001 8 3 m 0.76852924 0.04569085 <.0001 9 Least Squares Means for effect a*b Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 5 1 0.0121 0.0009 0.0001 0.0003 2 0.0121 0.0338 0.0078 0.0141 3 0.0009 0.0338 0.6010 0.6351 4 0.0001 0.0078 0.6010 0.9873 5 0.0003 0.0141 0.6351 0.9873 6 0.0008 0.0623 0.5721 0.2689 0.3171 7 0.4133 0.0109 0.0002 0.0002 0.0003 8 0.2544 0.5657 0.0375 0.0238 0.0275 9 0.4387 0.1121 0.0014 0.0008 0.0013 Least Squares Means for effect a*b Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj

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237 i/j 6 7 8 9 1 0.0008 0.4133 0.2544 0.4387 2 0.0623 0.0109 0.5657 0.1121 3 0.5721 0.0002 0.0375 0.0014 4 0.2689 0.0002 0.0238 0.0008 5 0.3171 0.0003 0.0275 0.0013 6 0.0006 0.0764 0.0036 7 0.0006 0.0586 0.1131 8 0.0764 0.0586 0.5226 9 0.0036 0.1131 0.5226 Squares Means Standard LSMEAN a b t Adj LSMEAN Error Pr > |t| Number 1 c 4 1.07258445 0.13478309 <.0001 1 1 c 9 0.72398916 0.12098042 <.0001 2 1 c 12 0.75895889 0.11734274 <.0001 3 1 c 24 0.73768451 0.11114654 <.0001 4 1 h 4 1.30695772 0.11528391 <.0001 5 1 h 9 0.33088124 0.11399883 0.0078 6 1 h 12 0.75617155 0.12459140 <.0001 7 1 h 24 0.25096830 0.11133798 0.0336 8 1 m 4 0.72597982 0.11112226 <.0001 9 1 m 9 0.67601551 0.11285700 <.0001 10 1 m 12 0.51816996 0.11125746 <.0001 11 1 m 24 0.15653314 0.12477071 0.2217 12 2 c 4 0.49761972 0.11099106 0.0002 13 2 c 9 0.58887274 0.11653498 <.0001 14 2 c 12 0.38400000 0.11092158 0.0020 15 2 c 24 0.48811175 0.11192069 0.0002 16 2 h 4 0.61774039 0.11186901 <.0001 17 2 h 9 0.30988433 0.16091359 0.0661 18 2 h 12 0.59850138 0.11665598 <.0001 19 2 h 24 0.43615979 0.15829658 0.0110 20 2 m 4 0.57212534 0.11128864 <.0001 21 2 m 9 0.69188124 0.11399883 <.0001 22 2 m 12 0.45485447 0.11202809 0.0005 23 2 m 24 0.48115562 0.11254456 0.0003 24 3 c 4 1.58258031 0.13952420 <.0001 25 3 c 9 0.82940918 0.13913204 <.0001 26 3 c 12 0.83033762 0.24820559 0.0027 27 3 c 24 0.32916762 0.13768203 0.0250 28 3 h 4 1.32907086 0.25179479 <.0001 29 3 h 9 0.38533744 0.25677690 0.1465 30

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238 3 h 12 0.72702326 0.13939112 <.0001 31 3 h 24 0.40790105 0.13599133 0.0062 32 3 m 4 1.27543308 0.13841207 <.0001 33 3 m 9 0.88456692 0.13841207 <.0001 34 3 m 12 0.63890567 0.14498685 0.0002 35 3 m 24 0.27521128 0.13699496 0.0559 36 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 5 6 7 8 1 0.0387 0.0639 0.0718 0.1640 0.0006 0.0568 <.0001 2 0.0387 0.8258 0.9351 0.0011 0.0330 0.8394 0.0074 3 0.0639 0.8258 0.8974 0.0019 0.0187 0.9861 0.0040 4 0.0718 0.9351 0.8974 0.0017 0.0167 0.9140 0.0051 5 0.1640 0.0011 0.0019 0.0017 <.0001 0.0020 <.0001 6 0.0006 0.0330 0.0187 0.0167 <.0001 0.0247 0.6240 7 0.0568 0.8394 0.9861 0.9140 0.0020 0.0247 0.0051 8 <.0001 0.0074 0.0040 0.0051 <.0001 0.6240 0.0051 9 0.0627 0.9905 0.8415 0.9411 0.0014 0.0197 0.8599 0.0060 10 0.0420 0.7820 0.6252 0.6988 0.0008 0.0378 0.6510 0.0137 11 0.0048 0.2294 0.1544 0.1745 <.0001 0.2471 0.1742 0.1036 12 0.0002 0.0062 0.0032 0.0017 <.0001 0.2863 0.0050 0.5847 13 0.0028 0.1777 0.1167 0.1399 <.0001 0.3070 0.1314 0.1292 14 0.0064 0.3991 0.2891 0.3692 0.0001 0.1392 0.3010 0.0441 15 0.0006 0.0492 0.0290 0.0337 <.0001 0.7413 0.0353 0.4057 16 0.0037 0.1759 0.1151 0.1251 <.0001 0.3274 0.1332 0.1483 17 0.0193 0.5354 0.4020 0.4525 0.0003 0.0807 0.4296 0.0297 18 0.0021 0.0586 0.0384 0.0377 <.0001 0.9143 0.0458 0.7677 19 0.0074 0.4329 0.3166 0.4008 0.0001 0.1260 0.3289 0.0389 20 0.0047 0.1566 0.1110 0.1328 0.0002 0.5969 0.1202 0.3469 21 0.0073 0.3570 0.2532 0.3042 0.0001 0.1466 0.2728 0.0517 22 0.0536 0.8549 0.6960 0.7744 0.0012 0.0304 0.7204 0.0114 23 0.0024 0.1253 0.0792 0.0844 <.0001 0.4381 0.0940 0.2117 24 0.0017 0.1411 0.0915 0.1198 <.0001 0.3666 0.1012 0.1560 25 0.0208 0.0002 0.0002 <.0001 0.1523 <.0001 0.0003 <.0001 26 0.2477 0.5882 0.7115 0.6088 0.0168 0.0090 0.7115 0.0037 27 0.4222 0.7127 0.8023 0.7350 0.1014 0.0746 0.7993 0.0450

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239 Least Squares Means Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 1 2 3 4 5 6 7 8 28 0.0011 0.0476 0.0294 0.0291 <.0001 0.9923 0.0362 0.6648 29 0.3909 0.0439 0.0544 0.0414 0.9378 0.0013 0.0573 0.0007 30 0.0337 0.2602 0.2102 0.2177 0.0038 0.8449 0.2236 0.6379 31 0.1062 0.9876 0.8670 0.9525 0.0047 0.0333 0.8831 0.0142 32 0.0018 0.0925 0.0606 0.0730 <.0001 0.6697 0.0684 0.3799 33 0.2771 0.0047 0.0072 0.0060 0.8589 <.0001 0.0075 <.0001 34 0.3646 0.4080 0.5081 0.4134 0.0313 0.0043 0.5147 0.0017 35 0.0568 0.6764 0.5473 0.5897 0.0021 0.0947 0.5738 0.0472 36 0.0005 0.0248 0.0147 0.0146 <.0001 0.7540 0.0186 0.8925 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 9 10 11 12 13 14 15 16 1 0.0627 0.0420 0.0048 0.0002 0.0028 0.0064 0.0006 0.0037 2 0.9905 0.7820 0.2294 0.0062 0.1777 0.3991 0.0492 0.1759 3 0.8415 0.6252 0.1544 0.0032 0.1167 0.2891 0.0290 0.1151 4 0.9411 0.6988 0.1745 0.0017 0.1399 0.3692 0.0337 0.1251 5 0.0014 0.0008 <.0001 <.0001 <.0001 0.0001 <.0001 <.0001 6 0.0197 0.0378 0.2471 0.2863 0.3070 0.1392 0.7413 0.3274 7 0.8599 0.6510 0.1742 0.0050 0.1314 0.3010 0.0353 0.1332 8 0.0060 0.0137 0.1036 0.5847 0.1292 0.0441 0.4057 0.1483 9 0.7538 0.1978 0.0021 0.1593 0.4071 0.0395 0.1430 10 0.7538 0.3258 0.0036 0.2724 0.6060 0.0774 0.2430 11 0.1978 0.3258 0.0375 0.8972 0.6684 0.4015 0.8498 12 0.0021 0.0036 0.0375 0.0540 0.0261 0.1857 0.0524 13 0.1593 0.2724 0.8972 0.0540 0.5739 0.4760 0.9525 14 0.4071 0.6060 0.6684 0.0261 0.5739 0.2151 0.5468 15 0.0395 0.0774 0.4015 0.1857 0.4760 0.2151 0.5151 16 0.1430 0.2430 0.8498 0.0524 0.9525 0.5468 0.5151

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240 17 0.4973 0.7137 0.5319 0.0091 0.4544 0.8624 0.1509 0.4167 18 0.0428 0.0703 0.2943 0.4390 0.3479 0.1843 0.7079 0.3666 19 0.4407 0.6465 0.6269 0.0234 0.5347 0.9516 0.1952 0.5098 20 0.1477 0.2317 0.6761 0.1839 0.7531 0.4406 0.7896 0.7918 21 0.3388 0.5215 0.7355 0.0224 0.6393 0.9171 0.2429 0.6014 22 0.8311 0.9203 0.2821 0.0027 0.2358 0.5472 0.0648 0.2074 23 0.0973 0.1716 0.6904 0.0784 0.7891 0.4249 0.6571 0.8339 24 0.1366 0.2403 0.8182 0.0750 0.9177 0.5013 0.5444 0.9658 25 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 26 0.5643 0.3911 0.0909 0.0009 0.0756 0.2113 0.0195 0.0644 27 0.7032 0.5708 0.2595 0.0185 0.2342 0.3989 0.1137 0.2160 28 0.0336 0.0596 0.2933 0.3439 0.3517 0.1726 0.7591 0.3742 29 0.0379 0.0252 0.0069 0.0003 0.0060 0.0146 0.0022 0.0052 30 0.2330 0.3029 0.6371 0.4111 0.6925 0.4885 0.9962 0.7138 31 0.9953 0.7741 0.2491 0.0037 0.2119 0.4689 0.0661 0.1876 32 0.0830 0.1439 0.5369 0.1902 0.6137 0.3191 0.8928 0.6539 33 0.0051 0.0030 0.0003 <.0001 0.0002 0.0007 <.0001 0.0002 34 0.3778 0.2463 0.0485 0.0004 0.0398 0.1252 0.0094 0.0335 35 0.6345 0.8365 0.5096 0.0112 0.4493 0.8002 0.1754 0.4078 36 0.0170 0.0314 0.1793 0.5154 0.2203 0.1000 0.5429 0.2366 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 17 18 19 20 21 22 23 24 1 0.0193 0.0021 0.0074 0.0047 0.0073 0.0536 0.0024 0.0017 2 0.5354 0.0586 0.4329 0.1566 0.3570 0.8549 0.1253 0.1411 3 0.4020 0.0384 0.3166 0.1110 0.2532 0.6960 0.0792 0.0915 4 0.4525 0.0377 0.4008 0.1328 0.3042 0.7744 0.0844 0.1198 5 0.0003 <.0001 0.0001 0.0002 0.0001 0.0012 <.0001 <.0001 6 0.0807 0.9143 0.1260 0.5969 0.1466 0.0304 0.4381 0.3666 7 0.4296 0.0458 0.3289 0.1202 0.2728 0.7204 0.0940 0.1012 Least Squares Means Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 17 18 19 20 21 22 23 24 8 0.0297 0.7677 0.0389 0.3469 0.0517 0.0114 0.2117 0.1560 9 0.4973 0.0428 0.4407 0.1477 0.3388 0.8311 0.0973 0.1366

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241 10 0.7137 0.0703 0.6465 0.2317 0.5215 0.9203 0.1716 0.2403 11 0.5319 0.2943 0.6269 0.6761 0.7355 0.2821 0.6904 0.8182 12 0.0091 0.4390 0.0234 0.1839 0.0224 0.0027 0.0784 0.0750 13 0.4544 0.3479 0.5347 0.7531 0.6393 0.2358 0.7891 0.9177 14 0.8624 0.1843 0.9516 0.4406 0.9171 0.5472 0.4249 0.5013 15 0.1509 0.7079 0.1952 0.7896 0.2429 0.0648 0.6571 0.5444 16 0.4167 0.3666 0.5098 0.7918 0.6014 0.2074 0.8339 0.9658 17 0.1251 0.9081 0.3600 0.7761 0.6417 0.3095 0.4029 18 0.1251 0.1704 0.5978 0.1958 0.0595 0.4613 0.3987 19 0.9081 0.1704 0.4129 0.8698 0.5853 0.3932 0.4643 20 0.3600 0.5978 0.4129 0.4880 0.2053 0.9243 0.8179 21 0.7761 0.1958 0.8698 0.4880 0.4636 0.4674 0.5682 22 0.6417 0.0595 0.5853 0.2053 0.4636 0.1447 0.2092 23 0.3095 0.4613 0.3932 0.9243 0.4674 0.1447 0.8713 24 0.4029 0.3987 0.4643 0.8179 0.5682 0.2092 0.8713 25 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 26 0.2410 0.0199 0.2299 0.0761 0.1651 0.4407 0.0438 0.0679 27 0.4377 0.0855 0.4179 0.1958 0.3550 0.6100 0.1759 0.2184 28 0.1133 0.9271 0.1581 0.6165 0.1853 0.0496 0.4808 0.4073 29 0.0158 0.0021 0.0158 0.0063 0.0114 0.0286 0.0039 0.0055 30 0.4098 0.8017 0.4686 0.8684 0.5137 0.2767 0.8039 0.7390 31 0.5409 0.0567 0.5005 0.1835 0.3979 0.8429 0.1352 0.1904 32 0.2464 0.6473 0.2947 0.8933 0.3585 0.1244 0.7928 0.6807 33 0.0013 0.0002 0.0008 0.0005 0.0005 0.0040 0.0001 0.0001 34 0.1421 0.0110 0.1376 0.0446 0.0936 0.2828 0.0222 0.0356 35 0.9069 0.1291 0.8382 0.3594 0.7217 0.7674 0.3136 0.4115 36 0.0626 0.8695 0.0908 0.4513 0.1072 0.0260 0.3159 0.2617 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 25 26 27 28 29 30 31 32 1 0.0208 0.2477 0.4222 0.0011 0.3909 0.0337 0.1062 0.0018 2 0.0002 0.5882 0.7127 0.0476 0.0439 0.2602 0.9876 0.0925 3 0.0002 0.7115 0.8023 0.0294 0.0544 0.2102 0.8670 0.0606 4 <.0001 0.6088 0.7350 0.0291 0.0414 0.2177 0.9525 0.0730 5 0.1523 0.0168 0.1014 <.0001 0.9378 0.0038 0.0047 <.0001 6 <.0001 0.0090 0.0746 0.9923 0.0013 0.8449 0.0333 0.6697 7 0.0003 0.7115 0.7993 0.0362 0.0573 0.2236 0.8831 0.0684 8 <.0001 0.0037 0.0450 0.6648 0.0007 0.6379 0.0142 0.3799 9 <.0001 0.5643 0.7032 0.0336 0.0379 0.2330 0.9953 0.0830 10 <.0001 0.3911 0.5708 0.0596 0.0252 0.3029 0.7741 0.1439 11 <.0001 0.0909 0.2595 0.2933 0.0069 0.6371 0.2491 0.5369

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242 12 <.0001 0.0009 0.0185 0.3439 0.0003 0.4111 0.0037 0.1902 13 <.0001 0.0756 0.2342 0.3517 0.0060 0.6925 0.2119 0.6137 14 <.0001 0.2113 0.3989 0.1726 0.0146 0.4885 0.4689 0.3191 15 <.0001 0.0195 0.1137 0.7591 0.0022 0.9962 0.0661 0.8928 16 <.0001 0.0644 0.2160 0.3742 0.0052 0.7138 0.1876 0.6539 17 <.0001 0.2410 0.4377 0.1133 0.0158 0.4098 0.5409 0.2464 18 <.0001 0.0199 0.0855 0.9271 0.0021 0.8017 0.0567 0.6473 19 <.0001 0.2299 0.4179 0.1581 0.0158 0.4686 0.5005 0.2947 20 <.0001 0.0761 0.1958 0.6165 0.0063 0.8684 0.1835 0.8933 21 <.0001 0.1651 0.3550 0.1853 0.0114 0.5137 0.3979 0.3585 22 <.0001 0.4407 0.6100 0.0496 0.0286 0.2767 0.8429 0.1244 23 <.0001 0.0438 0.1759 0.4808 0.0039 0.8039 0.1352 0.7928 24 <.0001 0.0679 0.2184 0.4073 0.0055 0.7390 0.1904 0.6807 25 0.0006 0.0125 <.0001 0.3818 0.0003 0.0002 <.0001 26 0.0006 0.9974 0.0156 0.0918 0.1335 0.5990 0.0413 27 0.0125 0.9974 0.0856 0.1308 0.1745 0.7141 0.1502 28 <.0001 0.0156 0.0856 0.0018 0.8464 0.0495 0.6888 29 0.3818 0.0918 0.1308 0.0018 0.0050 0.0449 0.0037 30 0.0003 0.1335 0.1745 0.8464 0.0050 0.2436 0.9390 31 0.0002 0.5990 0.7141 0.0495 0.0449 0.2436 0.1161

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243 Least Squares Means Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 25 26 27 28 29 30 31 32 32 <.0001 0.0413 0.1502 0.6888 0.0037 0.9390 0.1161 33 0.1391 0.0356 0.1368 <.0001 0.8549 0.0062 0.0116 0.0002 34 0.0013 0.7765 0.8476 0.0080 0.1313 0.0940 0.4204 0.0222 35 <.0001 0.3340 0.4974 0.1238 0.0235 0.3818 0.6523 0.2603 36 <.0001 0.0083 0.0591 0.7813 0.0011 0.7050 0.0276 0.4997 Least Squares Means for effect a*b*t Pr > |t| for H0: LSMean(i)=LSMean(j) Dependent Variable: Adj i/j 33 34 35 36 1 0.2771 0.3646 0.0568 0.0005 2 0.0047 0.4080 0.6764 0.0248 3 0.0072 0.5081 0.5473 0.0147 4 0.0060 0.4134 0.5897 0.0146 5 0.8589 0.0313 0.0021 <.0001 6 <.0001 0.0043 0.0947 0.7540 7 0.0075 0.5147 0.5738 0.0186 8 <.0001 0.0017 0.0472 0.8925 9 0.0051 0.3778 0.6345 0.0170 10 0.0030 0.2463 0.8365 0.0314 11 0.0003 0.0485 0.5096 0.1793 12 <.0001 0.0004 0.0112 0.5154 13 0.0002 0.0398 0.4493 0.2203 14 0.0007 0.1252 0.8002 0.1000 15 <.0001 0.0094 0.1754 0.5429 16 0.0002 0.0335 0.4078 0.2366 17 0.0013 0.1421 0.9069 0.0626 18 0.0002 0.0110 0.1291 0.8695 19 0.0008 0.1376 0.8382 0.0908 20 0.0005 0.0446 0.3594 0.4513 21 0.0005 0.0936 0.7217 0.1072 22 0.0040 0.2828 0.7674 0.0260 23 0.0001 0.0222 0.3136 0.3159

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244 24 0.0001 0.0356 0.4115 0.2617 25 0.1391 0.0013 <.0001 <.0001 26 0.0356 0.7765 0.3340 0.0083 27 0.1368 0.8476 0.4974 0.0591 28 <.0001 0.0080 0.1238 0.7813 29 0.8549 0.1313 0.0235 0.0011 30 0.0062 0.0940 0.3818 0.7050 31 0.0116 0.4204 0.6523 0.0276 32 0.0002 0.0222 0.2603 0.4997 33 0.0616 0.0052 <.0001 34 0.0616 0.2167 0.0041 35 0.0052 0.2167 0.0743 36 <.0001 0.0041 0.0743

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258 BIOGRAPHICAL SKETCH Andria Lea Desvousges was born in H ackensack New Jersey on September 16th 1971. Andria is the daughter of Albert and Barb ara Popoli and sister to Chritstian Popoli. She graduate from Champlain Valley Uni on High School in 1989 and began college that fall. Andria attended the University of Ma ryland at College Park for 2 years and then took some time off to ride on the professional jumpers circuit. She spent the next 4 years traveling and training a herd of 22 horses fo r the grand prix jumping level. She then returned to school part time at the University of Florida in 1996. A ndria graduated in the summer of 2000 with a Bachelor of Science. Following graduation sh e returned to school as a post-bac student and started a research project with Dr. Dan Sharp. Andria then enrolled in graduate school the following y ear under Dr. Sharp to pursue a Master of Science in the Animal Science Department with a specific focus on reproductive physiology. Andria plans on continuing with school and will be starting her Ph.D. in fall 2004 under Dr. Mats Troedsson and Dr. W illiam Buhi, with a focus on equine reproduction and protei n biochemistry.


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Permanent Link: http://ufdc.ufl.edu/UFE0008962/00001

Material Information

Title: Tissue Remodeling and Steroidogenesis in the Preovulatory Follicle of Cycling Pony Mares
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0008962:00001

Permanent Link: http://ufdc.ufl.edu/UFE0008962/00001

Material Information

Title: Tissue Remodeling and Steroidogenesis in the Preovulatory Follicle of Cycling Pony Mares
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0008962:00001


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Full Text












TISSUE REMODELING AND STEROIDOGENESIS IN THE PREOVULATORY
FOLLICLE OF CYCLING PONY MARES














By

ANDRIA L. DESVOUSGES


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2004




























Copyright 2004

By

Andria L. Desvousges
















ACKNOWLEDGMENTS

The road to this destination has been a very long and winding one. Over the course

of the last year I realized how fragile we are as people and that it is okay to ask for a bit

of help when you really need it. I have had a very difficult last year. If not for the people

who helped me along the way, I never would have made it. First and foremost, I must

thank Dr. Sharp for taking a chance on me, and helping me become a better researcher.

He taught me how to survive on my own, and forced me to think outside of the box to

find the answers I was looking for. From him I gained a greater knowledge of science and

research techniques. He encouraged me to think independently and to question

everything. His support the confidence to present this work. I would also like to thank the

members of my graduate committee, Dr. Lokenga Badinga, Dr. William Buhi, and Dr.

Gregory Schultz. They helped me in nay way possible achieve this goal of finishing my

research and thesis. I would especially like to thank Dr. Badinga for coming in to help me

at the last minute and offering me his patient technician, his students, and his laboratory

resources.

Additionally I would like to thank Dr. William Thatcher, who helped me with all of

my last minute statistics. Idania Alvarez ran many reverse zymography gels for me at the

end, when I was incapable of doing them myself. I thank the wonderful AMCB faculty

for their continued support and guidance when I needed it most. I thank Dr, Michael

Smith (and his students from the University of Missouri), for their help with all the









cloning and sequencing of my MMP-2 and TIMP-1 inserts. They accepted me as a part of

their lab and I still have a place to go to if I am ever in Missouri.

I must also thank all of my family and friends for putting up with me over the last

year. Things have been very difficult. They all are wonderful for being so patient, loving

and kind. I must thank my brother and my parents, who always believed in me no matter

what, and drove me to dust myself off and try again. They support me in whatever I do,

no matter how long it has taken me to get to this point. I appreciate their unconditional

love and know how lucky I am to have them all in my life. Last but not least, I thank my

husband, and best friend, Dan. He has seen me at my worst and best, and has always been

there for me no matter what. I am truly blessed because of him. He helped me push

through, at the end of this long strange trip that has been my masters program.
















TABLE OF CONTENTS

page

ACKNOW LEDGM ENTS ........................................ iii

LIST OF FIGURES ..................................... ix

ABSTRACT.................. .................. xii

CHAPTER

1 INTRODUCTION ................... ............................ ......... .. .......... 1

2 L ITER A TU R E R E V IEW ................................................................. ............... 4

R production in the M are .................................................. ...............4
Seasonality............................. ... ........... 4
Photoperiod.................. ...................................... ........ .. ........ .5
W inter Anestrus................................................ ... 6
Vernal Transition............ .... ................ .9
The Breeding Season ................... ............................. ....... .. .... .. .... ... 12
Autumnal Transition ............................................ 13
Endocrinology of the Estrous Cycle.................... ........ .......... 13
Ovarian Anatomy .............................................. .... .....14
Folliculogenesis...................................... ................... ..... .........15
Ovulation ............................................... ....... ...............17
Monitoring the Estrous Cycle.............................. ...............18
The Extracellular Matrix................ ......... ...............20
Matrix Metalloproteinases Structure and function ..........................................24
Matrix Metalloproteinases................... ........ ......... 24
M atrix M etalloproteinases Structure .................................................................24
Membrane Bound Matrix Metalloproteinases (MT-MMP's)..........................26
R egu lation of M M P 's ..................................................................................... 2 7
A ctivation of M M P's................................................... 27
Activation of M M P-2 ..................................... ..................... .. .............28
Tissue Inhibitor of Metalloproteinases (TIMP's).........................29
T IM P Structure .............. .................. ....................... ........ ......29
TIM P Expression and Regulation ..............................................29
MMP's/TIMP's and Their Role in the Ovulatory Cycle................ ...... .....30
Follicular Growth and Development..........................................................30
Follicular Atresia and Apoptosis ................................................................... 30


v









Follicular Rupture and Ovulation................................31
Types of MMP's: Role in Ovulation and Response to Gonadotropin Stimulation ....32
Collagenases .............. ................... .............. .32
Gelatinases ................... ................... ... ........ .......... 32
M embrane Type M M P's (M T-M M P's) .................... ..... .................... .. 34
TIMP's: Role in Ovulation and Relationship to Gonadotropin Stimulation ............35

3 MMP-2, TIMP-1 AND STEROIDOGENEIS IN THE PREOVULATORY
FOLLICLE ........................................................37

Introduction ...................................... ......... ........... .37
M materials and M methods ............................................................40
General Procedures............ .... .............. 40
M monitoring of the m ares .................................................................... 40
B lood processing ............... ....................................................................... .......40
Follicular fluid processing.............................. .................... 40
H orm one assays............... ............ .......... ..............41
Experiment 1: Effect of hCG on preovulatory steroids E2, P4 and Matrix
Metalloproteinase-2 and Tissue Inhibitor of Metalloproteinase-1 in the
Preovulatory Follicle of Cycling Pony M ares ............................................ 41
M materials and M ethods ............................................................. 41
Statistical A naly sis ........................ ........... .. .......... .. ................42
Experiment 2: Time trends of follicular P4, MMP-2 and TIMP-1 in untreated mares
by folliculocentesis. ....................................................... 42
M ethods and M materials .............................................. ............... 42
Statistical A naly sis .................. .................... .. ... .... .... .... ........ 43
Experiment 3: The time dependent effect of hCG administration on steriodogenesis
and tissue remodeling in the preovulatory follicle of cycling pony mares.........44
M ethods and M materials ............................................................. 44
Statistical Analysis ..... .......... .... ...... .. ... ............. 44
Experiment 4: Tissue expression of MMP-2 and TIMP-1 mRNA in gonadotropin
stim ulated ovarian tissue. .................................45............................
M ethods and M materials ............................................................. 45
Statistical Analysis ............................................. .. ......46
Experiment 1: Results and Discussion ..........................................46
Experiment 2: Results and Discussion ..........................................49
Experiment 3: Results and Discussion .......................................... 53
Experiment 4: Results and Discussion ..........................................56
Conclusions................................................ .58

4 INHIBITION OF THE TISSUE REMODELING SYSTEM AND
STERIODOGENESIS IN THE PREOVULATORY FOLLICLE OF CYCLING
PON Y M A RES ...... ... ...... .. ...... ........... .............. 61

Introduction .......... ......... ......... ............ ......61
M methods and M materials ............................................................62
General Procedures............ .... .............. 62









M monitoring of the m ares .................................................................... 62
B lood processing ............... ....................................................................... .......63
Follicular fluid processing.............................. .................... 63
Horm one assays................ ... ....... .. ...... ............ ... 63
Experiment 1: Inhibition of follicular P4 concentrations and its effects on MMP-2
and TIMP-1 ........................................ ......... 64
M ethods and M materials .............................................. ............... 64
Statistical A analysis ......................... ... ...... .. .. .... ..... .. .. ............65
Experiment 2: The Effects of MMP-2/9 cyclic inhibitor III on Follicular
Steriodogenesis and Tissue Remodeling in the Preovulatory Follicle of Cycling
Pony Mares. ........................................ .........65
M ethods and M materials ............................................................. 65
Statistical Analysis ............................................. .. ......66
Experiment: Results and Discussion ............................. ...............66
Experiment 2: Results and Discussion ..........................................69
Conclusions................................................ .72

5 INTRAFOLLICULAR ADMINISTRATION OF GNRH, P4 AND
MELATONIN AND THEIR EFFECTS ON TISSUE REMODELING AND
STEROIDOGENESIS IN THE PREOVULATORY FOLLICLE .............................74

Introduction...................................... .................................. ........ 74
M methods and M materials ............................................... ........75
General Procedures............ .... .............. 75
M monitoring of the m ares .................................................................... 75
B lood processing ............... ......................................................... .... ... ......75
Follicular fluid processing.............................. ................... 76
H orm one assays............................ .. .. ...... .. ..... .. ........... 76
Experiment 1: Intrafollicular Administration of GnRH, P4 or Melatonin and Their
Effects on MMP-2, TIMP-1, and Steroidogenesis in the Preovulatory Follicle of
Cycling Pony M ares................... .... ............................ ...... .. ............ 77
M materials and M ethods ............................................................. 77
Statistical Analysis ...................... ........ ........ ... ....78
Experiment 1: Results and Discussion ..........................................78
Conclusions................................................ .82

6 BLOCKADE OF LH AND/OR FSH AND THE EFFECTS ON TISSUE
REMODELING AND STEROIDOGENESIS IN THE PREOVULATORY
FOLLICLE OF CYCLING PONY M ARES .........................................................84

Introduction ...................................... .......... ........... .84
M materials and M methods ............................................................86
General Procedures............ .... .............. 86
M monitoring of the m ares .................................................................... 86
Follicular fluid processing.............................. .................... 86
Hormone assays............ .... ... .............. 87










Experiment 1: The effect of Exogenous P4 and E2+P4 Administration on
Follicular P4 Concentrations, and Matrix Metalloproteinase-2 and Tissue
Inhibitor of Metalloproteinase-1 Activity within the Preovulatory Follicle of
the Cycling Pony M are. .............................................. ............... 87
M ethods and M materials ............................................................. 87
Statistical Analysis ...................... .......... ..... ......... 89
Experiment 1: Results and Discussion .......................................... 89
Conclusions............................... .........96

7 CONCLUSION.................. ...... ...............99

APPENDIX

A PICTURES OF GELS AND BLOTS ................................. .... .... ............... 104

B GELATIN ZYMOGRAPHY AND REVERSE ZYMOGRAPHY ..........................111

C DOT (NORTHERN) BLOT PROCEDURE ............................................................119

D ESTROGEN RADIOIMMUNOAS SAY................................................................124

E PROGESTERONE RADIOIMMUNOASSAY ........................................ .................126

F A N O V A TA B LE S ...................................................... 128

LIST O F R EFER EN CE S ..................................... ...................................................... ...... 245

BIOGRAPHICAL SKETCH ............................................... 258
















LIST OF FIGURES


Figure page

3.1 Mean follicular P4 concentrations by treatment Experiment 1..............................47

3.2 Mean follicular MMP-2 activity by treatment, Experiment 1...............................47

3.3 Mean TIMP-1 follicular activity by treatment, Experiment 1 ..............................48

3.4 Mean follicular P4 concentrations for groups 1, 2, &3 (single aspiration groups)
Experiment 2 .....................................................50

3.5 Mean follicular P4 concentrations Group 4 (serial aspiration group)
Experiment 2 .....................................................50

3.6 Mean follicular MMP-2 activity Groups 1,2 &3 (single aspiration groups)
Experiment 2 .....................................................51

3.7 Mean follicular MMP-2 activity in Group 2(serial aspiration group)
Experiment 2 .....................................................51

3.8 Mean TIMP-1 activity in Groups 1, 2 &3 (single aspiration groups)
Experiment 2 .....................................................52

3.9 Mean TIMP-1 activity in Group 4 (serial aspiration group) Experiment 2...........53

3.10 Mean follicular P4 concentrations by group and time Experiment 3 ....................54

3.11 Mean follicular MMP-2 levels by group and time Experiment 3 .........................55

3.12 Mean follicular TIMP-1 levels by group and time Experiment 3 .........................56

3.13 MMP-2 mRNA expression levels by tissue and treatment, Experiment 4............57

3.14 TIMP-1 mRNA expression levels by tissue and treatment, Experiment 4 ...........58

4.1 Mean Follicular P4 concentrations by group Experiment 1 ..................................67

4.2 Mean MMP-2 activity by group, Experiment 1 ........................................... 67

4.3 Mean follicular TIMP-1 levels by group, Experiment 1 .............. ............ 68









4.4 Mean follicular P4 concentrations by group, Experiment 2..................................70

4.5 Mean follicular MMP-2 levels by group, Experiment 2 .......................................70

4.6 Mean follicular TIMP-1 levels by group, Experiment 2.......................................71

5.1 Mean follicular E2 concentrations by group, Experiment 1 .................................78

5.2 Mean follicular P4 concentrations by treatment and time, Experiment 1 .............80

5.3 Mean MMP-2 activity by group and time, Experiment 1 .....................................81

5.4 Mean TIMP-1 activity by group and time, Experiment 1 .......................................82

6.1 Mean follicular P4 concentrations by treatment, Experiment 1...............................90

6.2 Mean MMP-2 follicular activity by treatment, Experiment 1...............................90

6.3 Mean follicular TIMP-1 activity by treatment, Experiment 1..............................91

6.4 Mean MMP-2 mRNA expression by tissue and Treatment (In Vivo),
Experiment 1 ....................................................92

6.5 Mean Follicular TIMP-1 mRNA expression by tissue type and treatment
(In V ivo), Experim ent 1 ................................................................ 93

6.6 Mean P4 concentration in culture by treatment, Experiment 1 ..............................94

6.7 Mean MMP-2 production In Vitro by Treatment, Experiment 1 ..........................95

6.8 Mean TIMP-1 production In Vitro by treatment, Experiment 1 ............................96

A. 1 Hypothetical model of the remodeling system in the preovulatory follicle of
the mare based upon this series of experiments............................104

A.2 Sample Gelatin Zymography gel........................................... 105

A.3 Messenger RNA Dot Blot probed with TIMP-1 in Control and hCG treated
mares .............................. ................... ........ 106

A.4 Messgenger RNA Dot Blot probed with MMP-2 in control and hCG treated
mares .............................. ................... ........ 107

A.5 Messenger RNA Dot Blot probed with TIMP-1 in control P4 and P+E treated
m areas ........................................... .................. 108









A.6 Messenger RNA Dot Blot probed with MMP-2 in control, P4 and P+E treated
m a re s ....................................................... 1 0 9

A.7 Messenger RNA Dot Blot probed with 18s Bovine mRNA for standardization ...110
















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

TISSUE REMODELING AND STEROIDOGENESIS IN THE PREOVULATORY
FOLLICLE OF CYCLING PONY MARES

By

Andria L Desvousges

December 2004

Chair: Dan C. Sharp
Major Department: Animal Science

Ovulation is a poorly understood series of events that likely involves tissue

remodeling and steroidogenesis within the preovulatory follicle. We sought a better

understanding of the mechanisms by which the ovulatory process occurs in the horse, by

examining the effects of stimulation and inhibition of this remodeling system in the

preovulatory follicle. In response to administration of gonadotropin (hCG), there was an

increase in follicular progesterone (P4) concentrations, matrix metalloproteinase-2

(MMP-2), and tissue inhibitor of metalloproteinases-1 (TIMP-1) at 24 hours post

treatment (p<0.0001* for all factors) compared with controls. In untreated animals

sampled cross-sectionally at one of 3 time points (0, 48, or 72 after detection of a 30 mm

follicle), or serially every day (0,24,48, and 72 after detection of a 30 mm follicle), there

was no adverse effect on multiple sampling vs. single sampling on the microenvironment

of the follicle. Time trends of the response to gonadotropin administration were









examined. Results indicated different time trends for follicular P4, MMP-2 and TIMP-1

in treated animals compared with controls (p<0.0001*).

In another experiment, we examined mRNA expression of MMP-2 and TIMP-1

in response to gonadotropin administration. Results showed different responses by tissue

type and time in hCG treated versus control animals.

The next series of experiments examined the effects of blockade of MMP-2 and

P4 on the remodeling system and steroidogenesis. Results showed that there is a causal

relationship between follicular P4, MMP-2 and TIMP-1 demonstrated by blocking

follicular P4 with Ru486, or blocking MMP-2 with an inhibitor.

The next experiment examined the effects of intrafollicular administration of

hormones (GnRH 10g/100pl, P4 1Ig/100pl, or Melatonin 10g/100l) on tissue

remodeling and steroidogenesis. Results suggest that different hormones had different

effects on the remodeling system and steroidogenesis.

The last experiment examined the effects of blockade of LH and FSH on the

remodeling system and steroidogenesis. Results suggest that blockade of LH and FSH

significantly reduced the amount of tissue remodeling and steroidogenesis in the

preovulatory follicle of the cycling pony mares, both in vivo and in vitro. Based on this

series of experiments, it is our belief that the tissue remodeling system within the

preovulatory follicle of the cycling pony mare is dependent on a positive gonadotropin-

MMP-2 interaction, for ovulation to occur.














CHAPTER 1
INTRODUCTION

There is something fascinating about science that can be traced back to ancient

times. The overall study of science goes back far into our ancient history as humans. The

Greeks began with accepting philosophy as a true science, and it continued with the

discovery of medicine and natural causes for ailments that ancient people were suffering

from rather that the will of the gods. The application of logic and reason to the overall

study of science has become one of its strongest foundations.

Truly the discipline of science as we know it today was born out of the renaissance

(for example, Leonardo Da Vinci and his studies of anatomy, physiology, chemistry,

engineering, and metallurgy). Overall research has remained at the forefront of study

through the ages. Originally observation was a scientist's main tool of the trade. True

observational scientists or naturalists began their scientific studies through observation of

the natural world, by observing and collecting living plants and animals and studying

behavior. Yet observation had limits, and new and exciting tools were developed like the

microscope to delve deeper into the unseen world of cells and organisms.

In today's society we often take for granted the basic area of observational science.

Through basic observational techniques, scientists were able to extrapolate the reason

behind specific actions and behaviors, and apply this information to the physiology of an

animal. Today we use a plethora of techniques to understand the microenvironments of

the body, signal transduction systems, and cellular processes. We often take for granted

the tools such as polymerase chain reaction (PCR), immunohistochemistry, Northern









blotting, Southern blotting, Western Blotting, and genetic engineering (which are part of

our bag of tricks so to speak). Yet all the well developed techniques at our disposal are

useless without a basic understanding of how the system we study works. The pursuit of

basic science works hand in hand with the development and use of these everyday

techniques.

Horse reproduction has become a large business. It has helped the scientific

community to gain a better understanding of the reproductive physiology of the horse,

and to develop new techniques that can be used to improve reproductive efficiency.

Today the farms are split between mares and stallions and it is current practice to collect

and ship semen from one stallion to be sent to a mare thousands of miles away. To make

this process more efficient and economical for farm owners and veterinarians alike, we

need to understand the patterns and purpose of the physiological changes that occur

throughout the estrus cycle. The keys to unlocking the doors to the reproductive

mysteries of the horse are like those for any other animal; an understanding of the

hormone levels, sexual behavior, and the anatomical changes of the ovaries and the

uterus is needed to understand how these parameters work together in the whole animal.

In addition and understanding of the basic physiology can help us decipher the norm from

the abnormal. This will allow us to progress further into the areas of preventive medicine,

and timed Artificial insemination; and allows us to help solve infertility issues.

As we look back on the foundations laid for us by the hard work and inquisitive

nature of the scientists before us, it is amazing to discover how far we have come and

how far we have yet to go. The study of science is an endless journey with many roads to

travel down, and many unanswered questions to ask. To date, each new discovery opens









a thousand new questions to be answered, and there is no end in sight to this journey. I

ask myself, if there will ever be a day when we have nothing new to research. A limitless

wealth of knowledge is out there to be found. One must stop and discover the answers,

and pass them on to someone else.














CHAPTER 2
LITERATURE REVIEW

Reproduction in the Mare

Seasonality

Horses are often considered to be in the same category as other farm species, yet

they are different in many aspects of their reproductive cycle. A mare's estrous cycle is

typically 21 days in length and they are in standing estrus for anywhere from 2-7 days.

The typical estrus cycle is somewhat longer than other farm animal species, likely

reflecting the prolonged period of estrus. Thus, the "typical" length of the estrous cycle is

21 to 23 days, consisting of approximately 7 days of estrus and 14-16 days of diestrus.

The length of estrus is usually more variable (composite mean 6.5 days 2.6 [SD];

composite of 26 references) than the length of diestrus (composite mean 14.9 days 2.8

[SD]; composite of 10 references) (Ginther, 1992). Mares usually ovulate 24-48 hours

before going out of heat, making the prediction of ovulation time based on the beginning

of estrus uncertain. However, two separate studies, reported by (Ginther 1992) showed a

tendency for repeatability of length of estrus in individual mares. This suggests that the

root cause of estrus variation may be inherent to individual mares. Nonetheless, the

variability of this time period makes predicting the timing of ovulation difficult for farm

managers and veterinarians alike. The mare is considered a seasonal long-day or

(summer) breeder. The annual reproductive cycle of the mare can be divided into four

segments: anestrus, vernal transition, the breeding season, and the autumnal transition.









Photoperiod

Regulation of gonadotropin secretion throughout the year has been shown to be

independent of ovarian influence (Freeman et al. 1979, Affleck et al. 1991, Sharp et al.

1993 Porter et al. 1997.). Ovariectomized mares exhibit a spontaneous increase in

Luteinizing Hormone (LH) and Follicle Stimulating Hormone (FSH) in the spring,

continuously elevated concentrations during the summer, and a spontaneous decline in

the fall (Freedman et al 1979). This is in distinct contrast to sheep, in which negative

feedback by ovarian estrogen is required for reduced secretion of gonadotropins during

anestrus. Furthermore, Freedman et al. (1979) demonstrated that exposure to artificially

lengthened day resulted in earlier increase in LH and FSH in ovariectomized mares,

indicating that photoperiod is the determining factor of the annual gonadotropin secretary

pattern.

It has been shown by many researchers that artificial increase in day length during

the anestrus period hastens the onset of the breeding season in mares (Burkhart J. 1947,

Cleaver et al. 1995, Sharp et al. 1975, Sharp, 1980; Ginther, 1992). Results indicate that

increasing photoperiod hastened first ovulation of the breeding season by 2-3 months

(Sharp et al, 1975). Addition of artificial light at sunset was shown to be more effective

than addition at sunrise (Sharp 1980). However, other researchers showed that exposing

anestrous mares to Ih of artificial light during the scotophase (dark phase of photoperiod)

also stimulates earlier sexual recrudescence (Palmer et al. 1981). These latter authors

reported that this light schedule, a "night interruption" study, was effective only when the

light exposure began 9.5 h after onset of darkness.

For light stimulus to affect the seasonal reproductive cycle of the mare, the light

signal must be converted to an endocrine signal. This process involves the pineal gland,









and the hormone melatonin. Denervating the pineal gland, by removing the superior

cervical ganglia, ablated the secretary pattern of melatonin, with consequent delay of

entry into the breeding season (Sharp et al. 1979). Of interest, the delay was not observed

until the second post-surgery breeding season. The surgeries were performed during the

winter, and time of onset of the breeding season the following springtime was not

different among non-operated, sham operated, and superior cervical ganglionectomized

mares. However, the following year, onset of the breeding season was delayed by over

two months in the ganglionectomized mares compared with nonoperated or sham

operated controls. The authors suggested that the delay in onset of the breeding season in

superior cervical ganglionectomized mares likely reflected loss of photoperiodic timing

cues, and consequent expression of an endogenously controlled rhythm of reproductive

function Sharp et al. 1979). Results were similar following removal of the pineal gland

itself (Sharp 1982). Thus, these results suggest that pineal substances, such as melatonin

may convey photoperiodic cues to the central nervous system which then act to regulate

reproductive function directly or indirectly through phase control of an endogenous

rhythm. As a result of this research, it is now common practice on breeding farms to

expose mares to lights for 2.5 h after sunset nightly beginning in November to advance

the onset of the breeding season.

Winter Anestrus

The winter anestrus period is a time of reproductive quiescence which occurs

usually from the end of October through January. In the winter time mares are considered

to be in deep anestrus, with little-to-no sexual receptivity and little follicular

development. Of course as with all things there is great variability to the length of this

anestrus period between individual mares. Roughly about 20% of horse mares exhibit









continuous estrous cycles year round, whereas the percentage of pony mares exhibiting

complete estrous cyclicity ovulatoryy cycles), during the winter months is considerably

less, (Ginther, 1992). During anestrus, ovarian activity is decreased. There is little

follicular development, with most mares exhibiting follicles that are less than 10 mm in

size during this time period. If the ovaries are removed at this time they appear to be

mainly composed of ovarian stroma (Ginther, 1992). Accordingly along with a reduction

in follicular activity there is an overall reduction in the production of ovarian hormones,

estrogen and progesterone (Oxender, Noden & Hafs 1977). Usually these hormones

remain low until the onset of sexual awakening in the spring which is paralleled by an

increase in sexual receptivity (Sharp et al. 1993). A decreased receptivity response or

indifferent response by the mare towards the stallion is seen during this time (Ginther,

1992, Sharp, 1980).

During the early vernal transition mares may exhibit estrus behavior without any

associated ovulations. These behavioral signs can thus be deceiving, and should not be

used exclusively as breeding indications. Rather, the breeder or equine practitioner

should make use of ovarian changes to judge whether or not a mare has entered the

breeding season, and is displaying appropriate behavioral estrus. The overall lack of

gonadal function during winter time reflects the decrease in hypothalamic-pituitary

secretion activity, with greatly reduced gonadotropin secretion. Of special note, the

pituitary has been shown to have considerably reduced LH protein during the winter

months (Hart et al. 1984) which reflects the loss of messenger ribonucleic acid (mRNA)

encoding the subunits of LH (Sherman et al. 1992). The rise in gonadotropin levels

associated with onset of the breeding season is poorly understood. These seasonal









changes in gonadotropin levels occur with or without ovaries and shows that seasonal

influences, predominantly photoperiod, affect the CNS to regulate the secretion of

gonadotropins. Pelletier et al (1998a) demonstrated that the initial rise in LH prior to the

first ovulation of the year in intact pony mares occurred at the same time as in

ovariectomized pony mares, indicating that environmental influences likely played a

regulatory role. Alternatively, the timing of the increase in LH might reflect expression

of internal rhythm of some kind. .

Studies have shown that overall circulating Luteinizing Hormone (LH) and Follicle

Stimulating Hormone (FSH) are markedly low during this time period. Strauss et al.

(1979) and Silvia et al. (1986) showed that the GnRH content of the hypothalamus in

mares was significantly lower during anestrus than during the breeding season,

suggesting a potential mechanism for the lack of pituitary gonadotropin. Hart et al

(1984), using pituitaries collected from a local slaughter house throughout the year,

reported that the concentration of LH protein in the pituitaries was significantly lower

during the months of December and March compared with the months of July, and

October, reflecting the reduced LH secretion during anestrus and vernal transition. FSH,

on the other hand, was not significantly different throughout the year. Sherman et al.

(1992) demonstrated that the gene that regulates the synthesis of LH subunits was

undetectable during the months of December and March, indicating that the relative loss

of expression of the LH subunits serves as an explanation for the loss of circulating LH.

The failure of FSH secretion during winter anestrus, on the other hand does not appear to

reflect loss of gene expression, as Hart et al. (1984) demonstrated abundant presence of

the protein in pituitaries. Therefore, it is likely that a two-phase regulatory system is in









effect during anestrus: 1) FSH secretion is reduced because of the paucity of GnRH

secretion from the hypothalamus ( Strauss et al. 1979; Silvia et al. 1986; Sharp and

Grubaugh, 1987), and 2) LH is reduced because of the reduction in GnRH, but is also not

available for release, even if GnRH were introduced. Due to the reduction in both LH

and FSH, ovarian follicular development is reduced, and consequently, steroid secretion

is reduced, Studies of Douglas et al. (1974) and Lapin and Ginther (1977) indicated that,

although ovaries are inactive during the winter anestrus, they are capable of responding to

gonadotropins exogenously administered. These authors administered purified equine

pituitary extract to anestrus mares, and demonstrated follicular development and

ovulation. However, if they failed to continue providing pituitary extract, such

exogenously-treated mares reverted to the anestrus state.

Vernal Transition

As day length increases in the springtime, mares enter into a transitional period

referred to as "vernal transition". Vernal transition is the time period between winter

anestrus and the breeding season, during which sexual function is renewed. Shortly after

the shortest day of the year there is an increase in hypothalamic GnRH content and

secretion (Sharp and Grubaugh, 1987, Silvia et al. 1992. In response to the increased

GnRH secretion, peripheral FSH increases, generally in the month of January in mares

under ambient photoperiod, and remains elevated, but highly variable, throughout the

vernal transition period. Despite the elevated FSH concentrations, LH remains low until

just before the first ovulation of the year sometime in April or May, in horse mares or

pony mares, respectively (Ginther, 1992, Garcia et al.1979, Sharp et al. 1975, Sylvia,

1986). This unusual hormonal environment leads to development of a succession of

follicles that are: 1) slow growing, although they achieve pre-ovulatory follicle size, 2)









poorly steroidogenic, lacking key steroidogenic enzymes, 3) poorly vascularized, 4)

poorly invested with granulosa cells, and most significantly, 5) anovulatory (Sharp et al.

1975, Davis and Sharp, 1991, Tucker, 1993). In support of this finding, it has been

shown that the FSH receptor levels remain relatively constant independent of season and

stage of the mare's estrous cycle (Fay and Douglas, 1987). Furthermore, as previously

stated, administration of pituitary extract to mares in anestrus or early vernal transition

also leads to follicular development (Douglas 1974). During the vernal transition pony

mares develop an average of 3.7 0.9 anovulatory or transitional follicles (>30mm in

size) before the first ovulation of the year (Davis and Sharp, 1991; Watson et al. 2003).

Upon palpation or ultrasonic examination these transitional follicles are essentially

indistinguishable from pre-ovulatory follicles present during the breeding season.

Although mares during this time develop large follicles and may demonstrate estrus

behavior, peripheral E2 levels remain low, which suggests that these transitional follicles

are steroidogenically incompetent (Seamans, 1982, Davis 1987) In one study, the early

transitional follicles lacked androgens and estrogens whereas follicles late in the vernal

transition (presumably the last anovulatory follicles before the appearance of the first

ovulatory follicle), displayed increased androgens and estrogens, in follicular fluid, and in

in vitro culture, (Davis & Sharp, 1991). Seamans et al. (1982) reported that transitional

follicles cultured in vitro, were capable of converting both progesterone and androgen to

estrogen, indicating the presence of adequate amounts of aromatase enzyme. These data

suggest that the lack of estrogens in the transitional follicles may be due to a lack of

substrate earlier in the steroid biosynthetic pathway. In that regard, Tucker et al. (1992)

reported that follicles during early vernal transition did not exhibit 17-ac-hydroxylase









enzymes, suggesting that failure to produce estrogens reflected this lack of steroidogenic

catalysis. Watson et al. (2003) reported, in further support of that idea, that the side chain

cleavage enzyme is also undetectable in early vernal transition follicles. Therefore, the

main characterization of the vernal transition follicle is its poor steroidogenic capability,

likely due to the unusual high FSH to low LH ratio.

Just before the first ovulation of the year, there is a large surge of estrogen in the

peripheral plasma that reflects a large increase in intrafollicular estrogen, as well as an

increase in the in vitro estrogen synthetic capacity of the follicles. This estrogen surge,

which precedes the first ovulation of the year by approximately 5 to 6 days, is in close

temporal association with the first significant increase in peripherally circulating LH.

This secretion of LH indicates the reappearance of mRNA encoding the LH subunit

synthesis, but the signal for this gene expression is unknown. Estrogen secretion may be

an important signal in the regulation of LH synthesis and secretion, the E2 surge precedes

increased levels of LH by a few days (Davis, 1987).

The increase in E2 concentration could then act as a positive feedback mechanism

to stimulate the release of LH, and is certainly appropriately timed temporally for such a

role. Furthermore, Sharp et al demonstrated that estrogen administration to

ovariectomized pony mares during the equivalent time of early vernal transition resulted

in significantly increased mRNA encoding both the a and the LH 3 subunits (Sharp et al.

2001). Therefore, it is clear that estrogen can stimulate renewal of LH biosynthesis in

vernal transition, but the question remains as to whether that is the natural signal or not.

Pelletier et al (1998a 1998b) reported that the time of increased LH in ovary-intact and

ovariectomized pony mares was identical. Furthermore, the pre-ovulatory increase in









estrogen in the ovary-intact group was, of course not observed in the ovariectomized

mare group. Thus, in that study, the essential role of estrogen in LH biosynthesis renewal

is questionable. Thus, the question still remains as to what signal stimulates LH

biosynthesis renewal in the springtime. The fact that timing is similar in ovary-intact and

ovariectomized mares indicates that ovarian feedback is not likely the major signal,

leading to speculation about the potential role of environmental factors.

These large transitional follicles present a problem to farm owners and

veterinarians. For the Thoroughbred industry, foals that are born earlier in the year (close

to January 1st) bring the most money at yearling sales. Many of these owners attempt to

breed mares during this transitional period when the follicles are incompetent. This

overall practice has lead to the usually inappropriate use of ovulation stimulating

compounds to get mares bred earlier in the year. Yet breeding mares during this time

period not only exposes them to the possibility of uterine infection, but ends up costing

the breeder more money and lost time.

The Breeding Season

At the end of the vernal transition the first ovulation of the year marks the

beginning of the breeding season. The breeding season extends from April through

October in the northern hemisphere (Ginther, 1974, 1992). The mare's typical estrous

cycle is approximately 21 days in length (Daels and Hughes 1993). The cycle is divided

into an estrus and diestrus phase. The follicular phase, or estrus, is a time when the mare

is sexually receptive and develops large steroidogenically competent preovulatory

follicles. The estrus period lasts from 2-7 days, whereas diestrus lasts from 14-15 days

(Sharp, 1980). There is a lot of variation to the length of the estrus period as compared to









diestrus for individual mares (Ginther, 1974). Generally most mares (approximately 80%)

ovulate within 24-48 hours prior to the end of estrus (Hughes et al. 1975, 1980).

As ovulation occurs the follicle releases the oocyte into the oviduct. The follicle

cavity fills with blood and the granulosa cells begin to luteinize. A corpus

hemmoragicum (blood filled follicle) often forms, and is evident by ultrasound

examination for 2-4 days before becoming a mature corpus luteum. Loss of sexual

receptivity is temporally associated with progesterone concentrations above 1 ng/ml. The

luteal phase which lasts 14-15 days, ends with regression of the corpus luteum and a

return to estrus 2-4 days later

Autumnal Transition

It is a very poorly defined phase that is characterized by gradual loss of

reproductive function the end of the breeding season and the beginning of winter anestrus

(Sharp and Davis, 1993). This phase of the cycle begins with a rise in FSH, but is not

accompanied by an LH surge or ovulation (Ginther 1979, 1992, Snyder et al. 1978).

There is a tremendous amount of variability among mares entering into the autumnal

transition. It is believed that the transition involves a decline in GnRH secretion, and or

LH synthesis and secretion in response to seasonal or visual light changes (Sharp and

Davis, 1993). It may also be due to the failure of follicular growth and E2 production to

lead to luteolysis and the corpus lutem's overall life span may be extended (King and

Evans, 1988).

Endocrinology of the Estrous Cycle

The mares' entrance to the breeding season is induced by an increase in day length,

and an increase in GnRH secretion. This GnRH release triggers a gonadotropin release,

which has downstream effect on the ovarian production of E2 and P4. FSH occurs in two









surges one during early diestrus (postovulatory rise), and again around mid-diestrus (days

10-13) (Evans and Irvine 1975, Ginther 1992, Irvine 1981). FSH begins to decline about

5-7 days prior to ovulation, and there is a concomitant rise in LH secretion which

promotes ovulation (Pierson 1993). There is 12-18 hour LH surge prior to ovulation as in

other domestic farm species such as the cow and sheep. In contrast there is a slow rise in

LH over the course of 5-7 days, which reaches peak concentrations 1-2 days post-

ovulation and declines by approximately 5 days after ovulation (Pattison et al. 1974 ,

Greaves et al. 2000).

Growth of the dominant follicle stimulates the stimulates, the secretion of E2 which

then peaks 1-2 days prior to ovulation (Hughes et al. 1972, Hillman and Loy 1975,

Ginther 1992, Palmer and Jousset 1975, Palmer and Terqui 1977, Plotka et al. 1975).

Estrogen generally declines before ovulation, although one report suggested that estrogen

remained elevated for 1-2 days post ovulation (Pelehach 2000). In estrus P4 levels are

low, generally less than Ing/ml, and rise within 24 to 48 hours after ovulation, with

maximal P4 production by 6 days post ovulation. (Stabenfeldt et al. 1972, Plotka et al.

1972). It has been shown that P4 levels increase 140-160pg/ml 10 hour post ovulatory

and again increase up to 346pg/ml by 26 hours post ovulation (Plotka et al. 1975).

Progesterone remains elevated until regression of the CL at day 14-15 of the cycle

(Stabenfeldt et al. 1981). Another interesting point is that in the mare P4 does not block

follicular development (Daels and Hughes 1993).

Ovarian Anatomy

A distinctive and somewhat unusual feature in mares is their ovarian anatomy. The

mare has an inside-out ovary unlike other domestic farm species, companion animals or

laboratory animals. The ovary itself is kidney shaped with a prominent depression









(ovulation fossa) on the ventral border (Ginther 1992). The cortex and medullary tissue

are reversed in the mare, with the cortex central and the medulla peripheral. The cortical

tissue is almost completely surrounded by medullary tissue and only a small portion of

the ovary, the ovulation fossa, is invested with germinal epithelium (Stabenfeldt et al.

1975, Ginther 1992). This area of germinal epithelium covers the surface of the ovulation

fossa and it is the only location on the ovary where ovulation can occur. This anatomical

limitation may preclude more than one or two follicles from ovulating on each ovary.

Hence attempts to superovulate mares with exogenous gonadotropins is relatively

unsuccessful, with an average of only 4 to 5 embryos resulting from such attempts

(Squires et al. 2003) Another anatomical difference is that the CL when fully formed

does not project from the greater surface of the ovary as in other farm species (Ginther

1992).

Folliculogenesis

Follicular activity becomes suppressed during short days, but as the day length

increases in the spring there is an overall increase in the number and size of follicles

developing within the ovary (Ginther 1990). Mares have follicular waves that are less

defined than cattle and usually develop one dominant follicle, although some breed-types,

especially Thoroughbred, exhibit a propensity for development of more than one

dominant follicle (Stabenfeldt et al.. 1975, Ginther 1990, 1992, 2001). The follicle wave

that is initiated during the luteal phase consists of 3 to 6 follicles in a so-called "cohort",

which continues to grow throughout the luteal phase, until selection and deviation occur.

In the "selection" phase, a pool of small follicles is recruited to grow out of the

primordial pool. These follicles continue to develop until one of the cohort continues to

develop at the same or accelerated rate. At this time, follicular growth slows in the









remainder of the cohort, with subsequent decline in follicle diameter, and loss through

atresia. This point is called the point of deviation.

Deviation in mares occurs when the two largest follicles are on average 19 and

22.5mm in diameter, or approximately three days after the FSH peak (Ginther et al.

2003). The deviation mechanism is thought to reflect declining FSH concentrations in

response to inhibin produced by the entire follicle cohort (Ginther, 2003). E2, inhibin,

activin, and free IGF-1 increase in the dominant follicle approximately one day prior to

deviation (Ginther et al. 2003). Overall these changes may be due to a greater

responsiveness to LH and FSH or the earlier development of LH or FSH receptors, in the

future dominant follicle. As this dominant follicle grows the rest of the recruited follicles

undergo atresia. On average, more than 95% of all follicles become atretic (Pierson

1993).

Folliculogenesis is also associated with a rise in E2 which leads to ovulation. There

are differences between the follicular and luteal phases, with respect to E2 and P4. The

dominant follicle is the most steroidogenically competent and gonadotropin responsive

follicle (Pierson, 1993Tucker, 1992). The dominant follicle produces E2 approximately

1-3 days before ovulation (Hughes et al., 1972). This responsiveness of the pre-ovulatory

follicle to LH has been suggested to reflect an increase in the number of LH receptors in

the theca. This overall response may in fact be augmented by E2 (Pierson, 1993). The

periovulatory follicle is very highly vascularized compared with subordinate follicles.

The follicular fluid content of E2 is also increased within the periovulatory follicle

compared with other follicles (Tucker, 1993). Studies have suggested that the increased









vasculature allows the dominant follicle to accumulate more gonadotropins (Pierson,

1993).

On average the equine preovulatory follicle can grow up to 45-50mm prior to

ovulation. The preovulatory follicle in cows grows to almost 36,000 times larger in

square surface area from a primordial to a preovulatory follicle (Smith et al., 2000). You

can imagine the overall increase in surface area in an equine follicle as compared to a

bovine follicle. The dominant preovulatory follicle is usually the largest on the ovary

from about 6-7 days prior to ovulation, and it can grow on average 2-4mm per day or

more (Tucker, 1992) in size, as ovulation approaches (Pierson and Ginther, 1985,

Ginther, 1992). This provides an interesting dilemma for the mare, in that the only

portion of the ovary where ovulation can occur is the ventral pole at the ovulation fossa.

Wherever a dominant follicle grows and develops, within the internal cortex of the ovary

it must expand and/or migrate to contact the fossa in order to ovulate (Ginther, 1992). As

this follicle continues to grow, it often becomes conical or triangular in shape, as

ovulation becomes imminent.

Ovulation

As ovulation approaches in most species the dominant follicle expands internally

to the point of protruding from a considerable portion of the ovarian surface, and

ultimately contacting the ovulation fossa. At this time both LH and E2 are elevated.

Increased LH at the time of ovulation is associated with release of the oocyte and

follicular fluid, followed by luteinization of the remaining follicle. After the follicle is

evacuated, it fills with blood from the surrounding vasculature and forms a CH. During

palpation one can differentiate between a pre-ovulatory follicle and a CH only with great

difficulty. (Daels and Hughes 1993, Ginther 1992). It has been shown that changes in









follicle turgidity occur in 90% of pre-ovulatory follicles (Parker 1971, Ginther 1992).

Luteinization of the granulosa cells and theca intema cells into large luteal cells is a

process of differentiation, marked by both biochemical and morphological changes. This

formation of the CL and it subsequent production of P4 begins the diestrus period

(Senger 1997)

Monitoring the Estrous Cycle

Ultrasonography. During the early 1980s an important technology became

available to researchers and veterinarians, the use of ultrasonic monitoring of the

reproductive tract. The advent of ultrasound has provided both researchers and

veterinarians with a diagnostic tool to monitor the changes in the reproductive organs of

the mare throughout the estrous cycle. Ultrasound allows for evaluation of the

reproductive organs in real time. Ultrasound is used for many things, such as monitoring

follicular changes, examining the process of ovulation, CL morphology, and many others

(Squires et al. 1988, Ginther 1992).

Ultrasound is an efficient way to examine the reproductive tract of the mare using a

linear array transducer, and a forward facing transducer for transvaginal techniques. The

typical linear array transducer has a flat probe with the crystals arranged in a linear

fashion along the length of the transducer. The crystals themselves are energized via an

electric current that causes them to vibrate. Placement of the probe face against the

mare's rectal wall allows the transduction of sound waves through the tissues. The sound

waves are then reflected back to the probe with a greater or lesser energy depending on

the type of tissue. These sound waves are then converted to a grey and displayed in 2

dimensions on the screen (Ginther 1988, 1992).









Sound waves pass easily through fluid, and are not reflected back toward the sound

generating source. With B-mode ultrasound equipment, the reflected sound is expressed

electronically on a gray scale, with highest intensity echoes displayed as white, and

lowest intensity sound displayed as black. Thus, dense tissue reflects sound with a high

energy which is translated as white pixels on a video monitor. Such tissues or structures

are often referred to as echogenic. Other tissues appear in varying shades of grey which

depends on the amount of sound wave reflected (Ginther 1988). The use of high

frequency scanners (5-7.5Mz) permits good resolution, i.e. discrimination of two closely

located structures, yet such instruments do not penetrate very deeply in tissues.

Therefore, ultrasound scanners with a lower frequency of generated sound may be more

utilitarian for examination of tissues that are farther away from the probe, as near term

pregnancies. One must also consider the orientation of the transducer when proceeding

with an ultrasonic examination. If one places the probe toward the medial portion of the

ovary that is the image that appears on the top portion of the screen (Ginther, 1988).

The use of ultrasound has become an important research tool to examine the

ovaries collect oocytes for IVF (mostly bovine), and has potential for follicular fluid

collection and injection of various compounds into the follicular microenvironment.

Before the development of ultrasound, rectal palpation was the only means to determine

the reproductive status of the mare. One of the most important developments to research

is that it has aided is examination of the ovaries and pregnancy diagnosis. Now

researchers are able to accurately measure and map both follicular growth and regression.

Additionally it has also allowed for more precise ovulation detection. As follicles are

fluid-filled, they appear black on the monitor due to the fact that low intensity echoes are









interpreted electronically as black on a gray scale. The CL appears mottled in color,

because it is a mixture of dense and less dense tissues. The great profluence of cellular

margins presents highly echogenic surfaces which reflects sound with higher intensity,

hence the mixture of white and black pixels. Ultrasound also allows researchers to

accurately measure follicle size and examine small developing follicles of 5mm or

smaller. In a study by Squires et al. (1988) ovulation was determined by palpation and

also by ultrasound, the ultrasound operator was more accurate in 88% of the cases. It has

also helped clear up the identity of a CH, and CL. Blood is visualized as semi-echogenic

with fibrin lines, and this can now be distinguished from echogenic luteal cells (Squires

et al. 1988).

The Extracellular Matrix

Normal ovarian function it is dependent upon the dynamic and cyclic remodeling

of the extracellular matrix (ECM). The extracellular matrix is a network of molecules

that are bordered by cells, which benefit by changes in the composition and structure of

the ECM. In dynamic tissues such as the ovary, the thecal and granulosa cells are in

contact with ligands that bind these receptors (integrins) as the ECM is remodeled. The

process of differentiation both morphologically and biochemically, of granulosa and

thecal cells into large luteal cells which are steroidogenically competent may require

contact with ECM components (Smith et al. 2002). There are also many types of

interactions between the ECM and growth factors and cytokines, which may protect

them from proteolysis or alter their activity, yet are beyond the scope of this thesis

(Flaumenhaft and Rifkin 1992). The turnover of the ECM around these cells releases both

specific cytokines and growth factors (Logan and Hill 1992).









The ECM supports adhesion of cells and functions to transmit signals through cell-

surface adhesion receptors. The ECM contains collagens, glycoproteins and

proteoglycans. The basement membrane is a specialized ECM that separates epitheial

cells and the surrounding stroma, providing a barrier (Werb 1997). Collagen is composed

of 3 alpha chains that form a triple helix. In the ECM there are the typical fibrillar

collagens (I, II, III, V, and XI) which form fibrils that influence cellular function by their

interactions with specific integrins. The basement membrane (BM) is composed

primarily of collagen type IV and forms a network throughout the ECM. Cells can

interact with these type IV fibrils through intehrins, laminins and proteoglycans (Egeblab

et al. 2002).The overall product of the digestion of collagen by collagenase enzymes is

gelatin.

The glycoprotein component of the ECM is mainly made up of laminins, which are

heterotrimeric glycoproteins made up of 3 chains. Laminins are primarily located in the

BM where they also form a network with collagen type IV fibers. Laminins themselves

can affect cellular functions by binding directly to integrins and non integrin receptors

(Sakasi et al. 1999). The fibronectins are also glycoprotein's that are present in the ECM

and in the blood. They too form fibrils that have effects on cellular morphology,

migrations, adhesion, and differentiation via their interactions with integrins (Hynes et al.

1999, Gustafsson et al. 2000). Overall the proteoglycans also compose parts of the ECM

and help "decorate" collagen fibers yet much of their roles remain to be discovered. Basal

Components of the ECM are often altered via cleavage by various matrix

metalloproteinases (MMP's) (Nagase et al. 1999). When the ECM is remodeled by

MMP's this can have effects on cellular proliferation, differentiation and cell survival









(Whitelock et al., Manes et al. 1999, Boudreau et al. 1995, Sympson et al. 1994). The

control of MMP's by tissue inhibitors of matrix metalloproteinases (TIMP's) can help to

maintain the overall matrix stability

Ovarian Extracellular Matrix and Remodeling. The ovarian ECM itself provides a

unique microenvironment. The ECM of the preovulatory follicle consists of the theca

externa, and tunica albuginia tissue layers which are rich in type I, III, and IV collagen.

(Luck 1994). These collagen fibers provide the tensile strength and support to the follicle

wall. Ovarian follicles are composed of both epithelial and stromal tissue where cell

migration, division, differentiation and death occur. In cattle it has been shown that as the

antrum develops and at ovulation the epithelial cells of the follicle undergo a

morphological and biochemical transition to luteal cells (Rodgers et al. 2003). As the

follicle itself develops there are marked changes in the follicular basal lamina

composition from collagen type IV- 6 alpha chains to mainly alpha one and two chains

(Rodgers et al. 2003). In another study by Rodgers et al. (2002) in cattle they found that

there were changes in the granulosa membrane layer of the BM which was similar to the

formation of Call-Exner Bodies. Call-Exner bodies are small fluid fills spaces that

develop between granulosa cells in ovarian follicles, and they form rosette like structures,

during the beginning stages of tumor formation. The overall expression of this newly

reformed matrix was discovered to coincide with the exposure of granulosa cells to

steroidogenic enzymes (Rodgers et al. 2002). In the same study it was suggested that as

the granulosa cells mature and undergo differentiation this is accompanied by novel

changes in the composition of the ovarian matrix. In a comparative study by Bortolussim

et al. (1989) in pigs, rats, and cattle, they suggest that in all the follicles both laminin and









collagen type IV are localized to the basal lamina that separates the granulosa and thecal

cell layers. They also suggest that this basal lamina or basement membrane of growing

follicles is undergoing regular and continuous modification in its makeup.

The localized degradation of these collage fibers at the apex of the follicle wall is

required for ovulation in sheep, cattle, mice, rats, and primates. Breakdown of the

basement membrane is required for the release of the oocyte from the follicle cavity

(Espey and Linper 1994). It has been shown that MMP inhibitors prevented the

ovulatory process and that these enzymes play a critical role in follicular rupture

associated with ovulation, yet which specific one is required is still under investigation

(Brannnstrom et al. 1988, Butler et al. 1991). The ratio of degradation and inhibition by

MMP's and TIMP's can influence many cellular processes associated with ovarian

function, such as, differentiation, cellular migration, proliferation and apoptosis

(Boudreau et al. 1996, Brooks et al. 1996). In a study by Aten et al. (1995) it was shown

that rat granulosa cells cultured on a laminin matrix, promoted differentiation of these

cells into luteal cells, and this differentiation was blocked with an antibody to the integrin

beta 1 subunit.

As follicles proceed through growth they dramatically increase in surface area

within the ovarian stroma (Smith et al. 2002). The process of ovulation itself is

characterized by local ECM degradation of the follicle wall of the preovulatory follicle.

This usually occurs at the apex of the stigma of the antral cavity in sheep, cattle, mice and

primates (Lui et al. 1999). In the horse however this only occurs at the ovulatory fossa,

where the follicle contacts the germinal epithelium. After rupture of the ovulatory

follicle, the remaining thecal and granulosa steroidogenic cells become terminally









differentiated and reorganized. The overall mechanism of follicular rupture and CL

reformation are equivalent to the processes of wound healing and tumor formation (Smith

et al. 1994b). After ovulation the remaining cells within the follicular cavity are

remodeled into a CL, this process is equivalent to a 20 fold increase in tissue mass (Smith

et al. 2002). Dynamic remodeling of the ECM is found throughout the follicular cycle,

and is characteristic of growth, atresia, ovulation CL formation and regression.

Matrix Metalloproteinases Structure and function

Matrix Metalloproteinases

Matrix metalloproteinases are a super family of zinc metallo-endopeptidase which

are responsible for the turnover of extracellular matrix components. These are included

within the "MB Clan" of metallopeptidases, which contain a HEXXHXXGXXH motif

that acts as the zinc binding active site. They are commonly referred to as "Metzincins"

due to the fact that they all contain a conserved methionine reside that forms a turn eight

residues downstream from the active site. This group contains a number of families and

the MMP's are within the M10 group, which is further subdivided into subfamilies A and

B. MMP's belong to the A subfamily and are collectively known as matrixins, they are

usually designated with numbers. The MMP family currently consists of at least 26

members all of which share a common catalytic core, with a zinc molecule at their active

site (Stamenkovic I 2000).

Matrix Metalloproteinases Structure

All MMP family members are composed of a prodomain, a catalytic domain and a

highly conserved active site domain. The active site consists of an HEXGHXXGXXG

motif in which the three histidine residues constitute three zinc ligands and the glutamic

acid residue sits within the active site of the enzyme. There are glutamic and aspartic









rich sequences at both the N and C terminal ends of the domain that are thought to be

CA++ binding sites. MMP's contain a very distinctive PRCGVPD motif within the

prodomain which is responsible for maintaining the enzymes latency. All the MMP's

except MMP-7 and 26 contain a homeopexin domain which promotes the substrate

specificity of the enzymes. MMP-2/9 also contains a fibronectin gelatin binding domain.

The MMP family members are categorized based on both their structural and functional

characteristics. MMP's are either secreted as in 1-13, and 18-20, or they are anchored to

the cell membrane via a transmembrane domain 14-17 where they are referred to as MT-

MMP's. The MMP's are also functionally divided into categories based on the substrates

that they degrade, such as collagenases, stromelysins, and gelatinases (Chambers, et al.

1997, Van den Steen et al. 2002, Stamenkovic I 2000).

All of the MMP's demonstrate a high degree of sequence and structural homology

and are derivatives of the five domain archetypal collagenase enzyme, with either

insertions or deletions of specific domains.

In all the enzymes, the N-terminal domain consists of a short stretch of

hydrophobic amino acid residues that form the signaling sequence for secretion of the

protein into the extracellular space. This domain is cleaved off for activation to occur.

MMP's are maintained in a latent inactive state by a 77-78 residue propeptide which is

part of the N-terminal domain. This controls part of the regulation of the enzyme activity.

The pro-peptide is centered around the cysteine residue and interacts with the zinc atom

at the active site. This interaction excludes water from the active site and maintains the

inactive state of the pro-enzymes.









MMP's are produced as zymogens, they contain a signal sequence that must be

removed for activation. In the propeptide form of the enzymes there is a conserved

cysteine residue that chelates the active zinc site of the enzyme. The conserved sequence

PRCGVP surrounding this cysteine has been coined the "cysteine switch". Within the

MMP family there is a subset of MMP's including the MT-MMP's, as well as MMP-1 1,

21, 23, and 28 that contain a basic prohormone cleavage sequence RRKR, RRRR, or

RKRR, which is believed to be cleaved by the PACE/ furin family of enzymes. MMP's

differ in the placement of the active site groove which allows for different substrate and

inhibitor specificities.

All the MMP's except MMP-7 and 26 contain a regulatory hemopexin domain

which is separated from the catalytic domain by a hinge region. The homeopexin domain

is believed to confer much of the enzymes substrate specificity. The MMP's also are often

found to be associated with heparin sulfate gycosaminoglycans on the cell surface. The

overall structure of the hemopexin domain is a "four bladed propeller" with a calcium

binding site within the domain. The catalytic activation of the enzymes often includes

shedding the hemopexin domain and this domain is suggested to feedback on the enzyme

directly to down regulate it's activity( Itoh et al. 2002, Seiki M., 2002, Troederg, et al.

2002).

Membrane Bound Matrix Metalloproteinases (MT-MMP's)

MT-MMP's are localized to the cell surface, four of these MT-MMP 14, 15, 16, and

24 contain a hydrophobic transmembrane domain followed by a cytoplasmic domain.

MT-MMP 17 and 25 lacks the cytoplasmic domains are thought to be anchored to the

cells surface. The cytoplasmic domain is believed to be involved in cyto skeletal

signaling cascade systems, and may be directly phosphorylated by various protein









kinases. All of the MT-MMP's contain the transmembrane sequence downstream of the

homeopexin domain. MT-MMP 14 has been shown to provide the activation mechanism

for pro MMP-2, and both MMP-14 and 15 can activate pro MMP-13. MMP-14 degrades

native collagen type I, fibronectin, and laminin (Egeblad et al. 2002, Itoh et al. 2002,

Seiki 2002, Stamenkovic 2000).

Regulation of MMP's

The activity of MMP enzymes is regulated at three levels: transcription, protelytic

activation of the zymogen, and inhibition of the active enzyme. Data that suggest that a

variety of external stimuli such as cytokines, growth factors, and changes in the cell to

cell and cell to ECM interactions also help regulate enzyme activity. MMP expression is

primarily regulated at the transcriptional level. The 5' regions of the regulatory elements

of the MMP's contain an AP-1 cis element that is found to be proximal to the promoter,

roughly 70 nucleotides upstream of the initiation site. The promoter region itself contains

a PEA3 site which is another cis element that interacts with the AP-1 motif for optima

activation of the enzymes (Chambers et al. 1997, Seiki 2002, Stamenkovic 2000,

Yoshizaki, et al. 2002).

Activation of MMP's

MMP's are mostly secreted as latent proenzymes, the zymogen form, and they are

activated in the extracellular space. The latency is maintained by the "cysteine switch"

which is formed through the action of the sulfhydryl group of the conserved cysteine

residue which is within the propeptide and catalytic zinc binding site. This interaction

blocks the zinc dependent activation of a water molecule which mediates the nucleophilic

attack on the peptide bonds of the enzyme.









Activation of MMP-2

The activation of MMP-2 is the most widely elucidated and well understood

mechanism of MMP activation. The model for this activation relies on the formation of a

ternary complex on the cell surface made up of MT-MMP-14 (otherwise referred to as

MT-MMP-1), pro MMP-2 and the tissue inhibitor of matrix metalloproteinase 2, (TIMP-

2). TIMP-2 serves as the receptor for the homeopexin COOH terminal domain of MMP-

2, and the binding of pro MMP-2 to the MT-MMP14/ TIMP-2 complex localizes it to the

cell surface. Then proteolytic cleavage of the MMP-2 propeptide specifically at the

ASN37-LEU38 bond, results in MMP-2 activation, which is mediated by another TIMP-

2 free MT-MMP14 molecule (Seiki 2002, Stamenkovic 2000).

Assembly of the trimolecular complex of MT-MMP, MMP-2 and TIMP-2 is

critical for the docking, activation, and cross talk of MMP's and integrins. This is

involved in the regulation of focal matrix degradation and cell locomotion. MT-MMP-14

is in immediate proximity to integrin avp3. In this case both the integrin and MT-MMP-

14 are associated with the cytoskeleton. TIMP-2 links MT-MMP-14 (the receptor) and

the secretary MMP-2 proenzyme, together. The second molecule of MT-MMP-14 that is

free of TIMP-2 activates the integrin avp3 through a limited proteolysis of the 03

subunit. This activator initiates the activation or pro MMP-2 by cleaving the N-terminal

portion of the 68kDa latent zymogen. The activation intermediate a 64kDA molecule

associates with the activated integrin through the C-terminal domain of the enzyme itself.

The full maturation of the active enzyme from 64 to 62 kDa autocatalytic activation

occurs only if MMP-2 is completed with the integrin (Ratinkov et al. 2002).









Tissue Inhibitor of Metalloproteinases (TIMP's)

TIMP Structure

The overall proteolytic activity of the enzymes is primarily controlled by the tissue

inhibitors of metalloproteinases (TIMP's). TIMP's are small protein molecules ranging

from 21-28 kDa, and they specifically block MMP's by binding to the zinc binding site of

active MMP's. There are currently 4 identified TIMP's 1-4. All TIMP's possess a

formation of 12 conserved cysteine residues that are required for the formation of six

disulphide bonds. The amino terminal domain is required for the inhibition of the active

MMP's. TIMP-1 is very effective at inhibiting specifically collagenase activity. TIMP-1

and 2, are responsible for the inhibition of most of the MMP enzymes, and TIMP-1 can

also form a complex with MMP-9. This TIMP-1/MMP-9 complex is thought to be

responsible for the recruitment of MMP-3 and results in the inhibition of this enzyme.

TIMP-3 inhibits the activity of MMP-1,2,3,7,9, and 13. TIMP-4 also inhibits the activity

of MMP-2, 7, 1, 3 and 9 (Brew et al. 2000, Henriet et al. 1999).

TIMP Expression and Regulation

TIMP's are produced by a variety of different cell types including fibroblasts,

osteoblasts, keratinocytes and endothelial cells. They are differentially expressed in

different tissues and they follow the influx of the MMP's. TIMP-2 is constituently

expressed and normally paired with MMP-2. TIMP-3 is localized to the ECM and TIMP-

4 is mostly found in the vascular tissue. The TIMP's are slow tight binding inhibitors with

low nanomolar concentrations. TIMP-1 expression is usually regulated at the

transcription level by several cytokines and growth factors, such as TGF-P, TNF-a, EGF,

IL-1 and IL-6. TIMP-3 expression is regulated in a cell cycle dependent manner in many

cell types and in enhanced by phorbolesters, and TGF-P, while it is inhibited by TNF-a.









TIMP's 1, 2, and 4 are secreted by the cells in a soluble form, while TIMP-3 is

sequestered to the preicellular matrix (Brew et al. 2000, Henriet et al. 1999, Stamenkovic

2000). Yet interestingly it has been shown in a study by Roeb et al. (1999 utilizing

transfected TIMP-1 hepatoma cells, that an increase in TIMP-1 expression or treatment

to cells caused an increase in the activity of MMP-2 and 9 specifically. These results are

interesting due to the fact that there may be a complex activation mechanism of TIMP-1

to enhance MMP-2/9 proteolysis (Roeb et al. 1999). There may be more than one

regulatory effect of the TIMP molecules to either promote degradation or enhance matrix

stability, these complex systems remain to be elucidated.

MMP's/TIMP's and Their Role in the Ovulatory Cycle

Follicular Growth and Development

It has been shown that there may be an indirect role of MMP's in follicular growth

and development. In one study by Bagavandoss et al.(1998) it was shown that rat

preovulatory follicles that were stimulated with equine chrorionic gonadotropin (eCG),

resulted in increased gelatinase A (MMP-2) and B (MMP-9) immunoreactivity. It has

also been shown in other studies in rats that expression of the mRNA encoding MMP-2,

MMP-9 and TIMP-1 activity, along with gelatinolytic and collagenolytic activity was

increased in response to eCG administration (Cooke et al. 1999, Kennedy et al. 1996). It

has been elucidated that MMP expression is associated with follicular growth and

development but the biological role of this in follicular expansion remains to be

investigated.

Follicular Atresia and Apoptosis

The overall increase of some MMP's may help with the process of atresia and

apoptosis of non ovulatory follicles. In a study by Huet et al. (1998), there was an









increase in MMP-2 and 9 following hypophysectomy in sheep. It may be that the

enzymatic activity of MMP's breaks down the basement membrane, a characteristic sign

of follicular atresia. A stromeolysin-3 knock-out (KO) mouse model was utilized to

investigate the functional role of this MMP in atresia and granulosa cell apoptosis.

Hagglund et al. (2001) found that expression of stromelysin-3 mRNA is induced in wild

type mouse ovaries, during follicular atresia, yet there was no atresia or granulosa cell

apoptosis in the KO mouse model. It may be that MMP-3, (stromeolysin-3), may play a

role in ECM ovarian remodeling during follicular atresia.

Follicular Rupture and Ovulation

Follicular rupture and release of the oocyte are required for reproductive success.

The preovulatory surge of LH is the signal that triggers ovulation in many species, such

as sheep, cattle, mice, and primates. It has been believed that degradation of the ECM

surrounding the preovulatory follicle is a critical step in the ovulatory process that may be

triggered by the LH surge (Espey and Lipner 1994). Many studies have identified MMP's

that play role as the mediators of this process (Curry and Osten 2001). There must be

breakdown of the thecal collagenous layer, and the surface germinal epithelium at the

apex of the follicle for rupture to occur. Also there must be a breakdown of the basement

membrane which separates the thecal and granulosa cells for the release of the oocyte.

The basement membrane that separates these cells is predominately collagen type IV. In

2 studies it was shown that the administration of synthetic MMP inhibitors blocked

follicular rupture and ovulation (Butler et al. 1991, Brannstrom et al. 1988). The overall

regulation of these MMP's is important to maintain tissue homeostasis of the ovary. It

has been proposed that this occurs through three possible mechanisms: 1) the rate of









synthesis of the enzyme, 2) activation of the enzyme and 3) synthesis of their specific

inhibitors (Kane 1985).

Types of MMP's: Role in Ovulation and Response to Gonadotropin Stimulation

Collagenases

It has been shown that collagenolytic activity is increased in the preovulatory

follicles of rats and sheep, in response to an ovulatory stimulus (Curry et al. 1985,

Murdoch and McCormick 1992). The rise in the collagenolytic activity may be due to an

increase in either interstitially expressed collagenase or specifically collagenase III. It has

been shown in rats, macaque granulosa cells, and cattle, that there is an increase in

interstitial collagenase mRNA from preovulatory follicles, after an ovulatory stimulus

(Reich et al. 1991, Chaffin and Stouffer 1999, Bakke et al. 2000). Collagenase-3 on the

other hand may be more species specific in its regulation. It was found to be undetectable

in the mouse ovary during the periovulatory period (Hagglund et al. 1999). Yet in rats

and Cattle, it was expressed and differentially regulated by an ovulatory dose of hCG

(rats) or following the LH surge (cattle) (Balbin et al. 1996, Bakke et al. 2002). It may be

that an increase in the expression of either collagenase-3 or interstitial collagenase in the

preovulatory follicle may mediate the degradation of type I and III collagen fibers within

the ECM of the theca externa and tunica albuginea for ovulation to occur.

Gelatinases

In the ovulatory process it is thought that the gelatinases may play a key role in the

degradation of the basement membrane between the granulosa and thecal cells and may

further degrade the denatured collagen fibers after the initial reaction by the collagenase

enzymes. Overall the regulation and role of gelatinase A (MMP-2) and gelatinase B (

MMP-9) in the ovulatory process have been seen to be species specific. In rat and bovine









preovulatory follicles MMP-2 mRNA has been shown to be expressed constituently

(Bakke et al. 1999, Hagglund et al. 1999). Yet in contrast Cooke et al. (1996)

demonstrated increased in MMP-2 activity following an ovulatory stimulus with hCG

administration in the mouse. While in contrast to the rat and bovine model both MMP-2

mRNA and gelatinolytic activity increased following an ovulatory stimulus in mice

(Reich et al. 1991, Curry et al. 1992).

In sheep it has also been shown that MMP-2 may be a regulator of the ovulatory

process. Gottsch et al. (2000, 2001) Gottsch et al. (2001) demonstrated that

administration of antibodies to MMP-2 blocked ovulation in sheep. Russell et al. (1995)

immunized sheep against the N-terminal peptide of alpha-N-inhibin and showed a

significant decrease in MMP-2 activity in follicular fluid and the follicles failed to

ovulate. In another study in primates it was shown that there was an increase in both

MMP-2 and MMP-9 mRNA in response to an ovulatory stimulus by hCG (Chaffin and

Stouffer 1999). In rats it was also found that MMP-9 mRNA was increased following

administration of hCG and it increased in mouse ovaries following gonadotropin

stimulus (Curry and Osteen 2001 Robker et al. 2000).

In a study by Tsafriri, A. (1995) utilizing rat ovarian extracts, he demonstrated that

PMSG/hCG primed ovaries produced a significant increase in MMP-2 activity as

compared to controls. In a study by Curry et al. (2001) in mice it was shown that during

follicular development in rats MMP-2/9 was localized to the thecal and stromal layers of

the developing follicles. In the same study they also found that after hCG stimulation

MMP-2 mRNA increased in the granulosa cells as they underwent luteinization. They

also localized the overall gelatinalytic activity via in situ zymography, to the surrounding









tissue of developing follicles and the apex of preovulatory follicles, suggesting that both

MMP-2 and 9 play a role in growth of the follicles and ovulation (Curry et al. 2001).

In a study by Song et al. (1999) it was shown that equine ovarian stromal cells in

culture produced both pro and active MMP-2 and 9. In another study examining equine

follicular fluid it was shown that the predominant MMP in follicular fluid was both pro

and active MMP-2 from follicles of different sizes. MMP-9 was also present in the

follicular fluid and was significantly increased as the size of the follicles increased from

less than 10 mm to over 20 mm (Riley et al. 2001). Riley et al. (2001) also localized both

MMP-2 and 9 and found them to be present in the stromal, thecal and granulosa cell

layers. It has been suggested by Imai et al. (2003) that both pro-MMP-2 and Active

MMP-2 in the follicular fluid of cattle may serve as a marker for follicular health.

Membrane Type MMP's (MT-MMP's)

It is thought that pretenses that are localized to the cellular surface may promote or

assist in the breakdown of the collagen fibers within the ECM of the theca externa and

tunica albuginea during ovulation in sheep, cattle, and rodents (Espey and Lipner 1994).

The MT-MMP's may play a role in the ovulatory process by activating other MMP's and

maintaining their activity at the cell surface-ECM interface. (Knauper et al. 1996, Ohuchi

et al. 1997). MT-MMP-1 or 14 is the most widely studied member of this family in the

ovary. In a study by Bakke et al. (1999) mRNA expression was increased in bovine

preovulatory follicles following the LH surge. Yet in rodents, such as rats and mice, the

mRNA has been localized primarily to the thecal and granulosa cells within the

preovulatory follicle (Lui et al. 1998, Hagglund et al. 1999).

It has been demonstrated that there is a change in the regulation of cell type

expression of the MT-MMP-1 in response to gonadotropin stimulus. MT-MMP-1 both









mRNA and protein was shown to be down-regulated in the granulosa cells and up-

regulated in the thecal cells in response to hCG administration to rodents (Liu et al. 1998,

Hagglund et al. 1999). Yet in contrast in the bovine preovulatory follicle MT-MMP-1

mRNA is localized to the thecal cells prior to the LH surge and to the granulosa cells

after the LH surge (Bakke et al. 1999). It may be that the regulation is involved in the

activation of the trimolecular complex of MMP-2 and plays a role in the breakdown of

the basement membrane between the thecal and granulosa cells.

TIMP's: Role in Ovulation and Relationship to Gonadotropin Stimulation

Like the MMP's, TIMP expression is dynamically regulated throughout the ovarian

cycle. It has been shown that TIMP-1 mRNA and protein increase in the preovulatory

follicle of sheep following a preovulatory surge of LH (Smith et al. 1994a). Yet in

another study TIMP-2 expression was shown to be secreted constitutively throughout the

cycle (Smith et al. 1995). Utilizing immunohistochemical and in situ studies TIMP-1 was

found to be present in the granulosa cell layer of the preovulatory follicle in sheep (Smith

et al. 1994a, McIntush et al. 1996). When Smith et al. (1995) examined TIMP-2 they

discovered that it was localized primarily to the thecal cell layer in preovulatory follicles,

and it was constituently expressed after the preovulatory LH surge. Smith et al. (1999)

suggest that the localization and expression of TIMP-1 and TIMP-2 may indicate distinct

roles for each inhibitor during the preovulatory period. They suggest that TIMP-1 may

regulate the proteolysis within the granulosa cell layer and promote differentiation of

these cells into luteal cells, while TIMP-2 in the thecal cells enhances proteolysis via

localization of pro-MMP-2 to the thecal cell surface which expresses MT-MMP-1.

It has been shown in rats that there is an increased in TIMP-1 mRNA expression

following hCG stimulation (Mann et al. 1993). Curry et al. (2001) localized TIMP-1,2









and 3 to the stroma and thecal layers of developing follicles in rats. They also showed

that at 12 hours after hCG administration to rats that Luteinizing granulosa cells

expressed and increase in TIMP-1 and 3 mRNA, while TIMP-2 mRNA levels remained

unchanged (Curry et al. 2001). In a study by Chaffin et al. (1999) they also showed an

increase in TIMP-1 and TIMP-2 by 12 hours in primate granulosa cells following

administration of hCG. Song et al. (1999) demonstrated that cultured equine stromal cells

can produce TIMP-1 and -2 in the conditioned medium. They also found that treatment

with phorbol ester increased TIMP-1 production by these stromal cells significantly

(Song et al. 1999).

In a study by Riley et al. (2001) it was shown that follicular fluid from various

sized equine follicles contained TIMP-1-4 yet their content remained unchanged with the

size of the follicle. They also immunolocalized TIMP-1-4 in the ovarian tissue and found

that all 4 TIMP's were present in the thecal and granulosa cell layers of the follicles and

that they were also associated with the ovarian stromal ECM (Riley et al. 2001).

It has been suggested that these 2 dynamic TIMP's are important in the process of

ovulation, both in their roles as inhibitors to MMP's and TIMP-2's role in the activation

of MMP-2. Yet TIMP-1 may play a role in the process of biochemical and

morphological differentiation of the granulosa and thecal cells into luteal cells.














CHAPTER 3
MMP-2, TIMP-1 AND STEROIDOGENEIS IN THE PREOVULATORY FOLLICLE

Introduction

The statement that "structure implies function" is a basic tenet of physiology

research. The equine ovary is "inside-out" compared with other mammals in that

ovulation is restricted to a band of germinal epithelium at the ventral pole of the ovary,

the ovulation fossa ( Ginther 1992). Due to this "inside-out" configuration and the

requirement for ovulation to occur at a particular location, we propose that the prolonged

duration of estrus reflects the considerable amount of tissue remodeling required for

ovulation. Ovulation in the equine ovary, involves a complex series of events which are

not yet well understood. Mammalian ovulation is a process triggered by the preovulatory

surge of LH, resulting in the release of a mature ovum from the preovulatory follicle

(Hagglund et al. 1999). This process requires extensive tissue remodeling of the ECM

and degradation of the connective tissue of the follicle wall. In mares the unusually

prolonged estrus is accompanied by prolonged LH elevation, which lasts 5-7 days. It is

our belief that the prolonged estrus of mares reflects continued development of the

preovulatory follicles to reach the fossa and for ovulation to occur.

LH stimulates MMP's such as collagenase, and gelatinase, which likely play an

important role in the overall tissue remodeling associated with ovulation (Smith et al.

1999). Simultaneous expression of the enzyme and the inhibitor is believed to be the

mechanism that allows focal control of tissue degradation (Nothnick et al. 1996). In rats

administration of human chorionic gonadotropin (hCG), an LH like compound,









increased ovarian gelatinase and collagenase expression as well as enzymatic activity.

Cooke et al (1999) showed that gonadotropin induction of folliculogenesis stimulated

metalloproteinase activity concomitant with follicle growth and ovulation. In another

study it was shown that follicular levels of MMP-2 increased between the preovulatory

LH surge and the time of ovulation in ewes (Russel et al. 1995). It has been shown in

many species that gonadotropin stimulation can increase MMP activity (Smith et al.

2001). LH-like compounds such as hCG are commonly used throughout the horse

industry to stimulate ovulation, yet very little is known about how the use of

gonadotropins affects follicular tissue remodeling to promote ovulation.

In primates studies suggest that P4 concentrations and P4 receptor expression

increase with an ovulatory stimulus of LH, supporting the idea of a role for P4 in

preovulatory events associated with ovulation (Chaffin et al 1999). It has also been

shown in primates that follicular steroid production, particularly P4, and MMP-2 and

TIMP-1 demonstrated a temporal relationship (Chaffin et al. 1999, 2000). In mares, large

luteal cells biochemicallyy and morphologically differentiated granulosa cells) within the

preovulatory follicle have been shown to secrete P4 during estrus, which indicates that

the P4 producing capacity (luteinization) begins prior to ovulation (Ginther 1992).

Follicular fluid P4 increases prior to ovulation in horses, suggesting that luteinization

precedes follicular rupture (Watson et al. 1999). Such follicular P4 could possibly play a

role in the tissue remodeling process in horses.

Current knowledge of other species establishes the model for a series of projects to

examine the process of tissue remodeling and steroidogenesis in the preovulatory follicle

of pony mares. Further examination of the relationship of the steroid hormones and









MMP-2 and TIMP-1, may help us understand how this dynamic process occurs. This

series of experiments will help bring to light the remodeling system and its relationship to

steroid production using the preovulatory follicle of the pony mare as an experimental

model. This series of experiments will also allow for the development of intrafollicular

(singular and multiple) sampling of follicular fluid for further studies and research uses.

It has been previously shown in pony mares that hCG administration increases

steroid production in vivo but the effects of hCG on the expression of the tissue

remodeling system specifically MMP-2 and TIMP-1 have not been demonstrated (Song

et al.1999). Based upon other species models there appear to be time dependent changes

in the remodeling dynamics of the preovulatory follicle and some studies suggest that

there may be a relationship between the remodeling system and steriodogenesis (Chaffin

et al. 1999, Gottesh et al. 2000, Smith et al. 2000). Furthermore there is evidence to

suggest that there may be concerted stimulatory effects of E2 or P4 on MMP-2 and

TIMP-1, depending on the species that was examined. There appear to be conflicting data

as to the overall relationship of steroids and tissue remodeling enzymes within the

preovulatory follicle, depending on the species examined.

The hypotheses to be tested in this series of experiments are as follows: 1).

Gonadotropins will stimulate intrafollicular MMP-2, TIMP-1 E2, and P4. 2). Tissue

remodeling enzymes change as the time of ovulation approaches and these changes can

be assessed by folliculocentesis. 3). There are significant time trends for these factors in

response to hCG administration over a 24 hour period and these time trends are similar.









Materials and Methods

General Procedures

Monitoring of the mares

All mares were maintained on pasture with ample water provided and

supplementation of hay when necessary. Daily monitoring of the mares involved

collection of jugular vein blood samples and a teaser stallion was used to detect signs of

estrus. Teasing responses were recorded to keep track of the onset and duration of estrus

for each individual mare. Utilizing rectal palpation and ultrasonography the number,

location and size of ovarian follicles were measured and tracked and recorded daily.

Blood processing

Blood samples were collected daily in 10ml heparinzed vacutainer tubes and

temporarily stored on ice. Upon arrival at the lab, all blood samples were centrifuged at

4oC. at 3000 rpm for 15 minutes. Plasma was decanted and stored in plastic vials labeled

with the date and mare number at -200C. until being assayed for E2 and P4 via

radioimmunoassay.

Follicular fluid processing

Follicular fluid samples were collected at the appropriate times according to

experimental protocols via transvaginal ultrasound guided needle aspiration. This was

accomplished with a forward facing transvaginal ultrasound probe fitted with a 16 Ga

needle guide. Mares were tranquilized with Xylazine at a dosage of (1.5mg/kg IV). Their

tails were wrapped and the genital area cleansed and scrubbed with betadine solution and

rinsed clean. The probe was inserted vaginally while the appropriate ovary was held

rectally to guide the needle puncture into the follicle of interest. Follicular fluid was

completely or partially sampled and evacuated with a large 35ml syringe or a 1 ml









syringe and placed into a 50ml or a 1.5ml labeled conical tube. The conical tube was

placed on ice for transport and stored at -800C. until further analysis for steroid hormones

E2, P4 via radioimmunoassay, and enzyme and inhibitor protein gels were run for MMP-2

and TIMP-1.

Hormone assays

Commercially available radioimmunoassay kits from Diagnostic Products

Corporation (DPC) were used to determine estrogen and progesterone concentrations in

the blood samples and follicular fluid. Progesterone concentrations were determined

using DPC Coat-A-Count kit. The sensitivity, inter-assay, and intra-assay coefficients of

variation were 0.03ng/ml, 3.2% and 2.2%. Estrogen (estradiol-17b) concentrations were

determined using DPC double antibody kit. The sensitivity, inter-assay and intra-assay

coefficients of variation were 2.2pg/ml, 5.7% and less than 5%.

Experiment 1: Effect of hCG on preovulatory steroids E2, P4 and Matrix
Metalloproteinase-2 and Tissue Inhibitor of Metalloproteinase-1 in the Preovulatory
Follicle of Cycling Pony Mares

Materials and Methods

This experiment utilized 10 intact cycling pony mares. Beginning in May, mares

were monitored daily and the luteal phase was shortened by administration of

prostagalindin-2a to achieve estrus synchronization. Evaluation of mares involved rectal

palpation and examination via ultrasonography daily. Each mare was evaluated for

follicle size, number of follicles on each ovary, and location of each follicle. Blood

samples were collected daily from the jugular vein at first detection of estrus for all

mares. Mares were randomly assigned to a control or treatment group at the first

detection of a 25mm follicle. Mares were examined daily to monitor the growth of the

dominant follicle until it achieved a diameter of 30mm. At first detection of a 30mm









follicle accompanied by estrus, mares were treated with either saline control vehicle

2.5mls, or 2500IU hCG via jugular injection. Twenty four hours after treatment the

dominant follicle was completely evacuated of follicular fluid using a transvaginal probe.

Follicular fluid was evaluated for steroids, via radioimmunoassay and MMP-2 and

TIMP-1 were determined by gelatin zymography and reverse zymography. Quantification

of MMP-2 and TIMP-1 was done with densitometric analysis by the Alpha Imaging 3.0

software system.

Statistical Analysis

Data from this experiment were analyzed using Statistical Analysis System (SAS)

by least squares analysis of variance (ANOVA) for the main effect of group (control vs.

hCG). Plasma and follicular fluid estrogen and progesterone concentrations, and

follicular fluid MMP-2 Adjusted densitometric values, and TIMP-1 adjusted

densitometric values were the dependent variables examined in this experiment.. Least

squares means were calculated for group and tested for significance.

Experiment 2: Time trends of follicular P4, MMP-2 and TIMP-1 in untreated mares by
folliculocentesis.

Methods and Materials

This experiment utilized 20 intact cycling mares. Beginning in May mares were

monitored daily and treated with prostaglandin-2a to achieve estrus synchronization, and

to lyse any corpus lutem present on the ovaries. Evaluation of mares involved rectal

palpation and examination via ultrasonography daily. Each mare was evaluated for

follicle size, number of follicles on each ovary and location of each follicle. Follicular

fluid (200-500d1) was collected via transvaginal ultrasound guided folliculocentesis

either one time as assigned or 4 consecutive samples over 4 days ( Group 4). Mares were









randomly assigned to one of 4 groups as follows: Group 1 folliculocentesis at first

detection of a 30 mm follicle; time 0); Group 2 folliculocentesis 48 h after first detection

of a 30 mm follicle); Group 3 folliculocentesis 72 h after first detection of a 30 mm

follicle); or Group 4 sequential serial aspirations of the same follicle over time

(aspirations at time 0, 24 48 and 72 h after first detection of a 30 mm follicle). Follicular

fluid was collected by folliculocentesis with a 25ga needle and a total volume of 200-

500.l of follicular fluid was collected for analysis in all groups. Follicular fluid was then

analyzed for intrafollicular P4, by radioimmunoassay. MMP-2 activity was determined by

gelatin zymography and densitometric analysis through Alpha imaging 3.0 software.

TIMP-1 activity was determined by reverse zymography and densitometric analysis

through Alpha imaging 3.0 software.

Statistical Analysis

Data from this experiment were analyzed using Statistical Analysis System (SAS)

by least squares analysis of variance (ANOVA). Due to the dissimilar numbers of

observations, data from Groups and 1, 2 & 3 were analyzed separately (analysis 1) from

data in Group 4 (analysis 2) and comparisons between the two methods (cross sectional

sampling, Groups 1, 2 &3 and serial sampling (Group 4) were made visually only.

Progesterone concentrations, MMP-2 adjusted densitometric values, and TIMP-1

adjusted densitometric values were the dependent variables examined in this experiment.

The main effect tested in analysis 1 was group (Group 1, 2, &3). On analysis 2 potential

differences among consecutive sampling (0, 24, 48 72 h) were tested as the main effect.

Least squares means were calculated for groups and tested for significance.









Experiment 3: The time dependent effect of hCG administration on steriodogenesis and
tissue remodeling in the preovulatory follicle of cycling pony mares

Methods and Materials

Mares were randomly assigned to a control or treatment group, at the first detection

of a 25 mm follicle within group assignments mares were further randomly assigned to a

specific time of follicular fluid collection, 4, 9, or 24 h respectively. Mares were

examined daily to track the growth of the dominant follicle until the enlargement to

30mm. At the first detection of a 30mm follicle that was accompanied by estrus, mares

were treated with either saline control vehicle 2.5mls, or 2500IU hCG via jugular

injection. Depending on random assignment of mares to group the dominant follicle for

both groups was aspirated at 4, 9, or 24 h following saline or hCG injection ( Grps 1, 3 &

5 control injection at 4, 9 or 24 h, Grps 2,4,& 6 hCG injection at 4, 9 or 24 h). The

dominant follicle was aspirated after treatment using a transvaginal probe for complete

evacuation of the dominant follicle. Follicular fluid was evaluated for steroids, via

radioimmunoassay and MMP-2 and TIMP-1 was determined by gelatin zymography and

reverse zymography. All blood samples were assayed for steroid hormones via RIA.

Statistical Analysis

Data from this experiment were analyzed using Statistical Analysis System (SAS)

by least squares analysis of variance (ANOVA).Experimental model was a 2*3 factorial

with main effects of treatment (control vs. hCG) and time (4, 9 or 24 h) and the

interaction of treatment by time. E2, P4 concentrations, MMP-2 and TIMP-1 adjusted

densitometric values were the dependent variables examined in this experiment.

Additionally regression analysis was used to examine the time trends with tests for









homogeneity of regression Least square means were calculated for group, time and the

group*time interactions.

Experiment 4: Tissue expression of MMP-2 and TIMP-1 mRNA in gonadotropin
stimulated ovarian tissue.

Methods and Materials

This experiment utilized 9 intact cycling horse mares. Beginning in May, mares

were monitored daily and estrus was synchronized with prostagalindin-F2a, regressing

any corpus lutem present on the ovaries. Evaluation of mares involved rectal palpation

and examination via ultrasonography. Each mare was evaluated for follicle size, number

of follicles on each ovary, location of each follicle. Mares were randomly assigned to a

control or treatment group at the first detection of a 25mm follicle. Mares were examined

daily to track the growth of the dominant follicle until a diameter of 30mm was

achieved. At the first detection of a 30mm follicle accompanied by estrus mares were

treated with either a saline control vehicle 2.5mls, or 2500IU hCG via jugular injection.

Mares were paired into control and treatment groups as follows; Group 1 n=2; control 24

h, Group 2 n=2; hCG 24 h, Group 3 n=2; control 9 h, Group 4 n=2; hCG 9 h. Mares were

then sacrificed at either 9 or 24 h after treatment. Ovaries were collected under sterile

conditions and tissues were dissected into the following types, follicular wall and thecal

tissues. All tissues were snap frozen on liquid nitrogen and stored at -800 C for RNA

extraction. RNA was then dot blotted at a concentration of 5 [.g per sample. RNA was

then probed with equine MMP-2 or TIMP-1 radiolabled with P32. Blots were then

exposed to X-Ray film to quantify mRNA levels. All blots were then normalized to the

18s RNA Subunit for the bovine, this was acceptable due to species homology.









Quantification of MMP-2 and TIMP-1 was done with densitometric analysis by the

Alpha Imaging 3.0 software system.

Statistical Analysis

Data from this experiment were analyzed using Statistical Analysis System (SAS)

by least squares analysis of variance (ANOVA). Experimental model was a 2*2 factorial

with main effects of treatment (control or hCG) and time (9 or 24 h) and the interaction

of treatment by time. MMP-2 Adjusted densitometric values and TIMP-1 adjusted

densitometric values were the dependent variables examined in this experiment. The

main effect tested was group. Least squares means were calculated for groups and tested

for significance.

Experiment 1: Results and Discussion

There was a significant group effect (p<0.0001 see appendix table F. 1) for the hCG

treatment group that was characterized by an increase in intrafollicular P4 concentrations

compared with control animals (Figure 3.1). There was no significant difference between

control and hCG treatment in peripheral P4 (see appendix table F.2), and E2 (see

appendix table F.3), or intrafollicular E2 (see appendix table F.4).

There was a significant effect of group on MMP-2 (p<0.001 see appendix table

F.5) characterized by higher MMP-2 activity following hCG compared with control

animals (Figure 3.2). There was also a significant effect of group on TIMP-1 (p<0.0001

see appendix table F.6) characterized by higher TIMP-1 activity in the follicular fluid

following hCG compared with control animals (Figure 3.3).








47







Mean Follicular P4 Concentrations


S control *p<0.0001
F hCG




















Figure 3.1: Mean follicular P4 concentrations by treatment Experiment 1


Figure 3.2: Mean follicular MMP-2 activity by treatment, Experiment 1


Mean Follicular MMP-2 Levels


*p<0.0001


control
- hCG
















Maei FdliacarTlVP-1 levIs


Figure 3.3: Mean TIMP-1 follicular activity by treatment, Experiment 1

These results suggest that there may be a relationship among intrafollicular P4,


MMP-2 and TIMP-1. There were no differences in peripheral steroid hormones or in


intrafollicular E2, in response to hCG (gonadotropin) administration which suggests that


whatever remodeling events within the preovulatory follicle that involve E2 and/or P4 are


ot reflected in the peripheral circulation. There was an increase in intrafollicular P4


concentrations, and an increase in active MMP-2 and TIMP-1 protein level twenty four


hours after hCG administration.


These data indicate that there is a marked response to gonadotropin administration


reflected by increased intrafollicular P4, MMP-2 and TIMP-1 in the preovulatory follicle


of the pony mare. Gonadotropin stimulation increased MMP-2 which in turn may


stimulate remodeling of the basement membrane and other unknown events which may


aid in follicular apposition to the germinal epithelium where ovulation can occur. In turn


TIMP-1 may act either as a control point for MMP-2 or as a stimulator of biochemical


and morphological changes of the thecal and granulosa cells. The latter could contribute


12
*y 10 ^ o
a,
U)

E Q8
0
06

> 4


QO -A---^









to an increase in P4 steroidogenesis. It has been demonstrated in other species that TIMP-

1 can lead to premature luteinization of the granulosa and thecal cells and differentiation

to large luteal cells leading to an increased production of P4 within the preovulatory

follicle (Smith et al.1999).

Experiment 2: Results and Discussion

There was no significant group effect in follicular P4 concentrations (p<0.13 see

appendix table F.7) in analysis one (Grp 1, 2, &3) over time figure 3.4. The 48 and 72 h

folliculocentesis showed an arithmetic decline in follicular P4 concentrations compared

with time 0, it approached significance by pdiff analysis. In the serially sampled animals

(Grp 4) there was a significant group effect (p<0.0001 see appendix table F.8) Figure 3.5

Pdiff analysis revealed that P4 concentrations decreased over time in the 24, 48 and 72 h

groups compared with time 0. Of interest, the serially aspirated groups showed the same

basic profile as the single aspiration group with respect to P4 concentrations by visual

inspection.

There was a significant group effect (Grps 1, 2 & 3) in follicular MMP-2 levels

(p<0.0001 see appendix table F.9) for analysis 1 over time (Figure 3.6) Group 3 showed a

significant increase in MMP-2 activity compared with both time 0 and 48 h by pdiff

analysis. In the serially sampled animals (Grp 4) there was a significant time effect

(p<0.0005 see appendix table F.10), (Figure 3.7) Pdiff analysis demonstrated that MMP-2

levels increased over time from 48 to 72 h. By visual inspection collecting follicular fluid

for analysis cross sectionally and serially appeared similar and suggests that serial

sampling may be a viable research technique.

















P4 Follicular Concentrations Single Aspirations
by Group


600-


400


200 -


Group=p<0.13


0 -------u-----~---------
____~__ 0 hours 24 hours 48 hours


72 hours


Individual group by time comparisons by pdiff analysis
Bars with differentsuperscriptletters were different p<0.06*




Figure 3.4: Mean follicular P4 concentrations for groups 1, 2, &3 (single aspiration

groups) Experiment 2


Individual group bytime comparisons by pdiff analysis
Bars with different superscript letters were different p<0.01*





Figure 3.5: Mean follicular P4 concentrations Group 4 (serial aspiration group)

Experiment 2


P4 Follicular Concentrations Multiple Aspirations
by Time


1200

Group=p<0.0001*
1000


800


600


400 r


200


Hours 24 hours 48 hours 72 hours














MMP-2 Follicular Levels Single Aspirate Group
by Time



2 5 Group=p<0.0001*


020




C a


E 5


0 hours 24 hours 48 hours 72 hours
Individual group by time comparisons bypdiff analysis
Bars with different superscript letters were different p<0.0001*


Figure 3.6: Mean follicular MMP-2 activity Groups 1,2 &3 (single aspiration groups)
Experiment 2


MMP-2 Follicular Levels Multiple Aspirates
over Time

25
:h Group=p<0.0005*

20

I |
E 15

S10


05


Hours 24 hours 48 hours 72 hours
Individual group bytime comparisons by pdiff analysis
Bars with different superscript letters were different p<0.0001*


Figure 3.7: Mean follicular MMP-2 activity in Group 2(serial aspiration group)
Experiment 2


There was a significant group effect (Grp 1, 2, & 3) in follicular TIMP-1 (p<0.0001


see appendix table F.11) (Figure 3.8). TIMP-1 levels increased from 0 to 48 and from 48







52



to 72 h by Pdiff analysis. In Grp 4 animals there was a significant time effect (p<0.0001


see appendix table F.12)(Figure 3.9), which demonstrated that TIMP-1 levels increased


over time from time 0 to 24, 48 and 72 h. Pdiff analysis also showed that there was also a


significant increase in TIMP-1 levels at all 3 times when compared with time 0. Group 4


showed the same basic profile for TIMP-1 levels as Grps 1, 2, & 3.


TIMP-1 Follicular Levels Single Aspirates
by Time


14
cn Group=p<0.0001*
C 12 c

.
S 10 b

E 08




04




00
0 hours 24 hours 48 hours 72 hours
Individual group bytime comparisons bypdiff analysis
Bars with different superscript letters were different p<0.0001*


Figure 3.8: Mean TIMP-1 activity in Groups 1, 2 &3 (single aspiration groups)
Experiment 2







53




TIMP-1 Follicular Levels Multiple Aspirates
by Time


1 4 Group=p<0.0001*

D 12

-* 10

0 08

06

>04

02

0 hours 24 hours 48 hours 72 hours
Individual group bytime comparisons bypdiff analysis
Bars with different superscript letters were different p<0.004*



Figure 3.9: Mean TIMP-1 activity in Group 4 (serial aspiration group) Experiment 2

Experiment 3: Results and Discussion

Data from experiment one data indicated that there was a relationship between

intrafollicular P4 concentrations, MMP-2 active form and TIMP-1 levels. All rose by 24

hours after hCG. In this experiment we examined the potential time trends of these three

factors within the preovulatory follicle of the cycling pony mare.

There was a significant effect of group characterized by increased P4, MMP-2 and

TIMP-1 compared with controls (p<0.005 see appendix table F. 13). There was a

significant group by time interaction characterized by sharply increased P4 in hCG treated

vs. control mares by 24 (p<0.0001* see appendix table F.13) (Figure 3.10). Regression

analysis indicated that the time trends for control and hCG treatment were not

homogeneous and the data were best represented by 2 separate curves. (p>0.001* see

appendix table F. 14).There were no significant time trends in P4 or MMP-2 in control











mares whereas TIMP-1 in control mares was significantly elevated by 24 hours


(p<0.0001* see appendix table F .15).





Mean Follicular P4 Concentrations

2500 4 hrs b
S9 hrs
I 24 hrs
Group=p<0.005*
2000 Group by Time interaction=p<0.001*


1500-


1000


500- a



control hCG
Individual group by time comparisons by pdiff analysis
Bars with different superscript letters were different p<0.001*



Figure 3.10: Mean follicular P4 concentrations by group and time Experiment 3

Statistical analysis indicated there was a significant effect of group, time and a


group*time interaction (p>0.0001* for group, and time g*t p<0.002* see appendix table


F. 16) for intrafollicular MMP-2 levels. hCG treated mares exhibited markedly elevated


MMP-2 activity compared with controls (Figure 3.11). Pdiff analysis exhibitied


significant differences at 24 hours, with MMP-2 levels increased compared to 4 or 9 h.


Regression analysis of the time trends indicated that data were best represented by 2


separate curves, with the controls showing no significant time trends and the hCG-treated


animals best represented by a second order curve with a sharp upswing from 9 to 24 h


(p>0.001* see appendix table F.17).




































Individual group bytime comparisons bypdiff analysis
Bars with different superscript letters were different p<0.003*




Figure 3.11: Mean follicular MMP-2 levels by group and time Experiment 3


Statistical analysis of follicular fluid TIMP-1 indicated significant group time and


group*time interactions (p>0.0001*: see appendix table F.18) (Figure 3.12). The control


group showed a significant increase in TIMP-1 levels from 9 to 24 h, while the treated


group showed a significant increase in TIMP-1 over all 3 time points. Regression analysis


of time trend data indicated homogeneity of regression which demonstrated that these


data were best represented by one single line which was an approximate average of the 2


groups over time (p>0.005* see appendix table F.19).


Mean Follicular MMP-2 Levels

4 -


Z I 24 hrs
3 Group=p<0.0001*
Group by Time interaction=p<0.002*
0
2

control hCG
>1



control hCG




































Individual group bytime comparisons bypdiff analysis
Bars with different superscript letters were different p<0.001*




Figure 3.12: Mean follicular TIMP-1 levels by group and time Experiment 3


Experiment 4: Results and Discussion


Data indicated that there was a significant effect of treatment on the levels of


MMP-2 expression within the ovarian tissue that was collected (p<0.05* see appendix


table F. 19). Data also indicate that there was a significant effect of time (p<0.05* see


appendix table F.20) (Figure 3.13).Results indicate that there was a significant group by


time interaction (p<0.01* see appendix table F.20) Results suggest that there were


different levels of MMP-2 mRNA expression in follicular wall and theca internal tissue in


response to hCG administration as compared with control tissue collected at the same


time point. There was a significant decrease in the amount of MMP-2 mRNA expression


in control animals in both tissue types at 24 h when compared to the 9 h controls


(p<0.05* see appendix table F.20) (Figure 3.13). Data also indicated that there was


significantly less MMP-2 mRNA expression in hCG treated mares at both time points


Mean Follicular TIM P-1 Levels

14- 4 4 hours Group=p<0.0001*
V 4 9 hours Group byTime interaction=p<0.0001*
12
U e
10 -
E b
o 08 d
F 06

> 04- A

02

00
Control hCG










compared to the 9 h control tissues (p<0.05* see appendix table F.20). There were also

significant differences in the MMP-2 mRNA expression at 24 h following hCG as

compared to 9 h after hCG administration (p<0.05* see appendix table F.20)(Figure

3.13). Yet interestingly there were no significant differences between the 24 h control

animals or the 24 hCG treated animal levels of MMP-2 mRNA expression.



MMP-2 mRNA Expression by Tissue and TX

10 -
Group p<0.05* Follicle Wall
Time p<0.05*
a Group by Time p<0.01* Theca
8


6 a
















(p<0.05 see appendix table F.21) (Figure3.14).
0)

Control 9 hrs Control 24 hrs hCG 9 hrs hCG 24 hrs

Individual group comparisons bypdiff analysis
Bars with different superscript letters are different p<0.05*


Figure 3.13: MMP-2 mRNA expression levels by tissue and treatment, Experiment 4

TIMP-1 mRNA levels overall were not significantly different between groups

(p
significant differences in time (p
significant group by time interactions (p<0.276 see appendix table F.21). Yet Pdiff

analysis indicated that there were significant differences among tissue types and by time

in response to treatment when compared with the control animal tissue expression at 9 h

(p<0.05* see appendix table F.21) (Figure3.14).







58




TIMP-1 mRNA Expression by Tissue and Tx

25 -
a Group p<0.117* m Follicle wall
a Time p<0.1159* Theca
3 20 Group by Time p<0.276*

0a
S15 -









Control 9 hrs Control 24 hrs hCG 9 hrs hCG 24 hrs
Individual group comparisons by pdiff analysis
Bars with different superscript letters are different p<0.05*


Figure 3.14: TIMP-1 mRNA expression levels by tissue and treatment, Experiment 4

Conclusions

Gonadotropin administration (hCG) stimulated follicular P4 concentrations, MMP-2

and TIMP-1 levels. There appears to be a relationship among these three factors.

Follicular E2, and both peripheral E2 and P4 showed no differences in response to

treatment, but P4 exhibited marked changes in the follicle, suggesting that P4 may be

involved in, or affected by the remodeling system at the local level. This contrasts to E2

which has been shown to be the main factor in other species such as cattle, and rodents

(Hagglund et al., 1999, Bakke et al., 2000). The temporal relationship among follicular

P4 concentrations, MMP-2 and TIMP-1 levels in response to treatment with hCG

suggests an interrelationship between intrafollicular P4 and the tissue remodeling system.

Visual inspection of results from two different sampling methods, serially and cross

sectionally suggests that there is no difference, and therefore may provide a more









efficient sampling tool via serial sampling. It appears that intrafollicular sampling of

small volumes is a viable means to examine small volumes of follicular fluid without

disrupting the follicular environment. The data obtained from this experiment obtained

with two different sampling methods may represent the microenvironment of the

preovulatory follicle of the mare with respect to P4 concentrations, MMP-2 and TIMP-1

levels. These techniques are viable tools for further research by utilizing a small 25 Ga

needle for follicular sampling or for microinjection. We have demonstrated that there is

no adverse effect to the follicle, any apparent tissue disruption or visible ultrasonic

changes.

Experiment three suggests that gonadotropin administration hastens ovulation by

stimulating tissue remodeling enzymes. The increase in P4 at 24 h suggests that this

increase is delayed compared with MMP-2 and TIMP-1 levels. MMP-2 and TIMP-1

levels showed an increase in treated animals compared to controls. MMP-2 levels

increased significantly from 9 to 24 h and TIMP-1 levels increased over all 3 time points

in response to treatment. The time trends in P4, MMP-2 and TIMP-1 suggest that MMP-2

and TIMP-1 rose steadily while P4 rose rapidly between 9 and 24 h. One interpretation of

this observation is that the increase in P4 in response to exogenous gonadotropin may lag

behind the changes in MMP-2 and TIMP-1. If that is true, it is interesting to speculate

whether MMP-2 and/or TIMP-1 are causally associated with the increase in P4.

Experiment 4 demonstrated that there are changes in the expression of both MMP-2

and TIMP-1 mRNA levels in both follicular wall and theca intema tissue. These changes

appear to occur most significantly by 9 h in both MMP-2 and TIMP-1 mRNA levels. Yet

interestingly there appears to be no significant differences in the MMP-2 mRNA






60


expression in control or treated animals at 24 h, but there were differences in MMP-2

mRNA expression levels in both tissue types and in response to hCG treatment at 9 h.

This suggests that changes in MMP-2 mRNA expression in response to gonadotropin

administration occur before 24 h. However TIMP-1 mRNA expression levels were

different by the tissue type in response to treatment and time. Overall these experiments

demonstrate that hCG stimulated the tissue remodeling system in the equine preovulatory

follicle, and that P4 is likely a key player in this 3 part regulatory relationship.














CHAPTER 4
INHIBITION OF THE TISSUE REMODELING SYSTEM AND STERIODOGENESIS
IN THE PREOVULATORY FOLLICLE OF CYCLING PONY MARES

Introduction

Based on results from experiments one and three, a relationship exists among

follicular P4 concentrations, MMP-2 and TIMP-1 levels within the preovulatory follicle.

In this series of experiments we examined the effects of inhibition of follicular P4

concentrations and MMP-2 activity. These experiments examine the remodeling system

(MMP-2 and TIMP-1) and steroidogenesis (P4) to better understand how these 3 factors

function together. We chose to examine this relationship through the use of mifepristone

(Ru486) and a cyclic inhibitor of MMP-2.

It has been shown in pregnant rats that administration of Ru486 at a concentration

of (2mg/kg) on day 12 of pregnancy induced preterm delivery and significantly reduced

the luteal 30-HSD enzyme activity by 72 hours after injection (Telleria et al. 1995). In

another study by Telleria et al. 1994, they examined the effects of both Ru486 and a

specific rat progesterone antibody. Their results suggested that both the antibody and

Ru486 act by decreasing 30-HSD enzyme activity. Furthermore Ru486 action was

exerted through an antiprogesterone action, that is Ru486 may have acted by directly

blocking the effects of P4. Results from a study examining Ru486 effects in ovulation in

the rat, after gonadotropin administration, suggested that ovarian 30-HSD enzyme

activity was inhibited in hCG stimulated animals when 20mg/kg Ru486 was administered

2 hours prior to hCG stimulation (Tanaka et al. 1993). The results from this study









indicated that ovarian 30-HSD activity depended on P4 concentrations and suggest that

there may be an autocrine regulation of P4 production during ovulation in stimulated

animals (Tanaka et al. 1993). Uilenbroek et al. 1992, examined the effects of Ru486 on

preovulatory follicles isolated from rats and cultured in the presence or absence of LH.

Their results suggested that in the absence of LH there were no significant differences in

the accumulation of E2, P4 and T in the medium. Yet in the presence of LH, E2, P4 and T

were significantly reduced in culture in the Ru486 treated animals, as monitored by the

inability to produce P4. Overall the data suggested that 30-HSD activity was reduced by

administration of RU486 in vivo (Uilenbroek et. al. 1992).

MMP-2/9 cyclic inhibitor III from calbiochem is a cyclic peptide that acts as strong

inhibitor of MMP-2 and 9. It s a heterocyclic inhibitor with the molecular formula

C52H71N13014S2, that has an inhibitory concentration of 50% (IC5o), specifically to MMP-

2 and MMP-9 in a concentration of 10IM/ml in culture. It has been shown to prevent the

activation of the enzyme through preventing cleavage of the pro-form to the active form

of the enzyme. It is supplied as a lyophilized solid that is water soluble, and

has a 2 month stability when reconstituted and frozen at -200C (Koivunen et al.

1999). We propose to test the dual hypotheses that: 1. blockade of intrafollicular P4

results in associated blockade of MMP-2 and TIMP-1, and 2. blockade of MMP-2 results

in an associated blockade of intrafollicular P4.

Methods and Materials

General Procedures

Monitoring of the mares

All mares were maintained on pasture with ample water provided and

supplementation of hay when necessary. Monitoring of the mares involved collection of a









jugular vein blood sample and a teaser stallion was used to detect signs of estrus daily.

Teasing responses were recorded to keep track of the onset and duration of estrus for

each individual mare. Utilizing rectal palpation and ultrasonography the number, location

and size of ovarian follicles were measured and tracked and recorded daily.

Blood processing

Blood samples were collected daily in 10ml heparinized vacutainer tubes and

temporarily stored on ice. Upon arrival at the lab, all blood samples were centrifuged at

4oC. at 3000 rpm for 15 minutes. Plasma was decanted and stored in plastic vials labeled

with the date and mare number at -200C. until being assayed for E2 and P4 via

radioimmunoassay.

Follicular fluid processing

Follicular fluid samples were collected at the appropriate times according to

experimental protocols via transvaginal ultrasound guided needle aspiration. This was

accomplished with a forward facing transvaginal probe fitted with a 16 Ga needle guide.

Mares were tranquilized with Xylazine (1.5mg/kg). Their tails were wrapped and the

genital area cleansed and scrubbed with betadine solution and rinsed clean. The probe

was inserted vaginally while the appropriate ovary was held rectally to guide the needle

puncture into the follicle of interest. Follicular fluid was collected and placed into a

conical tube. The conical tube was placed on ice for transport and stored at -80oC. until

further analysis for steroid hormones E2, P4 via radioimmunoassay, and enzyme and

inhibitor protein gels were run for MMP-2 and TIMP-1.

Hormone assays

Commercially available radioimmunoassay kits from Diagnostic Products

Corporation (DPC) were used to determine estrogen and progesterone concentrations in









the blood samples and follicular fluid. Progesterone concentrations were determined

using a DPC Coat-A-Count kit. The sensitivity, inter-assay, and intra-assay coefficients

of variation were 0.07ng/ml, 4.4% and 2.2%. Estrogen (estradiol-17b) concentrations

were determined using a DPC double antibody kit. The sensitivity, inter-assay and intra-

assay coefficients of variation were 3.6pg/ml, 5.5% and less than 5%.

Experiment 1: Inhibition of follicular P4 concentrations and its effects on MMP-2 and
TIMP-1

Methods and Materials

This experiment utilized seven cycling pony mares. Beginning in May mares were

monitored daily and treated with prostaglandin-2a to achieve estrus synchronization, and

to regress any corpus lutem present on the ovaries. Evaluation of mares involved rectal

palpation and examination via ultrasonography. Each mare was evaluated for follicle

size, number of follicles on each ovary and location of each follicle. Blood samples were

collected daily from the jugular vein at the first signs of estrus for all mares. Mares were

randomly assigned to control or treatment (Ru486) group at the first detection of a 25mm

follicle. Mares were examined daily to track the growth of the dominant follicle until the

enlargement to 30mm.

At the first detection of a 30mm follicle that was accompanied by estrus mares

were treated with either sesame oil control (10mls) IM or Ru486 (500mg) in 10 mls of

sesame oil IM, followed by a second treatment of the same volume and concentration 48

hours later. Mares were examined daily to track the growth of the dominant follicle.

Forty-eight hours after the second treatment, the dominant follicle was completely

aspirated by transvaginal ultrasound folliculocentesis. Blood samples were collected on

all mares daily, from the first day of treatment to five days post aspiration. Follicular









fluid was evaluated for steroids via RIA and MMP-2 and TIMP-1 levels were determined

by gelatin zymography and reverse zymography. All blood plasma samples were assayed

for steroid hormones via RIA.

Statistical Analysis

Data from this experiment were analyzed using Statistical Analysis System (SAS)

by least squares analysis of variance (ANOVA), with the main effect of group (control

vs. Ru486). Estrogen concentrations, progesterone concentrations, MMP-2 Adjusted

densitometric values, and TIMP-1 adjusted densitometric values were the dependent

variables examined in this experiment. Least squares means were calculated for groups

and tested for significance.

Experiment 2: The Effects of MMP-2/9 cyclic inhibitor III on Follicular Steriodogenesis
and Tissue Remodeling in the Preovulatory Follicle of Cycling Pony Mares.

Methods and Materials

This experiment utilized eight cycling pony mares. Beginning in May mares were

monitored daily and treated with prostaglandin-2a to achieve estrus synchronization, and

to lyse any corpus lutem present on the ovaries. Evaluation of mares involved rectal

palpation and examination via ultrasonography. Each mare was evaluated for follicle

size, number of follicles on each ovary and location of each follicle. Blood samples were

collected daily from the jugular vein at the first signs of estrus for all mares. Mares were

randomly assigned at the first detection of a 25mm follicle to receive control vehicle

(100[l1) or treatment (100[l inhibitor 100 M). Mares were examined daily to track the

growth of the dominant follicle until the follicle reached 30mm.

Mares were scanned daily by ultrasound to monitor diameter of the largest follicle,

and treatment was administered by folliculocentesis, (using a custom made 25 Ga.









injection needle to be placed within the 16Ga transvaginal aspiration needle) on the day

that the largest follicle achieves a diameter of 30mm. Forty-eight hours after

folliculocentesis, the dominant follicle was completely aspirated by transvaginal

ultrasound needle guided aspiration. Blood samples were collected daily from the day of

treatment to 5 days post aspiration. Follicular fluid was evaluated for steroids via RIA

and MMP-2 and TIMP-1 levels were determined by gelatin zymography and reverse

zymography. All serum samples were assayed for steroid hormones via RIA.

Statistical Analysis

Data from this experiment were analyzed using Statistical Analysis System (SAS)

by least squares analysis of variance (ANOVA) with main effect of group (control vs.

inhibitor). Estrogen concentrations, progesterone concentrations, MMP-2 Adjusted

densitometric values, and TIMP-1 adjusted densitometric values were the dependent

variables examined in this experiment. Least squares means were calculated for groups

and tested for significance.

Experiment: Results and Discussion

There was a significant effect of group (p>0.0007*: see appendix table F.22) that

was characterized by a decrease in intrafollicular P4 compared with controls (Figure 4.1).

There was no significant differences in peripheral E2 or P4 or intrafollicular E2 (see

appendix table F.23,F.24, F.25).








67





Mean Follicular P4 Ru486


1200 -
1 Control
1000 Ru486


800 -


600 -


400 -
p<0.0007*
200 -


0





Figure 4.1: Mean Follicular P4 concentrations by group Experiment 1


There was a significant group effect on follicular MMP-2 (p>0.0006*: see


appendix table F.26), that was characterized by a significant decrease in follicular MMP-


2 levels compared with controls (Figure 4.2).


Figure 4.2: Mean MMP-2 activity by group, Experiment 1


Mean MMP-2 Levels Ru486


5
S Control
S Ru486
D 4
.2

E 3
Sp p<0.0006*
U)
- 2




0)
0 I--------------------







68


There was also a significant group effect of follicular TIMP-1 (p<0.05*: see

appendix table F.27), that was decreased compared with controls (Figure 4.3). The

overall decrease in TIMP-1 was not as dramatic as the decrease in MMP-2. The overall

levels of TIMP-1 were significant yet they do not represent such a dramatic decrease as

seen in MMP-2 and P4.




Mean TIMP-1 Levels Ru486

07
Control
C 06 Ru486

0C 05- p<0.05*

E 04
0
03

02



00 -





Figure 4.3: Mean follicular TIMP-1 levels by group, Experiment 1

The large decrease in P4 was expected due to the actions of Ru486 through either a

mechanism of blocking the P4 receptor or more likely through it's actions on the 33HSD

enzyme. Based upon the results from this experiment it would explain the overall

decrease in intrafollicular P4 concentrations. The decrease in MMP-2 was an interesting

result from this experiment; and strongly suggests that MMP-2 may be functionally

associated with changes in P4.Yet interestingly the reduction in TIMP-1 in response to

Ru486 administration, was less dramatic. If MMP-2 and TIMP1 function on an eqimolar









association (Shapiro, et. al 1995), then the lesser reduction in TIMP-1 might reflect

alternative regulatory mechanisms. As these data only represent one point in time

reduction in P4 could affect MMP-2 and TIMP-1 with different timing. Overall these data

show that there is a close and interwoven relationship between these factors. This was

demonstrated by administering Ru486 and examining the overall decreases in these three

intrafollicular factors. This experiment also sheds light on the role of P4 in this system,

and that it may be a stimulating factor for MMP-2 activity, but may overall be enhanced

somehow through TIMP- 's actions. These 3 factors and their relationship within the

preovulatory follicle of the mare need to be clarified.

Experiment 2: Results and Discussion

There was a significant effect of group (p>0.006*: see appendix table F.28) that

was characterized by a decrease in intrafollicular P4 concentrations in treated mares

compared with controls (Figure 4.4). There was no significant difference in peripheral E2

or P4 or in intrafollicular E2 concentrations between groups (see appendix table F.29,

F.30, F.31).

There was a significant effect of group on MMP-2 follicular levels (p>0.0008*: see

appendix table F.33) compared with control animals characterized by a decrease in

intrafollicular MMP-2. The data suggests that inhibitor treatment blocked the activation

of MMP-2 resulting in an overall mean decrease in the activity levels compared with

controls (Figure 4.5). This was expected, due to the nature of the inhibitor used and its

capability to block the activation of the enzyme within the follicle.








70





Mean Follicular P4 MMP-Inhibitor


1400
Control
1200 m MMP-Inhibitor

1000 -

E 800 -


c 600 -
a-
400 p<0.006*


200 -

0







Figure 4.4: Mean follicular P4 concentrations by group, Experiment 2


Figure 4.5: Mean follicular MMP-2 levels by group, Experiment 2


Mean MMP-2 Levels for MMP-Inhibitor


6
4 Control
E I MMP-Inhibitor

. 4

E
0
3 p-<0.0008*

C,










There was also a significant effect of group on TIMP-1 follicular levels (p>0.02*:

see appendix table F.34) compared with controls (Figure 4.6) characterized by a decrease

in TIMP-1 in response to inhibitor administration.



Mean TIMP-1 Levels MMP-Inhibitor


(A Control
c MMP-Inhibitor
D 08-
os-
S06 p<0.02*

0 0-









Figure 4.6: Mean follicular TIMP-1 levels by group, Experiment 2

This experiment allowed us to examine the interrelationship of these three factors

in yet another way. By examining the effects of inhibiting the MMP-2 enzyme activation

we could see its effects on follicular P4 concentrations and TIMP-1. It also allowed us to

test our intrafollicular injection system to see if it was a viable tool for future research in

this area. Administration of the MMP-2/9 cyclic inhibitor III as expected decreased the

overall MMP-2 levels in the follicular fluid of the preovulatory follicle. In turn there was

also a significant decrease in follicular P4 concentrations this again suggests that there is

some relationship between MMP-2 and P4. Using this inhibitor the data indicated a

significant decrease in TIMP-1 levels. This is not surprising, as TIMP-1 functions in an

equimolar relationship to inhibit MMP-2 (Shapiro et al. 1995) and by using a cyclic









specific inhibitor to reduce MMP-2 we observed a similar decrease in TIMP-1.Yet it is

still perplexing as to the interrelationships of TIMP-1 and P4. It may be that due to the

lower levels it cannot promote P4 steroidogenesis beneath a certain threshold, which is

why we do not see the large increase in P4 concentrations.

Yet the data still point to the fact that these three factors are functionally related

and may act in concerted fashion to promote ovulation in the mare. The data overall

suggest a strong relationship among these factors. This experiment suggests that further

research in this area is necessary to examine these factors and their relationships to each

other.

Conclusions

These experiments examined inhibition of both follicular P4 and MMP-2. Blockade

of either P4 or MMP-2 resulted in reduction in MMP-2, suggesting a functional

relationship between P4 and MMP-2, within the preovulatory follicle. Experiment 1

emphasized the important relationship of follicular P4 concentrations and the tissue

remodeling system, through the use of Ru486. Data would suggest that RU486 exhibited

reduction in follicular P4 through possibly decreasing 30-HSD enzyme activity which

reduced P4 synthesis within the follicle. We did not do any binding studies to examine

whether or not Ru486 binds to the P4 receptor in the horse follicle. Yet interestingly there

was no effect of Ru486 on follicular E2, or peripheral E2 and P4. Data would suggest that

follicular P4 exhibits some effect on MMP-2 activity and that these factors may possible

regulate each other.

In experiment 2 we examined the opposite effect of inhibiting MMP-2 activity

through the use of a cyclic inhibitor. Data from this experiment again suggested that there

is an interrelationship between P4 and MMP-2 activity. When the enzyme was inhibited






73


there was a significant decrease in P4 concentrations within the follicle. Overall takes

together these studies suggest that P4 and MMP-2 are intimately involved likely through a

positive feedback relationship leading to tissue remodeling in the preovulatory follicle

which is necessary for ovulation.














CHAPTER 5
INTRAFOLLICULAR ADMINISTRATION OF GNRH, P4 AND MELATONIN AND
THEIR EFFECTS ON TISSUE REMODELING AND STEROIDOGENESIS IN THE
PREOVULATORY FOLLICLE.

Introduction

Many studied have shown that gonadotropins play a major role in the remodeling

process (Hagglund et al. 1999; Smith. et al. 2002). Data from previous studies have

shown that MMP-2 and TIMP-1 are present in the follicular fluid of pony mares

(Desvousges et al. 2002, 2001; Popoli et al. 2000, 1999). Furthermore hCG increased

MMP-2, TIMP-1 and follicular P4 24 hours after administration to mares during the

breeding season.

Recent studies in human granulosa cell cultures suggest that melatonin plays a role

in ovarian steroidogenesis (Woo et al. 2001). Melatonin has also been shown to up

regulate the LH-receptor (LH-RC) and down regulate the GnRH receptor mRNA in the

human ovary (Woo et al. 2001). Addition of GnRH to human stromal cells resulted in a

decrease in TIMP-1 in the medium (Chou et al. 2003). Addition of progesterone to

cultured human endometrial cells resulted in a significant reduction of the active form of

MMP-2 (Zhang et al. 2000). In an experiment by Nothnick et al. (2003) utilizing TIMP-1

null mice, data from this study suggested that there was a significant reduction in the

circulating levels of progesterone, following administration of hCG.

Based upon our results that intrafollicular injection does not seriously disrupt the

microenvironment of the dominant follicle and our ability to utilize this methodology to

sample small volumes of follicular fluid from the same follicle, we feel that this









technique is a viable research tool for this experiment. Our objective with experiment

seven was to examine the effects of intrafollicular administration of GnRH, P4 or

Melatonin on MMP-2, TIMP-1 and steroidogenesis within the preovulatory follicle of

cycling pony mares during the breeding season. The hypothesis to be tested in this

experiment is that GnRH will stimulate MMP-2 and TIMP-1, and P4; P4 Will not stimulate

MMP-2, TIMP-1 and P4 significantly above control levels, and Melatonin will stimulate

MMP-2, TIMP-1 and P4 within the preovulatory follicle.

Methods and Materials

General Procedures

Monitoring of the mares

All mares were maintained on pasture with ample water provided and

supplementation of hay when necessary. Daily monitoring of the mares involved

collection of a jugular vein blood sample and a teaser stallion was used to detect signs of

estrus. Teasing responses were recorded to keep track of the onset and duration of estrus

for each individual mare. Utilizing rectal palpation and ultrasonography the number,

location and size of ovarian follicles were measured and tracked and recorded daily.

Blood processing

Blood samples were collected daily in 10ml heparinzed vacutainer tubes and

temporarily stored on ice. Upon arrival at the lab, all blood samples were centrifuged at

4oC. at 3000 rpm for 15 minutes. Plasma was decanted and stored in plastic vials labeled

with the date and mare number at -200C. until being assayed for E2 and P4 via

radioimmunoassay.









Follicular fluid processing

Follicular fluid samples were collected at the appropriate times according to

experimental protocols via transvaginal ultrasound guided needle aspiration. This was

accomplished with a special forward facing transvaginal probe fitted with a 19 Ga needle

guide. Mares were tranquilized with Xylazine (1.5mg/kg IV). Mares were administered

the tranquilizer via jugular vein injection. Their tails were wrapped and the genital area

cleansed and scrubbed with betadine solution and rinsed clean. The probe was inserted

vaginally while the appropriate ovary was held rectally to guide the needle puncture into

the follicle of interest. Follicular fluid, was, (total volume of 200- 500.l) was collected

and placed on ice for transport and stored at -800C. until further analysis for steroid

hormones E2, P4 via radioimmunoassay, and enzyme and inhibitor protein gels were run

for MMP-2 and TIMP-1.

Hormone assays

Commercially available radioimmunoassay kits from Diagnostic Products

Corporation (DPC) were used to determine estrogen and progesterone concentrations in

the blood samples and follicular fluid. Progesterone concentrations were determined

using a DPC Coat-A-Count kit. The sensitivity, inter-assay, and intra-assay coefficients

of variation were 0.06ng/ml, 3.0% and 1.8%. Estrogen (estradiol-17b) concentrations

were determined using a DPC double antibody kit. The sensitivity, inter-assay and intra-

assay coefficients of variation were 1.2pg/ml, 2.7% and less than 5%.









Experiment 1: Intrafollicular Administration of GnRH, P4 or Melatonin and Their
Effects on MMP-2, TIMP-1, and Steroidogenesis in the Preovulatory Follicle of Cycling
Pony Mares.

Materials and Methods

This experiment utilized 30 intact pony mares. Beginning in May mares were

monitored daily and estrous synchronized by administration of prostaglandin-2a.

Evaluation of mares involved rectal palpation and examination via ultrasound daily. Each

mare was evaluated for follicle size, number of follicles on each ovary, and location of

each follicle. Blood samples were collected daily from the jugular vein at the first

detection of estrus for all mares. Mares were randomly assigned to a control or treatment

group at first detection of a 25 mm follicle. Mares were examined daily to monitor the

growth of the dominant follicle until it achieved a diameter of 30 mm. At the first

detection of a 30mm follicle accompanied by estrus, mares were treated with one of 4

treatments as follows: Group 1 saline control vehicle (100.l); Group 2 GnRH

(10pg/l00pl); Group 3 P4 (1Ig/l00pl); Group 4 Melatonin (10lg/100pl), all were

physiologically relevant doses established by our lab in previous work beyond the scope

of this thesis. Treatment was given intrafollicularly with a 25 GA needle via transvaginal

follicular injection into the dominant follicle. At 24 and 48 hours after injection the

dominant follicle was sampled with the same 25 Ga and a small volume 200-5001p of

follicular fluid was collected for further analysis. Follicular fluid was evaluated for

steroids, via RIA, and MMP-2 and TIMP-1 were determined by gelatin zymography and

reverse zymography. Quantification of MMP-2 and TIMP-1 was done with densitometric

analysis by the Alpha Imaging 3.0 software system.











Statistical Analysis

Data from this experiment were analyzed using Statistical Analysis System (SAS)


by least squares analysis of Variance (ANOVA). Experimental model was a 2*4 factorial


with main effects of treatment (control, GnRH, P4, or Melatonin) and time (24 or 48 h)


and the interaction of treatment by time. E2, P4 concentrations, MMP-2 and TIMP-1


adjusted densitometric units were the dependent variables examined in this experiment.


Orthogonal contrasts were used to examine the differences between groups.


Experiment 1: Results and Discussion

There was a significant effect of group (control; GnRH; P4; or Melatonin) (p<0.01*


see appendix table F.35) for follicular E2 concentrations that was characterized by an


increase in follicular E2 in the GnRH and P4 groups compared with control animals


(Figure 5.1).


Figure 5.1: Mean follicular E2 concentrations by group, Experiment 1


E2 Follicular Concentrations by Group and Time

400000
Group po.05-
4 Group by time interaction p<0.05-
300000
E b

C 200000 -

100000 -


Individual group comparisons bypdiff analysis
Bars with different superscript letters are different p<0.05*









There was also a significant effect of time in all groups except the melatonin group

(p<0.05* see appendix table F.35) that was characterized by an increase over time from

24 to 48 h (Figure 5.1). There was no significant group by time interaction for follicular

E2 concentrations (p<0.53* see appendix table F.35). The increase in E2 concentrations

in the control group over time was expected, yet interestingly E2 was elevated compared

with controls overall in response to follicular GnRH, and P4 administration. Further

studies are indicated to examine this response in increased E2 yet; at this point

interpretation of this data becomes difficult.

There was a significant effect of group (control; GnRH; P4; Melatonin) (p<0.01*

see appendix table F.36) for follicular P4 concentrations that was characterized by a

significant decrease in the P4 group as compared to controls. Overall group means were

significantly reduced in the GnRH and P4 treatment groups compared with controls

(p<0.01* see appendix table F.36) (Figure 5.2). There was also a significant effect of time

in all groups with varied responses to treatments (p<0.0001* see appendix table F.36),

that was characterized by an increase in all groups except P4 treatment, (Figure 5.2).

There was also a significant group by time interaction (p<0.01* see appendix table F.36)

Results suggest that the follicular injection of P4 did not stimulate any further production

of P4, but may have acted in a negative feedback manner and blocked any increase above

the 24 h control values. Results also suggest that GnRH somehow reduced the amount of

follicular P4 overall compared with controls over time, while melatonin showed a marked

increase in P4 concentrations overall and especially by 48 h after injection.










































Figure 5.2: Mean follicular P4 concentrations by treatment and time, Experiment 1


There was no significant effect of group (control; GnRH; P4; or Melatonin) on


peripheral E2 or P4 concentrations (see appendix table F.37, F.38).


There was a significant effect of group (control; GnRH; P4; Melatonin) on MMP-2


activity within the follicular fluid (p<0.001* see appendix table F.39) that was


characterized by an increase in MMP-2 in the P4 treatment group, while there was a


significant decline in the MMP-2 activity in the GnRH and Melatonin treated animals as


compared with controls (Figure 5.3). There was no significant effect of time (p<0.3 1* see


appendix table F.39). There was an overall group by time interaction (p<0.0002* see


appendix table F.39). This was most dramatically characterized by the decrease in the


GnRH and Melatonin treatment groups compared with controls, (Figure 5.3). Results also


suggest that there was an inversion in the response over time from the control animals to


the P4 treated animals, showing and increase in MMP-2 from 24-48 h in the control group


P4 Follicular Concentrations Group by Time

5000
500 Group p<0.01 signified by
24 hours Group by time interaction p<0.01*
m 48 hours
4000
b

E 3000

2000

000 1


Control GnRH P4 Melatonin

Overall group means were significantly decreased p<0.05* in GnRH and P4 treatment group
Individual group comparisons bypdiff analysis
Bars with different superscript letters are different p<0.01*












while there was a decrease in the P4 group from 24-48 h. The interpretation of this data is


interesting yet puzzling and further work needs to be done in this area to fully understand


the treatment responses.


MMP-2 Follicular Levels by Group and Time

1 6 -
1o 6Group p<0.001*
Group by time interaction p0.01 24 hours
1E 4 b 48 Hours
D b
S 1 2


08 a



> 02
00
Control GnRH P4 Melatonin

Overall group means were decreased in GnRH and Melatonin p>0.03*


Individual group comparisons bypdiff analysis
Bars with different superscript letters are different p<0.001*





Figure 5.3: Mean MMP-2 activity by group and time, Experiment 1


There was a significant effect of group for TIMP-1 activity (p<0.0001* see


appendix table F.40). There was a significant effect of time in (p<0.0001* see appendix


table F.40). There was also a significant group by time interaction (p<0.0001* see


appendix table F.40) (Figure5.4). Indicating that TIMP-1 activity responded very


differently to treatments. These data are complex and further work needs to be done


elucidate the intricacies of the responses of follicular E2, P4, MMP-2 and TIMP-1


responses to follicular treatment.









































Figure 5.4: Mean TIMP-1 activity by group and time, Experiment 1

Conclusions

MMP-2, TIMP-1 and P4 all increase from 24 to 48 h after first detection of a 30mm


follicle and intrafollicular saline administration. Interestingly intrafollicular P4


concentrations in experiment 1 disagree with our previous data from experiment 3


chapter one. This may be due to different experimental design and the use of different


animals and animal numbers for each experiment. Intrafollicular administration of GnRH


appeared to block the increase in MMP-2 activity, and markedly increased TIMP-1


activity at 24 h compared with controls. Despite the increase in TIMP-1 at 24 h, it


declined below control levels by 48 h. GnRH also decreased P4 concentrations overall


compared with controls.


Follicular administration of P4 did not alter overall mean P4 MMP-2 activity,


however it was increased by 24 compared with controls, and was reduced by 48 h after


TIMP-1 Follicular Levels by Group and Time


S14 4 hours Group p 4 hours Group by Time interaction p<0.0001*
12
O e e
10

06
E d0



S02


00
Control GnRH P4 Melatonin
Overall group means were ranked by treatment effect as follows P4>Mel>GnRH>Control
Individual group comparisons bypdiff analysis
Bars with different superscript letters are different p<0.0001*









treatment. TIMP-1 levels were significantly higher when compared with controls by 24 h,

and continued to rise above controls by 48 h. There were no significant differences in

follicular P4 concentrations at either time point. Follicular Melatonin administration

reduced MMP-2 activity at 24 and 48 h when compared with controls. TIMP-1 levels

were increased compared with controls at 24 h, but declined below controls by 48 h after

treatment. P4 follicular concentrations were below controls at 24 hours yet rose sharply

by 48 h after treatment.

Intrafollicular administration does not appear to adversely affect follicular

development or biochemical function, indicating it's utility again as a study model.

Furthermore, intrafollicular administration of hormones affects both tissue remodeling

and steroidogenesis within the preovulatory follicle. Control animals followed similar

trends from previous experiments with increasing MMP-2, and TIMP-1 over time.

Overall these data suggest that there is again an intimate relationship of MMP-2, TIMP-1

and P4. Hormone administration affects both the balance in the remodeling system and

steroidogenesis. However more work needs to be done in this area to elucidate the

relationship between these three factors.














CHAPTER 6
BLOCKADE OF LH AND/OR FSH AND THE EFFECTS ON TISSUE REMODELING
AND STEROIDOGENESIS IN THE PREOVULATORY FOLLICLE OF CYCLING
PONY MARES

Introduction

Though the structure and secretary patterns of LH secretion are similar in many

species, there are some animals that do have unique secretary patterns. In some species

such as cows, sheep and humans, LH secretion is low in amplitude during estrus and is

secreted infrequently in high amplitude during diestrus (Hauger et al. 1977, Barid 1978,

Ellinwood and Norman 1983). These differing patterns have brought about the idea that

P4 suppresses LH pulse frequency, while E2 inhibits pulse amplitude; this was best shown

by the work of Goodman and Karsch in sheep in 1980.

In contrast to sheep the secretion of equine LH has been shown to be episodic in

nature (Evans et al. 1979, Porter et al. 2001). The episodic release of LH and FSH is

induced by the secretion of GnRH from the hypothalamus (Ginther 1992). Endocrine

feedback and the overall interactions between levels of the hypothalamus and pituitary

are important when studying the estrous cycle of the mare. Data suggest that in many

species that the follicle secretes E2 due to FSH secretion from the pituitary gland (Knobil

1980). The E2 then feeds back to the hypothalamus and pituitary gland to increase LH

and GnRH (Tonnetta and diZerga 1989, Ginther 1992). The LH in turn causes ovulation

of the dominant follicle. Once ovulation has occurred and the large luteal cells of the

follicle begin to produce P4. The P4 levels increase to averages greater than Ing/ml at









about the time of the decline in LH (Plotka et al. 1975, Ginther 1992) data suggest that

there is a negative feedback of ovarian P4.

E2 has also been shown to have a negative feedback on the LH secretion which

causes an initial decline prior to the onset of the LH surge (Yamaji et al. 1972). In rats it

has been shown that LH can be suppressed by either E2 or P4 (Goodman and Daniel

1985). In sheep data indicate that there is a synergy between E2 and P4 when they are

administered to sheep at low doses and it causes a greater inhibition of LH (Goodman et

al. 1981). E2 can have both positive and negative feedback depending on the species

examined. In primates (Knobil 1974) it has been shown to have a negative feedback on

gonadotropins, and also in sheep (Coppings and Malven 1976).

In the mare positive feedback effects of E2 were originally suggested to be involved

in the ovulatory surge of LH due to the timing of the decline in E2 and LH simultaneously

and the peak in E2 which precedes the maximum LH concentrations (Pattison et al. 1972,

1974). During vernal transition in the mare data indicate that E2 levels increase prior to

the LH serum increase (Davies et al. 1987). Administration of E2 to ovariectomized mares

during vernal transition increased LH secretion and pituitary content (Sharp et al. 1991).

In another study by Garza et al. 1986b, data suggested that injections of E2 benzoate to

ovariectomized mares during the breeding season resulted in an increase in LH secretion

over the course of treatment.

P4 has also been shown to have both stimulatory and inhibitory effects on

gonadotropin secretion depending on the time of administration in relation to the estrous

cycle. In E2 treated menopausal women progesterone inhibited the effects of the E2

treatment (Nillius and Wilde 1971, Wise et al. 1973). In another study by Leyendecker et









al. 1972, it was shown that administration of large doses of P4 reduced the length of the

LH surge in humans. Yet there is much information in the literature that suggest that E2

priming is required in ovariectomized animals for P4to exert it's effects (Mcpherson et al.

1975).

In mares, LH remains elevated for 2-3 days post ovulation before declining over

the next few days to diestrus levels (Evans and Irvine 1975, Ginther 1992). This delay in

the decline of LH may be due to the lack of P4 feedback following ovulation. This

information sets the backdrop to use P4 and E2+P4 in combination to examine the effects

of the negative feedback on LH and /or FSH and the effects upon the tissue remodeling

system and steroidogenesis within the preovulatory follicle of the cycling pony mare.

Materials and Methods

General Procedures

Monitoring of the mares

All mares were maintained on pasture with ample water provided and

supplementation of hay when necessary. Daily monitoring of the mares involved estrus

detection with a teaser stallion. Teasing responses were recorded to keep track of the

onset and duration of estrus for each individual mare. Utilizing rectal palpation and

ultrasonography the number, location and size of ovarian follicles were measured and

tracked and recorded daily.

Follicular fluid processing

Follicular fluid samples were collected at the appropriate times according to

experimental protocols. Follicular fluid was completely evacuated with a large 35ml

syringe and placed into a 50ml conical tube. The conical tube was placed on ice for

transport and stored at -800C. until further analysis for steroid hormones E2, P4 via









radioimmunoassay, and enzyme and inhibitor protein gels were run for MMP-2 and

TIMP-1.

Hormone assays

Commercially available radioimmunoassay kits from Diagnostic Products

Corporation (DPC) were used to determine estrogen and progesterone concentrations

follicular fluid. Progesterone concentrations were determined using DPC Coat-A-Count

kit. The sensitivity, inter-assay, and intra-assay coefficients of variation were 0.04ng/ml,

3.0% and 2.1%. Estrogen (estradiol-17b) concentrations were determined using DPC

double antibody kit. The sensitivity, inter-assay and intra-assay coefficients of variation

were 2.0pg/ml, 5.3% and less than 5%.

Experiment 1: The effect of Exogenous P4 and E2+P4 Administration on Follicular P4
Concentrations, and Matrix Metalloproteinase-2 and Tissue Inhibitor of
Metalloproteinase-1 Activity within the Preovulatory Follicle of the Cycling Pony Mare.

Methods and Materials

This experiment utilized 8 intact cycling pony mares. Beginning in May, mares

were monitored daily and the luteal phase was shortened by administration of

prostagalindin-F2a to achieve estrus synchronization. Evaluation of mares involved rectal

palpation and examination via ultrasonography daily. Each mare was evaluated for

follicle size, number of follicles on each ovary, and location of each follicle. Mares were

randomly assigned to group and treated from the day of prostaglandin injection. Mares

were administered control vehicle, Group 1 (sesame oil; n=3), Group 2 P4 (150mg/day;

n=3), and Group 3 E2+P4 (10mg+150mg/day; n=2) respectively until detection of a

30mm follicle. Mares were examined daily to monitor the growth of the dominant follicle

until it achieved a diameter of 30mm. Mares were then taken to surgery the day of

presentation of a 30mm follicle and via a lateral flank incision the dominant ovary was