FATE, TRANSPORT, AND TOXICITY OF PERFLUOROCHEMICALS IN WASTEWATER DOMINANT SYSTEMS By IGNACIO ALEJANDRO RODRIGUEZ JORQUERA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMEN T OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2014
2014 Ignacio Alejandro Rodr guez Jorquera
To my parents my wife and my lovely daughter
4 ACKNOWLEDGMENTS My first acknowledgment needs to be expressed to my two great advisors: Dr Nancy Denslow and Dr Gurpal Toor. I thank UF students and Post Doctoral Associates: Alvina Mehinto, Daniel Spade, Claudio Verdugo, Cristina Colli Dula, Viet Dang; Kevin Kroll, Erika Brockme ier, Candice Lavelle, Roxanne Werner, Lauren, Felipe Hernandez, Marianne Kozuch Todd Osborne. I thank my committee members Dr Peter Frederick and Dr Christopher Wilson. I thank Dr Mike Avery and Kandy Keacher (USDA National rida Field Station in Gainesville), The Horse Unit (IFAS), Chris Bird and Robin Hallbourg (Alachua County Environmental Protection Lyn Branch (WEC), Christine Housel (Florid a Fish and Wildlife Conservation Commission ). I thank the College of veterinary medicine for an intramural seed grant and the Chilean government scholarships Becas Chile CONICYT.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................... 12 CHA PTER 1 LITERATURE REVIEW ................................ ................................ .......................... 14 Background ................................ ................................ ................................ ............. 14 Genomics Tools to Assess Water Pollution ................................ ...................... 17 The Relevance of Assessing Pollution Effects on Wildlife ................................ 19 Impact of Endocrine Disrupting Chemicals on the Environment ....................... 20 Micropollutant Mixtures in Aquatic Systems ................................ ..................... 21 Fate and Transport of PFCs in the Aquatic Environment ................................ 21 Degradation of PFCs in the Aquatic Environment ................................ ............ 26 Effects of PFCs in Animals ................................ ................................ ............... 27 Overall Hypothesis ................................ ................................ ................................ .. 29 Significance of this Dissertation Research ................................ .............................. 30 2 U SING TRANSCRIPTIONAL RESPONSE OF FATHEAD MINNOW S ( Pimephales promelas ) TO EVALUATE T HE IMPACT OF WASTEWATERS IN WILDLIFE PRESERVATION AREAS ................................ ................................ ..... 35 Introduction ................................ ................................ ................................ ............. 35 Materials and Methods ................................ ................................ ............................ 37 Study Site and Water Collection ................................ ................................ ....... 37 Runoff Wastewater (RW) ................................ ................................ ........... 37 Treated City Wastewater (TCW) ................................ ................................ 38 On Campus Treated Wastewater (OTW) ................................ ................... 38 Water Analyses ................................ ................................ ................................ 39 Fish Exposure and Tissue Collection ................................ ............................... 40 Microarray Analysis ................................ ................................ .......................... 40 Bioinformatics ................................ ................................ ................................ ... 41 T otal Cholesterol Determination ................................ ................................ ....... 42 Results and Discussion ................................ ................................ ........................... 42 Water Chemistry ................................ ................................ ............................... 42 General Biological Response ................................ ................................ ........... 43 Cluster Analysis ................................ ................................ ................................ 44
6 Enrichment Analysis ................................ ................................ ......................... 45 System Biology Analysis ................................ ................................ .................. 46 Cholesterol Related Genes ................................ ................................ .............. 47 Generating hypotheses ................................ ................................ .................... 47 Cholesterol Levels in Plasma ................................ ................................ ........... 5 0 Overall implications ................................ ................................ .......................... 51 Summary of Findings ................................ ................................ .............................. 52 3 PERFLUOROCHEMICALS IN AN URBAN STREAM: THE OCURRENCE OF PFCs IN TREATED WASTEWATERS INSIDE A WILDLIFE PRESERVE ............. 63 Introduction ................................ ................................ ................................ ............. 63 Materials and Methods ................................ ................................ ............................ 65 Sample Collection and Storage ................................ ................................ ........ 65 Solid Phase Extraction ................................ ................................ ..................... 66 HPLC and Mass Spectrometry Analysis ................................ ........................... 67 Results ................................ ................................ ................................ .................... 68 Discussion ................................ ................................ ................................ .............. 69 PFCs Concentration from Source to Sink ................................ ......................... 70 Seasonal Variability in PFCs Concentrations ................................ ................... 71 Restoration Project ................................ ................................ ........................... 72 Final Considerations ................................ ................................ ......................... 73 Summary of Findings ................................ ................................ .............................. 75 4 COMPARATIVE TRANSCRIPTOMIC RESPONSE OF FATHEAD MINNOW LIVER AND BLOOD AFTER 48 HOURS EXPOSURE TO ENVIRONMENTALLY RELEVANT PERFLUORCHEMICALS CONCENTRATIONS ................................ ................................ .............................. 82 Introduction ................................ ................................ ................................ ............. 82 Materials and Methods ................................ ................................ ............................ 85 Exposure Water Preparation ................................ ................................ ............ 85 Water Anal yses ................................ ................................ ................................ 86 Fish Exposure and Tissue Collection ................................ ............................... 87 Microarray Analysis ................................ ................................ .......................... 88 Bioinformatics ................................ ................................ ................................ ... 89 Results ................................ ................................ ................................ .................... 90 Water Chemistry ................................ ................................ ............................... 90 General Biologic al Responses ................................ ................................ ......... 91 Cluster Analysis ................................ ................................ ................................ 92 Both tissues ................................ ................................ ............................... 92 Liver ................................ ................................ ................................ ........... 92 Blood ................................ ................................ ................................ .......... 92 Enrichment Analysis ................................ ................................ ......................... 93 System Biology Analysis ................................ ................................ .................. 93 Discussion ................................ ................................ ................................ .............. 96 Levels of Exposure and Low Dose Effects ................................ ....................... 97
7 Similarities Between L iver and Blood ................................ ............................. 100 Carbohydrates and Lipid Metabolism ................................ ............................. 100 Immune System ................................ ................................ ............................. 101 Cell Membrane Effects and Blood Cell Sensitivity ................................ .......... 101 The role of PPAR and ER ................................ ................................ ............... 103 Non Carcinogenesis Estrogen Like Effects ................................ .................... 104 Thyroid Hormone ................................ ................................ ............................ 104 Final Considerations ................................ ................................ ............................. 105 Summary of Findin gs ................................ ................................ ............................ 106 5 CONCLUSIONS ................................ ................................ ................................ ... 136 Assessing Urban Water Pollution Impacts in Protected Areas ....................... 139 Hypercholesterolemia in fish exposed to urban waters ................................ .. 139 Perfluorochemicals Occurrence in an Urban Stream with Wastewater Influence ................................ ................................ ................................ ...... 141 The Effects of Environmentally Relevant Concentrations of PFCs ................. 142 The Blood as Non invasive Sampling Tissue ................................ ................. 142 Final Considerations ................................ ................................ ............................. 143 APPENDIX : SUPPORTING INFORMATION ................................ ............................. 145 LIST OF REFERENCES ................................ ................................ ............................. 172 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 191
8 LIST OF TABLES Table page 2 1 Perflurochemicals detected in urban waters in Gainesville area, Florida 27 and current study. ................................ ................................ ................................ ...... 53 2 2 Selected GO biological processes altered in fish liver from each site after 48 hours exposure (Fisher exact test) (p<0.05). ................................ ...................... 54 3 1 Concentrations (ng/L) of organic compounds in historical samples (ACEPD, 2010) ................................ ................................ ................................ .................. 76 3 2 Transitions numbers, limits of determination (LOD) and limit of quantification (LOQ) used for PFCs determination ................................ ................................ .. 77 3 3 Average PFCs concentrations (ng/L) determine in Sweet water branch downstream of a WWTP during the wet season, 2012. ................................ ...... 78 3 4 Average PFCs concentrations (ng/L) determine in Sweetwater branch downstream of a WWTP during the dry season, 2013. ................................ ...... 78 4 1 Eff ects of PFCs in Fish species. General, liver or blood related effects are reviewed. ................................ ................................ ................................ .......... 107 4 2 Actual concentrations of PFCs (ug/L) in exposure waters used in this study. Concentrations below quantifications limit (4 ng/L) were s et to zero. ............... 111 4 3 Gene Enrichment Analysis: Biological categories (GO) from liver of fathead minnow exposed to PFCs. ................................ ................................ ................ 112 4 4 Gene Enrichment Analysis: Biological categories (GO) from blood of fa thead minnow exposed to PFCs. ................................ ................................ ................ 114
9 LIST OF FIGURES Figure page 1 1 Step and processes in cDNA microarray hybridization in a toxicogenomic study ................................ ................................ ................................ ................... 32 1 2 General diagram of genomics processes and analyses used in this dissertation research. ................................ ................................ ......................... 33 1 3 Conceptual model of the fate and t oxicity of PFCs in a waste water dominant system. ................................ ................................ ................................ ............... 34 2 1 Main PFCs determined in water used for fish exposure. ................................ .... 56 2 2 Comparison o f overall gene regulation from liver samples among sites. ............ 57 2 3 Gene cluster analys is for controls and each of the three urban water types.. .... 58 2 4 Pathway studio analysis representation of highly affected genes from each of 3 sites ................................ ................................ ................................ ................. 59 2 5 ................... 62 3 1 Sampling points along Sweetwater Branch canal. ................................ .............. 79 3 2 Detected PFCs along Sweetwater branch stream during the wet and dry season. ................................ ................................ ................................ ............... 80 3 3 PFOS and PFOA concentrations (ng/L) in Sweetwater branch stream during the wet season and dry season. ................................ ................................ ......... 81 4 1 General procedures of bloo d and liver gene expression analysis in fish using cDNA microarrays. ................................ ................................ ........................... 117 4 2 Venn diagrams depicting similarities in gene expression between liver and blood from fathead minnows after 48 h of P FCs exposures. ............................ 118 4 3 Cluster analysis of both blood and liver tissues from FHM after 48 h of PFCs exposure. ................................ ................................ ................................ .......... 119 4 4 Cluster anal ysis of both blood and liver tissues from FHM after 48 h of PFCs exposure. ................................ ................................ ................................ .......... 120 4 5 Cluster analysis of blood tissues from FHM after 48 h of PFCs exposure. ....... 121 4 6 The central role of PPARD: Peroxisome proliferator Activator Receptor Beta in liver from fathead minnow exposed to PFOS high treatment. ....................... 122
10 4 7 Lipid and carboh ydrates metabolism from the liver of fathead minnow exposed to PFOS high. ................................ ................................ .................... 123 4 8 Lipid and carbohydrates metabolism from the liver of fathead minnow exposed to PFOS low. ................................ ................................ ...................... 124 4 9 Lipid and carbohydrates metabolism from the liver of fathead minnow exposed to PFOS mix. ................................ ................................ ...................... 125 4 10 Pathways analysis of the estrogen receptor exp ression targets in liver from fathead minnow exposed to PFCs. ................................ ................................ ... 126 4 11 Upregulated genes involve in DNA repair from liver of fathead minnow exposed to PFOS high treatment. ................................ ................................ .... 127 4 12 Altered genes involve in DNA repair from liver of fathead minnow exposed to PFOS low treatment. ................................ ................................ ........................ 128 4 13 Lipid and carbohydrates metabolism fr om the blood of fathead minnow exposed to PFOS high. ................................ ................................ .................... 129 4 14 Lipid and carbohydrates metabolism from the blood of fathead minnow exposed to PFOS low. ................................ ................................ ...................... 130 4 15 Lipid and carbohydrates metabolism from the blood of fathead minnow exposed to PFOS low. ................................ ................................ ...................... 131 4 16 Pathways analysis of the estrogen receptor expression targets in blood from fathead minnow exposed to PFCs. ................................ ................................ ... 132 4 17 Expression target for Very Low Density Lipoprotein (VLDL) in blood from fathead minnow exposed to PFCs Mixture. ................................ ...................... 133 4 18 Alteration of histone related genes from blood exposed to PFOS low in fathead minnow. ................................ ................................ ............................... 134 4 19 Immune related genes altered in blood from fathead minn ow exposed to PFOS low (left) and PFOS high (right) treatments. ................................ ........... 135
11 LIST OF ABBREVIATIONS EDCs Endocrine Disrupting Chemicals FHM Fath ead Minnow PFBA Perfluorobutanoate PFCs Perfluorochemicals PFDA Perfluorodecanoic acid PFDoA Perfluorododecanoic acid PFHpA Perfluoroheptanoic acid PFHxA Perfluorohexanoic acid PFNA Perfluorononanoic acid PFOA Perfluoro Octanoic Acid PFOS Perfluoro Octanoic Sulfonate PFTeDA Perfluorotetradecanoic acid PFTrDA Perfluorotridecanoi c acid PFUdA Perfluoroundecanoic acid RBC Red Blood Cell VTG Vitellogenin WWTP Waste Water Treatment Plant
12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requir ements for the Degree of Doctor of Philosophy FATE, TRANSPORT, AND TOXICITY OF PERFLUOROCHEMICALS IN WASTEWATER DOMINANT SYSTEMS By Ignacio Alejandro Rodrguez Jorquera May 2014 Chair: Gurpal S. Toor Cochair: Nancy D. Denslow Major: Interdisciplinary Ec ology Urban surface waters frequently receive treated or untreated wastewater. In these w astewater dominant systems, endocrine disrupting compounds (EDCs) are commonly present. These EDCs have the potential to produce toxic effects in organisms at very l ow concentrations (ug/L to ng/L) To assess the effects of urban waters with wastewater influence, a 48 hour fish exposure was performed exposing fathead minnows to surface water from three locations. Gene expression microarrays were used to determine the influence of exposures on gene expression in fish. Water from the sites was analyzed for the presence of organic contaminants using a h igh performance liquid chromatography (HPLC) Fish serum cholesterol was measured using a f luorometric kit. These urba n waters altered transcription of fish cholesterol metabolism and DNA repair genes. Moreover, exposed fish had elevated cholesterol levels. These findings resemble the effects caused by perflurochemicals (PFCs ) commonly found EDCs in urban waterways. Inve stigated urban waters contain PFCs in the 200 250 ng/L range Further research demonstrates the occurrence of PFCs during a water year in an urban stream that enters a protected area. The dominant PFCs found
13 in this stream were PFOS (Perfluorooctanesulfoni c acid), PFOA (Perfluorooctanoic acid), and PFHxA (Perfluorohex anoic acid). To confirm the findings of the field study we exposed fathead minnows during 48 h through wate r spiked with three different PFOS concentrations (0; 500 and, 25000 ng/L) and a mixt ure of PFCs; in an attempt to mimic typical conce ntrations of PFCs found in urban waters. Additionally, to explore non destructive sampling methods, blood was used as starting material for microarray analysis. When compared with liver, the number of altere d genes in blood was five times greater. The c oncentrations of PFCs used in this study alter ed gen e expression in fish being the mos t prominent effects related to DNA repair, lipid, mitochondrial and thyroid hormone metabolism. Altered expression of genes involved in metabolic pathways was common to both liver and blood. Also, PFC exposure led to up regulation of estrogen receptor in both liver and blood. Surprisingly, the up regulation of vitellogenin genes was observed in blood tissue. These gene expressi on profiles fit into adverse outcome pathways suggesting effects on survival and reproduction on fish
14 CHAPTER 1 LITERATURE REVIEW Background Aquatic systems are the final destination of pollutants with largely unknown long term effects on aquatic and t errestrial organisms (Schwarzenbach et al., 2006; Zanden and Rasmussen, 1996) For example, wastewater from wastewater treatment plants (WWTP) is typically discharged into surface water bodies. About 7.4% of the w astewater is reused in U.S., primarily for irrigation of golf courses and public parks, (Miller, 2006) These wastewaters contain several compounds associated with human use, including veterinary and human pharmaceuticals, and personal care products, collectively known as emerging contaminants or contaminants of emerging concern. Discharge of wastewater containing emerging contaminants into water bodies is of growing conce rn and notoriety due to their unknown effects on water quality. The use of advanced treatment (e.g., reverse osmosis, ozone treatment) can improve removal of emerging contaminants in wastewater, but is not widely practiced due to cost, and thus some contam inants are not remove from wastewater (Lee et al., 2004) Wildlife, particularly aquatic biota or terrestrial fauna closely associated with water bodies are constantly exposed to contaminants. Wastewat er may contain compounds that may bioaccumulate in wildlife food and pose a threat by poisoning wildlife (Lemly and Ohlendorf, 2002) S ubstances such as hormone s (e.g., ethinylestradiol ) and pharmaceuticals ( carbamazepine, fluoxetine, and ibuprofen ) commonly present as mixtures in wastewater have high biological activity and can become toxic to aquatic wildlife at the concentrations typically present in surface w aters
15 (De Lange et al., 2006; Jobling et al., 2006; Oetken et al., 2005) Further, presence of other contaminants such as persistent organic pollutants and/or metals in water cause bioamplification of contaminants in the food (such as invertebrates and fish) affecting aquatic and terrestrial wildlife (Daley et al., 2011) P articular attention must be placed on the persistent emerging contaminants due to their insufficie nt removal by the conventional wastewater treatment. As a result, they have the potential to ends in water bodies result ing in chronic exposure to wildlife (Calisto and Esteves, 2009) In the context of human development and the inevitable outcome of increasing contaminants in water bodies, it is important to assess the impact of pollution on wildlife and develop ways to protect it, especially in areas set aside t o protect nature such as the State or National preserves. Historically, most of the water quality research has been focused on the two most common contaminants, nitrogen and phosphorus, which occur at concentrations of milligrams per liter (macropollutants ) (Hunt, 2009) However, several other contaminants occur at much smaller con centrations (micropollutants), ranging from nanograms to micrograms per liter with known or potential deleterious health effects (Templeton et al., 2009) Wastewater treatment plants are one major source of these contaminants, which can be released as chemical mixtures that es cape treatment and make it into surface waters (Templeton et al., 2009) Among the chemicals in the mixture are some that behave as endocrine disrupters and can exert biological effects at the low concentrations (ug/L to ng/L ) usually found in the environment (Templeton et al., 2009) For example, p resence of 5 ng/L of ethinylestradiol in an experimental lake in Canada caused the collapse of a fish population (Kidd et al., 2007) It is estimated that every population on earth is
16 chronically expose to pollution (Guillette and Iguchi, 2012) Nevertheless we do not know what the effects really are at population levels. Within the contaminants classified as micropollu tan t s, perfluorochemicals (PFCs) represent a class of endocrine disrupter compounds (EDCs) with high thermal, chemical, and biological inertness. These accumulate in the food chain (Lau et al., 2007) liver and blood (Jensen and Leffers, 2008; Lau et al., 2007) A wide range of toxic effects of PFCs have been reported in laboratory animals and cultured cells, but the route of exposure and current and future risk are still unknown (Nakayama et al., 2007) as are the mechanisms of toxicity (Hagenaars et al., 2013; Hekster et al., 2003) Much research has shown that PFCs are ubiquitous in the aquatic environment (reviewed by Houde et al., 2011) Moreover, some authors suggest that two PFCs, PFOA and PFOS, are present in the blood, liver and kidneys o f every person in our society (Kannan et al., 2004) and their levels in blood may represent the highest human exposure to exogenous chemicals, exceeding that of m ore well DDE or PCBs (Jensen and Leffers, 2008) The presence of P FCs and their potential effect o n humans and wildlife in the aquatic environment has been recognized as one of the emerging issues in environmental chemistry (Ahrens, 2011; Jensen and Leffers, 2008) Therefore, there i s an urgent need to study the fate, transport, and toxicity of PFCs PFOS and PFOA in the environment, particularly in aquatic systems (Suja, 2009). To assess the effects of contaminants, traditional toxicology analyses are restricted to measuring dramati c effects as mortality (LD 50 ) or gross changes in organs architecture and histology, but these fail to reflect toxicant stress at an early exposure time point (Jung et al., 2010) However, t he fact that toxicity is preceded by alterations
17 in gene expression, allows the use of novel molecular tools (i.e. genomics) to perform gene expression profiling giving a better insight into the mechanisms of toxicity, at earlier time points and lower conc entrations than normally required for toxicity studies. Genomics T ool s to A ssess W ater P ollution The study of contaminants (toxic) effects using genome wide information (genomics) is called toxicogenomics, an approach that undoubtedly is changing the way researche r s perform toxicology studies (Afshari et al., 2011) One type of genomics tools currently used in environmental toxicology is microarrays. Microarrays are used to measure gene expression and have been shown to be particularly useful in determining the molecular effects of toxicity. Basically a microarray is a solid s urface such as microscope s lide where single strand DNA fragments have been attached in a precise known position at a high density T hus, they can act as molecular detectors of gene expression (Holloway et al., 2002) Microarrays, allow the determination of gene expression differences between samples by measuring the relative amount of messenger RNA (mRNA). In the case of cDNA microarrays, first it is necessary to extract the RNA from a tissue sample. Afterward s a synthetic DNA (cDNA) copy which is complimentary of each target mRNA is produced by a reverse transcription process. Then, a fluorescen t dye is added to the produced cDNA. These now dyed cDNAs are templates of the original RNA from the tissue sample and can hybridize with their corresponding (complementary) sequences on the solid surface ( F igure 1 1). Fluorescence principle is used to det ect and determine the amount of gene sequences that got attached by measuring the light intensity and calculating the relative amount of each mRNA from each sample. Thus, m icroarray analyses give new understanding of toxicity pathways (Nuwaysir et al., 1999) and can be used to detect
18 early biomarkers of toxicity (Wang et al., 2008) Since microarrays are able to measure thousands of genes at a time, they provide an accelerated method to discover altered toxicity pathways that lead to disease (Waters et al., 2002) making it feasible to understand the mechanisms of toxicity in different wildlife species (Snape et al., 2004) One of the advantages of genomics tools is their sensitivity for discovery of biomarkers of toxicity In the environmental toxicology context the term biomarker is a xenobiotically induced variation in cellular or biochemical comp onents or processes, structures, or functions that are measurable in a biological system or (NRC, 1987) Other technologies such as quantitative polymerase chain reaction (qPCR), are important as well to provide orthogonal confirmation of biomarker discovery of relevant genes identi fied by microarray approaches. Nevertheless, a drawback of the above mentioned techniques for assessing toxicity is the need to euthaniz e the organism to assess gene expression in the target organ typically the liver losing the advantages of repeated sa mpling on the same individuals for monitoring purposes. Recent improvements make it po ssible to use blood from humans and rodents as a starting material to analyze gene expression signatures for toxicity determination (Fricano et al., 2011; Jung et al., 2010) To date the use of blood in non mammalian vertebrates (i.e. fish and birds) in toxicogenomics studies is virtually absent. Different from mammals, the blood of non mammalian vertebrates (fish, reptile, amphib ian and birds) have nucleated red blood cells (Rowley, 1988) This attrib ute makes the use of blood to asses toxicity a fertile area for research. Thus, the potential to use these new molecular approaches in wi ldlife to answer old unresolved questions in ecotoxicology is promising.
19 The overall goal of this dissertation research was to use genomics tools to understand the impact of micropollutants, such as PFCs in a wastewater dominant system, on the gene expression of aquatic (fish) organ isms and test the feasibility of using blood as a non invasive sampling method in both fish and birds. The R elev ance of Assessing Pollution Effects on W ildlife For more than 50 years, the deleterious effects of synthetic chemicals have been reported in the scientific literature, showing developmental and reproductive impairment in fish and birds (among other species) from the wild and in some cases corroborated with the use of confined or laboratory animals (Colborn et al., 1993) Since water bodies are the final destinati on of contaminants, we should expect a greater impact of this type of hazard on aquatic bio ta One way to analyze the level of threat to a particular group of species is to analyze the worst case outcome: the total extirpation of the species or its extinc tion. Currently, a concern arises because contemporary rates of species extinction are increasing due to anthropogenic activities, being orders of magnitude greater than background rates (calculated using the evidence in fossil s) (Pimm et al., 1995) Freshwater fish have the highest extinction rate worldwide among all vertebrate classes, with the modern extinction rate for North American fresh water fish being 877 times grea ter than the background extinction rate for this class (Burkhead et al., 2012 ). For example, 39 species of North American freshwater fishes have become extinct s ince 1900 including five geographically distinct populations of commercially important fish t hat have be en extirpated from North America (Burkhead, 2012; Jelks et al. 2008) Within the causes of fish extinction about 17% are attributable to water pollution, the third leading cause after habitat loss (32%) and introduction of non nativ e fish (29%) (Burkhead et al., 2012 ).
20 Impact of Endocrine Disrupting C hemicals o n t he Environment Several contaminants present in the aquatic environment have been shown to mimic or antagonize the actions of steroid hormones, a problem often described as (Jobling et al., 1998) The feminization o f vertebrates by endocrine disrupting chemicals (EDCs) is a key environmental issue that affects both terrestrial and aquatic wildlife (Harris et al., 2011) T he hypothesis that EDCs in water can impact the reproductive health of various fish species has been shown (Mills and Chichester, 2005) ; with the intersex phenomena often viewed as a remarkable effect caused by the exposure of fish to EDCs (Leatherland and Woo, 2010) s imultaneous presence of male and female gonada l tissue in an individual (Tyler and Jobling, 2008) Thus, intersex has the potential to negatively impact reproductive fitness. F or instance, male intersex fish produce lower amounts of semen and lesser sperm density, with reduced spermatozoid moti lity (Jobling et al., 2002) Intersex has been documented histologically in an extensive and rapidly growing list of wild and laboratory animals (Kramer et al., 2011) While most of the research has been focused on androgenic or estrogenic effects, less attention has been placed o n other types of impacts on the endocrine system, such as the thyroid system (Colborn et al., 1993) or altered immune function (Vos et al., 2000) T he diverse effects of EDCs on the thyroid, retinoid, androgen, estrogen, and corticosteroid systems of a wide range of animals exposed to EDCs, make it imperative to research the extent of the risk posed by EDCs to wildl ife (Jobling et al ., 2006) New evidence from research on EDCs can help to propose an effective and sustain able strategy against this insidious and unseen pollution (Schwarzenbach et al., 2006)
21 Micropollutant M ixtures in A quatic S ystems The fate, transport and effects of nutrients, and natural organic matter occurring in the ug/L to mg/L concentrations on aquatic systems are relatively well understood For example, deleterious effects such as oxygen depletion or hypoxia and toxic algal blooms are the result of elevated nutrient concentr ations in the water bodies (Jackson et al., 2001) Understanding the impact of micropollutants (including EDCs) is much harder, because high number of contaminants may be present in the ng/L range (Jobling et al., 1998) and in the form of countless potential interactions among contaminants. Organisms are exposed t o complex chemical mixtures conta ining individual chemical components at concentrations too low to raise concern. But the additive or synergistic effects can raise the potency of these chemical cocktails. Examples of this phenomenon include the induction of the yolk protein vitellogenin (VTG) in male fish by mixtures of estrogenic compounds containing individual concentrations below dangerous levels (Brian et al., 2005) Moreover, the inherent va riability and complexity of the envir onment contributes on the difficulty for the identification of the contaminants impacting the ecosystems H ence, to demonstrate just the presence and the biological effects of a single and /or simple chemical mixtures become an enormous task (Schwarzenbach et al., 2006) Fate and T ransport of PFCs in the Aquatic E nvironment The PFCs known to be globally distributed and persistent became part of the EDCs because of their ability to produce estrogenic effects (Liu et al., 2007a) PFCs comprise a diverse group of several of hundred chemicals, consisting of a hydrophilic functional group and a lipophilic fluorinated chain (this gives the su rfactant properties) which vary in length. These compounds have been widely used as processing additives
22 during fluoropolymer production and as surfactants in consumer applications for over 50 years (Kissa, 2001) PFCs can be cl assified into two types: neutral and ionic (Ahrens, 2011) Ionic PFCs are very persistent because of the strong carbon fluorine bond but neutral PFCs are less persistent and prone to volatiliz ation (Kissa, 2001) consequently ionic PFCs are the ones th at have been the subject of grea ter investiga tion (reviewed by Ahrens, 2011; Houde et al., 2011) The occurrence and fate of PFCs in the aquatic environment has been recognized as an emerging topic in environmental che mistry. PFOA and PFOS are the most common PFCs metabolites found in the environment (reviewed by Ahrens, 2011) However, field data on environmental fate and transport of PFCs, su ch as the distribution among water, sediment, and biota is still limited (Ahrens, 2011) and few laboratory studies on sorption processes in sediment exist (Higgins and Luthy, 2006; Higgins et al., 2007) Thus, the extent to which the resulting distribution parameters and mechanistic inferences apply to conditions in the field are not yet clear (Kwadijk et al., 2010) The environmental fate of PFCs resu lts from the interplay of numerous processes including physical transport and multimedia partitioning in the environmental matrices Different transport pathways of PFCs exist in the environment for example atmospheric transport is the main pathway for v olatile (mainly neutral and short length carbon chain) PFCs W hile ionic PFCs can enter the aquatic environment d irectly, as point sources from w astewater treatment plant s (WWTP) and as non point sources from stormwater runoff (Ahrens, 2011) Deposition of PFCs from atmospheric sources has
23 been detected from degradation products of their precursors (Martin et al., 2003) as well as their sorption into sediments (Ahrens et al., 2011) Because of the chemical structure and properties of PFCs it is difficult to estimate their enrichment on solid su rfaces using the o ctanol to water partition coefficients K OW F or example, the o ctanol to water partition coefficients (log K OW ), which is generally used to estimate enrichment in sediments cannot be determined for compounds having s urfactant properties (Theobald et al., 2012) The behavior of surfactant (surface active agents) compounds in water is different from typical compounds, since they are amphiphilic they possess the ability to reduce water surface tension because their hydrophilic heads and hydrophobic tails (Giesy and Kannan, 2002) Therefore they contain both water soluble and water insoluble components. Determination of partition coefficients and sorption are important to verify the environmental fate of PFCs (Ahrens, 2011; Theobald et al., 2012) The sorption behavior of PFCs has been investigated in terms of the solid to water partition coefficient (K d ) (Ahrens et al., 2010a; Ahrens et al., 2009; Higgins and Luthy, 2006) where chain length and functional group of PFCs has great i nfluence in the ir partitioning T hus, PFCs with carbon chain length s < 7 carbons were exclusively found in the dissolved phase, while long chain s (>7 carbons ) appeared to bind to particles. Short chain PFCs have a higher potential for transport in water w hile long chain s tend to be distributed in biota or the abiotic environment like sediment, which cou ld act as a sink for PFCs (Becker et al., 2008; Martin et al., 2004) Previous work demonstrated that for a given PFC class, chain length was the dominant structural feature influencing sorption onto river sediment s (Higgins and Luthy, 2006) Labadie and Chevreuil (2011)
24 observed that log K OC ( the ratio of the concentration of a substance in the soil/sludge to the concentration of the substance in the aqueous phase at adsorption equilibrium) increased li nearly with increasing chain length from perfluoro hexanoic acid ( P FHxA =6 carbon alkylates ) to perfluoro dodecanoic acid ( PFDoA = 11 carbons) and from perfluoro butanoate sulfonate ( PFBS = 4 carbon sulfonates ) to perfluoro octanoic sulfonate (PFOS = 8 carbon s). C hain length and functional groups of PFCs influence the sorption and environmental fate. Sediment to water distribution coefficients (log K d ) of PFCs range from 0.8 to 4.3 and are also positively correlated with perfluoroalkyl chain length (Labadie and Chevreuil, 2011) thus, PFCs sorption to sediment increases with increasing perfluorocarbon chain length (Myers et al., 2012) Higgins et al., ( 2005) concludes that PFCs with an even number of ca rbons dominate in sediments. Accordingly, it has been shown that the sorption of PFCs increases by a factor of 3.5 for the a ddition of each successive carbon on their backbone arrangement (Higgins and Luthy, 2006) Another factor affecting the pa rtitioning i s the organic carbon content; as increasing sorption was found with increasing organic carbon content. Higgins and Luthy (2006) showed that PFC s sorption onto sediment strongly correlated with the sediment total organic carbon ( T OC). For example Theobald et al., (2012) found t he highest PFC s concentrations at stations with high silt and TOC contents; opposed to the lowest concentrations in sandy sediments, suggesting a strong influence of sediment TOC on PFCs concen trations. T herefore, it is particularly difficult to compare concentrations of PFCs in sediments from different locations without knowing the TOC.
25 PFCs have been determined mainly in biota and water samples. Only very few studies deal with PFCs in sedime nts (Theobald et al., 2012) The role of sediments in the fate of PFCs may be more important than expected In general, enrichment of PFCs from water to sediment is moderate; as a result, suspended matter and sediments may act as sinks for PFOS (Martin et al., 2004) Myers et al., (2012) found that spatial distribution patterns of sediment concentrations of PFCs in a Lake follow similar trends of contaminants such as mercury and polychlorinated biphenyls (PCBs) Factors that may influence migr ation of PFCs in sediments include the movement of sediments by plants or other biota ( bi oturbation ) (Ahrens et al., 2009; Higgins et al., 2007; Martin et al., 2004) and b iotransformation of precursors (Higgins et al., 2007; Rhoads et al., 2008) For example, i ncreasing PFOS concentrations may be a result of ong oing degradation of precursors (Myers et al., 2012) As indicated by other authors (Ahrens et a l., 2009; Higgins et al., 2005) the role of sediment partitioning and cycling must be better understood to consider the overall fate of PFCs in aquatic systems. Thus, the understanding of PFC fate in aquatic systems is still incomplete and there is only limited data on their distribution between water, sediment and biota (Kwadijk et al., 2010; Myers et al., 2012) F or example, the re suspension of PFCs from the sediment into the water column is not fully understo od (Myers et al., 2012) neither is the environmental fate and behavior of PFCs in sediment / water (K d ), nor the bio accumulation factors (BAF) and b iota/sediment accumulation factors (BSA Fs) (Ahrens, 2011) Finally, some researche r s such as Theobald et al ., (2012) do not recommend the use of sediments to determine the fate of PFCs as the first choice matrix because
26 sediment parameters like TOC strongly inf luence the enrichment process in addition to the high spatial variability not related to pollution I n contrast, the water phase could be a better matrix for investigating the fate and distribution of PFC concentrations because it allows the identification of the sou rces. Degradation of PFCs in the Aquatic E nvironment PFCs are released in to the environment indirectly as a result of environmental degradation of precursor compounds or directly through manufacturing and consumer products (Prevedouros et al., 2005) One of the potential precursors of PFCs is N ethyl perfluorooctane sulfonamidoethanol ( N EtFOSE), a volatile compound present in protective paper coatings There is evidence that it is oxidized in the atmosphere to PFC As and PFSAs (D'Eon et al., 2006) Consequently, PFCs can be degraded in the atmosphere or under aerobic conditions to perfluoroalkyl carboxylates (PFCA s like PFOA ) and perfluoroalkyl sulfonates (PFSAs like PFOS ), after which they can be transported and deposited in water. Also, neutral PFCs (nonionic) can be transformed by hydrolysis, photolysis and biodegradation (Martin et al., 2005; Rhoads et al., 2008) But their final metabolites such as PFOS and PFOA are recalcitrant in the environment, as a result PFCs are generally not believed to undergo metabolic or other degradation in the environment (Kissa, 2001) Thus, h istorical losses of PFCs via degradation are assumed to be negligible often less than 1 to 3% of original concentration in different matrices Several studies have shown that mass flows of PFCAs and PFSAs increase in WWTPs indicating biological transformation of precursors (Higgins and Luthy, 2006; Schultz et al., 2006; Sinclair and Kannan, 2006) Liou et al., (2010) reported no microbial degradation of PFOA in f ive different anaerobic microbial communities These researche r s were not able to document any alteration of PFOA, concluding that under
27 the conditions examined, PFOA is microbiologically inert, hence environmentally persistent (Liou et al., 2010) For instance WWTP mass flow studies found similar or higher PFC s c oncentrations when comparing effluent to the influent, which indicate s that conventional WWTPs are not effective in the removal of PFCs The biotransformation of widely used PFCs could lead to greater concentrations of PFCs in wastewaters thus, PFCs have been found ubiquitously in the aquatic environment (Yamashita et al., 2005) Since the fi rst research of the global occurrence of PFOS in wildlife (Giesy and Kannan, 2001) the scientific literature on the environmental and toxicological aspects of PFCs has expanded rapidly, with the current rate of publication being over 400 articles per year (Buck et al., 2011) Nevertheless, there still is no clarity on the b iotransformation, transformation pathways or partitioning behavior of PFCs (Rayne and Forest, 2009) PFOA and PFOS, tend to be fin al metabolites from PFCs precursors, thus they are detected more because of their h igh persistence in water and sediments with no relevant or obvious degradation process es observed to date Effects of PFCs in A nimals It is clear that there is widespread occurrence of PFCs in the environment, including wildlife from all over the world (for a review visit Houde et al., 2011) Most of the research on toxic effects of PFCs has been done in mammals, where researchers have documented re duced body weight, increased liver weight, and alteration of cholesterol metabolism (Luebker et al., 2002) as well as an increase in the occurrence of hepatocellular adenomas and thyroid follicular cell adenomas (Seacat et al., 2002) In general, a variety of effects including the alteration of lipid metabolism, changes in the thyroid and immune system, developmental effects, and hormonal
28 effects have been determined in different classes of laboratory and wi ldlife animals (Liu et al., 2007b) At sub cellular levels, a gonism of the peroxisome proliferator activated receptor alpha (PPAR (DeWitt et al., 2009; Liu et al., 1996; Shipley et al., 2004; Yang et al., 2002) S everal perfluorinated compounds can activate PPAR peroxisome prol iferation by perturbing lipid metabolism and transport. Also, the activation of PPAR followed by altered expression of genes involved in peroxisome proliferation, cell cycle control, and apoptosis have been reported (DeWitt et al., 2009) Fish can be exposed to PFCs t hrough water (Sinclair et al., 2006) The main body compartments where PFCs tend to accumulate are blood and liver (Lau et al., 2007) In fish, t he disruption of fatty acid metabol ism, lipi d and cholesterol transport (Wei et al., 2008) cell death (Wei et al., 2009) alteration of cytochro me P450 superfamily enzymes (Liu et al., 2008) c ell membrane related effects (Hu et al., 2002) oxidative stress and apoptosis (Liu et al., 2008) as well as the disruption of the hypothalamus pituitary thyroid axis (Shi et al., 2009) h a ve been listed as common adverse effects of PFCs. Thus, signaling pathways altered by PFCs in teleost fish are led by estrogen receptor ( ER ) aryl hydrocarbon receptor ( AHR ), thyroid receptor (TR), and proliferator activator receptor ( PPAR ). Particularly PFOS is able to exert estrogenic li ke effects by altering the expression of estrog enic rela ted genes, such as VTG and cytochrome aromatases ( cyp19a and cyp19b ) with the potential outcome of developmental and reproductive abno rmalities in some teleost fish (Du et al., 2009; Shi
29 et al., 2008; Wei et al., 2 009) All studies motioned above were done with high concentrations, which are uncharacteristic of the environmental levels typically found. Overall, toxicity data for PFCs at the concentrations found in the environment are non existent. Toxicity report s of PFCs from fish species demonstrate the toxic influence at higher concentratio ns, but it is difficult to extra polate the effects to low PFCs concentrations that exist in the environment. Despite the widespread distribution of PFCs their toxicity and biological effects on organisms, especially on fish species, at ambient levels in the wild remain largely unknown (Shi et al., 2008; Wei et al., 2008; Wei et al., 2009) Overall Hypothesis An understanding of the f ate, transport, and effect s of pollutants in aquatic system s is necessary to characterize risks and effects t o organisms due to exposure to PFCs. This knowledge would allow us to mitigate PFCs impacts on wildlife and perhaps adapt manufacturing and/or was tewater treatment practices to attenuate PFCs at the source. T his dissertation research investigate d the fate, transport and effects of PFCs in urban waters from the wastewate r treatment plant to the receiving system, a we tland located in a preservation a rea. Figure 1 3 shows the overall conceptual model. Specific objectives of this research were as follows: De termine the fate of PFCs in water, in a gradient from the source (WWTP) to the sink (stream, wetland). Determine gene expression alterations in an aquatic vertebrate model (fathead minnow fish Pimephales promelas ) Investigate the usefulness of using blood from fathead minnow as starting material for toxicogenomics studies.
30 This objectives allowed determination of : (1) the general impact of PFCs mi xtures at environmentally relevant concentrations on the liver and blood, (2) the utility of using blood as a starting material on species with nucleated red cells, and (3) potential biomarkers of the effects of PFCs. The overall hypothesis was that due t o environmental persistence and bio accumulative properties, PFCs typically exist in water bodies downstream of WWTP in concent rations that will exert toxic effects on liver and blood gene expression in aquatic vert ebrate species. Significance of t his Diss ertation Research This project improve d risk assessment of PFCs in the environment by determ ining levels of PFCs that enter in to preservation areas as well as understanding the toxici ty effects of those realistic concentrations of PFCs on vertebrates from ex periments with confined animals Moreover, this approach determine d the use fulness of sampling blood from fish to assess effects of other stressors using molecular tools including for example, harmful algal blooms or effects on immune system in vertebra tes exposed to contaminants. Some genes that are considered biomarkers in the liver were expressed in blood cells, supporting the use of non destructive sampling to replace liver for this type of assessment. The possibility of re sampling the same individ uals, before and after exposure has the advantage of eliminating individual differences and make s more accurate longitudinal studies possible This is especially important for vertebrates that are on the endangered species list but the technique also allo ws researchers to understand effects with far smaller sample sizes of any species This research contributed valuable information for refin ing sampling techniques and reducing the amount of animal s
31 needed for toxic o genomic studies. I anticipate that the re sults from this dissertation research will have considerable impact on the design of vertebrate monitoring efforts since the starting material (blood) is more accessible and less expensive than using whole animals, allowing the use of larger numbers of ind ividuals at a population scale, one of the current gaps in ecotoxicology area.
32 Figure 1 1. Step and processes in cDNA microarray h ybridization in a toxicogenomic study (Adapted from Agilent One color microarray protocol)
33 Figure 1 2 General diag ram of genomics processes and analyses use d in this dissertation research (Adapted from Mittler and Shulaev, 2013)
34 Figure 1 3 Conc eptual model of the fate and toxicity of PFCs in a waste water dominant system.
35 CHAPTER 2 U SING TRANSCRIPTIONAL RESPONSE OF FATHEAD MINNOW S ( Pimephales promelas ) TO EVALUATE THE IMPACT OF WASTEWATERS IN WILDLIFE PRESERVATION AREAS Introduction Th ere is significant evidence linking water pollution caused by urban development to the loss of biodiversity in water ecosystems through toxicity and eutrophication, among other processes (SCBD, 2010). Urban development causes changes in the water quality o f lakes, streams, rivers, and estuaries and reduces biotic richness due to hydrologic feature modifications and greater pollutant loading (Brown et al., 2009; Paul and Meyer, 2008; Walsh et al., 2005) The population of cities has grown enormously in the l ast decades, thus, the importance of city governance to tackle the challenges of biodiversity loss has increased (Puppim de Oliveira et al., 2011). Since diferent types of green areas, like parks and urban forests, are the major sources of biodiversity in the urban landscape, it is fundamental to provide a network of green spaces to preserve and enhance urban biodiversity (Niemela, 1999). Moreover, an exclusive protection of species. To estimate the impact of water pollution on wildlife species or on ecosystems is a titanic task. The complex mixtures of chemicals found in surface water, runoff wastewater, and treated wastewater makes it difficult to elucidate the impact of individual chemicals in biota (Denslow et al., 2007) None theless, the fact that toxicity is preceded by alterations in transcriptional responses allows the use of approaches like microarrays for early detection of toxic effects (N uwaysir et al., 1999) DNA microarray analysis can measure the expression of thousands of genes at a time, potentially
36 accelerating the discovery of toxicant pathways (Waters et al., 2002) Also, transcriptional response profiling provides more specifici ty than the use of traditional biomarkers helping to establish links between the mechanisms of action exerted by certain chemicals (Poynton et al., 2008) T herefore a toxicogenomics approach is useful to determine transcriptional response profiling to ass ess effects produced by complex environmental mixtures of chemicals (Snape et al., 2004) Perfluorochemicals (PFCs) are among the most common emerging chemicals detected in surface waters due to the wide use in domestic and industrial applications and thei r environmental persistence ( reviewed by Ahrens, 2011 ). Some are known to be endocrine disruptors. Domestic wastewater is one of the main sources of PFCs (Kolpin et al., 2002; Plumlee et al., 2008; Tang et al., 2006) but runoff and leaching from urban are as can also contribute PFCs to the aquatic environment (Ahrens, 2011; Furl et al., 2011; Murakami et al., 2009) The broad use of PFCs has resulted in their detection in wildlife and humans, and in particular in aquatic biota (Houde et al., 2011) Several studies in fish have reported that liver is a main target tissue of PFCs (Lau et al., 2007; Martin et al., 2003a, b) with the disruption of fatty acid metabolism, lipid and cholesterol transport (Wei et al., 2008) cell death (Wei et al., 2009) alteration of cytochrome P450 superfamily enzymes (Liu et al., 2008) cell membrane related effects (Hu et al., 2002) oxidative stress and apoptosis (Liu et al., 2008) as common adverse effects. The most important simple lipid in fish is cholesterol (Tocher, 2003) Particularly relevant for reproduction are the potential effects exerted on the metabolism of cholesterol due to its connection with the biosynthesis of sex hormones in the gonad and with vitellogenesis, an area requiring further research (Ankley et al., 2005)
37 Previous (ACEPD., 2010) and current analysis of water samples from the urban waters investigated here, showed the consistent presence of several PFCs. We used a toxicogenomic approach to make associations between altered genes in the livers of fathe ad minnows and the known presence of chemicals in the waters used for the exposure. Our main hypothesis was that levels of PFCs typical of wastewater effluent will be associated with altered transcriptional response patterns in fish downstream The approac h t o test this hypothesis was to use microarrays to assess gene expression changes on fish exposed to urban waters in a laboratory setting. Thus, it was possible to detect altered transcriptional response patterns attributable to PFCs pollution among other s in fish exposed to these effluents Materials a nd Methods Study Site and Water C ollection Three sites were chosen for the collection of wastewaters within the Orange Creek Bas in, Gainesville, Florida, USA. The sites where chosen based on their know n pre sence of PFCs their differences in water treatment system (from advance treatment to no treatment) and because they discharge into protected areas. Runoff W astewater (RW) This water came from A180 acre Lake, which is part of the Tumblin Creek watershed. The outflow of the lake then travels to a State Preserve where it enters the Florida Aquifer via the LaChua Sink. The Tumblin Creek watershed includes 8.9 square miles of urban Ga inesville, wi t h 60% correspond ing to an impervious area and nearly 80% of the basin correspond ing to residential and commercial areas (CH2M HILL, 1985). The lake itself was designated as a State of Florida wildlife sanctuary (Bill No 1356 Chap. 65 1005). Water quality at this site is impaired due to the runoff containing
38 fertilize rs from residential areas; commercial and agricultural activities; leachate from a landfill ; sanitary sewer lines ; and septic tank systems. Treated City W astewater (TCW) Surface water was collected in Sweetwater Branch creek. The Sweetwater Branch Watershe d encompasses 3.3 square miles; with major land uses being : 60% low density residential, 20% commercial, 14% mixed forests and wetlands. The City of Gainesville wastewater reclamation facility, a tertiary level domestic wastewater treatment plant which treats domestic wastewaters from the southeast and northeast areas of Gainesville city, discharges treated wastewater into the Sweetwater Branch creek. After treatment, the discharges State Preserve finally entering into LaChua Sink. About 67% of Sweetwater Branch creek base flow is provided by the wastewater treatment plant. The maximum amount of discharge permitted is 7.5 million gallons per day (ACEPD, 2010). On Campus Treated W astewater (OTW) This site is a wastewater treatm ent facility from a university campus. The w astewater treatment plant effluent is used for landscaping irrigation About 90% of the irrigation water used on campus is reclaimed water which is treated to Class I water quality standards (potable water) (UF Physical Plant 2005). Since wastewater runoff is probably the greatest source of water for t he university nature conservation area Lak e Alice (Wells, 2005), it is very likely that water use d for irrigati on will end up in the lake. Water from each site was collected two days prior to the fish exposure experiment using Chemfluor tubing and 120 L steel barrel s coated with polyester resin (gel coat) to avoid cross contamination Basic parameters such as t emperature, pH, d issolved o xygen and electrical c ondu ctivity were measured on site using field
39 meters (Table S1, Supporting information Appendix A ) W ater from the barrel w as transported to the laboratory and pumped in to four fiberglass cylinders in the aquatic toxicology facility The facility has an aver age temperature of 25 C (room temperature). W ater from each cylinder was then pumped in to four replicate aquariums per treatment (Figure S1, Supporting information Appendix A ) and kept for 1 day without fish (pre tre atment); extremely high pH (> pH 9) wa s adjusted for one of the effluent waters. On day 2, four male f athead minnow s from a common tank were transferred to each replicate aerated aquarium and ke pt for 48 h with one 75% water change after the first 24 h Water A nalyses Water samples were coll ected at the start of the exposures from the collection barrels. One L sample s of each effluent was collected in an amber glass bottle with a Teflon cap and stored at 4 C until analysis for nutrients and metals. EPA standard methods 300.1 and 3120 (EPA, 1993) were used to analyze n on acidified samples for chlorine, conductivity and pH; s ulfuric acid was added to preserve nutrients and nitric acid was added to pres erve trace metals. Also, 500 mL water samples were collected using EPA Method 537 in polypropylene bottles for PFCs analysis of 10 types of perfluorinated carboxylic acids from the 3 sites, the water used as control, and a field blank. Previously, concentrations of organic contaminants including 10 PFCs with 5 to 12 carbon backbone length as perfluorinated carboxylic acids and 5, 6, 8 carbon backbone length as perfluorinated sulfonic acids were measured for treated city wastewater and on campus treated wastewater by the Ala chua County Environmental Protection Department (ACEPD., 2010)
40 Fish Exposure and Tissue C ollection Sixty four r eproductively mature pond reared fathead minnow (FHM) m ales were separated from the common tank two weeks before the experiment and p laced in the treatment aquaria for 48 h. The exposure system consisted of 40 L glass aquaria. Each exposure was conducted in quadruplicate and each aquarium contained four male FHM in 25 L of treatment water. The water used in the control treatment was carbon filt ered de chlorinated tap water. The positions of the treatment tanks were randomized and test initiation times were staggered to ensure an exposure/sampling interval of 48 h. The fish were not fed during the experiment The t emperature range of the water was 24 26 C with a photoperiod of 16 h light: 8 h dark. Temperature, dissolved oxygen, and pH were measured at the beginning of the experiment and in each tank on ce a day. At the conclusion of the exposures, fish were anesthetized with MS 222 and weighed to the nearest 0.1 g The testes were removed and preserved fo r histological analysis to confirm sex and sexual stage. Liver tissue was flash frozen using liquid nitrogen and stored at 80 C until RNA extraction. Liver was isolate d from four males for ea ch treat ment except for the control group (n=3) All procedures involving live fish were reviewed and approved by the University of Florida Institutional Animal Care and Use Committee (IACUC). Microarray A nalysis Total RNA was extracted using the RNA STAT 60 reagent (Tel Test, Friendswood, TX, USA), reconstituted in RNAsecure (Ambion), and DNase treat ed with Turbo DNA free (Ambion) RNA quantity for microarray analysis was measured using the NanoDrop ND 1000 (Nanodrop Technologies, Wilmington, DE) and RNA quality was evaluated using the Agilent 2100 BioAnalyzer with the RNA 6000 Nanochip. RNA
41 integrity values (RIN) were between 8.0 and 9.9 for all samples used in the analysis. Fathead minnow oligonucleotide microarrays used in this study were manufactured by Agilent (Palo Alto, CA) and were designed in our laboratory (GEO: GPL9248). F our biological replicate FHM RNA samples were isolated from FHM liver s after exposure to each effluent. Microarray hybridizations were performed according to th e Agilent One color microarray protocol (document no. G4140 90040 v6.5) using Cyanine 3 (Cy3) (Agilent, Palo Alto, CA). One g total RNA per sample was used to produce cDNA. Each sample contained a specific activity >9.8 pmol Cy3/ug, and amounts were adjusted to a fina l mass of 1.65 g for 8x15K microarray hybridizations. A final volume hybridization proceeded for 17 h at 65 C. Microarrays were washed the following day according to the Agilent protocol and, kept in the dark until scanning on an Agilent G2505B microarray scanner (same day ). Data extraction was performed using Agilent Feature Extraction software (v9.5) Bioinformatics Raw expression data (gProcessedSignal) were imported into J MP Genomics v5 (SAS, Cary, NC) and log 2 transformed and normalized by LOESS before performing ANOVA to identify d iffe rentially regulated transcripts. Gene Ontology (GO) annotations were used to perform functional enrichment analysis (Fisher Exact Test p <0.0 5) to determine the over represented genes in each GO biological process. Differentiall y regulated transcripts ( p < 0.01, fold change greater than 1.5 ) were subjected to hierar chi cal clustering. Distance calculations were performed with Cluster 3. 0 soft ware (Eisen et al., 1998) Euclidean distance was used as a similarity metric and an average linkage in the clustering method. We used the Java Tree View software for clustering
4 2 visualization (Saldanha, 2004) To visualize the changes in transcriptional response between sites exposed to water types and controls, we use d Pathway Studio TM ( http://www.elsevier.com/online tools/pathway studio ). M icroarray data were submitted (http://www.ncb i.nlm.nih.gov/geo/ query/acc.cgi ?acc=GSE37550 ). For pathway analysis and hypothesis generation, all significantly regulated genes ( p <0.05) were used (without a false discovery rate cut off) to ob tain as many genes as possible. Total Cholesterol Determinati on Total cholesterol was determined in plasma from eight (n=8) exposed fish per treatment Diego, CA; Catalog Number: STA 390) was used for this purpose. Briefly, approximately 5 uL of fish plasma was diluted 1:4000 with 1X assay diluent (Triton X 100 0.5% and Sodium Hydroxide 10 N). Standard curves were prepared following the manufacturer protocol and used to calculate the concentration of total cholesterol ( u M), then transformed to ng/mL of fish plasma. Results and Discussion Water C hemistry Table 2 1 shows the amount of perfluorochemicals (PFCs) found in the water types investigated here In general, historical data shows levels of organic wastewaters c ontaminants (OWCs) were lower or within the range of those detected in other urban wastewaters studies in the US (Fent et al., 2006) except for two of the PFCs, PFOS that fluctuated between 97 170 ng/L values that are in the higher range for a treated city wastewater site compare d to similar studies in the US (20 187 ng/L and 1 130 ng/L (Plumlee et al., 2008) ; 3 68 ng/L (Sinclair et al., 2006) ; 26 ng/L (Boulanger et al., 2005 );
43 and perfluorooctanoic acid (PFOA) in the on campus treated wastewater site that fluctuated between 92.7 1 10 ng/L Overall, the concentrations of PFCs tend to be similar among sampling periods, with PFOS, PFOA and perfluorohexanoic acid (PFHxA) being the major PFCs detected in these waters. Also, the actual PFCs determined from exposure waters were similar to concentrations shown in the historical data ( Table 2 1), with the exception of PFOS concentrations in runoff wastewater that were lower (Figure 2 1). The overall PFCs trend observed for all periods was PFOA> PFHxA > PFOS. The highest individual PFC was PFOA (118.9 ng/L ) in on campus treated wastewater site (Figure 2 1). Runoff wastewater (RW) and treated city wastewater had similar patterns of individual PFC concentrations, with PFOS concentrations of 14.3 to 21.2 ng/L respectively. In our study, conc entrations of PFOS ranged from 97 to 170 ng/L in treated city wastewater. These concentrations are in the high range compared with other studies (Plumlee et al., 2008; Sinclair and Kannan, 2006) In the case of PFOA, the concentrations determined were in the middle range (17.1 37 ng/L ) when compared with other studies (Ahrens, 2011; Plumlee et al., 2008; Sinclair and Kannan, 2006) General Biological R esponse There was no fish mortality in any of the treatments. Among all water types runoff wastewater a ltered the highest number of genes (1028 748 of which were well annotated ), followed by treated city wastewater (787, 655 annotated genes), and on campus treated wastewater (559, 344 annotated genes) compared to controls (ANOVA, p < 0.05 ). The treated cit y wastewater used here was collected downstream of a wastewater treatment plant, thus we expected similar biological responses between treated city wastewater and on campus treated wastewater. Unexpectedly, we found
44 more similarities in biological respons es between runoff wastewater and treated city wastewater. C oincidently the composition of PFCs and the concentrations determined in runoff wastewater and treated city wastewater was very s imilar for these two sites (F igure 1). When changes in steady state levels of mRNAs were compared among the groups, the patterns of up and down regulated genes were similar for runoff wastewater and treated city wastewater and less similar compared to on campus treated wastewater (Supporting information, Appendix A Tabl es S5 S7 contain lists of annotated genes that are differentially regulated for each of these scenarios). In Figure 2 2 we analyze the entire set of differentially expressed genes together and they are listed in order of their fold expression over control s for the runoff wastewater site (Figure 2 2A), followed by treated city wastewater (Figure 2 2 B) and then by on campus treated wastewater (Figure 2 2 C). If the expression of a gene was not significant for a particular treatment, we set the expressio n to zero, so that we could focus only on those genes that had a p value < 0.05 and compare them across treatments. In general the signals from runoff wastewater and treated city wastewater were stronger (higher fold change and larger number of genes chan ged). Cluster A nalysis To examine transcriptional response patterns across water types we performed a cluster analysis of the genes identified as significantly regulated ( p < 0.01) after 48 h of exposure The hierarchical clustering of all samples (Figur e 2 3 ) group s the fi sh exposed to on campus treated wastewater in a different node, suggesting that the transcriptional response pattern for these fish is distinct. In the case of fish exposed to runoff wastewater, the cluster analysis forms a nearly dist inct group except for one
45 individual that was grouped within the treated city wastewater group effectively splitting the treated city wastewater group This analysis reinforces the previous gene ordering analysis (Figure 2 2) suggesting that there are more similarities between fish exposed to treated city wastewater and runoff wastewater than fish exposed to on campus treated wastewater. Enrichment A nalysis Enrichment analysis (Subramanian et al., 2005) showed overrepresented GO term categories in the set of regulated genes from the FHM array for groups of fish exposed to each water type (Fisher exact test, p < 0.05 ). Eighteen biologi cal processes were overrepresented for runoff wastewater, 19 for treated city wastewater, and 12 for on campus treated wast ewater Selected biological processes affected along with the Fisher exact test values are listed in Table 2 2 the complete list of biological processes affected are in Table S8 (Supporting information Appendix A ) Three common biological processes that were overrepresented in fish exposed to runoff wastewater and treated city wastewater were GO: 0008299 isoprenoid biosynthetic process ; GO: 0007050 cell cycle arrest ; and GO:0007049, cell cycle However only one biological process (GO: 0006298 mismatch rep air ) was commonly overrepresented in treated city wastewater and on campus treated wastewater exposed fish Overrepresentation of DNA damage related genes were present for all water types. Enriched sub network analysis showed down regulation of DNA repli cation ( p < 0.001) for runoff wastewater and protein folding ( p < 0.001) for treated city wastewater exposed fish (Table S9 Supporting information Appendix A ). Also, fish exposed to runoff wastewater showed up regulation of the metallothionein 2 gene (2.8 fold change; p = 0.04) along with several genes related with zinc and metal transport (Table S5,
46 Supporting information Appendix A ) suggesting that this water also contained metal contamination. For on campus treated watewater the sub network enrichment analysis showed down regulation for protein folding ( p < 0.01) and up regulation for DNA repair processes ( p < 0.01 ) (Figure S 9 Supporting Information Appendix A ). System Biology A nalysis From the larger set of genes that were differentially regulated ( ANOVA p < 0.05), a subset of genes that have corresponding human homologs were selected to examine cellular pathways that may have been altered by the exposures using PathwayStudio TM This analysis helps to visualize interactions among gene products and th eir cellular localization s (Garcia Reyero et al., 2009) Within the enriched pathways with the lowest p values ( p <0.001) for both runoff wastewater and treated city wastewater were isoprenoid ; sterol ; cholesterol ; lipid and steroid biosynthetic process pat hways (Table S9 Supporting information Appendix A ). This is very interesting because it is made up of genes involved in cholesterol biosynthesis. Response to drug pathway was another common enriched pathway between runoff wastewater and treated city was tewater ( p < 0.001). In the on campus treated wastewater group enriched pathways ( p < 0.002) were mainly related with apoptosis processes such as anti apoptosis apoptosis regulation of apoptosis induction of apoptosis induction of apoptosis by extrac ellular signals Also there were several processes involved with cell membranes, such as c ell cell junction organization ; cell junction assembly ; cell wall macromolecule catabolic process Moreover, the m ismatch repair process was targeted for all exposed fish (runoff wastewater, p < 0.01; treated city wastewater, p < 0.003; and on campus treated wastewater, p < 0.001).
47 Cholesterol Related G enes Among the most altered genes was a group related to cholesterol metabolism including HMGR ( 3 hydroxy 3 methy lglu taryl coenzyme A reductase) that was down regulated by 13 and 6.6 fold change for runoff wastewater and treated city wastewater exposed fish, respectively. Another down regulated transcript, the MVD (mevalonate pyrophosphate decarboxylase) showed 2.94 and 3.05 fold change for runoff wastewater and treated city wastewater exposed fish, respectively. Both genes (HMGR and MVD) are part of the mevalonate pathway, related to cholesterol metabolism pathways (Figure 4 A, B). Other genes in this pathway such as FDPS (farnesyl pyrophosphate synthetase) and IDI1 (I sopentenyl diphosphate delta isomerase 1 ) showed 2.56 and 2.18 fold down regulation respectively in treated city wastewater (Figure 4A). In our study, two other sterol metabolism related transcripts were down regulated including Sterol C5 desaturase Homolog (2.6 and 2.5 fold) and Sterol C4 methyl Oxidase like (5.5 and 3.5 fold) for runoff wastewater (RW) and treated city wastewater (TCW), respectively, suggesting a dominant effect on sterol biosynthe sis molecules. Generating hypotheses Overall our result helps to generate hypotheses about the potential causes of these patterns PFCs are most likely responsible for the pattern observed. Alternatives hypotheses to explain some of the effects, such as dioxin like effects, are also plausible. For example, dioxins are capable to a cause hypercholesterolemia and alter cholesterol genes in rats (i.e. Fletcher et al., 2005) Since Aryl Hydroc arbon Receptor 2 genes were slightly up regulated with 1.7 and 1.6 for RW and TCW respectively the dioxins can be marked down as responsible for the pattern observed. Nevertheless, the
48 expression of cytochrome p450 enzyme s such as CYP1A were not altered in these fish, indeed no cytochrome enzyme s were altered except for Cytochrome P450, Family 51 which were down regulated 4.7 and 2.5 fold for RW and TCW respectively. It is known the ability of dioxins to activate the aryl h ydrocarbon r eceptor but this is t ightly related with the up regulation of genes such as CYP1A. In fish, as in other organism s CYP1A is considered the gene most strongly induced enzyme by dioxins (Carney et al., 2006; Handley Goldstone et al., 200 5) Thus we focus ed the analysis on the PFCs like effects. W astewater (Kolpin et al., 2002; Plumlee et al., 2008; Tang et al., 2006) and r unoff (Ahrens, 2011; Furl et al., 2011; Murakami et al., 2009) are known sources of PFCs in the environment. It is k nown that PFOS and PFOA are the most commonly detected PFCs in the aquatic environment (Ahrens, 2011) with PFOS being more frequently detected in biota (Houde et al., 2011; Meyer et al., 2009) Both of these contaminants have been noted in previous studi es to cause the disruption of fatty acid metabolism, lipid and cholesterol transport (Wei et al., 2008) cell membrane related effects (Hu et al., 2002) oxidative stress and apoptosis (Liu et al., 2007) In general, the effects on the transcriptional res ponse observed in fish exposed to waters from all si tes, derived for all the analyses made here, are related to fatty acid metabolism (and cholesterol) DNA repair, oxidation reduction process es, cell membrane related processes and apoptosis These findin gs suggest that the effluents were capable of modifying key transcripts required for fish genome integrity which could potentially cause deleterious effects such as ca rcinogenesis on aquatic biota in the wildlife refuge sites where these effluents go throu gh.
49 Two pathways in particular were most conspicuous; cholesterol biosynthesis and DNA repair, both of which are likely adverse outcome pathways since it is expected that severe alterations of these pathways would have repercussions in reproduction (Tocher 2003 ) or tumor i genesis (David et al., 2004) We observe that f ish exposed to runoff wastewater and treated city wastewater showed similarities in their decreased steady state levels of transcripts for several key intermediates in cholesterol biosynthes is Currently, it is well known that fish liver is an important target organ of PFCs (Lau et al., 2007; Wei et al., 2008; Wei et al., 2009) and the predominant effects observed by other s research in biota are related with cholesterol biosynthesis (Haughom and Spydevold, 1992; Luebker et al., 2002; Wei et al., 2008; Wei et al., 2009) The biggest effect observed in fish exposed to two of the water types, runoff wastewater and treated city wastewater was the down regulation of 3 hydroxy 3 methylglutaryl Coen zyme A Reductase (HMGR), a limiting step in th e cholesterol biosynthesis pathway (Estey et al., 2008) HMGR is found in virtually all tissues, but the liver expresses one of the highest levels of this enzyme (Ness and Chambers, 2000) Consequently, t he l iver play s a major role in regulating cholesterol levels (Turley and Spady, 1993) Moreover, the shutdown of genes involved in the metabolism of cholesterol was in agreement with the elevated levels of cholesterol measured in plasma. For on campus treat ed wastewater exposed fish the most down regulated genes were the CDH11 (cadherin 11, type 2) and HSP90B1 ( heat shock protein 90 kDa beta ) with 19.3 fold and 2.8 fold down regulation, respectively, and the most up regulated genes were CYP27B1 ( cytochrome P450, famil y 27, subfamily B, polypeptide 1), G6PD ( glucose 6 phosphate dehydrogenase ) and RAD23B ( RAD23 homolog B )
50 (2.86, 2.27 and 2.90 fold change, respectively). Pathway analysis showed down regulated genes connected with membrane related cell process n etworks ( Figure 4, C) PFOA was the most predominant PFC in our On campus treated wastewater (97.2 118.3 ng/L Table 2 1) For fish exposed to this site, the main sub sets of altered transcripts were related to the DNA repair process (up regulation) and pr otein folding (down regulation) (Figure S2, Supporting information Appendix A ). DNA metabolism disturbance has been observed in liver tissue in fish exposed to PFCs (Hoff et al., 2003) Enrichment analysis showed over representation of biological proces ses such as biosynthesis of phospholipids and cell wall catabolism in exposed fish (Table 2 2). In addition, membrane related genes such as MPP5 (membrane protein, palmitoylated 5); LYSMD3 (LysM, putative peptidoglyca n binding, domain containing 3), and C DH11 (cadherin 11, type 2) were over represented. A ll of these genes encode proteins related w ith cell cell adhesion (Figure 4 C) and are related to other proteins encoded by INADL (InaD like) which are localized in tight junctions. PFC membrane related ef fects have been observed previously in vitro (Upham et al., 1998) and in vivo (Hu et al., 2002) in rat liver. Particularly Hu et al. (2003) suggests that the amphiphillic properties of PFCs can primarily disturb cell membranes in hepatoma cells. Moreover effects on membrane integrity in fish exposed to PFCs have also been observed (Hoff et al., 2003) Cholesterol Levels in Plasma Interestingly, fish exposed to treated city wastewater and runoff wastewater; revealed significantly higher levels of total ch olesterol (Figure 5) compared with the control group (one tail t test; p< 0.05). Cholesterol metabolism is very fine tuned, thus light variations in cholesterol plasma levels represent a bigger change in the amount of
51 cholesterol on fish. Fish exposed to on campus treated wastewater did not show differences in plasma cholesterol levels compared to controls. Overall i mplications It is well known that the main pollutants, in terms of concentrations in urban waters, are nutrients (Schwarzenbach et al., 2006; Walsh et al., 2005), but the presence of toxicants, even at much lower concentrations than nutrients, can disturb aquatic biota, particularly fish (Kidd et al., 2007) Our findings suggest that concentrations of toxicants at levels typical of urban wastew aters can affect relevant processes such as DNA repair, cholesterol biosynthesis, oxidation reduction cell wall catabolism, and apoptos is. Cholesterol biosynthesis is required for sex hormone production and other researchers found that circulating sex st eroids in fish, including fathead minnows, can be affected by exposure to fluorinated surfactants at higher exposure concentrations (Ankley et al., 2005; Oakes et al., 2005; Oakes et al., 2004) Moreover, long term exposure of sexually mature fathead minn ows to PFOA depressed estradiol, testosterone, and 11 ketotestosterone concentrations in both males and females (Oakes et al., 2004) The surfactant properties of PFCs are likely to cause damage to cell membranes (Beach et al., 2006) and increase the perme ability of other toxic compounds (Hu et al., 2003), which will further increase damage to cell homeostasis. This may be the source for DNA damage. The waters used here are complex mixtures of chemicals with environmentally relevant concentrations of PFC s. Exposure to these waters induced alteration in adverse pathways that have previously been identified in studies using much higher concentrations (g to mg/L) of individual PFCs or PFC mixtures. The main identified signatures, for example in the lipid b iosynthetic process cholesterol biosynthesis, and
52 cell membrane related effects and DNA damage suggests a strong influence of PFCs. Other signatures also were present, probably due to additional chemicals typically found in wastewater effluents, but non e of the predominant signatures point to a specific type of contaminant. The fathead minnow is the most sensitive species to PFC exposure (Beach et al., 2006), thus it is likely that it is responding to the PFCs present in the water in the same way that o ther organisms respond to much higher concentrations (g/L to mg/L) in laboratory in vivo studies. Exposures using similar low concentration range s of PFCs (ng/L) in our laboratory confirm that low concentrations of PFCs are able to alter FHM at transcript ional levels. It is relevant to highlight the impact of water pollution that enters wildlife preservation areas since are these areas the one that will help to maintain the biodiversity which is currently in crisis. Summary of Findings In conclusion, urba n waters were able to exert effects at transcriptional levels on genes that play a vital role in fish reproduction and survival. Particularly interesting was the fact that the transcripts of genes such as HGMR, which is key in various metabolic pathways we re down regulated suggesting effects on pivotal molecules in fish physiology like cholesterol. These could have several physiological repercussions from cell membrane stability to sex hormones production. Additionally, transcripts from genes used in DNA r epair mechanisms were altered in all exposed fish. Although the cell uses these genes as a repair mechanism against DNA attacks, this outcome is not guaranteed, especially when the mechanism is in constant challenge.
53 Table 2 1 Perflurochemicals detecte d in urban waters in Gainesville area, Florida 27 and current study. TCW a TCW b OTW b RW c TCW c OTW c Control c Field Blank c Perfluoro Butanoic Acid (PFBA) NM NM NM 7.5 3.4 6.8 3.24 2.98 Perfluoro Pentanoic Acid (PFPA) 4.6 8.2 2 1 NM NM NM NM NM Perfluoro Hexanoic Acid (PFHxA) 16 18 36 13.7 21.8 30.8 ND ND Perfluoro Heptanoic Acid (PFHpA) 5.2 6.8 9.2 12.3 13 13.8 ND ND Perfluoro Octanoic Acid (PFOA) 37 35 110 23.9 40.8 118.9 1.1 ND Perfluoro Nonanoic Acid (PFNA) 22 26 15 4 8.4 11.3 ND ND Perfluoro Decanoic Acid (PFDA) 4.7 2.8 16 3.7 11.1 31.6 ND ND Perfluoro Undecanoic Acid (PFUnA) 0.89 ND 1.1 ND ND ND ND ND Perfluoro Octylsulfonic Acid (PFOS) 170 97 6.5 14.3 21.2 5.7 ND ND Perfluoro Dodecanoic Acid (PFDoA) ND 1 0.6 1 ND ND ND ND ND Perfluoro Tridecanoic Acid (PFTrDA) NM NM NM ND ND ND ND ND Perfluoro Tetradecanoic acid (PFTeDA ) NM NM NM ND ND ND ND ND ND: Not Determined; NM: Not Measured a Feb 2009 sampling; b August 2009 sampling ; c Current study.
54 Table 2 2. Selected GO biological processes altered in fish liver from each site after 48 hours exposure (Fisher exact test) (p<0.05). GO Biological Process Category Fisher Raw p value Runoff wastewater GO:0008299; isoprenoid biosynthetic process 0.0003844 GO:0007050; cell cycle arrest 0.0024918 GO:0007049; cell cycle 0.0074055 GO:0006512; ubiquitin cycle 0.0211385 GO:0000226; microtubule cytoskeleton organization and biogenesis 0.0218518 GO:0006284; base excision repair 0.0387724 GO:0016310; phosphory lation 0.0387724 GO:0048268; clathrin cage assembly 0.0387724 GO:0050930; induction of positive chemotaxis 0.0387724 GO:0008202; steroid metabolic process 0.0388635 GO:0009615; response to virus 0.0388635 GO:0016481; negative regulation of transcripti on 0.0485779 GO:0006614; srp dependent cotranslational protein targeting to membrane 0.0494227 GO:0042742; defense response to bacterium 0.0494227 Treated city wastewater GO:0008299; isoprenoid biosynthetic process 1.00E 04 GO:0006457; protein foldi ng 0.0012033 GO:0007050; cell cycle arrest 0.0025079 GO:0006614; srp dependent cotranslational protein targeting to membrane 0.0030573 GO:0006605; protein targeting 0.0084778 GO:0001889; liver development 0.0103108 GO:0007275; multicellular organismal development 0.0140328 GO:0006812; cation transport 0.0155787 GO:0016310; phosphorylation 0.0230372 GO:0048268; clathrin cage assembly 0.0230372 GO:0042127; regulation of cell proliferation 0.0296971 GO:0007049; cell cycle 0.0362156 GO:0006298; misma tch repair 0.036921 GO:0009058; biosynthetic process 0.0419263 GO:0016568; chromatin modification 0.0419263
55 Table 2 2. Continue d GO Biological Process Category Fisher Raw p value On campus treated w astewater GO:0005975; carbohydrate metabolic process 0.0031858 GO:0030154; cell differentiation 0.0051887 GO:0042981; regulation of apoptosis 0.015963 GO:0006298; mismatch repair 0.0179481 GO:0016998; cell wall catabolic process 0.0179481 GO:0043065; positive regulation of apoptosis 0.0189263 GO:0007275; multicellular organismal development 0.0200178 GO:0006289; nucleotide excision repair 0.0307637 GO:0051260; protein homooligomerization 0.0307637 GO:0006816; calcium ion transport 0.0326926 GO:0007018; microtubule based movement 0.0381376 GO:0008654; phospholipid biosynthetic process 0.0457857
56 F igure 2 1. Main PFCs determined in water used for fish exposure. RW TCW OTW Sites
57 Figure 2 2. Comparison of overall gene regulation from liver samples among sites (ANOVA p < 0.05 ) A) Treated cit y wastewater (TCW); B) Runoff wastewater (RW) and C) On campus treated wastewater (OTW) Genes are expressed as fold change over expression of control fish. All genes were arranged from the most highly up regulated to most highly down regulated for all sit es based on the sequential order from sites A to B to C Genes whose fold expression data w ere not significant at p value < 0.05 were set to 0.
58 Figure 2 3. Gene cluster analysis (p < 0.05 ; fold change > 1.5) for co ntrols and each of the three urban wate r types. OTW: On campus treated wastewater; RW: Runoff wastewater; TCW: Treated city wastewater; CTL: Control. Red color represents up regulated genes, green color represents down regulated genes, and black color represents no change, with respect to the m edian expression level for each gene.
59 Figure 2 4. Pathway studio analysis representation of highly affecte d genes from each of 3 sites A) Treated city wastewater (TCW); B) Runoff wastewater (RW) and C) On campus treated wastewater (OTW) Blue: Do wn regulated genes, Red: Up regulated genes Green: Cellular complex. Abbreviations: IDI1 : isopentenyl diphosphate delta isomerase 1 ; IDI2 : isopentenyl diphosphate delta isomerase 2 ; MVK : mevalonate kinase ; OSBP: Oxysterol binding protein; MVD: mevalonate pyrophosphate decarboxylase; HMGCR: 3 hydroxy 3 methylglutaryl coenzyme A reductase; FDPS: farnesyl pyrophosphate synthetase ; PVRL4 : Poliovirus receptor like proteins ; MPP 5 : membrane protein, palmitoylated 5 (MAGUK p55 subfamily member 5 ); LYSMD3 : LysM, p utative peptidoglycan binding, domain containing 3 ; NCOA7: nuclear receptor coactivator 7 ; MYC v myc myelocytomatosis viral oncogene homolog ; DLG1 : discs, large homolog 1 CRELD2: cysteine rich with epidermal growth factor like domains 2 ATP2A1: ATPase, C a++ transporting, cardiac muscle, fast twitch 1. INADL: InaD like
60 Figure 2 4 Continued
61 Figure 2 4 Continued
62 Figure 2 Control; TCW: Treated City Wastew ater; RW: Runoff Wastewater; OTW: On Campus Treated wastewater. Significantly different from Control group (One tail T test; p< 0.05).
63 CHAPTER 3 PERFLUOROCHEMICALS IN AN URBAN STREAM: THE OCURRENCE OF PFCs IN TREATED WASTEWATERS INSIDE A WILDLIFE PRES ERVE Introduction E cological degradation has been consistently observed in urban streams Stressors such as combin ed or sanitary sewer overflows and wastewater treatment plant effluents (WWTPs) are listed as causes of urban streams impairment (Walsh et al. 2005). Historically, nutrients (nitrogen and phosphorus) were major pollutants discharged in wastewater from WWTPs ( Schwarzenbach et al., 2006). As such, WWTPs are now required to meet nutrient threshold limits when discharging wastewater in to surface wa ter bodies. The implementation of WWTPs to control the gross pollution positively changed the quality of surface waters as well as help ed restore aquatic biodiversity (Sumpter, 2009) However, it is now known that several organ ic contaminants including per fluorinated compounds (PFCs) are not effectively removed in WWTPs and persist in discharged wastewater (Ratola et al 2012). Various sources of the highly persistent and potentially toxic PFCs include WWTPs, leachate from land fill, and stormwater runoff from streets ( Ahrens et al., 2009; Bossi et al., 2008; Schultz et al., 2006). WWTPs are considered a major source of PFCs, particularly those WWTPs that treat industrial effluents (Sinclair and Kannan, 2006; Becker et al., 2008 a ) It is well known that PFCs are chemicals with high environmental persistence as well as they escape removal in WWTPs (Ahrens 2011 ; Giesy et al., 2001; K im and Kannan 2007). Further, WWTP can augment (in plant production) the amount of PFCs in wastewate r due to precursor degradation (Heidler and Halden 2008). However the net mass balance increase of PFCs in WWTP effluents depends on PFCs functional groups (Guo et al., 2010). For example, in the
64 WWTP effluent, concentrations of perfluoro octanoic sulfon ate (PFOS) tend to decrease while perfluoro octanoic acid (PFOA) increase (Pan et al., 2011; Guo et al., 2010; Schultz et al., 2006). PFCs input from WWTPs to the receiving water bodies seems to remain constant throughout the year, as no major seasonal cha nges in their concentrations have been observed in research done on wastewater dominant aquatic systems ( Arvaniti et al., 2012; Yu et al., 2009; Ahrens et al., 2009; Loganathan et al., 2007). Due to the highest global occurrence and persistence of dominant types of PFCs (PFOA and PFOS) in various environmental matrices (reviewed by Houde et al., 2011), monitoring the presence of an expanded group of PFCs is necessary to understand their impacts on ecological health (Liu et al., 2013). Furthermore, in the co ntext of urban expansion and the corresponding impact on protected areas ( Radeloff et al., 2010), it is important to protect the areas set aside to preserve nature. Within the known threats that preserve areas face, pollution is mentioned (Machils, 1985) b ut in depth assessment is typically missing. In the State of Florida, the system of parks and recreation areas aims to provide resource based recreation while preserving and restoring natural resources. Six State parks are located within the Alachua County boundary (Florida St.) including two preserve parks. Treated wastewater from a WWTP is discharged in to Sweetwater branch, a highly modified stream that Payne's Prairie is a 5600 ha sub basin resulting from solution of the underlying limestone ( Jacobs et al., 2002) located 10 miles south of Gainesville city, Alachua County The p resence of limestone beneath much of the surface has resulted in the formation of sinkholes, large shallow lakes and broad wet prairies ( Ja cobs et al., 2002) A report from Alachua County Environmental
65 Protection Department show s the presence of several o rganic c ontaminants including PFCs in Sweetwater branch ( Table 3 1) (ACEPD 2010 ) The preliminary determination of relatively high concentr ations of the persistent PFCs in Sweetwater branch and their potential toxicity concerns prompted the need to investigate the longitudinal occurrence of PFCs from the source (WWTP) to sink (where surface water flows to groundwater) in order to determine th eir potential impacts on aquatic and terrestrial biota. T he hypothesis was that due to the h igh persistence of PFC s in the environment, their concentrations will be similar from source to final destination in a stream that passes throughout two wildlife pr eserve s Materials and Method s Sample Collection and Storage G rab surface water samples were collected two times each in the dry (October May) and wet (June September) season during 2012 2013 water year To cover the longitudinal area along Sweetwater bran ch, a sampling point was set every 1 km starting immediately downstream of a WWTP (point 0, Figure 3 1 ) and finishing at the Alachua sinkhole. This resulted in six sampling points along the 5 km stretch of the stream. A total of 48 samples were collected ( 6 sites x 2 samples per site x 4 times in a year). A field blank was taken at the last sampling site for each sampling day (a total of 4 samples). At each sampling site, a one liter water sample was taken, in duplicate, in pre washed polypropylene (PP) bot tles because PFCs may be adsorbed onto glas s surfaces (Martin et al., 2004). Samples were collected in flowing water 10 to 20 cm below the surface Before sampling, all PP bottles were washed three times with soap, then three times with deionized water and finally with HPLC grade methanol.
66 A customized blend of Tris [Tris (hydroxymethyl) amino methane] and Tris HCL [Tris (hydroxymethyl) amino methane hydrochloride] with a weigh t ratio of 15.5/1 Tris HCL/TrisBase was prepared Five grams of this blend was a dded into cleaned PP bottles before sampling to produce a pH near 7.0 at 25 C. This blend functions as a buffer, and removes free chlorine in chlorinated waters such as treated wastewater. Finally, the level of water was marked on the sample bottles for f inal volume determination after extraction. All samples were transported on ice to the laboratory, st ored at 20 C o an d analyzed within two weeks of the sampling date. No filtration step was required for water samples as there was not a high amount of su spended particulate matter thus solid phase extraction (SPE) cartridges were not clogged Historical data that includes other organic pollutant besides PFCs was provided by Alachua Environmental Protection Department (Table 3 1). Solid Phase Extraction Eleven types of PFCs were analyzed: PFBA (Perfluorobutanoate); PFHxA (Perfluorohexanoic acid); PFHpA (Perfluoroheptanoic acid); PFOA (Perfluorooctanoic acid); PFO S (Perfluorooctanesulfonic acid); PFNA ( Perfluorononanoic acid ) ; PFDA (P erfluorodecanoic acid ) ; PFUdA (P erfluoroundecanoic acid ) ; PFDoA (P erfluorododecanoic acid ) ; PFTrDA ( Perfluorotridecanoic acid ) ; PFTeDA ( Perfluorotetradecanoic acid ). The detection of PFCs was performed using a high performance liquid chromatograph couple d with tandem mass spect rometry (HPLC/ESI MS/MS) The limit of detection was 1.0 ng/L and the limit of quantification was 4 ng/L A rigorous quality control was performed using standards as suggested by others ( Shoemaker et al.,
67 2009; Ahrens et al., 2010 b ). In brief, mass labele d perfluoroalkylcarboxylic aci d (MPFOA: Perfluoro n [1,2,3,4 13C4] octanoic acid ) and a mass l abeled perfluoroalkylsulfonate (MPFOS: Sodium perfluoro 1 [1,2,3,4 13C4]octanesulfonate ) were purchased from Wellington Labs and used as internal standards (IS). Dilutions of IS were prepared to achieve 40 ng/L of MPFOA and 40 ng/L MPFOS in each sample. IS were added to each sample before the beginning of the solid phase extraction (SPE). Oasis HLB 6 cc cartridges (Waters) with 200 mg sorbent per cartridge and 30 m particle size were used for the SPE of the water samples. A previous preparation of the cartridge with 15 mL of methanol was performed, followed by a rinse of the cartridge with 18 mL of reagent water, without allowing the water to drop below the top ed ge of the packing Polypropylene tubing (also pre washed with methanol) was used to transfer the water from bottles to cartridges. The vacuum was adjusted in the manifold to allow two drops of water per second approximately. Finally, the extract was concen trated to dryness with nitrogen in a heated water bath, and then adjusted to a 1 mL volume with 96:4% (vol/vol) methanol: water after and injected to the liquid chromatographer. HPLC an d Mass Spectrometry Analysis The analysis of PFC compounds was perform ed using an Agilent 1100 series liquid chromatograph coupled to a 4000 QTRAP mass spectrometer (AB sciex). A C18 and the injector to eliminate any flourochemicals from poly(tetrafluoroethylene) instrument components. After extraction and dryness all samples were solubilized in 1 mL of 96% methanol and 5 u L were injected into the column. Separation of compounds was do ne using 2mM
68 ammonium acetate solution as solvent A, and methanol as solvent B at a flow rate of 300 nL/min. The initial condition was set at 40% of solvent B and held for 2 min, followed by a gradient from 40% to 80% of solvent B in 16 min, and held for 2 min. Solvent B was increased to 100% in 1 min and held for another 2 min to ensure that all compounds would elute. Finally solvent B was dropped to 40% in 1 min and held for 7 min to allow the reconditioning of the column for the next injection. The colum n elution was directly sprayed into the mass spectrometer using a turbo V ion spray interface operated in negative mode. The instrument parameters were set as follow s: desolvation ray voltage 4500 V, nebulizer gas 1 and 2 were set at 20 AU and CAD was set high, and a dwell time of 40msec. Mass spectrometry data was acquired in MRM mode. All instrument parameters for PFC standards were optimized and at least two MRM transitions per compound were used whenever was possible and are shown in Table 3 2. Results A total of 52 water samples were collected (including 4 field blanks) for PFCs determination. Recoveries of PFCs ranged from 50 to 150% when labeled IS were used; these values are within the accepted limits of 67 to 127% (Ahrens et al., 2010b) From the 11 PFCs analyzed, seven were ng/L ) in the dry and wet season water samples (Tables 3 3 and 3 4). In both seasons, most detected PFCs were PFOS > PFOA > PFHxA > PFHpA > PFDA > PFNA Further, PFCs with 11 to 14 carbons backbone were below the detection limit and the smalle st PFCs in terms of its carbon backbone (Perfluoro butanoate 4 carbons ) was barely detected (Table 3 3, 3 4). During the wet season, the concentrations of detected PFCs were similar at all sampling points with a slight
69 reduction at the sinkhole site (Figu re 3 2). The reduction of PFCs concentration at the sinkhole sampling point is very likely due to dilution phenomena. At this location, the surface water from Sweetwater branch also receives input canal contributes water from Alac hua Lake (Protection, 2013) ; this means, the water on canal. In the dry season, PFCs concentrations in some cases were 3 fold higher than the previous upstream sampling point ( Table 3 3 and Figure 3 3). This anomaly in the concentration of PFCs may be the result of the diversion of all Sweetwater canal water; this means that the remaining stagnant water available during the dry season sampling results in a more concentrated dete ction of PFCs due to their inability to volatilize. Overall, PFCs with an even number of carbon s were dominant in water samples at all sampling points. Also, the p erfluoroheptanoic acid (PFHpA) / PFOA ratio at the sampling points w as lower than 1, confirm i ng that the source of PFCs are non atmospheric ( Simcik and Dorweiler, (2005) Simcik and Dorweiler (2005) determine d that the ratio between PFHpA and PFOA represent s the amount of a tmospheric or urban contribution of PFCs into a particular aquatic system. For example, if the PFHpA / PFOA ratio is higher than 1 then atmospheric deposition (rain) of PFCs is the leading cause. Contrary, if the ratio is smaller than 1 (as observed in th is research) it means that) the majority of PFCs determined in the water environment came from either runoff or a wastewater source s Discussion The use s of PFCs ( including direct ly from manufacturing or from products that contain PFCs ) can result in the i r direct or indirect ly (from degradation products) release
70 into the aquatic environment in several forms ( Boulanger et al., 2005; Prevedouros et al., 2006; Lau et al., 2007). PFCs can be toxic (e.g., Liu et al., 2013), bioaccumulative (e.g., Froese et al., 1998) and they constantly occur in wastewater dominant systems (e.g., Ahrens 2011 ). Thus, detailed data about their occurrence, composition, and concentrations in water bodies entering preservation areas will help to understand potential risk of chronic exposure as well as to devise management options to reduce their impact on wildlife. PFCs Concentration from Source to Sink In general, no substantial longitudinal differences in total PFCs concentrations were observed in the water body (except for the div ersion anomaly during the dry season). The most frequently detected PFCs ( PFOS > PFOA > PFHxA > PFHpA > PFDA > PFNA ) showed a consistent trend in all points, with a small reduction at the sinkhole sampling point. For instance, the trend of the two most con centrated PFCs was very consistent longitudinally. For example, during the wet season, the PFOA concentration range from point s in the first 4 kilometers was 41 45 ng/L (mean: 42 ng/L ), with a drop of 30 % (29 ng/L ) at the sinkhole point. In the case of PF OS, during the wet season the range was 93 111 ng/L (mean: 103 ng/L ) with a drop of 23% at the sinkhole point. This reduction at the sinkhole sampling point is very likely due to dilution. The contribution of the relatively PFCs free water from a third wa ter body that come s from this point. In the past, all of Newnans Lake flowed into Payne's Prairie but actually, due to channelization, just about the half of the flow now comes from this lake ( Jacobs et al., 2002) concentration roughly by a factor of 25 %.
71 for similar wastewater effl uents where PFOA tend s to be the dominant PFC (Zareitalabad et al., 2013) during these sampling events This contradicts the results from others researche r s such as Pan et al. (2011), Guo et al. (2010), and Schultz et al. (2006) who found that PFOS concent rations tend to decrease while PFOA tend s to increase after they pass through a WWTP. Moreover, Kim and Kannan (2007) investigated the amount of PFCs in s now, rain, surface runoff and lakes and reported that PFOA was the predominant PFCs type in any of the environmental matrices. Why is PFOS more prevalent in this stream? One p otential explanation includes the contribution of runoff from urban and commerc ial areas. Xiao et al. (2012) found that in the runoff from urban areas PFOS was dominant with concentrations followed by PFOA. Thus, runoff contribution also can expla in why the concentrations of PFCs in general are greater during the wet season and why PFOS is even more prevalent than PFOA during wet season. Also, u nlike PFOS, PFOA concentrations are heavil y influenced by point source type emissions from industrial fac ilities (Pistocchi and Loos, 2009) Seasonal Variability in PFCs Conce n trations As discussed above, among all PFCs types, PFOS was the most predominant in our wastewater dominant stream, with mean and median concentrations of 99 ng/L and 93 ng/L for the wet season and 96 ng/L and 49 ng/L for the dry season, respectively. PFOA was the second dominant PFC in the system, with mean and median concentrations of 41 ng/L and 39 n g/L for the wet season and 47 ng/L and 34 ng/L for the dry season, respectively. Nevertheless, if the abnormal points from the water diversion in the canal are taken out of the calculation, important seasonal differences can be observed. For insta nce, the average of the dry season for PFOS and
72 PFOA without considering the abnormal points is 42 and 31 ng/L respectively (versus 99 ng/L and 40 ng/L for the wet season). Thus the difference between wet and dry season are marked; mainly for PFOS with co ncentrations in the wet season that represent more than 50% of the dry season. Seasonal or time trends of PFCs concentration in surface waters are not common findings. For example, Sakurai et al., (2010 ) found no seasonal trends in a 2 year study in Tokyo, Japan. In an other study from Japan, Tsuda et al., (2010) found seasonal differences for PFOS and PFOA but just in two out of 14 sampling sites. Average annual precipitation for Gainesville city area is 1370 millimeters. More than half of this amoun t falls from June to September. August, the month with the greatest precipitation, averages 208 millimeters. The least precipitation occurs in November, averaging 44 millimeters (Easley and Judd, 1990). The general higher concentrations, which were particu larly high for PFOS concentrations observed, are likely due to street runoff from urban areas in Gainesville. Comparing the mean and median concentrations values of PFOS and PFOA in our system with other s reported elsewhere (reviewed by Ahrens, 2011; Zarei talabad et al., 2013), it is clear that our stream is receiving unusual amounts of PFCs. For example, in the WWTP effluents, the reported PFOS and PFOA median concentrations are 11 ng /L and 24 ng/L respectively (Zareitalabad et al., 2013). Thus, in our study site, the median concentrations of PFOS and PFOA were 8 and 1.6 times greater during the wet season and 4.5 and 1.4 times greater during the dry season, respectively. Restoration Project A proposed Sweetwater Branch Sheet Flow Restoration Proje ct has been designed primarily to reduce the excess loads of total nitrogen ( TN ) total phosphorous
73 ( TP ) and total suspended solids ( TSS Sweetwater Branch (FDEP, 2006). This restoration effort is in place in ord er to comply with designated total maximum daily loads (TDMLs). Currently, several stages of this restoration project has been completed, including the construction of a wetland cell (an artificial wetland ) in order to restore historical sheet flow in Payn the dry season sampling, the two p oints downstream of the restoration project specifically downstream fr o m the first constructed cell (diversion point) show ed an abnormally high concentration of PFCs in relation to the concentrat ion measured up stream and replicates) show an increase of 50% of more in the amount of PFCS with eight or nine carbons. The amount of water in the canal in those two points was ve ry low during the two dry season sampling s (almost completely dry). Thus due to the diversion of the water in this stream, the remnant stagnant water could evaporate producing a very concentrate d amount s of eight and nine carbons PFCs that are unlikely to volatilize (Ahrens, 2011) Final Considerations PFCs have been detected in wildlife worldwide (Giesy and Kannan, 2001; Houde et al., 2011) and it is considered that they could be the most prevalent contaminant in human blood (Kannan et al., 2004) The toxic effects of PFCs in aquatic biota have been recognized (reviewed by Lau et al. 2007) Nonetheless, most of literature considers that the typical concentrations found in the aquatic environment are too low to be consider ed a risk for aquatic biota. Lin et al., (2010) discussed that high levels of PFCs (e.g. PFOS 293 ng/L and perfluo rohexanoic acid 406 ng/L ) which are at least twice the levels found here pose a risk for aquatic communities downstream of
74 WWTPs. But the traditional approach to determine toxicity does not take in consideration sub chronic effects or more sensitive endpo ints as gene expression changes. The new approaches to evaluate toxicity are pushing down the levels that are considered dangerous. Moreover, since protected areas are set aside to maintain biodiversity, special consideration should be taken in order to p rotect these types of areas. Surprisingly, aquatic pollution impacts on protected areas are rarely if not completely absent in the scientific literature. The occasional research papers that discuss pollution pressure on protected areas highlight the impact of the types of pollution that apparently are less obvious and harmful such as light, air and noise pollution (i.e. Radeloff et al., 2010; Usenko et al., 2010). Considering the current biodiversity crisis the known impact of pollutant s on wildlife and the recognized ability of preserved areas to conserve biodiversity enhance of understanding of the presence (as well as toxicity) of pollutants inside preservation areas is urgent. Also, s ources of water flowing into the basin include the Sweetwater Branch, which drains Eastern Gainesville and has along its course a WWTP flows through a canal and enters directly into LaChua Sink ( Jacobs et al., 2002) T he existence of geological formations as karst features that create sinkholes (Katz et a l., 1995) provides natural hydrological connections between surface waters and groundwater. Since water for human consumption is retrieved from the groundwater investigation of the occurrence of highly persistent chemicals such as PFCs is increasingly imp ortant.
75 Summary of Findings PFCs were detected along all stream sampling sites with a minor reduction in concentration in the sinkhole area, likely due to dilution. The m ost detected PFCs were PFOS>PFOA>PFHxA>PFHpA>PFDA>PFNA The Perfluoroheptanoic acid ( PFHpA) / PFOA ratio for all sites investigated here w as lower than 1, confirming that wastewater effluent was a major source of PFCs. Two p oints downstream of restoration projects s of PFOS (156 and 225 ng/L ) and PFOA (64 and 8 0 ng/L ), but further research is needed to determine this phenomenon Overall, this system receives relatively high amount s of PFCs compared to global concentrations for similar water bodies.
76 Table 3 1. Concentrations (ng/L) of organic compounds in histo rical samples (ACEPD, 2010) Compound Feb 09 Aug 09 Atrazine 2.6 2.5 Bisphenol A 22 ND Caffeine 41 ND Carbamazepine 330 48 DEET 110 130 Diazepam 19 1.4 Diclofenac ND 17 Fluoxetine 27 17 Gemfibrozil 36 ND Iopromide 170 420 Meprobamat e 35 55 Phenytoin 180 90 Methadone 11 ND Salicylic acid 320 11 Perfluoropentanoic Acid 4.6 8.2 Perfluorohexanoic Acid 16 18 Perfluoroheptanoic Acid 5.2 6.8 Perfluorooctanoic Acid (PFOA) 37 35 Perfluorononanoic Acid 22 26 Perfluorodecanoi c Acid 4.7 2.8 Perfluoroundecanoic Acid 0.89 ND Perfluorobutanesulfonic Acid 5.4 7.7 Perfluorohexylsulfonic Acid 12 11 Perfluorooctylsulfonic Acid (PFOS) 170 97 Perfluorododecanoic Acid ND 1
77 Table 3 2. Transitions numbers, limits of determ ination (LOD) an d limit of quantification (LOQ) used for PFCs determination ID Monitoring transitions STDev 3 STDev 10 LOD (ng/mL) LOQ (ng/mL) PFBA 384 126.5 0.0001 0.00032 PFHxA 458.5 199.5 0.00009 0.0002 PFHpA 403.9 125 .7 0.0001 0.00032 PFOA 423.6 143.2 0.00009 0.00028 PFNA 444.9 138.1 0.00009 0.00029 LPFOSK 399.3 151 0.0001 0.00026 PFDA 42 5.3 107.5 0.00009 0.00037 PFUdA 404 114.4 0.0001 0.00035 PFDoA 457.6 167.3 0.00009 0.00024 PFTrDA 401.2 122.7 0.0001 0.00033 PFTeDA 399.1 156.8 0.0001 0.00026 713 MPFOA 394.5 168.1 0.0001 0.00024 MPFOS 399.3 151 0.0001 0.00026
78 Table 3 3 Average PFCs concentrations (ng/L) determine in Sweetwat er B ranch downstream of a WWTP during the wet season, 2012. Wet Season PFCs Type Dist ance from WWTP Point 0 1k 2k 3k 4k Sink Field Blank PFBA (Perfluorobutanoate) 1.37 1.75 3.44 0.85 1.58 2.55 0.46 PFHxA (Perfluorohexanoic acid) 30.23 29.43 32.69 29.48 29.77 15.38 0.02 PFHpA (Perfluoroheptanoic acid) 13.28 14.61 15.87 15.14 15.68 13.1 4 0.02 PFOA (Perfluorooctanoic acid) 40.71 42.86 45.27 41.31 42.07 29.24 0.42 PFOS (Perfluorooctanesulfonic acid) 99.35 96.13 106.93 105.54 104.78 78.74 0.04 PFNA (Perfluorononanoic acid) 4.66 4.36 4.88 4.31 4.66 8.19 0.04 PFDA (Perfluorodecanoic acid) 10.54 8.71 8.30 7.67 7.38 3.64 0.05 PFUdA (Perfluoroundecanoic acid) 0.36 0.24 0.25 0.25 0.37 0.13 0.08 PFDoA (Perfluorododecanoic acid) 0.14 0.07 0.09 0.05 0.13 0.15 0.02 PFTrDA (Perfluorotridecanoic acid) 0.03 0.04 0.06 0.03 0.04 0.25 0.03 PFTeDA (P erfluorotetradecanoic acid) 0.02 0.02 0.02 0.01 0.03 0.12 0.01 Table 3 4 Average PFCs concentrations (ng/L) determine in Sweetwater B ranch downstream of a WWTP during the dry season, 2013. Dry Season PFCs Type Distance from WWTP Point 0 1k Diversi on Point 3k 4k Sink Field Blank PFBA (perfluorobutanoate) 1.95 1.38 0.71 1.02 1.18 0.76 0.55 PFHxA (Perfluorohexanoic acid) 24.20 32.48 16.40 22.14 48.10 15.06 0.07 PFHpA (Perfluoroheptanoic acid) 12.06 26.62 7.55 20.21 58.35 16.43 0.01 PFOA (Perfluoro octanoic acid) 32.60 41.20 28.54 64.33 79.62 27.55 0.45 PFOS (Perfluorooctanesulfonic acid) 44.92 49.56 43.08 224.91 156.42 31.69 0.52 PFNA (Perfluorononanoic acid) 4.28 3.97 3.98 6.21 5.71 7.94 0.02 PFDA (Perfluorodecanoic acid) 7.89 6.87 6.29 9.21 7. 05 0.95 0.04 PFUdA (Perfluoroundecanoic acid) 0.21 0.18 0.20 0.51 0.47 0.22 0.08 PFDoA (Perfluorododecanoic acid) 0.06 0.06 0.05 0.16 0.13 0.08 0.01 PFTrDA (Perfluorotridecanoic acid) 0.01 0.02 0.01 0.05 0.05 0.18 0.02 PFTeDA (Perfluorotetradecanoic ac id) 0.01 0.02 0.02 0.02 0.04 0.04 0.02
79 Figure 3 1. Sa mpling points along Sweetwater B ranch canal. Alachua Sink
80 Figure 3 2. Detected PFCs along Sweetwater branch stream during the wet and dry season. Concentrations in ng/L are depicted from the source (Point 0) downstream WWTP and each 1000 meters until the sinkhole which i s the final destination (Sink). Average calculated from four samples per site for each season. Standard error bars are shown. Field Blank PFCs concentrations are also plotted for reference.
81 Figure 3 3. PFOS and PFOA concentrations (ng/L) in Sweetwater branch stream during the wet season and dry season. Average calculated from four samples per site for each season. Standard error bars are shown. PFOS & PFOA Dry Season
82 CHAPTER 4 COMPARATIVE TRANSCRIPTOMIC RE SPONSE OF FATHEAD MINNOW LIVER AND BLOOD AFTER 48 HOURS EXPOSURE TO ENVIRONMENTALLY RELEVANT PERFLUORCHEMICALS CONCENTRATIONS Introduction Changes in the size and shape of internal organs as well as histological alterations have been used in toxicology for years to assess the effects of toxicants in animals. Also, the presence of metabolites in blood such as glucose, cholesterol, and enzymes that are assessed by clinical chemistry and hematology have been largely used to understand the influence of toxicant s in vertebrates (i.e. Petterino and Argentino Storino, 2006) In recent times, the application of the genomics technology tools to assess toxic effects has revolutionized the field of toxicology (Afshari et al., 2011) Genomics tools applied to toxicolog y (toxico genomics) is a current and fertile research area in environmental toxicology. Genomics tools allow researchers to perform analysis of thousands of genes at a time to identify expression changes that relate to the exposures. However, the necessity to euthanize animals to assess toxicant effects on target organs is a major drawback of these applications with regards to monitoring wildlife in ecotoxicology studies (particularly for endangered species). In addition, there is increasing public concern about animal welfare and their use in experimental paradigms. New advances in genomics approaches, especially for dealing with specific issues affecting human subjects, allow the use of blood as a non invasive sampling method to analyze gene expression si gnatures for toxicity determination (Fricano et al., 2011; Jung et al., 2010) Moreover, blood is a logical choice for assessing immuno toxicity and is a much easier and cheaper tissue that can be sampled repeatedly from the same individual. This approach allows a refinement of sampling methodologies for
83 toxicology and also reduces the number of animals required since the use of same individuals for multiple comparisons will reduce variability in the experimental design improving the statistical power. Endo crine disrupting chemicals (EDCs) are widely known to alter key features in fish physiology such as alteration in reproduction and sexual behavior, immune response and organism development (Bernanke and Khler, 2009) EDCs are found in the aquatic environ ment in low to very low concentrations (ug/L to ng/L ) (Bergman, 2013) For instance, the fully fluorinated alkyls, denominated perfluorochemicals (PFCs), are considered one of the most ubiquitous types of EDCs. The ir wide use, high persistence and bioaccum ulative properties have led to a n omnipresent occurrence of these chemicals in humans and the environment The most prominent PFCs in the environment are perfluoro octanoic sulfonate ( PFOS ) and perfluro octanoic acid ( PFOA ). Particularly, the presence of P FCs in the aquatic environment has been commonly documented (Ahrens, 2011; Houde et al., 2011) and recognized as an emerging research topic (Suja, 2009) The accumulation of PFCs has been widely detected in freshwater and marine fish (i.e. Giesy and Kanna n, 2001) with the liver and blood serving as the main body compartments for the bioaccumulation of PFCs in fish (Martin et al., 2003), particularly as documented in fathead minnows (Ankley et al., 2005) Toxic effects of PFCs on biota have been studied fo r more than three decades in animals ( Griffith and Long, 1980) and are currently largely documented (reviewed by Lau et al., 2007) but the mode of action is still elusive. Within aquatic biota fish appear to be even more sensitive than invertebrates to t he toxic effects of PFCs (Ji et al., 2008)
84 and the fathead minnow ( Pimephales promelas ) has been labeled as the most sensitive fish species for exposure to PFCs ( Dro ttar and Krueger, 2000a). A variety of toxic effects documented for fish are summarized in Table 4 1. The r eported health related changes in fish range from general effects such as reduced growth, survival and reproductive organ impairment to alteration of specific cellular pathways like lipid metabolism, oxidative stress, DNA damage, as well as immunotoxicity and estrogenic effects. The induction of the peroxisome proliferator receptors (PPAR) seems to have a pivotal role in triggering several deleterious effects in mammals such as DNA damage, production of reactive oxygen species (ROS) and alt eration of lipid metabolism (DeWitt et al., 2009) The alteration of PPAR was one of the first discovered effects of short term acute exposures of rats to PFCs (Takagi et al., 1991) In fish, however, it is not clear if PFCs can alter the expression of the three isoforms of PPAR ( ) and the available data suggest both up and down regulated outcomes that can be ascribed to these receptors (i.e. Fang et al., 2013; Fang et al., 2012; Krvel et al., 2008) In some fish species, such as rainbow trout, e xposure to PFCs increased the expression of the estrogen receptor (ER), a response related with carcinogenesis, more so than alterations of expression of PPAR isoforms. This is akin to the response seen in exposed human s where insensitiv ity to peroxisome proliferation after PFCs exposures has been described (Benninghoff et al., 2011; Benninghoff et al., 2012) The effect of PFCs on the immune system of fish, in particular in liver, brain, and spleen also has been previously observed (Hoff et al., 2003) M oreover, s everal genes, such as the pro
85 tokines ( TN F 8), have been highlighted as indicators of immune toxicity produced by PFOS exposure (Corsini et al., 2011; Fang et al., 2013) Most of the reported concentrations of PFCs in surface waters and wastewaters are in the range of parts per trillion (e.g. ng/L ) (i.e Ahrens 2011; Houde et al., 2011; Plumlee et al., 2008) But, the majority of laboratory exposures use concentrations in th e part per million (mg/L) ranges which a re several orders of magnitude higher than commonly found in surface or wastewaters (i.e. Liu et al., 2007; Oakes et al., 2005; Wei et al., 2008). These high concentrations are chosen to mimic PFCs released during sporadic spills and because it is assume d that toxicity is unlikely to occur at environmentally relevant concentrations (i.e Oakes et al., 2005; Wei et al., 2008). The main hypothesis was that low levels of PFCs are able to exert changes at transcriptional level on liver and blood of exposed fi sh. The main objectives of this research project were to : 1) elucidate if environmentally relevant concentrations of single or mixtures of PFCs are able to alter gene expression in fathead minnows; 2) compare the transcriptome response of liver and blood i n order to investigate non destructive sampling options. Materials and Methods Exposure Water Preparation Four different treatments were prepared (Table 4 2). A control (0 ug/L ); two PFOS concentrations: PFOS High (25 ug/L ), PFOS Low (0.5 ug/L ); and a P FCs Mixture, consisting of 7 types of PFCs at concentrations similar to those found previously downstream from a wastewater treatment plant (Chapter 2 and Rodriguez Jorquera et al., 2014, in prep.) in an attempt to mimic the mixtures usually found in water s with wastewater influence (i.e. Plumlee et al., 2008) The preparation of all
86 exposure treatments was accomplished by dissolving the need ed amount of each PFC in a 50 mL pol ypropylene falcon tube using 1. 9 mL of Triethylene glycol (TEG) as vehicle, vorte x for 2 min and then poured into the distribution cylinders. Then, the 50 m L preparations were poured into a pre cleaned fiberglass distribution cylinder containing 38 liters of carbon filtered and de chlorinated city water. The distribution cylinder had an air bubble system, which constantly mixed the spiked preparation. For the control group, Milli Q water and TEG were mixed in equivalent volumes as above, in a falcon tube. All PFCs were purchased from Wellington labs ( Ontario, Canada ). At the end of the exposures 1 L spiked water samples w ere collected for each treatment in polypropylene bottles for analysis of the 7 types of perfluorinated carboxylic acids (including PFOA) using EPA Method 537. Water Analyses To confirm the concentrations used in the exposures, seven types of PFCs were analyzed: PFBA (Perfluorobutanoate); PFHxA (Perfluorohexanoic acid); PFHpA (Perfluoroheptanoic acid); PFOA (Perfluorooctanoic acid); PFO S (Perfluorooctanesulfonic acid); PFNA ( Perfluorononanoic acid ) ; PFDA (P erfluorodeca noic acid ). The detection of PFCs was performed using a high performance liquid chromatography system couple d with tandem mass spectrometry (HPLC/ESI MS/MS) allowing for a limit of detection of 1.0 ng/L and a limit of quantification of 4 ng/L Detailed ex planation can be found in chapter 3. S tandards for quality control accepted elsewhere ( Ahrens, 2010; Shoemaker, 2009) were followed. In brief, a heavy isotope labeled perfluoroalkylcarboxylic aci d (MPFOA: Perfluoro n [1, 2, 3, 4 13 C4 ] octanoic acid ) and a mass l abelled perfluoroalkylsulfonate (MPFOS: Sodium perfluoro 1 [1, 2, 3,
87 4 13 C4 ] octanesulfonate ) were purchased from Wellington Labs and used as internal standards (IS). Dilutions of IS(s) were prepared to achieve 40 ng/L of MPFOA, and 40 ng/L MPFOS i n each 1 L sample. IS(s) were added to each sample before the beginning of the solid phase extraction (SPE). Oasis HLB 6 cc cartridges (Waters) with 200 mg sorbent material per cartridge and 30 m particle size were used for solid phase extraction of the water samples. A previous preparation of the cartridge with 15 mL of methanol was performed, followed by a rinse of the cartridge with 18 mL of reagent water, without allowing the water to drop below the top edge of the packing Polypropylene tubing (also pre washed with methanol) was used to transfer the water from bottles to cartridges. The vacuum was adjusted in the manifold in order to allow a flow of approximately two drops per second before adding the sample to the cartridge. Finally, the extract was concentrated to dryness with nitrogen in a heated water bath, and then adjusted to a 1 mL volume with 96:4% (vol/vol) methanol: water after adding the IS(s ) and injected to the HPLC. Fish Exposure and Tissue Collection Reproductively mature pond reared f athead minnow (FHM) were purchased from Andersen Minnow Farm (Lonoke, Arkansas) approximately 6 months before the exposures. Thirty two males were separated from the common tank two weeks before the experiment and placed in the treatment aquaria for 48 h. The exposure system consisted of 10 L glass aquaria. Each exposure was conducted in quadruplicate and each aquarium contained two male FHM in 4 L of treatment water. The water used in the control treatment was carbon filtered, dechlorinated tap water. The positions of the treatment tanks were randomized and test initiation times were staggered to ensure an exposure/sampling interval of 48 h. The fish were not fed during the experiment. The
88 temperature range of the water was 24 26 C and the photoperiod of 16 h light: 8 h dark was used At the conclusion of the exposures, fish were anesthetized with MS 222 and weighed to the nearest 0.1 g. The testes were removed and preserved for histological analysis to confirm sex and sexual maturity stage. Liver tissue a nd whole blood was flash frozen using liquid nitrogen and stored at 80 C until RNA extraction. Liver and blood were isolated from all fish The liver and blood from the same individuals (four for individual for each treatment) were used for RNA extractio n and microarray processing ( Figure 4 1) All procedures involving live fish were reviewed and approved by the University of Florida Institutional Animal Care and Use Committee (IACUC). Microarray Analysis Total RNA was extracted following the RNA STAT 60 reagent protocol (Tel Test, Friendswood, TX, USA). RNA was reconstituted in RNAsecure (Ambion ; New York, USA ), and DNase treated with Turbo DNA free (Ambion ; New York, USA ). In order to extract RNA from blood, column purification was performed. RNeasy M ini Kit (Qiagen ;Limburg, Germany) were used for the column purification of DNAse treated RNA. The RNA quantity for microarray analysis was measured using the NanoDrop ND 1000 (Nanodrop Technologies, Wilmington, DE) and RNA quality was evaluated using the Agilent 2100 BioAnalyzer with the RNA 6000 Nanochip. RNA integrity values (RIN) range d from 8.1 to 9.3 for blood samples and 7.5 to 9.4 for liver samples. RNeasy Mini Kit (Qiagen; Limburg, Germany) were used for the column purification of DNAse treated R NA were performed. A f athead minnow oligonucleotide microarray manufactured by Agilent (Palo Alto, CA) and designed in our laboratory (GEO: GPL9248) was used in this study After the exposure of fish to each treatment, f our biological replicate s (one for
89 e ach tank replicate) were used for RNA isolation from FHM livers and blood using the same individuals in order to reduce variability among fish Microarray hybridizations were performed according to the Agilent "One color microarray (document no. G4140 900 40 v6.5) using Cyanine 3 (Cy3) (Agilent, Palo Alto, CA). For blood and liver samples, 25 ng and 50 ng of total RNA per sample respectively were used to produce cDNA by reverse transcription using the poly (A) RNA present in the starting total RNA sample (u sing reverse transcriptase enzyme) Single stranded cDNA is then converted into double stranded cDNA and purified After this, RNA polymerase is used to produce the cRNA using the second cDNA strand (with a poly A tail) previously produced as template. Thi s new cRNA is the one that will be labeled with the Cy3 dye and hybridized to the microarray slide. Each sample co ntained a specific activity >8.15 pmol Cy3/ug, and amounts were adjusted to a final mass of 600 ng per sample for 8x15K microarray hybri dizati on. A final volume of 25 microarrays, and then hybridization proceeded for 17 h at 65 C. Then m icroarrays were washed according to the Agilent protocol and, kept in the dark until scanning on an Agilent G2505 B microarray scanner (same day). Data extraction was performed using Agilent Feature Extraction software (v9.5). Bioinformatics Raw expression data (gProcessedSignal) were imported into JMP Genomics v5 (SAS, Cary, NC) and log 2 transformed and normalized by LOESS before performing ANOVA to identify differentially regulated transcripts. Gene Ontology (GO) annotations were used to perform functional enrichment analysis (Fisher Exact Test p <0.05) to determine the over represented genes in each GO biological process. Differentially regulated transcripts p < 0.01; fold change greater than 1.5 were subjected to
90 hierarchical clustering analysis Distance calculations were performed with the program Cluster 3.0 (Eisen et al., 1998) using Euclidean distance as a similarity metric and average linkage as a clustering method and visualized usin g the Java Tree View software (Saldanha, 2004) To visualize the changes in gene expression between sites exposed to water types and controls, we used Pathway Studio TM 9 ( http ://www.elsevier.com/online tools/pathway studio/biological database ). Microarray database ( http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54506 ). A 20 % false discovery rate (FDR) was used for the analysis of overall changes in gene expression. Results Water Chemistry The actual concentration s determined in each of the treatments are detailed in table 4 2. No cross contamination was detected in the control group as we wer e unable to measure any of the target PFCs in the water from this group. The actual concentration of the PFOS high treatment was 25 ug/L which is lower than the lowest exposure concentrations (1 mg/L) reported from experiments using fish (i. e. Fang et al ., 2012 ; Fang et al., 2013; Oakes et al., 2004; Wei et al., 2008; Wei et al., 2009) The concentration of the lower dose of PFOS was 0.5 ug/L which is much lower than the lowest concentration reported in the literature. The mixture of PFCs, consisting of six types of perfluoro alkyls and one perfluorosulfonate (PFOS) (Table 4 2), contains concentrations within the range reported in surface waters and wastewater worldwide (Ahrens, 2011)
91 General Biological Responses No fis h mortality was observed during the 48 hours of exposure. The number of altered genes after the 20% FDR application by treatment were liver high= 99; liver low= 137; liver mix= 84; blood high = 557; blood low = 1421 and blood mix=471. The first noticeable result was that the low treatment of PFOS caused changes in a larger number of genes than the higher concentration in both the liver and blood of exposed fish. PFOS Low was the treatment that exerted the largest overall response in both investigated tissue s with application of 20% FDR. For the PFOS low treatment, the largest number of altered genes was observed in the blood tissue. Overall, blood transcriptomic response appears to be 5 to 10 times more sensitive than liver to PFCs exposure. A Venn diagram ( Figure 4 2) illustrates similarities in gene expression (without FDR correction) for the experiment. Considering the response from the liver as the primary reference and selecting all altered genes without FDR restriction, the similarities between liver and blood tissue showed in Venn diagrams (Figure 4 2 ) were 15% for PFOS High (81 genes), 10% for PFOS Low (143 genes) and 13% for PFCs Mix (59 genes). GO categories for common genes between liver and blood included for PFOS high GO:0006915; apoptosis GO: 0006917; induction of apoptosis ; GO:0006955; immune response ; GO:0007155; cell adhesion ; GO:0005975; carbohydrate metabolic process ; GO:0006096; glycolysis ; GO:0006281; DNA repair For PFOS Low : GO:0006006; glucose metabolic process ; GO:0005975; carbohydr ate metabolic process ; GO:0045454; cell redox homeostasis ; GO:0006629; lipid metabolic process ; GO:0016337; cell cell adhesion ; GO:0008286; insulin receptor signaling pathway ;
92 GO:0006915; apoptosis ; GO:0006281; DNA repair ; GO:0006325; establishment and/or maintenance of chromatin architecture ; and for PFCs mix: GO:0006725; aromatic compound metabolic process ; GO:0030070; insulin processing ; GO:0006629; lipid metabolic process GO:0008203; cholesterol metabolic process GO:0008202; steroid metabolic process ; GO:0006915; apoptosis ; GO:0006281; DNA repair Cluster Analysis Both tissues In general this analysis showed complete segregation of the r esponses for both liver and blood ti ssues (Figure 4 3) Nevertheless, the process used to generate this analysis igno res known functions of genes that can be related, over representing the distance among genes that can be actually related. To avoid these limitations it is necessary to incorporate biological knowledge in other types of analyses that includes common functi onal response between related genes (Huang et al., 2012) Thus, the similarities between both tissues need to be assessed using more than just clustering analysis, for example using gene ontology (GO) categories or pathway analyses. Liver This analysis showed the total segregation of all treatment groups, with the exception of an individual from the mixture group that was more related with the control. The nodes from PFOS low and PFCs mix treatments were closer than the node representing PFOS high treatm ent (Figure 4 4). Blood T his analysis showed the total segregation of all treatment groups. In this case, the node representing PFOS low was the one more dissimilar than the control group (Figure 4 5).
93 Enrichment Analysis The amount of GO categories iden tified as significant by the Fisher Exact Test by treatment were liver high, 24; liver low, 23; liver mix, 35; blood high, 28; blood low, 41 and blood mix, 20. All GO categories from liver tissue and blood for all treatments are shown in Table 4 3 and 4 4 respectively. In general, the main GO categories altered were related with carbohydrates and lipid metabolism, cell communication, electrolytes transport, DNA metabolism and thyroid metabolism. Particularly, in blood tissue the immune related categories we re enriched. System Biology Analysis The prominent relationships among the main altered genes in this study are highlighted in this section. Individual genes are mentioned as well because of their relevance in the PFCs exposure literature. The main relati onships among altered genes are depicted using pathway studio software. Liver: PPAR and ER are known genes altered by PFCs and also key genes because their central role in cell metabolism such as for lipids and carbohydrates as well as being central in car cinogenesis. No up regulation of any PPAR isozyme was observed in any treatment. Specifically, PPAR genes were down regulated in fish liver exposed to all treatments of PFCs (e.g. Figure 4 6). PPAR is pivotal in cholesterol transport; cell cycle; cell g rowth; fatty acid import, biosynthesis and oxidation; glucose metabolism, ROS generation, DNA degradation, apoptosis and respiratory chain. Figures 4 7, 4 8 and, 4 9 depict the similarities and differences of altered genes in fish exposed to PFOS high, PFO S low and PFCs mix respectively. In these series of figures, pathway representations for the liver response to PFCs show that some of the main pathways include genes altered in the mitochondria affecting the respiratory chain, as
94 well as membrane genes inv olved in fatty acid and glucose metabolism. For liver PFOS high treatment, key genes in cholesterol metabolism were altered resulting in the shutdown of this metabolic pathway. Sterol metabolism was up regulated in this group as well as the glucose metabol ism. Estrogen receptor 2a genes were up regulated in fish exposed to PFOS high and low treatments in a non monotonic dose response manner (Figure 4 10) but not changed by PFCs in the mixture. Additionally, estrogen receptor 1 was up regulated (3.5 fold ch ange) in the low dose treatment. PFCs clearly produced effects in genes involve d in DNA metabolism in the liver. Interestingly, GADD45A: growth arrest and DNA damage inducible alpha genes were consistently down regulated in liver from all treatments. This gene is highly conserved between species (Rosemary Siafakas and Richardson, 2009) and is involved in cell cycle arrest, DNA damage and apoptosis induction. The repression of this gene is associated with uncontrolled proliferation of cells (Rosemary Siafaka s and Richardson, 2009) Additionally a set of genes that induced DNA repair mechanism are shown for the PFOS high treatment (Figure 4 11) and PFOS low (Figure 4 12). Thyroid receptor beta genes were altered in liver from fish exposed to PFOS high (2.7 f old change; p = 0.034) and almost significant in the PFCs mix treatments (2.2 fold change; p = 0.07). Blood: PPAR genes were down regulated in blood from fish exposed to low levels of PFOS but not altered in the other two treatments (Figures 4 13 to 4 15) Despite the differences in individual genes, the general response of blood with regard of glucose and lipid metabolism as well as the effects on mitochondrial genes were similar
95 to the effects observed in liver (Figures 4 13 to 4 15), with the alteration of steroid, cholesterol and glucose metabolism and mitochondria respiratory chain. Other peroxisome associated gene, (the p eroxisome assembly factor 1 ) was down regulated in blood from fish exposed to both level s of PFOS. It is known that the oxyra dical metabolism in peroxisomes is related with cell inju ry, peroxisome proliferation and oxidati ve stress, catabolis m of nucleic acids, and senescence ( del Ro et al., 1992) In the case of blood from PFOS low treatment, we observed the up regulation of estrog en receptor instead of the characteristic PPAR up regulation after PFCs exposure. A comparison of all blood treatments were performed in order to visualized the similarities in the effects related with the ER (Figure 4 16). An unexpected result related to known estrogenic effects such as the up regulation of vitellogenin (VTG, Vitellogenin 3, Phosvitinless 3.4 fold changes) was observed in the blood from the fish exposed to the PFC mixture. Different from the other two treatments, the response of blood fro m the PFCs mix treatment also triggers the down regulation of genes involved in VLDL production and the up regulation of steroidogenic acute regulatory protein (STAR) (Figure 4 17 ). It is known that PFCs have the ability to impair lipoproteins (Bijland et al., 2011) involved in lipid metabolism (triglycerides). The finding of these types of genes in blood tissue is of high interest since they represent genes typically related with liver metabolism. The effects on DNA metabolism were also detected in blood expo sed to PFCs. For example, histone acetylation enzymes were affected and chromatin remodeling induced in blood from fish exposed to PFOS low treatment (Figure 4 18 )
96 treatments. This is significant since thyroid hormones have a wide influence on growth, development and general metabolism in vertebrates mediated mainly through their nuclear receptors. Moreover, in fish t he biological activities of thyroid hormones are predominantly mediate d at the transcription level by binding to thyroid receptors in the hypothalamus pituitary thyroid axis (Liu et al., 2000) as well as the known effects of waterborne PFCs on the disruption of the hypothalamus pituitary thyroid axis in fish (Shi et al., 20 09) The immune system genes were also altered through interleukin signaling. For example, in fish exposed to PFOS low treatment, genes such as interleukin 16 gene IL6: interleukin 6 (interferon, beta 2); STAT1: signal transducer and activator of transcri ption 1, 91kDa; JAK2: Janus kinase 2; JAK3: Janus kinase 3; STAT3: signal transducer and activator of transcription 3 (acute phase response factor) were up regulated, but in the PFOS high treatment genes such as IL6R and STAT 1 were down regulated ( Figure 4 19). Discussion The persistence and omnipresence of PFCs in the aquatic environment make the study of the toxic effects of these compounds on fish species a relevant issue. Moreover, the higher sensitivity of fathead minnow to PFCs, and the po wer of geno mics tools provides new opportunities to test relevant hypothesis such as the ability of PFCs to cause a measurable effect at environmentally relevant concentrations in fish. This can help to propose protective management for aquatic biota in a realistic s cenario. Also, the evaluation of the use of non destructive approaches such as the utilization of blood as starting material for microarrays studies can provide new perception on
97 ecotoxicology studies, because understanding the response of this tissue in n on mammalian vertebrates will provide a more accessible monitoring tool in the field; as well as refine the animal experimentation in the lab realms. Consequently, one of our initial hypotheses was confirmed: environmentally relevant level of PFCS altered gene expression in fathead minnows. Additionally, new hypotheses have been generated. First, low dose effects were clearly observed, with higher quantity of genes been altered in both tissues at the lower dose of exposure. Secondly, unexpectedly blood app ears to be more sensitive to PFCs gene expression alterations than liver. Perhaps blood will give advantageous opportunities for early discovery of PFCs effects. Levels of Exposure and Low Dose Effects In this study, three different dosage treatment s of PF Cs were used. Two were single doses of PFOS due to their prominent occurrence and greater toxic impact and the other a PFCs mixture mimicking levels commonly found in surface waters. The PFOS high treatment is equivalent to the serum levels of PFOS found i n human blood worldwide (Harada et al., 2003) Also in the PFCs mix treatment, 7 types PFCs were included taking as reference our previous study in urban waters which are as well similar to levels reported in surface water internationally (Ahrens, 2011; H oude et al., 2011; Plumlee et al., 2008) Accordingly, in this study we use d concentrations of PFCs in the range of parts per trillion (ng/L) to parts per billion (ug/L) which is more environmentally relevant since those concentrations in the ppt range a re more likely to be found in sur face waters, wastewaters (Ahrens, 2011) and also even more similar than those concentrations found in human blood (Harada et al., 2003)
98 In our study, the low dose treatment of PFOS showed the highest response in gene expression from both tissues. Low dose effects can be defined as occur in the range of human exposures or effects observed at dose s below those used for traditional toxicological studies (Vandenberg et al., 2012) In fish the aqueous exposure is considered the main route of accumulation of PFOS (Martin et al., 2003 a ) In the case of fathead minnows, which is considered the most sens itive fish species to PFCs exposure ( Drottar and Krueger 2000a), three noticeable studies report the low observed effect concentrations (LOEC) from water exposures: 5.4 mg/L ( Drottar and Krueger 2000a); 3 mg/L (Oakes et al., 2005) and 0.58 mg/L ( Drottar and Krueger 2000b). As noted, all these concentrations range at least one order of magnitude higher than the low dose (PFOS low 0.5 ug/L ) used here. In terms of the methodologies of exposure, the study that reports 5.4 ug/L as LOEC is the one more comp arable with this study, since the researchers perform a 96h exposure in a static system with water renewals ( Drottar and Krueger 2000a). Nonetheless, it is important to highlight that none of those studies used an endpoint as sensitive as gene expression profiles. More recently, researchers used a proteomic analysis to assess the effect of 96h exposure of PFOS on European bullhead ( Cottus gobio ) finding a greater effect at their lower dose (0.1 mg/L) when compared with the higher (1 mg/L) used dose (Dorts et al., 2011) Nevertheless, the concentrations used in Dorts et al.,(2011) are still very high compared with the lower concentration used here. Gene expression has been used to assess the effects of PFCs on the transcriptome of fish. Wei (2008) used gene expression as an endpoint to evaluate the toxicity of PFOA after 28 days, but at a dose of 10 mg/L. In the Wei et al study,
99 alterations observed in rare minnow liver genes showed these genes were involved in lipid metabolism and transport, hormone action, immune response and mitochondrial function ; specifically inhibiting thyroid hormone biosynthesis genes and significantly inducing estrogen responsive genes (Wei et al., 2008) These researchers also studied the effects of a mixture of six PFCs on primary cultured hepatocytes from rare minnows (Wei et al., 2009) but again using doses ( 15 mg /L PFOS and PFOA; PFCs mixtures totaling concentrations in the mg/L range) orders of magnitudes higher than our study. Despite this, commonalities on the general effect s with our study exist, such as the induction of fatty acid transport and metabolism, induction of xenobiotic metabolism (clearance), induction of mitochondria effects, induction of telomerase associated genes and induction of immune system related genes. Moreover, these authors defend the relevance of testing these high levels of PFCs because of potential spill episodes and the high bioaccumulation properties of PFCs. Others studies using gene expression as endpoint s but with shorter duration of exposure i nclude the one performed using zebra fish embryos (Shi et al., 2008) where researchers found up regulation on genes related with thyroid development only upon exposure to lower concentrations of PFOS (0.1; 0.5 and 1 mg/L). Another study investigated gene expression in Atlantic salmon hepatocytes after PFOS exposure, detecting the alteration of genes previously found in other animals (Krvel et al., 2008) Nonetheless, the concentrations used in Krvel et al., study were at least one order of magnitude hig her than this study. Thus, this could be the first study to shown gene expression alteration in such low doses.
100 Similarities Between Liver and B lood Even considering that the cluster analysis showed a clear distinct pattern of gene expression between tissu es (Figure 4 3), the other analyses performed, mainly the system biology pathways, demonstrate that enough similarities exist between the response from both tissues to consider the use of blood as a useful matrix for monitoring purposes. The fact is that we found similarities among potential biomarkers such as thyroid hormone receptor, PPAR, and estrogen receptors were altered in similar manner in both liver and blood. But also importantly, similar pathways were altered in similar ways. For example, the pa thway targeting the estrogen receptor was very similar between blood and liver for the PFOS low treatment (Figures 4 10 and 4 16). Also, the general effects on cholesterol metabolism, sterol metabolism, and fatty acid import and mitochondria respiratory ch ain were very consistent among treatments and tissues. Carbohydrates and Lipid Metabolism Through the activation of PPAR, PFCs can i nterfere with carbohydrates and fatty acid metabolism, cholesterol synthesis, and lipid transport (Haughom and Spydevold, 19 92; Krvel et al., 2008) basically increasing the intracellular free fatty acids and free cholesterol in hepatocytes (Luebker et al., 2002) Oakes et al, (2005) observed that in fathead minnow the h epatic fatty acyl CoA oxidase activity increased after PF Cs exposure as well as the levels of sex steroids in blood. In our study pathway analyses showed that the c holesterol metabolism genes were generally suppressed and steroid metabolism was induced in all treatments. The glucose import er was induced in the l iver in fish exposed to all treatment s (Figures 4 6 to 4 9). Glucose metabolism was induced in both PFOS high and low treatments, but inhibited in PFCs mix. The alteration of
101 steroid metabolism genes leads to a repression of this metabolism in blood from a ll PFCs treatments. Immune S ystem The immune system is affected for endocrine disrupters, particularly those that exert estrogen like activity (Ansar Ahmed, 2000) Some consensus exist s with regard to the ability of PFOS and PFOA to alter the immune proce ss in vitro and in vivo (DeWitt et al., 2009; Wei et al., 2008) The effect of PFCs on the immune system of fish liver, brain, and spleen has been also previously observed (Hoff et al., 2003) Moreover, several genes, such as pro 8), have been highlighted as indicators of immune toxicity produced by PFOS exposure (Corsini et al., 2011; Fang et al., 2013) Here, the pathway involving interleukin genes were altered in different manner in blood from fish exposed to the PFOS low and PFOS high treatments (F igure 4 19) Also, tumor necrosis factor ( TNF ) and Natural Killer (NK) genes pathways were altered in blood All of these effects can impair the interac tion of organisms with infectious agents and tumor development surveillance and increase disease susceptibility These kinds of effects have been already observed in mammals after exposure to P F Cs (Keil et al., 2008) Finally, the erythrocyte in fish is also involve d in the immune respon se (Morera and MacKenzie, 2011) so the deleterious effects of PFCs on t his abundant type of cell can have additional repercussion on the immune system besides the logical repercussion on the oxygen carbon dioxide transport. Cell Membrane Effects and Blood Cell Sensitivity The adverse effect of PFCs on cell membrane s has been observed (Hu et al, 2002, 2005) as well as the affinity of PFCs for blood plasma proteins, with the result of higher accumulation of PFCs in this body compartment. Because the cell membrane
102 forms the first barrier between the cell and exogenous exposures a ssessing the related effects of PFCs on RBCs is important since their main function the transport of oxygen (O 2 ) and carbon dioxide (CO 2 ) are strongly dependent on the permeability properties of the RBCs membrane (Thomas and Ege, 1998) Moreover, it is known th at anoxic conditions are generated after strong rain episodes in urban stream s caused by excess of nutrients loading (Walsh et al 2005). Thus, it is logical to speculate that PFCs (which are also carried by street runoff) will worsen the oxygen d epletion condition since the RBC will be unable to transport the lower amount of dissolved oxygen to the body organs. Our results show effects on cell membrane from whole blood, including several genes related with hemoglobin as well as ion flux es which ar e strongly related with hemoglobin function (Russo et al., 2008) Fish erythrocytes are rich in mitochondria and thus use aerobic metabolism (Ferguson et al., 1989) I t is known that PFCs can induce injury on mitochondria in mammals, t h rough interference o f mitochondrial metabolism processes such as proton leak of the mitochondrial inner membrane, which resemble s a surfactant like change in membrane fluidity ( Hu et al., 2002) M itochondrial damage also has been demonstrated in fish after PFCs exposure (Star kov and Wallace, 2002) Mitochondria are involve d in oxidative phosphorylation, a process vital to ATP production. Different than mammals, in normal oxygen conditions the nucleated fish erythrocytes obtain more than 90% of their energy from oxidative phosp horylation (Ferguson et al., 1989). Thus, if oxidative phosphorylation is a ffected by PFCs this will cause an imbalance in the energy metabolism in the fish red blood cell impairing the general performance of this type of cell.
103 T he R ole of PPAR and ER P robably one of the most interesting results from exposures of fish to PFCs was the mechanism involved in liver carcinogenesis produced by these compounds. In fish species, there is information that carcinogenesis is mediated through the estrogen receptor and this is more similar with the human response to these chemicals than in other experimental animals. Benninghoff and collaborators (2011) found that dietary PFCs bind to the ER in vitro (human and trout cell lines) and in vivo (juveniles of rainbow tro ut). Further, the same group of researchers tested the ability of PFCs to cause cancer in trout via an estrogen receptor lik e mechanism (instead of via the PPAR mechanism), but at concentrations higher than those commonly found in human exposures. They fou nd that PFCs caused an increase in liver tumor incidence (PFOS 26%; PFOA 62%; PFDA 88%) (Benninghoff et al., 2012) Our high PFOS treatment used concentrations in the range typically found in humans worldwide (Harada et al., 2003) and we observed the up re gulation of the ER (Figure 4 10), as well as the consistent down regulation of growth arrest DNA damage inducible 45 alpha gene ( GADD45 It is known that when GADD45 is deleted or repressed it cause s the uncontrolled proliferation of cells. The down regulation of GADD45 expression is considered a survival mechanism for cancer cells since they can avoid apoptosis, which w ill increase tumor i genesis (Rosemary Siafakas and Richardson, 2009) Surprisingly, in this study the effects on the ER appear to be greater in the lower level of PFOS, which also shows the down regulation of the GADD Furthermore GADD45 was down regul ated in all treatments and tissues except for the blood PFOS high treatment (Figures 4 10 and 4 16). Alteration of this gene after PFC exposure has been reported in rats (Ren et al., 2009)
104 Non Carcinogenesis Estrogen Like E ffects Estrogen like activitie s ha s been reported by PFCs in fish and human cell lines In fish cell lines PFCs are potent inducers of VTG (Benninghoff et al., 2011) For example, (Wei et al., 2007) demonstrated a significant increase of VTG e xpression in the livers of mature male fi sh after 14 and 28 d of exposure to 3, 10, and 30 mg/L PFOA In our study, the estrogen receptor genes were up regulated in both tissues from almost all treatments. Additionally, the up regulation of VTG was also observed in blood. The exposures were shor t, only 48 h, so it is possible that with a prolonged exposure we would have seen other Vtg transcripts up regulated. Thyroid Hormone The up regulation of thyroid receptor beta ( TR ) in the blood tissue from all treatments is relevant since it confirms that this type of receptor can be influenced by low concentrations of organic contaminants in whole blood. The mRNA levels of TR have been observed in Sea Bream ( Sparus aurata ) heart intestine, brain, kidney, skeletal muscle, liver, and gill in similar quantities (Nowell et al., 2001) Shi et al (2009) found that PFOS altered expression differentially for TR isoforms in zebra fish For instance and down regulate d, respectively. This d ifferential specificity (Liu et al., 2000). Because Shi et al, used the whole body from zebra fish embryos, it is difficult to determine which tissues were most involved for t he down regulation of The alteration of can lead to alterations of thyroid hormone binding and consequently, altering the respons e cascades with the resulting disruption of the hypothalamus pituitary thyroid axis (Shi et al., 2009).
105 Final C onsider ations Since this study used very low dose s of PFOS and PFCs mixtures never reported before it is important to highlight that even after the application of a relatively conservative restriction (e.g. FDR) for the gene analysis experiment both blood and liver showed considerable alterations in expression of set s of genes. Also, the similarities found with other studies in terms of individual genes, such as PPAR or ER, as well as entire pathways such as those related with lipid metabolism reinforce the id ea that PFCs produce measurable effects at environmentally relevant doses. Non monotonic response have been reported in PFCs exposures from fish (Dorts et al., 2011) in other animal models (Jacquet et al., 2012) as well as in human cell lines (Hu et al., 2014) By far the most abundant cell type in circulation is the erythrocyte, which is nucleated in fish (Morera and MacKenzie, 2011), thus it is plausible to think that the blood responses observed here came predominantly from effects on RBC s Moreover, even con sidering that the transcriptomic response s differed between tissue s (liver and blood) in terms of the number of total genes affected if the analysis is based on the pathways and GO categories the conclusion s change dramatically, since many similarities c an be observed in the key altered pathways in both tissues. Nucleated RBCs in fish and some other vertebrates offers an excellent opportunity for non invasive sampling for transcriptomic analysis Certainly this approach can be developed as an independen t diagnostic tool for contaminant exposure. With time, targeting the altered genes profiles and their networks and pathways in blood from wildlife will logically help to understand the effects of toxicants, independently of other organ responses. This
106 may be easily projected for effects at the population level and perhaps at the community level. Summary of Findings Environmentally relevant concentrations of PFCs were able to alter key transcripts in fathead minnows. The concentrations of PFOS used include d levels equivalent to those found in human blood and mixtures typically found in urban waters. The effects on fish include key genes involve d in steroid production, DNA repair, c ell proliferation, and mitochondria l activity These genes are involve d in fu ndamental metabolic pathways related with energy, reproduction, and important deleterious effects such as carcinogenesis. The use of blood provide d a considerable amount of information that could be linked specifically for some genes, and in general with p athways changed in the liver. When considering all differentially regulated genes after the application of the FDR, t he number amount of altered genes in blood was at least 5 times more than in liver of fish exposed to the low concentrations of PFCs. Furth er work is need it to prove is blood is more sensitive than liver to PFCs exposures. The finding of the up regulation of VTG genes is very significant since vitellogenin is probably the more accepted biomarker to assess fish estrogenicity. Further confirma tion of VTG using qPCR is necessary The particular features of non mammalian vertebrates blood, their apparent sensitivity and the non destructive sampling possibility make this tissue very attractive for this type of research. We expect that the further use of blood in toxico genomics studies will contribute to advance in unmasking the effects of the EDCs in a real world scenario. Hopefully, as demonstrated by this work, the availability of blood can be useful for field studies and drive researchers and p ractitioners to use toxicogenic tools beyond the bounds of the laboratory.
107 Table 4 1. Effects of PFCs i n f ish species. General, liver or blood related effects are reviewed. PFOS General Liver Blood (Related) Reference Lipid Metabolism Lipids droplets Du et al., 2009 Alteration of hepatocytes membrane and structure Hoff et al., 2005 Increase intracellular free fatty acids and free cholesterol Seacat et al., 2002 Hepatocellular hypertrophy Seacat et al., 2002 Liver damage Hoff et al., 2003 Elevated hepatosomatic index (HSI) Han and Fang, 2010 Reproduction Alteration of plasma androgens and estrogens Oakes et al., 2004, Oakes et al., 2005 and Ankley et al., 2005 VTG induction Wei et al., 2007; Liu et al., 2007; Ishibashi et al., 2008.,Han and Fang, 2010 Elevated gonadal somatic index (GSI) Han and Fang, 2010; Ji et al., 2008 VTG up regulation estrogenic response Du et al., 2009 Reduced Hatchability Drottar and Krueger, 2000; Ji et al.,2008 Genotoxicity Altered homeo stasis of DNA metabolism Hoff et al., 2003 Increase DNA single strand breaks Kim et al., 2010 Immunotoxicity
108 Table 4 1. Continued PFOS General Liver Blood (Related) Reference Immunosuppression Fang et al., 2012 Thyroid Metabolism Inhibi t thyroid hormone homeostasis Ji et al., 2008 Hyperplasia, hypertrophy, and colloidal depletion Ji et al., 2008 Growth Elevated the condition factor Oakes et al., 2005; Ji et al., 2008 Inhibition effects on growth/malformation Du et al., 2009; Drottar and Krueger, 2000; Ji et al.,2008 Mortality Reduced survival Ankley et al., 2005; Ji et al., 2008 Membrane related Enhance of membrane fluidity Enhance of membrane fluidity Hu et al., 2000 Disturb the structure of membranes Schreier et al., 2000 Hepatocyte damage Hoff et al., 2005 Increase the permeability of cell membranes Increase the permeability of cell membranes Hu et al.,2003) Ion regulatory distress Hoff et al., 2005 Gap junction intercellular communication Hu et al ., 2002 Increase of proton leakage of the inner mitochondrial membrane Starkov and Wallace, 2002 Bind to blood protein Jones et al., 2003
109 Table 4 1. Continued PFOS General Liver Blood (Related) Reference mitochondrial membrane potential Hof f et al., 2005 Others Increase hematocrit / Decrease total serum protein content PFOA Lipid Metabolism Interfere with fatty acid metabolism, cholesterol synthesis, and lipid transport Haughom and Spydevold, 1992 Reproduction Redu ced Hatchability Ji et al.,2008 VTG induction Kim et al., 2010 Declines in circulating plasma steroids /Decrease in egg production Oakes et al., 2009 Genotoxicity increases the incidence of liver tumors Biegel et al., 2001 Immunotoxicit y Down regulated immune response genes Wei et al., 2008
110 T able 4 1. Continued. PFOS General Liver Blood (Related) Reference Suppress the expression of iodothyronine deiodinase type II Wei et al., 2008 Growth Mortality Reduce survival Ji et al.,2008 Others Produce of reactive oxygen species (ROS) Liu et al., 2007
111 Table 4 2. Actual concentration of PFCs (ug/L) in exposure waters used in this study. Concentrations below quantifications limit (4 ng/L ) were set to zero. PF OS PFOA PFBA PFHxA PFHpA PFNA PFDA PFOS_Low 0.5 0 0 0 0 0 0 PFOS_High 25 0 0 0 0 0 0 PFCs_Mix 0.35 0.2 0.05 0.1 0.1 0.05 0.05 CTRL 0 0 0 0 0 0 0
112 Table 4 3. Gene Enrichment Analysis: Biological categories (GO) from liver of fathead minnow exp osed to PFCs. Category p Value Liver High GO:0006355; regulation of transcription, dna 0.000967607 GO:0007517; muscle development 0.002227232 GO:0008206; bile acid metabolic process 0.002274561 GO:0001702; gastrulation with mouth forming second 0.004 278617 GO:0007369; gastrulation 0.007044274 GO:0006446; regulation of translational initiation 0.010736805 GO:0009615; response to virus 0.010736805 GO:0006397; mRNA processing 0.010875101 GO:0042221; response to chemical stimulus 0.017179407 GO:0007 154; cell communication 0.017439231 GO:0030901; midbrain development 0.026088715 GO:0048484; enteric nervous system development 0.026088715 GO:0007398; ectoderm development 0.027302943 GO:0009410; response to xenobiotic stimulus 0.027302943 GO:0030878 ; thyroid gland development 0.027302943 GO:0006865; amino acid transport 0.030254191 GO:0008380; RNA splicing 0.037391713 GO:0006098; pentose phosphate shunt 0.038815106 GO:0030917; midbrain hindbrain boundary development 0.038815106 GO:0043123; posit ive regulation of i kappab kinase/nf kappab cascade 0.040150435 GO:0006457; protein folding 0.043533132 GO:0006629; lipid metabolic process 0.043581599 GO:0030574; collagen catabolic process 0.043947438 GO:0006754; atp biosynthetic process 0.004802491 GO:0006606; protein import into nucleus 0.011843341 GO:0006412; translation 0.01692271 GO:0006468; protein amino acid phosphorylation 0.018752378 GO:0001503; ossification 0.019592339 GO:0043066; negative regulation of apoptosis 0.019592339 GO:0007166 ; cell surface receptor linked signal transduction 0.020288206 GO:0006829; zinc ion transport 0.025470775 GO:0008150; biological_process 0.026631829 GO:0007565; female pregnancy 0.029437355 GO:0018108; peptidyl tyrosine phosphorylation 0.029437355 GO: 0006281; DNA repair 0.030242756 GO:0007010; cytoskeleton organization and biogenesis 0.035872298 GO:0046907; intracellular transport 0.036226599 GO:0005977; glycogen metabolic process 0.041754786 GO:0006098; pentose phosphate shunt 0.041754786 GO:0007 413; axonal fasciculation 0.041754786 GO:0007519; skeletal muscle development 0.041754786 GO:0030308; negative regulation of cell growth 0.041754786 GO:0018298; protein chromophore linkage 0.044273978 GO:0006334; nucleosome assembly 0.044861216
113 Tabl e 4 3. Continued Category p Value Liver Mix GO:0016192; vesicle mediated transport 0.045654478 GO:0008206; bile acid metabolic process 0.001492699 GO:0001702; gastrulation with mouth forming second 0.002831883 GO:0007602; phototransduction 0.0028318 83 GO:0006268; DNA unwinding during replication 0.004701897 GO:0007242; intracellular signaling cascade 0.005771259 GO:0007067; mitosis 0.006971058 GO:0007275; multicellular organismal development 0.009785979 GO:0008203; cholesterol metabolic process 0.010164069 GO:0008150; biological_process 0.011141954 GO:0007411; axon guidance 0.013192607 GO:0006904; vesicle docking during exocytosis 0.017997586 GO:0048484; enteric nervous system development 0.017997586 GO:0006777; mo molybdopterin cofactor bio synthetic process 0.020853311 GO:0007158; neuron adhesion 0.020853311 GO:0007565; female pregnancy 0.020853311 GO:0009411; response to uv 0.020853311 GO:0009435; nad biosynthetic process 0.020853311 GO:0007601; visual perception 0.025042799 GO:000660 5; protein targeting 0.026668536 GO:0018298; protein chromophore linkage 0.02814526 GO:0008272; sulfate transport 0.029864025 GO:0009615; response to virus 0.03403574 GO:0035023; regulation of rho protein signal transduction 0.037772174 GO:0006003; fr uctose 2,6 bisphosphate metabolic process 0.039928877 GO:0006071; glycerol metabolic process 0.039928877 GO:0006325; establishment and/or maintenance of chromatin architecture 0.039928877 GO:0006694; steroid biosynthetic process 0.039928877 GO:0007266; rho protein signal transduction 0.039928877 GO:0030903; notochord development 0.039928877 GO:0048048; embryonic eye morphogenesis 0.039928877 GO:0048268; clathrin cage assembly 0.039928877 GO:0008202; steroid metabolic process 0.040432868 GO:0006811; ion transport 0.046191685 GO:0008284; positive regulation of cell proliferation 0.047303827 GO:0006265; DNA topological change 0.050858819
114 Table 4 4. Gene Enrichment Analysis: Biological categories (GO) from blood of fathead minnow exposed to PFCs. Category Blood High p Value GO:0006814; sodium ion transport 0.001917 GO:0048484; enteric nervous system development 0.002332 GO:0042074; cell migration involved in gastrulation 0.002615 GO:0008152; metabolic process 0.005187 GO:0001503; ossificatio n 0.007035 GO:0001763; morphogenesis of a branching structure 0.007598 GO:0001841; neural tube formation 0.007598 GO:0019221; cytokine and chemokine mediated signaling pathway 0.007598 GO:0045494; photoreceptor cell maintenance 0.007598 GO:0007242; in tracellular signaling cascade 0.007917 GO:0042157; lipoprotein metabolic process 0.011092 GO:0016070; RNA metabolic process 0.014836 GO:0006351; transcription, DNA dependent 0.015116 GO:0006835; dicarboxylic acid transport 0.015116 GO:0006412; transla tion 0.017826 GO:0016337; cell cell adhesion 0.019919 GO:0015031; protein transport 0.020709 GO:0048514; blood vessel morphogenesis 0.02456 GO:0006810; transport 0.037534 GO:0006470; protein amino acid dephosphorylation 0.038013 GO:0006511; ubiquitin dependent protein catabolic process 0.038742 GO:0000226; microtubule cytoskeleton organization and biogenesis 0.041577 GO:0006817; phosphate transport 0.044904 GO:0006811; ion transport 0.047123 GO:0006306; DNA methylation 0.047857 GO:0006725; aromat ic compound metabolic process 0.047857 Blood low GO:0006350; transcription 0.000638 GO:0006355; regulation of transcription, dna dependent 0.000861 GO:0042592; homeostatic process 0.001555 GO:0048514; blood vessel morphogenesis 0.002488 GO:0016573; hi stone acetylation 0.002655 GO:0006865; amino acid transport 0.003543 GO:0007267; cell cell signaling 0.006457 GO:0007257; activation of jnk activity 0.012345 GO:0015816; glycine transport 0.012345 GO:0030509; bmp signaling pathway 0.012345 GO:0030833 ; regulation of actin filament polymerization 0.012345 GO:0030574; collagen catabolic process 0.012607 GO:0006118; electron transport 0.013746 GO:0045941; positive regulation of transcription 0.014516 GO:0006306; DNA methylation 0.015337 GO:0001568; b lood vessel development 0.016089
115 Table 4 4. Continued. Category p Value Blood low GO:0007275; multicellular organismal development 0.01747 GO:0007155; cell adhesion 0.018085 GO:0006897; endocytosis 0.0208 GO:0006396; RNA processing 0.020888 GO:00 07368; determination of left/right symmetry 0.021061 GO:0000184; mrna catabolic process, nonsense mediated decay 0.022035 GO:0046854; phosphoinositide phosphorylation 0.022035 GO:0009117; nucleotide metabolic process 0.02288 GO:0007156; homophilic cell adhesion 0.026847 GO:0007049; cell cycle 0.027608 GO:0007411; axon guidance 0.027908 GO:0001756; somitogenesis 0.031529 GO:0006281; DNA repair 0.032925 GO:0006351; transcription, dna dependent 0.034451 GO:0007004; telomere maintenance via telomerase 0.034451 GO:0016310; phosphorylation 0.034451 GO:0042384; cilium biogenesis 0.034451 GO:0048015; phosphoinositide mediated signaling 0.034451 GO:0006817; phosphate transport 0.039293 GO:0006810; transport 0.041368 GO:0005975; carbohydrate metabolic process 0.041409 GO:0001666; response to hypoxia 0.044966 GO:0045786; negative regulation of cell cycle 0.045156 GO:0006548; histidine catabolic process 0.049298 GO:0046488; phosphatidylinositol metabolic process Blood mix GO:0006605; protein targeti ng 0.049298 0.002373 GO:0006777; mo molybdopterin cofactor biosynthetic process 0.008111 GO:0030878; thyroid gland development 0.008111 GO:0006730; one carbon compound metabolic process 0.016196 GO:0000079; regulation of cyclin dependent protein ki nase activity 0.020887 GO:0007623; circadian rhythm 0.020887 GO:0009401; phosphoenolpyruvate dependent sugar phosphotransferase system 0.020887 GO:0006310; DNA recombination 0.024758 GO:0006493; protein amino acid o linked glycosylation 0.026122 GO:00 06825; copper ion transport 0.026122 GO:0007205; protein kinase c activation 0.026122 GO:0006916; anti apoptosis 0.031461 GO:0006259; DNA metabolic process 0.035095 GO:0006470; protein amino acid dephosphorylation 0.038247 GO:0035023; regulation of rh o protein signal transduction 0.039945 GO:0008152; metabolic process 0.042899
116 Table 4 4. Continued Category p Value Blood mix Blood low GO:0007605; sensory perception of sound 0.0441 GO:0030574; collagen catabolic process 0.047045 GO:0007155; c ell adhesion 0.048856 GO:0006817; phosphate transport 0.049388
117 Figure 4 1. General procedures of blood and liver gene expression analysis in fish using cDNA microarrays.
118 Figure 4 2. Venn diagrams depicting similarities in gene expression between liver and blood from fathead minnows after 48 h of PFCs exposures.
119 Figure 4 3. Cluster analysis of both blood and liver tissues from FHM after 48 h of PFCs exposure.
120 Figure 4 4. Cluster analysis of both blood and liver tissues from FHM after 48 h of PFCs exposure.
121 Figure 4 5. Cluster analysis of blood tissues from FHM after 48 h of PFCs exposure.
122 Figure 4 6. The central role of PPARD: Peroxisome proliferator Activator Receptor Beta in liver from fathead minnow expos ed to PFOS high treatment. LDLRAP1: low density lipoprotein receptor adaptor protein 1 ; PPARD: peroxisome proliferator activated receptor delta (Alias: peroxisomal proliferator activator receptor beta); HSP17B4: heat shock 70kDa; OSBPL1A: oxysterol bindin g protein like 1A; VLDLR: very low density lipoprotein receptor; LRP5: low density lipoprotein receptor related protein 5; CYP27A1: cytochrome P450, family 27, subfamily A, polypeptide 1.
123 Fig ure 4 7. Lipid and carbohydrates metabolism from the liver of fathead minnow exposed to PFOS high. LDLRAP1: low density lipoprotein receptor adaptor protein 1; UQCRFS1: ubiquinol cytochrome c reductase, Rieske iron sulfur polypeptide 1; SDHC: succinate dehydrogenase complex, subunit C, integral membrane protein, 1 5kDa; COX1: cytochrome c oxidase 1 mitochondrial; COX17: cytochrome c oxidase assembly homolog; COX4I1 : cytochrome c oxidase subunit IV isoform 1 ; COX15: COX15 homolog, cytochrome c oxidase assembly protein (yeast); ALDH2: aldehyde dehydrogenase 2 famil y (mitochondrial); ALDH9A1: aldehyde dehydrogenase 9 family, member A1; SLC25A27: solute carrier family 25, member 27; SLC27A4: solute carrier family 27 (fatty acid transporter), member 4; SLC27A5: solute carrier family 27 (fatty acid transporter), member 5; PPARD: peroxisome proliferator activated receptor delta (Alias: peroxisomal proliferator activator receptor beta); PLIN2: perilipin 2; HSPA5: heat shock 70kDa protein 5 (glucose regulated protein, 78kDa); NPC1: Niemann Pick disease, type C1; OSBPL5: ox ysterol binding protein like 5; OSBPL8: oxysterol binding protein like 8; OSBPL10: oxysterol binding protein like 10; HSD3B7: hydroxy delta 5 steroid dehydrogenase, 3 beta and steroid delta isomerase 7; VLDLR: very low density lipoprotein receptor; LRP5: low density lipoprotein receptor related protein 5; SLC2A9: solute carrier family 2 (facilitated glucose transporter), member 9; FBP1: fructose 1,6 bisphosphatase 1 Fructose 1,6 bisphosphatase 1; PFKL: phosphofructokinase, liver
124 Figure 4 8. Lipid and c arbohydrates metabolism from the liver of fathead minnow exposed to PFOS low. LDLRAP1: low density lipoprotein receptor adaptor protein 1; UQCRFS1: ubiquinol cytochrome c reductase, Rieske iron sulfur polypeptide 1; SDHC: succinate dehydrogenase complex, s ubunit C, integral membrane protein, 15kDa; COX1: cytochrome c oxidase 1 mitochondrial; COX17: cytochrome c oxidase assembly homolog; COX4I1 : cytochrome c oxidase subunit IV isoform 1 ; COX15: COX15 homolog, cytochrome c oxidase assembly protein (yeast); ALDH2: aldehyde dehydrogenase 2 family (mitochondrial); ALDH9A1: aldehyde dehydrogenase 9 family, member A1; SLC25A27: solute carrier family 25, member 27; SLC27A4: solute carrier family 27 (fatty acid transporter), member 4; SLC27A5: solute carrier famil y 27 (fatty acid transporter), member 5; PPARD: peroxisome proliferator activated receptor delta (Alias: peroxisomal proliferator activator receptor beta); PLIN2: perilipin 2; HSPA5: heat shock 70kDa protein 5 (glucose regulated protein, 78kDa); NPC1: Nie mann Pick disease, type C1; OSBPL5: oxysterol binding protein like 5; OSBPL8: oxysterol binding protein like 8; OSBPL10: oxysterol binding protein like 10; HSD3B7: hydroxy delta 5 steroid dehydrogenase, 3 beta and steroid delta isomerase 7; VLDLR: very lo w density lipoprotein receptor; LRP5: low density lipoprotein receptor related protein 5; SLC2A9: solute carrier family 2 (facilitated glucose transporter), member 9; FBP1: fructose 1,6 bisphosphatase 1 Fructose 1,6 bisphosphatase 1; PFKL: phosphofructoki nase, liver; SORD: sorbitol dehydrogenase; SLC27A1: solute carrier family 27 (fatty acid transporter), member 1; GGPS1: geranylgeranyl diphosphate synthase 1;
125 Figure 4 9. Lipid and carbohydrates metabolism from the liver of fathead minnow exposed to PFOS mix. LDLRAP1: low density lipoprotein receptor adaptor protein 1; UQCRFS1: ubiquinol cytochrome c reductase, Rieske iron sulfur polypeptide 1; SDHC: succinate dehydrogenase complex, subunit C, integral membrane protein, 15kDa; COX1: cytochrome c oxidase 1 mitochondrial; COX17: cytochrome c oxidase assembly homolog; COX4I1 : cytochrome c oxidase subunit IV isoform 1 ; COX15: COX15 homolog, cytochrome c oxidase assembly protein (yeast); ALDH2: aldehyde dehydrogenase 2 family (mitochondrial); ALDH9A1: aldehy de dehydrogenase 9 family, member A1; SLC25A27: solute carrier family 25, member 27; SLC27A4: solute carrier family 27 (fatty acid transporter), member 4; SLC27A5: solute carrier family 27 (fatty acid transporter), member 5; PPARD: peroxisome proliferator activated receptor delta (Alias: peroxisomal proliferator activator receptor beta); PLIN2: perilipin 2; HSPA5: heat shock 70kDa protein 5 (glucose regulated protein, 78kDa); NPC1: Niemann Pick disease, type C1; OSBPL5: oxysterol binding protein like 5; OS BPL8: oxysterol binding protein like 8; OSBPL10: oxysterol binding protein like 10; HSD3B7: hydroxy delta 5 steroid dehydrogenase, 3 beta and steroid delta isomerase 7; VLDLR: very low density lipoprotein receptor; LRP5: low density lipoprotein receptor r elated protein 5; SLC2A9: solute carrier family 2 (facilitated glucose transporter), member 9; FBP1: fructose 1,6 bisphosphatase 1 Fructose 1,6 bisphosphatase 1; PFKL: phosphofructokinase, liver; SORD: sorbitol dehydrogenase; SLC27A1: solute carrier famil y 27 (fatty acid transporter), member 1; GGPS1: geranylgeranyl diphosphate synthase 1
126 Figure 4 10. Pathways analysis of the estrogen receptor expression targets in liver from fathead minnow exposed to PFCs CYP19A1: cytochrome P450, family 19, subfa mily A, polypeptide 1; TMPRSS2: transmembrane protease, serine 2; SHBG: sex hormone binding globulin; SYT2: synaptotagmin II; HGF: hepatocyte growth factor (hepapoietin A; scatter factor); FSTL1: follistatin like 1; ADM: adrenomedullin; CD40 : CD40 molecu le, TNF receptor superfamily member 5; CDH1: cadherin 1, type 1, E cadherin (epithelial); EGFR: epidermal growth factor receptor; CNR1: cannabinoid receptor 1 (brain); RAB31: RAB31, member RAS oncogene family; CA12: carbonic anhydrase XII; CKB: creatine ki nase, brain; CCND1: cyclin D1; BCL2: B cell CLL/lymphoma 2; GADD45A: growth arrest and DNA damage inducible, alpha; KIFAP3: kinesin associated protein 3; STAR: steroidogenic acute regulatory protein; SYP: synaptophysin; FOXA1: forkhead box A1; HSF1: heat s hock transcription factor 1; EGR3: early growth response 3; NQO1: NAD(P)H dehydrogenase, quinone 1
127 Figure 4 1 1 Upregulated genes involve in DNA repair from liver of fathead minnow exposed to PFOS high treatment. ALKBH1: alkB, alkylation repair homolog 1 (E. coli); RAD23B: RAD23 homolog B; BRIP1: BRCA1 interacting protein C terminal helicase 1; XPA: xeroderma pigmentosum, complementation group A; RECQL4: RecQ protein like 4; ERCC1: excision repair cross complementing rodent repair deficiency, complementa tion group 1 (includes overlapping antisense sequence); FEN1: flap structure specific endonuclease 1; REV3L: REV3 like, catalytic subunit of DNA polymerase zeta (yeast)
128 Figure 4 1 2 Altered genes involve in DNA repair from liver of fathead minnow exp osed to PFOS low treatment. ALKBH1: alkB, alkylation repair homolog 1 (E. coli); RAD23B: RAD23 homolog B; BRIP1: BRCA1 interacting protein C terminal helicase 1; XPA: xeroderma pigmentosum, complementation group A; RECQL4: RecQ protein like 4; ERCC1: excis ion repair cross complementing rodent repair deficiency, complementation group 1 (includes overlapping antisense sequence); FEN1: flap structure specific endonuclease 1; REV3L: REV3 like, catalytic subunit of DNA polymerase zeta (yeast)
129 Figure 4 1 3 Lip id and carbohydrates metabolism from the blood of fathead minnow exposed to PFOS high. LDLRAP1: low density lipoprotein receptor adaptor protein 1; UQCRFS1: ubiquinol cytochrome c reductase, Rieske iron sulfur polypeptide 1; SDHC: succinate dehydrogenase c omplex, subunit C, integral membrane protein, 15kDa; COX1: cytochrome c oxidase 1 mitochondrial; COX17: cytochrome c oxidase assembly homolog; COX4I1 : cytochrome c oxidase subunit IV isoform 1 ; COX15: COX15 homolog, cytochrome c oxidase assembly protein (yeast); ALDH2: aldehyde dehydrogenase 2 family (mitochondrial); ALDH9A1: aldehyde dehydrogenase 9 family, member A1; SLC25A27: solute carrier family 25, member 27; SLC27A4: solute carrier family 27 (fatty acid transporter), member 4; SLC27A5: solute carr ier family 27 (fatty acid transporter), member 5; PPARD: peroxisome proliferator activated receptor delta (Alias: peroxisomal proliferator activator receptor beta); PLIN2: perilipin 2; HSPA5: heat shock 70kDa protein 5 (glucose regulated protein, 78kDa); NPC1: Niemann Pick disease, type C1; OSBPL5: oxysterol binding protein like 5; OSBPL8: oxysterol binding protein like 8; OSBPL10: oxysterol binding protein like 10; HSD3B7: hydroxy delta 5 steroid dehydrogenase, 3 beta and steroid delta isomerase 7; VLDLR : very low density lipoprotein receptor; LRP5: low density lipoprotein receptor related protein 5; SLC2A9: solute carrier family 2 (facilitated glucose transporter), member 9; FBP1: fructose 1,6 bisphosphatase 1 Fructose 1,6 bisphosphatase 1; PFKL: phosph ofructokinase, liver; SORD: sorbitol dehydrogenase; SLC27A1: solute carrier family 27 (fatty acid transporter), member 1; GGPS1: geranylgeranyl diphosphate synthase 1
130 Figure 4 1 4 Lipid and carbohydrates metabolism from the blood of fathead minnow expo sed to PFOS low. LDLRAP1: low density lipoprotein receptor adaptor protein 1; UQCRFS1: ubiquinol cytochrome c reductase, Rieske iron sulfur polypeptide 1; SDHC: succinate dehydrogenase complex, subunit C, integral membrane protein, 15kDa; COX1: cytochrome c oxidase 1 mitochondrial; COX17: cytochrome c oxidase assembly homolog; COX4I1 : cytochrome c oxidase subunit IV isoform 1 ; COX15: COX15 homolog, cytochrome c oxidase assembly protein (yeast); ALDH2: aldehyde dehydrogenase 2 family (mitochondrial); ALDH 9A1: aldehyde dehydrogenase 9 family, member A1; SLC25A27: solute carrier family 25, member 27; SLC27A4: solute carrier family 27 (fatty acid transporter), member 4; SLC27A5: solute carrier family 27 (fatty acid transporter), member 5; PPARD: peroxisome p roliferator activated receptor delta (Alias: peroxisomal proliferator activator receptor beta); PLIN2: perilipin 2; HSPA5: heat shock 70kDa protein 5 (glucose regulated protein, 78kDa); NPC1: Niemann Pick disease, type C1; OSBPL5: oxysterol binding protein like 5; OSBPL8: oxysterol binding protein like 8; OSBPL10: oxysterol binding protein like 10; HSD3B7: hydroxy delta 5 steroid dehydrogenase, 3 beta and steroid delta isomerase 7; VLDLR: very low density lipoprotein receptor; LRP5: low density lipoprotein receptor related protein 5; SLC2A9: solute carrier family 2 (facilitated glucose transporter), member 9; FBP1: fructose 1,6 bisphosphatase 1 Fructose 1,6 bisphosphatase 1; PFKL: phosphofructokinase, liver; SORD: sorbitol dehydrogenase; SLC27A1: solute ca rrier family 27 (fatty acid transporter), member 1; GGPS1: geranylgeranyl diphosphate synthase 1;
131 Figure 4 1 5 Lipid and carbohydrates metabolism from the blood of fathead minnow exposed to PFOS low LDLRAP1: low density lipoprotein receptor adaptor prot ein 1; UQCRFS1: ubiquinol cytochrome c reductase, Rieske iron sulfur polypeptide 1; SDHC: succinate dehydrogenase complex, subunit C, integral membrane protein, 15kDa; COX1: cytochrome c oxidase 1 mitochondrial; COX17: cytochrome c oxidase assembly homolo g; COX4I1 : cytochrome c oxidase subunit IV isoform 1 ; COX15: COX15 homolog, cytochrome c oxidase assembly protein (yeast); ALDH2: aldehyde dehydrogenase 2 family (mitochondrial); ALDH9A1: aldehyde dehydrogenase 9 family, member A1; SLC25A27: solute carri er family 25, member 27; SLC27A4: solute carrier family 27 (fatty acid transporter), member 4; SLC27A5: solute carrier family 27 (fatty acid transporter), member 5; PPARD: peroxisome proliferator activated receptor delta (Alias: peroxisomal proliferator a ctivator receptor beta); PLIN2: perilipin 2; HSPA5: heat shock 70kDa protein 5 (glucose regulated protein, 78kDa); NPC1: Niemann Pick disease, type C1; OSBPL5: oxysterol binding protein like 5; OSBPL8: oxysterol binding protein like 8; OSBPL10: oxysterol b inding protein like 10; HSD3B7: hydroxy delta 5 steroid dehydrogenase, 3 beta and steroid delta isomerase 7; VLDLR: very low density lipoprotein receptor; LRP5: low density lipoprotein receptor related protein 5; SLC2A9: solute carrier family 2 (facilitat ed glucose transporter), member 9; FBP1: fructose 1,6 bisphosphatase 1 Fructose 1,6 bisphosphatase 1; PFKL: phosphofructokinase, liver; SORD: sorbitol dehydrogenase; SLC27A1: solute carrier family 27 (fatty acid transporter), member 1; GGPS1: geranylgeran yl diphosphate synthase 1;
132 Figure 4 16. Pathways analysis of the estrogen receptor expression targets in blood from fathead minnow exposed to PFCs CYP19A1: cytochrome P450, family 19, subfamily A, polypeptide 1; TMPRSS2: transmembrane protease, serine 2; SHBG: sex hormone binding globulin; SYT2: synaptotagmin II; HGF: hepatocyte growth factor (hepapoietin A; scatter factor); FSTL1: follistatin like 1; ADM: adrenomedullin; CD40 : CD40 molecule, TNF receptor superfamily member 5; CDH1: cadherin 1, type 1 E cadherin (epithelial); EGFR: epidermal growth factor receptor; CNR1: cannabinoid receptor 1 (brain); RAB31: RAB31, member RAS oncogene family; CA12: carbonic anhydrase XII; CKB: creatine kinase, brain; CCND1: cyclin D1; BCL2: B cell CLL/lymphoma 2; GAD D45A: growth arrest and DNA damage inducible, alpha; KIFAP3: kinesin associated protein 3; STAR: steroidogenic acute regulatory protein; SYP: synaptophysin; FOXA1: forkhead box A1; HSF1: heat shock transcription factor 1; EGR3: early growth response 3; NQO 1: NAD(P)H dehydrogenase, quinone
133 Figure 4 17. Expression target for Very Low Density Lipoprotein (VLDL) in blood from fathead minnow exposed to PFCs Mixture. STAR: steroidogenic acute regulatory protein; HSF1: heat shock transcription factor 1; CYP27A1 : cytochrome P450, family 7, subfamily A, polypeptide 1; EGFR: epidermal growth factor receptor; CCND1: cyclin D1.
134 Figure 4 18. Alteration of histone related genes from blood exposed to PFOS low in fathead minnow. NCOR1: nuclear receptor corepressor 1 ; NCOR2: nuclear receptor corepressor 2; NCOA1: nuclear receptor coactivator 1; NCOA3 : nuclear receptor coactivator 3 ; SIN3A: SIN3 homolog A, transcription regulator (yeast); EP300: E1A binding protein p300; CREBBP: CREB binding protein; KPNA2: karyopher in alpha 2 (RAG cohort 1, importin alpha 1); KAT2B: K(lysine) acetyltransferase 2B; KAT2A: K(lysine) acetyltransferase 2A; SIRT1: sirtuin 1
135 Figure 4 19. Immune related genes altered in blood from fathead minnow exposed to PFOS low (left) and PFOS hig h (right) treatments. IL6 : interleukin 6 (interferon, beta 2) ; IL6ST: interleukin 6 signal transducer (gp130, oncostatin M receptor); IL6R : interleukin 6 receptor; STAT1: signal transducer and activator of transcription 1, 91kDa; IL6: interleukin 6 (int erferon, beta 2); IL16 Interleukin 16 (lymphocyte chemoattractant factor); TYK2: tyrosine kinase 2; JAK1: Janus kinase 1; JAK2: Janus kinase 2; JAK3: Janus kinase 3; STAT3: signal transducer and activator of transcription 3 (acute phase response factor)
136 CHAPTER 5 CONCLUSIONS C lean water is required for human consumption and crop irrigation Thus, the relevance to understand the effects of water pollution is high. Nonetheless, an important part of the equation about water pollution issues is often forgott en: The fact that biodiversity (aquatic as well as terrestrial organism s ) are directly or indirec tly affected by water pollution Effects of water pollution on biodiversity are tightly related with the water quality for human consumption and development. For example, a global joint analysis showed that threats to human water security and biodiversity are highly spatially correlated with nutrient, pesticide and organic loads which are common t h reats for both human health and the environment (Vrsmarty et a l., 2010) Hence by setting and applying standards to protect sensible aquatic organism, we will be improving water quali ty for other species including humans Accordingly, understand ing the impact of pollution in the aquatic system is a long desired go al for many researche r s, managers, and practitioners All who recognize the vital role of water in living organism and the widespread phenomena of water pollution will realize the necessity to improve current knowledge. It Is important to consider that ne ither the sole detection of compounds nor the toxicity determination in reductionist settings will give the much needed answers to better understand and resolve the water pollution problem. In fact, the complexity and dynamic variability of aquatic systems paired with complex mixtures of thousands of c hemicals that reach the aquatic environment at dissimilar concentrations make assessing the impact of water pollution a subject that is seriously challenging. So far, the best knowledge about the impact of po llution on aquatic ecosystem s has been produced from the research on the
137 most conspicuous pollution such as metals (Sumpter, 2009) and the highly concentrated nutrient loading (Schwarzenbach et al., 2006) But actually little is known about the impact of p ollution on the majority of living species (Sumpter, 2009) Moreover, the impact of hormone like pollutants branded as endoc rine disrupters chemicals commonly found in low concentrations in urban waters pose a significant concern to public health; these ef fects are produced th r ough mechanisms that involve divergent pathways that are highly conserved in wildlife and humans (Diamanti Kandarakis et al., 2009) The best knowledge will arise from research that combines approaches from several disciplines To avoid the gross pollu tion of water, societies ha ve been installing wastewater treatment plants (WWTP s ). The installation of WWTP s contributes positively to the water quality. For example, in UK, river water quality in urban areas improved considerably after the installation of WWTP s (Wheeler 1979) As a consequence, biodiversity improved considerably (the aq uatic organisms returned) (Wheeler, 1979) However, not all rivers have fully recovered, even in developed countries such as the UK (Sumpter, 2009) It is known that WWTP s are unable to eliminate 100% of the hundreds of chemicals that enter them pharmaceutical and personal care products which end in the rivers and streams with unknown effects on aquatic biota (Kolpin et al., 2002) Thus, wastewater dominant system s can represent a t h reat for species and ecosystems. One prominent researcher in the area Dr. Sumpter (Sumpter, 2009) state s that if we want to protect aquatic organisms from water pollution, we need to adequately answer the following questions:
138 1. What chemicals are present in the aquatic environment? 2. At what concentrations are these chemicals present, and where? 3. What species receive exposure to these chemicals? 4. W hat ar e the effects of this exposure? This disse rtat ion deals with most of these question s to some extent. This research dissertation combines laboratory and field research approaches as well as using water chemistry and molecular biology techniques to understand the effects of pollution in fish. Fish have been used as bio indicators to establish the impa ct of pollution in the aquatic environment. Moreover b y assessing the impact of pollution on fish, it is possible to establish potential links between the adverse outcomes observe d in one species to other f ish species. S ometimes the links can be draw n to other vertebrate species, and in some case s perhaps link s with human effects can be indicated. Maybe the most prominent example of the usefulness of fish as bioindicators is the estrogenicity effect s caused by xenoestrogen s discharged in the aquatic environment worldwide. For example, Jobling et al., (1998) first documented the widespread phenomena of fish sexual and dev elopment disruption associated with waste water dominant systems in the UK The extrapolation of toxic effects found in laboratory animals and wildlife to human is a controversial issue. For one hand, the potency of hormone like chemicals is lower compared with natural hormones, for the other hand mixtures can have an additiv e impact greater than the sum of the individual chemicals concentrations ( Schwarzenbach et al., 2006 ) Also, chronic exposures are more likely to occur in animals that inhabit the aquatic environment. A s tatement from the work session on health effects of contemporary use of pesticides (Short, 1999) highlights the wildlife human connection: Wildlife data deserve more attention than they have had in the past because the y have proven to be an early warning system of adverse
139 ecosystem and human health effects Many examples of this connection have been reported, f or instance, fish cellular response to PFCs can be more similar to cell response than other known mamm al laboratory models ( Benninghoff et al., 2011) Assessing Urban Water Pollution Impacts in Protected A reas To protect biodiversity as well as natural resources like water, societies have used protected areas to preserve nature, which include wetlands and aquatic species among others. Nonetheless, research literature about the impact of water pollution inside preserved areas is rare. The invisible water pollution is able to trespass protected areas borders, most of the time inadvertently The field study in this dissertation research (C hapter 2) was useful to grasp the complexity of the aquatic system and its response ag ai nst pollution. This study shows that urban waters were able to exert effects at transcriptional levels on genes that play a vital role in fish reproduction and survival. The causal association about the chemical present in these waters and the effects observed were not possible, which exemplify the difficulty to determine water pollution effects in a non reductionist setting. Nevertheless, r elevant information such as the type of altered genes or the elevated cholesterol plasma level on fish were critical pieces of information to generate new hypotheses further developed in this research. Hypercholesterolemia in fish exposed to urban waters O ne p articularly interesting result from the field study was the fact that the transcripts of gene such as HGMR, which is key in various metabolic pathways were down regulated in fish exposed to waters that enters into protected areas. This suggests effects on pivotal molecules in fish physiology like cholesterol. Cholesterol is consider one of the most important molecule in fish physiology (Tocher, 2003) The alteration of
140 cholesterol metabolism can produce s everal repercussions on cell membrane stability as well as in sex hormones production. Indeed, some fish exposed to these urban waters had besides the alteration of cholesterol genes, elevated cholesterol in plasma (hypercholestero lemia) H ypercholesterolemia seems to be a normal condition in preparation for sp a w n ing in fish (Larsson and Fnge, 1977) N evertheless, some researchers identified severe (bu t reversible) deleterious effects on cardiovascular system in salmonids with high levels of cholesterol during the spawning season (i.e.Van Citters and Watson, 1968) Moreover, elevated total plasma cholesterol has been observed in fish exposed to polychlorinated biphenyl PCBs (Johansson et al., 1972) Also, effects of PFCs in biota are related with cholesterol biosynthesis (Haughom and Spydevold, 1992; Luebker et al., 2002; Wei et al., 2008; Wei et al., 2009). Interestingly, the associations between high levels of PFCs with high level of cholesterol in blood have been commonly reported in epidemiological studies (Emmett et al., 2006; Nelson et al., 2010; Steenland et al., 2009) Additionally, transcripts from genes used in DNA repair mechan isms were altered in all fish exposed t o these urban treated wastewaters Although the cell uses these genes as a repair mechanism against DNA attacks, this outcome is not guaranteed, especially when the mechanism is in constant challenge. Several toxic s can trigger DNA repair mechanism s incl uding PFCs (Hoff et al., 2003) Furthermore, several DNA repair genes were altered in the laboratory exposure (C hapter 4) of fathead minnows to PFCs which confirmed the ability of PFCs to alter DNA metabolism at very low concentrations
141 Despite the fact that these waters contain several contaminants that could altered others transcripts (i.e.: metallothionein), it was possible to describe patterns in transcriptional that resembles the effe cts caused by chemicals as PFCs. These waters contain several chem icals that are the result of typical activities in medium sized cities, a scenario that is common across the US. Additionally in Northern Florida, the existence of natural hydrological connections between surface waters and water for human consumption a quifers through karst features as sinkholes (Katz et al., 1995) and practices like deep injection for reuse of wastewater, increases the relevance of our findings. It is important to determine whether these natural connections to the aquifer or the inje ction practices are exposing aquatic biota or humans to contaminants such as PFCs, and whether this exposure results in alterations in cholesterol metabolism or DNA damage. Perfluorochemicals Occurrence in an Urban Stream with Wastewater Influence WWTPs an d runoff are known as major contributors of PFCs to the aquatic environment (Ahrens, 2011) Moreover, the processes on WWTPs can result in an increase of some PFCs (Heidler and Halden 2008). In this study PFCs were detec ted along all stream water canals that enter into a protected area with a minor reduction in concentration in the sinkhole area, likely due to dilution. Most detected PFCs were: PFOS>PFOA>PFHxA>PFHpA>PFDA>PFNA Simcik and Dorweiler (2005) determine that the ratio between PFHpA and PFOA represent the amount of atmospheric or urban contribution of PFCs into a particular aquatic system. For example, if the PFHpA / PFOA ratio is higher than 1 means that the atmospheric depo sition (rain) of PFCs is the leading cause. If the ratio is smaller than 1; the majority of PFCs determined in the water environment came from either runoff or
142 wastewater source. The Perfluoroheptanoic acid (PFHpA) / PFOA ratio for all sites investigated h ere were lower than 1, confirming that wastewater effluent as a the maj or concentration of PFOS, further research is needed to determine this phenomena. Overall, this system receive s relatively high amount of PFCs compared with a global concentrations for similar water bodies. The mean of the total PFCs determined was 197 ng/L and 200 ng/L for the wet and dry season respectively. The Effects of Environmentally Relevant Concentrations of PFCs Low levels effects caused by pollution can be traceable since the 70`s (Wilson, 1978). Since pollutant such EDCs tend to be present in low concentrations in the real world, the use of similar level to assess pollution effects become more relevant for the environment. This dissertation proves that e nvironmentally relevant concentrations of PFCs were able to alter key transcripts in fathead minnows. The concentrations of PFOS used include levels equivalent to those found in human blood and mixtures t ypically found in urban waters. The effects on fish include key genes involve in steroids production, DNA repair, cell proliferation, and mitochondrial activity. These genes are involved in fundamental metabolic pathways related with energy, reproduc tion, and important deleterious effects such as carcinogenesis. The Blood as Non invasive Sampling Ti ssue One of th e drawbacks of genomics tools is the necessity to euthanize the animal to assess the toxicity from the target organs (typically the liver). In thi s study, t he use of blood provides a considerable amount of information that could be linked specifically for some genes, and in general pathways with those changes in the liver. The response from b lood to the low concentrations of PFCs exposures was at le ast 5 times higher
143 than the liver in terms of the number of altered genes Furthermore, t he discovery of the up regulation of VTG genes in blood is very significant since vitellogenin is probably the more accepted biomarker to assess fish estrogenicity. Be cause the particular features of non mammalian vertebrates blood, their sensitivity and the non destructive sampling possibility we expect that the further use of blood in toxico genomics studies will contribute to advance in unmasking the effects of the E DCs in a real world scenario. Hopefully, the use of the blood can drive the researches and practitioners to use the toxicogenomic tools beyond the laboratory bounds. Final C onsiderations It may be premature to conclude that the chemicals at the concentrati ons present today in rivers and streams pose little or no threat to aquatic wildlife since there is no data for the vast majority of aquatic organisms; thus, currently we do not know whether or not they have been impacted by chemical pollution (Sumpter, 20 09) As in the past, wastewater treatment plants help to reduce gross pollution; new technology will be adopted to control the new challenge in water pollution. Hopefully, t he contribution like this dissertation research will help to better understand and size the problem of micro pollution Also, the use of interdisciplinary approach es will improve our understanding of this phenomenon The main contribution s of this dissertation in the field of environmental toxicology are the demonstration that environmen tal (low level) of PFCs can alter gene expression in fish and the feasibility of using blood as non destructive sampling method in non mammalian vertebrates Optimistically, the WWTPs of the future will include technology to eliminate most of the EDCs impr oving even more the health of the aquatic ecosystem as well as the availability of water for human development. Moreover, with time the use
144 of blood to research the impact of pollution on wildlife can be more widespread as it is actually in human research. Also, t he non destructive method proposed can be used to design studies to monitor the impact of pollution in the field, re sampling same individuals before and after the impact, or sampling a greater number of individuals, perhaps assessing the impact of pollution at population levels. Finally, the use of genomics tools to assess the response of blood to other non toxic stressor can be applied to understand physiological changes in other areas of animal science.
145 APPENDIX A SUPPORTING I NFORMATION Table S 1. Water parameters measured in each site during effluent collection.* Original on site pH. Site pH DO ( mg/L ) Temp. (C) Conductivity siemens Runoff wastewater 9.86* 7.3 18.8 560 Treated city wastewater 7.77 6.7 19.6 1540 On campus treated wastewater 7.55 4.9 24.1 1820 Control w ater 6.2 4.55 21.1 Table S2. Water pH, dissolved oxygen and temperature (Celsius) from the cylinder. Parameters measured in Cylinders Site Date Time pH Temperature Stormwater 3 Feb 11 6:00 PM 6.5* 22 Streamwater 3 Fe b 11 6:00 PM 5.7 22.5 Wastewater 3 Feb 11 6:00 PM 6.2 22.5 Control 3 Feb 11 6:00 PM 6.2 21.1 Adjusted pH.
146 Table S3.Concentrations ( ng/L )c of organic compounds in historical samples ((ACEPD). 2010) TCW Feb 2009 TCW Aug 2009 OCTW Aug 2009 OCTW Aug 2009* Atrazine 2.6 2.5 ND ND Bisphenol A 22 ND ND 11 Caffeine 41 ND ND ND Carbamazepine 330 48 ND ND DEET 110 1 30 250 270 Diazepam 19 1.4 ND 1.4 Diclofenac ND 17 ND 26 Fluoxetine 27 17 2.8 21 Gemfibrozil 36 ND ND ND Iopromide 170 420 ND ND Meprobamate 35 55 55 91 Phenytoin 180 90 220 86 Methadone 11 ND ND ND Salicylic acid 320 11 ND 27 Perfluoropentanoic Acid 4.6 8.2 4.8 4.5 Perfluorohexanoic Acid 16 18 16 16 Perfluoroheptanoic Acid 5.2 6.8 0.61 ND Perfluorooctanoic Acid (PFOA) 37 35 9.2 9.2 Perfluorononanoic Acid 22 26 36 39 Perfluorodecanoic Acid 4.7 2.8 1.8 2 Perfluorou ndecanoic Acid 0.89 ND 15 16 Perfluorobutanesulfonic Acid 5.4 7.7 110 130 Perfluorohexylsulfonic Acid 12 11 6.5 6.1 Perfluorooctylsulfonic Acid (PFOS) 170 97 21 20 Perfluorododecanoic Acid ND 1 1.1 0.87
147 Table S4. Concentrations (mg /L ) of nu trients, chloride and metals in collected samples TCW TCW OCTW Control NOx N 0.04 4.62 2.74 0.05 Cl 14.02 76.92 92.06 28.6 NH3 N 0.19 0.35 0.24 0.15 TKN 5.73 1.06 1.1 0.13 P nd 1.18 0.26 nd K 2.67 15.5 17.28 0.89 Ca 31.45 42.16 42.35 3 2.16 Mg 4.84 21.59 28.23 23.7 Zn 0.02 0.07 0.09 0.02 Cu nd 0.02 nd nd Al 0.31 0.49 0.02 nd B nd 0.16 0.06 nd Cd nd 0.02 nd nd Mo nd 0.03 nd nd nd: not detected
148 Table S5. Annotated genes differentially regulated from fish exp osed to runoff wastewater Gene Title Log(2) Expression Fold Probability Homeo Box C6a / 1.990 0.0000 Stromal Cell Derived Factor 4 / 2.669 0.0000 Kv Channel Interacting Protein 1 / 2.238 0.0001 Related RAS Viral Oncogene Homolog 2 / 0.976 0.0001 KI AA0284 / 1.068 0.0001 CREB Binding Protein / 1.313 0.0001 Serologically Defined Colon Cancer Antigen 10 / 0.793 0.0002 Si:ch211 214k9.1 / 1.145 0.0002 Chloride Channel 3 / 1.809 0.0003 Wu:fk63e10 / Similar To Wu:fk63e10 Protein / 2.768 0.0004 El ongation Of Very Long Chain Fatty Acids like 2 / 1.661 0.0004 Nucleolar Protein 5 / 0.890 0.0004 Oxysterol Binding Protein / 1.294 0.0006 Similar To Ribosomal L1 Domain Containing 1 / 1.065 0.0006 Zona Pellucida Glycoprotein 3c / Zgc:66432 / Hypothet ical LOC555835 / 1.660 0.0006 Elongation Factor, RNA Polymerase II, 2 / 1.435 0.0007 Furin B / Similar To FurinB Preproprotein / 1.199 0.0007 Ankyrin Repeat Domain 57 / 1.290 0.0007 3 hydroxy 3 methylglutaryl Coenzyme A Reductase / 3.705 0.0007 Amp lified In Osteosarcoma / 1.913 0.0008 RIKEN CDNA 1810073N04 Gene / 1.751 0.0008 Peptidylprolyl Isomerase B / 0.919 0.0008 Similar To Metalloprotease Disintegrin 15 With Thrombospondin Domains / 0.623 0.0009 Transmembrane Emp24 Protein Transport Doma in Containing 3 / 1.866 0.0009 p rotein Disulfide Isomerase Associated 4 / 2.987 0.0010 Staphylococcal Nuclease And Tudor Domain Containing 1 / 0.920 0.0010 Dystroglycan 1 / 1.557 0.0012 Similar To Krt4 Protein / 1.310 0.0012 Similar To Basic, Immu noglobulin like Variable Motif containing Protein / Basic, Immunoglobulin like Variable Motif Containing / 2.771 0.0013 Si:dkey 159a18.7 / 1.155 0.0014 RIKEN CDNA 2410014A08 Gene / 1.633 0.0014 Damage specific DNA Binding Protein 1, 127kDa / 0.636 0.0 015 Chromosome X Open Reading Frame 39 / 0.856 0.0016 Similar To Serine Proteinase Inhibitor, Clade B, Member 1 / Hypothetical Protein LOC792004 / Zgc:173729 / Serpin Peptidase Inhibitor, Clade B, Member 1, Like 1 / Serpin Peptidase Inhibitor, Clade B, Member 1, Like 4 / 1.675 0.0017
149 Eukaryotic Translation Initiation Factor 2B, Subunit 3 Gamma / 0.697 0.0017 Lyric like / 1.234 0.0018 Cytochrome P450, Family 51 / 2.249 0.0020 N acetyltransferase 13 / 0.682 0.0021 Single stranded DNA Binding Prote in 3 / 0.621 0.0021 Polyhomeotic Homolog 1 / 2.310 0.0022 Signal Sequence Receptor, Beta / 0.991 0.0022 Aminopeptidase Puromycin Sensitive / 2.462 0.0022 Eukaryotic Translation Initiation Factor 3, Subunit G / 0.883 0.0023 Protein Disulfide Isomerase Family A, Member 6 / 1.016 0.0023 Si:dkeyp 114f9.4 / 1.356 0.0023 Si:dkeyp 89d7.2 / 0.811 0.0024 Leucine Rich Repeat Transmembrane Neuronal 1 / 2.503 0.0024 ADP ribosylation Factor like 3 / 1.098 0.0024 ISL1 Transcription Factor, LIM/homeodomain / 0.747 0.0024 Isochorismatase Domain Containing 1 / 1.014 0.0026 Anaphase Promoting Complex Subunit 7 / 0.851 0.0026 Similar To IgGFc binding Protein Precursor / 1.917 0.0027 Lectin, Mannose binding, 1 / Similar To Lman1 Protein / 1.909 0.0028 Caspa se 8, Apoptosis related Cysteine Peptidase, Like 2 / Similar To Caspase 8b / 0.860 0.0028 Transient Receptor Potential Cation Channel, Subfamily M, Member 1 0.777 0.0029 Sterol C4 methyl Oxidase like / 2.460 0.0030 RAB14, Member RAS Oncogene Family / 1.833 0.0030 Zinc Finger And BTB Domain Containing 11 / 1.006 0.0031 Humpback / 0.818 0.0031 Si:rp71 1n7.1 / 0.962 0.0032 Prostaglandin D2 Synthase / 2.369 0.0032 Similar To Protocadherin / Acyl CoA Synthetase Long chain Family Member 1 / 1.320 0.0032 Mitofusin 2 / 1.129 0.0032 Neural Cell Adhesion Molecule 3 / 2.400 0.0033 Transferrin Receptor 2 / 1.996 0.0033 Nuclear Receptor Subfamily 4, Group A, Member 2 / 0.837 0.0034 Similar To SH3 And Multiple Ankyrin Repeat Domains 2 / 1.338 0.00 35 Similar To SIL1 Homolog, Endoplasmic Reticulum Chaperone / 1.197 0.0035 Ntl dependent Gene 5 / 2.404 0.0036 Chromosome 19 Open Reading Frame 62 / 1.103 0.0037 BMS1 like, Ribosome Assembly Protein / Similar To LOC553241 Protein / 1.380 0.0037 Ubiq uitin like Modifier Activating Enzyme 3 / 1.012 0.0038 Sorting Nexin 4 / 1.066 0.0038 Eukaryotic Translation Initiation Factor 3, Subunit M / 1.646 0.0039
150 General Transcription Factor IIF, Polypeptide 1, 74kDa / 1.261 0.0039 Apoptotic Protease Activa ting Factor / 1.041 0.0040 Heat Shock Protein 5 / Heat Shock 70kDa Protein 5 / 2.807 0.0040 Type I Cytokeratin, Enveloping Layer / Zgc:92533 / Si:dkeyp 113d7.7 / Zgc:136388 / 1.279 0.0042 Homer Homolog 1 / 2.256 0.0042 MARCKS like 1 / 1.584 0.0042 C anopy1 / 2.164 0.0043 Similar To Plectin 1 / 1.258 0.0044 Cell Adhesion Molecule With Homology To L1CAM / 2.384 0.0045 Tweety Homolog 3 / 1.144 0.0045 Similar To HCG40928 / 1.181 0.0046 Similar To Pre B cell Leukemia Transcription Factor Interactin g Protein 1 / 2.256 0.0046 MAD1 Mitotic Arrest Deficient like 1 / 0.609 0.0046 Si:ch211 168n16.1 / 1.890 0.0048 Solute Carrier Family 30, Member 5 / 0.779 0.0049 TNF Receptor associated Factor 3 Interacting Protein 1 / 1.391 0.0049 Rap2 Interacti ng Protein / Similar To Rap2 Interacting Protein / 1.775 0.0049 TAR DNA Binding Protein / 0.639 0.0051 Par 3 Partitioning Defective 3 Homolog / 1.471 0.0051 Similar To Somatostatin Receptor Type Two / 1.959 0.0052 Spindle Assembly 6 Homolog / 0.874 0 .0052 Chloride Intracellular Channel A / 0.712 0.0053 Phosphoribosyl Transferase Domain Containing 1 / 1.415 0.0054 Intraflagellar Transport 88 Homolog / 0.927 0.0056 Similar To EPS15 Protein / 1.569 0.0057 B cell Receptor associated Protein 31 / 1 .014 0.0057 Ribosome Binding Protein 1 Homolog 180kDa / 2.096 0.0058 Similar To 240K Protein Of Rod Photoreceptor Cng channel / 2.203 0.0059 Pirin / 2.015 0.0061 EPH Receptor B2 / 4.455 0.0061 Twinfilin, Actin binding Protein, Homolog 1b / 0.947 0. 0062 PR Domain Containing 8 / 0.673 0.0062 Ubiquitin activating Enzyme E1 domain Containing 1 / 1.169 0.0063 Similar To SPACR / 0.633 0.0064 Potassium Intermediate/small Conductance Calcium activated Channel, Subfamily N, Member 1 / 1.036 0.0065 U biquitin like Modifier Activating Enzyme 5 / 1.595 0.0066 Defender Against Cell Death 1 / 1.449 0.0066 Mutated In Colorectal Cancers / 0.885 0.0069 Similar To SNX26 Protein / 1.353 0.0071 Similar To Sarcoma Antigen NY SAR 41 / Hypothetical LOC55544 2 / Hypothetical Protein LOC100004835 / 0.875 0.0072
151 Implantation associated Protein / 1.132 0.0072 GDP mannose Pyrophosphorylase B / 2.161 0.0075 Ubiquitin conjugating Enzyme E2I / 0.836 0.0076 Prolyl 4 hydroxylase, Beta Polypeptide / 0.864 0.0077 DEP Domain Containing 6 / 2.962 0.0078 CAMP regulated Phosphoprotein 19 / 2.117 0.0079 Limb Bud And Heart Development Homolog / 1.604 0.0079 Farnesyl Diphosphate Synthase / 1.439 0.0079 Cell Cycle Associated Protein 1 / 1.253 0.0080 Similar To ZNF3 18 Protein / 1.722 0.0080 Solute Carrier Family 39, Member 7 / 1.131 0.0080 Similar To KIAA0692 Protein / 0.741 0.0081 Similar To SIL1 Homolog, Endoplasmic Reticulum Chaperone / 1.667 0.0081 Sorbin And SH3 Domain Containing 3 / 1.492 0.0081 Uracil DNA Glycosylase / 1.523 0.0084 Dentin Sialophosphoprotein / 1.124 0.0085 Similar To Peroxisomal Coenzyme A Diphosphatase NUDT7 / 0.992 0.0086 Myosin VB / 2.465 0.0086 Signal Sequence Receptor, Gamma / 1.036 0.0086 Ankyrin Repeat And Sterile Alpha Motif Domain Containing 6 / 1.233 0.0087 Inhibitor Of Growth Family, Member 2 / 0.796 0.0087 Cathepsin L1, A / 1.095 0.0087 Septin 7 / 1.077 0.0089 Zinc Finger CCCH type Containing 15 / 0.769 0.0089 Bruno like 5, RNA Binding Protein / 0.807 0.0090 Leukemia Inhibitory Factor Receptor Alpha / 1.166 0.0090 Carnitine Acetyltransferase / 0.939 0.0090 RAD50 Homolog / 0.724 0.0090 U2 Small Nuclear RNA Auxiliary Factor 2 / 1.072 0.0090 Bromodomain Containing 8 / 0.632 0.0091 Si:ch211 173p18.3 / 0.79 9 0.0092 Glucose 6 phosphate Dehydrogenase / 0.944 0.0093 Cyclin dependent Kinase like 1 / 1.513 0.0096 Titin like / 2.254 0.0097 Nedd4 Family Interacting Protein 1 / 1.655 0.0099 Jumonji Domain Containing 1B / 0.703 0.0099 Cysteine And Glycine ric h Protein 1 / 1.942 0.0103 UDP Gal:betaGlcNAc Beta 1,4 Galactosyltransferase, Polypeptide 1 / 0.846 0.0103 Similar To Keratin Alpha 2 / 0.686 0.0104 Inhibitor Of DNA Binding 2, Dominant Negative Helix loop helix Protein / 1.637 0.0104 Si:dkey 34f16 .1 / 1.067 0.0105
152 Tectonic Family Member 3 / 0.618 0.0106 Bmi1 Polycomb Ring Finger Oncogene / 1.013 0.0106 Glycogen Synthase Kinase Binding Protein / 0.835 0.0106 T Cell Receptor Gamma Variable 3 / 2.158 0.0107 Similar To Collagen Alpha 1(VIII) Ch ain Precursor / 0.634 0.0107 NCK associated Protein 1 / 1.180 0.0109 RIKEN CDNA 2310008M10 Gene / 1.397 0.0111 Sterol C5 desaturase Homolog / 1.395 0.0113 Similar To Flocculin like Protein / 2.254 0.0113 SEC31 Homolog A / 0.872 0.0114 Zinc Fing er, Matrin Type 2 / 0.896 0.0114 Similar To Multiple C2 Domains, Transmembrane 2 / 2.749 0.0115 Similar To ZBTB40 Protein / 0.693 0.0117 UPF2 Regulator Of Nonsense Transcripts Homolog / 1.876 0.0117 Transforming Growth Factor Beta Regulator 1 / 1.000 0.0117 Low Density Lipoprotein Receptor Adaptor Protein 1 / 1.188 0.0120 MAX Dimerization Protein 3 / 0.713 0.0121 Neurogenic Differentiation 1 / 1.583 0.0122 Der1 like Domain Family, Member 1 / 1.347 0.0122 Similar To HCG16936 / 0.888 0.0122 WD R epeat Domain 91 / 0.662 0.0123 RAB26, Member RAS Oncogene Family / 0.885 0.0125 Si:rp71 1h10.1 / 2.765 0.0128 Sec1 Family Domain Containing 1 / 0.649 0.0129 Lysophospholipase II / 0.638 0.0130 LIM Domain Kinase 2 / 1.489 0.0131 Glutamate cysteine Ligase, Catalytic Subunit / 0.652 0.0131 Similar To Keratin Type IE / 0.983 0.0131 Similar To KIAA1607 Protein / 1.138 0.0132 Bromodomain Containing 9 / 1.693 0.0132 Similar To MGC98945 Protein / 1.378 0.0132 Mediator Complex Subunit 17 / 1.481 0.0 134 Jagunal Homolog 1 / 1.080 0.0139 YY1 Transcription Factor / 1.128 0.0139 Similar To RIKEN CDNA 2810004N23 Gene / 0.909 0.0142 Peptidyl tRNA Hydrolase 1 Homolog / Hypothetical LOC559192 / 0.881 0.0142 SAR1 Gene Homolog A / 0.605 0.0143 Nucleobi ndin 1 / 1.270 0.0145 Cleavage And Polyadenylation Factor Subunit Homolog / 0.719 0.0145 Chromosomal Passenger Complex Protein Dasra A / 2.059 0.0145 MutL Homolog 1, Colon Cancer, Nonpolyposis Type 2 / 0.778 0.0145 FERM Domain Containing 4A / 1.769 0 .0147 Mitochondrial Carrier Homolog 2 / 0.618 0.0147 N acetylneuraminic Acid Synthase / 1.145 0.0147 Acetyl CoA Acetyltransferase 2 / 1.760 0.0150
153 Table S6. Annotated genes differentially regulated from fish expo sed to treated city wastewater Gene Title Log(2) Expression Fold Probability Related RAS Viral Oncogene Homolog 2 / 0.910 0.0001 Homeo Box C6a / 1.642 0.0003 Furin B / Similar To FurinB Preproprotein / 1.348 0.0003 Carnitine Acetyltransferase / 1.429 0.0005 Sec1 Family Domain Containi ng 2 / 0.827 0.0008 KIAA0284 / 0.815 0.0008 Peptidylprolyl Isomerase B / 0.920 0.0008 Similar To Collagen Alpha 1(VIII) Chain Precursor / 0.927 0.0009 Cyclin E / 0.705 0.0009 Dedicator Of Cytokinesis 6 / 1.164 0.0009 Similar To Tripartite Motif containing 32 / 0.386 0.0010 SEC31 Homolog A / 1.204 0.0014 Oxysterol Binding Protein / 1.138 0.0015 Stromal Cell Derived Factor 4 / 1.747 0.0017 Epoxide Hydrolase 1, Microsomal / 2.111 0.0017 Similar To Wu:fi20h07 Protein / 0.952 0.0021 CREB Binding Protein / 0.906 0.0023 Stomatin / 0.884 0.0024 Solute Carrier Family 30, Member 5 / 0.868 0.0024 Protein Disulfide Isomerase Associated 4 / 2.637 0.0025 GrpE like 1, Mitochondrial / 1.527 0.0028 Pirin / 2.271 0.0028 Isochorismatase Domain Containing 1 / 0.976 0.0033 RIKEN CDNA 2410014A08 Gene / 1.450 0.0033 BCL2/adenovirus E1B Interacting Protein 3 like, 2 / 0.737 0.0036 Similar To Secreted Frizzled related Protein 1 / 1.992 0.0038 Caspase 8, Apoptosis related Cysteine Peptidase, L ike 2 / Similar To Caspase 8b / 0.823 0.0038 Transmembrane Protein 38A / 1.529 0.0039 Zona Pellucida Glycoprotein 3c / Zgc:66432 / Hypothetical LOC555835 / 1.286 0.0040 Damage specific DNA Binding Protein 1, 127kDa / 0.552 0.0041 Similar To SIL1 Homo log, Endoplasmic Reticulum Chaperone / 1.166 0.0042 Similar To Basic, Immunoglobulin like Variable Motif containing Protein / Basic, Immunoglobulin like Variable Motif Containing / 2.328 0.0044 Mutated In Colorectal Cancers / 0.947 0.0045 F box And W D 40 Domain Protein 4 / 0.538 0.0045
154 Acyl Coenzyme A Dehydrogenase, Very Long Chain / 0.696 0.0045 Similar To TAFI95 / 1.157 0.0046 Staphylococcal Nuclease And Tudor Domain Containing 1 / 0.740 0.0046 Wu:fk63e10 / Similar To Wu:fk63e10 Protein / 1.92 4 0.0050 Regulatory Factor X, 1 / 0.754 0.0051 Activin A Receptor, Type 1B / 0.990 0.0051 Ubiquitin Carboxyl terminal Esterase L1 / 1.765 0.0052 Elongation Of Very Long Chain Fatty Acids like 2 / 1.153 0.0052 Tudor Domain Containing 9 like / 1.625 0.0054 WW Domain Containing Adaptor With Coiled coil / 1.081 0.0054 Kinesin Family Member 22 / 1.373 0.0056 Solute Carrier Family 37, Member 2 / 1.006 0.0056 Plasticity Related Gene 3 / 2.096 0.0057 Splicing Factor, Arginine/serine rich 8 / 0.575 0.0057 Amyloid Beta Precursor Protein / 1.242 0.0057 ST6 Beta galactosamide Alpha 2,6 sialyltranferase 2 / 1.490 0.0058 Similar To Ribosomal L1 Domain Containing 1 / 0.769 0.0058 Zinc Finger And BTB Domain Containing 11 / 0.911 0.0058 Proteasome 26S Subunit, Non ATPase, 6 / 1.850 0.0059 Transient Receptor Potential Cation Channel, Subfamily M, Member 1 / 0.694 0.0060 Solute Carrier Family 39, Member 7 / 1.185 0.0061 Similar To RING Finger Protein C13orf7 Homolog / 1.250 0.0061 3 hydroxy 3 methy lglutaryl Coenzyme A Reductase / 2.725 0.0062 Potassium Intermediate/small Conductance Calcium activated Channel, Subfamily N, Member 1 / 1.040 0.0063 Similar To IgGFc binding Protein Precursor / 1.676 0.0065 Si:ch211 264e16.2 / Si:ch211 219a15.4 / 0 .630 0.0066 Eukaryotic Translation Initiation Factor 3, Subunit M / 1.511 0.0067 MAD Homolog 1 / 0.733 0.0068 G Protein coupled Receptor 123 / 1.344 0.0068 Aldehyde Dehydrogenase 9, Subfamily A1 / 1.324 0.0070 Sp8 Transcription Factor / 0.892 0.0070 Transmembrane Emp24 Protein Transport Domain Containing 9 / 1.874 0.0070 Eukaryotic Translation Initiation Factor 3, Subunit G / 0.743 0.0070 Zinc Finger Protein 236 / 1.136 0.0071 Friend Leukemia Integration 1a / 0.990 0.0071 Eukaryotic Translation Initiation Factor 3, Subunit B / 0.819 0.0072 Lyric like / 0.994 0.0073 Similar To Protocadherin / Acyl CoA Synthetase Long chain Family Member 1 / 1.156 0.0075 Canopy1 / 1.979 0.0075 Septin 7 / 1.106 0.0076 Chemokine Ligand 12a / 1.852 0.0077
155 RIK EN CDNA 2310008M10 Gene / 1.487 0.0077 Transferrin Receptor 2 / 1.742 0.0078 Zinc Finger, FYVE Domain Containing 21 / 0.739 0.0078 Transmembrane Emp24 Protein Transport Domain Containing 3 / 1.359 0.0080 Si:ch211 245h14.1 / 0.908 0.0081 Hormonally Upregulated Neu associated Kinase / 1.089 0.0084 Forkhead Box B1 / 0.415 0.0084 HIV 1 Tat Interactive Protein 2 / 0.808 0.0084 ATPase, Ca++ Transporting, Cardiac Muscle, Fast Twitch 1 / ATPase, Ca++ Transporting, Cardiac Muscle, Fast Twitch 1 Like / 0 .795 0.0086 Ral GEF With PH Domain And SH3 Binding Motif 1 / 0.836 0.0087 Nuclear Receptor Subfamily 4, Group A, Member 2 / 0.716 0.0089 Similar To Heterogeneous Nuclear Ribonucleoprotein A3 / Predicted Gene, EG432946 / 1.955 0.0090 Inhibitor Of DNA Binding 2, Dominant Negative Helix loop helix Protein / 1.675 0.0092 Prostaglandin D2 Synthase / 2.000 0.0092 Myelin Associated Glycoprotein / 0.912 0.0092 Chloride Channel 3 / 1.093 0.0092 Makorin, Ring Finger Protein, 1 / 0.901 0.0094 Sec1 Family Domain Containing 1 / 0.685 0.0096 N myc Downstream Regulated Gene 3 / 0.555 0.0099 G Protein Beta Subunit like / 0.522 0.0104 Aldehyde Dehydrogenase 18 Family, Member A1 / 1.383 0.0104 WD Repeat Domain 91 / 0.682 0.0105 TAR DNA Binding Protein / 0.565 0.0106 Protein Kinase C, Theta / 1.250 0.0107 MARCKS like 1 / 1.357 0.0107 Si:ch211 214k9.1 / 0.649 0.0108 Farnesyl Diphosphate Synthase / 1.362 0.0108 Tumor Necrosis Factor Superfamily, Member 10 Like 4 / 1.586 0.0110 Myocyte Enhancer Facto r 2D / 0.793 0.0114 NADPH Oxidase Organizer 1 / 1.104 0.0116 Similar To Putative Extensin / 1.208 0.0116 Coiled coil Domain Containing 132 / 0.889 0.0118 Peptidyl tRNA Hydrolase 1 Homolog / Hypothetical LOC559192 / 0.912 0.0118 ISL1 Transcription Factor, LIM/homeodomain / 0.579 0.0119 Jagged 1 / 1.018 0.0122 Similar To Serine Proteinase Inhibitor, Clade B, Member 1 / Hypothetical Protein LOC792004 / Zgc:173729 / Serpin Peptidase Inhibitor, Clade B, Member 1, Like 1 / Serpin Peptidase Inhibitor, Clade B, 1.225 0.0124
156 Member 1, Like 4 / Mediator Complex Subunit 4 / 0.459 0.0132 Interferon Regulatory Factor 5 / 1.006 0.0136 Calreticulin Like / 1.658 0.0136 RIKEN CDNA 1810073N04 Gene / 1.138 0.0137 Crystallin, Gamma N2 / 1.990 0.0137 Simila r To Novel Zinc Finger Protein / 0.321 0.0138 ATPase, Na+/K+ Transporting, Alpha 1 Polypeptide / ATPase, Na+/K+ Transporting, Alpha 1a.4 Polypeptide / 1.003 0.0139 Cell Cycle Associated Protein 1 / 1.132 0.0141 Formin like 2 / 0.943 0.0142 Similar To KIAA0692 Protein / 0.670 0.0143 Si:rp71 1n7.1 / 0.748 0.0143 Cysteine rich With EGF like Domains 2 / 2.222 0.0144 Twinfilin, Actin binding Protein, Homolog 1b / 0.815 0.0144 CUB Domain Containing Protein 1 / 0.515 0.0146 Heterogeneous Nuclear Ri bonucleoprotein A0 / 0.917 0.0147 Tectonic Family Member 3 / 0.582 0.0147 Similar To Growth arrest specific Protein 2 / 2.311 0.0147 N acetyltransferase 13 / 0.495 0.0148 Suppressor Of Ty 5 Homolog / 0.485 0.0149 Similar To F box And WD 40 Domain P rotein 10 / 0.996 0.0149 RNA Binding Motif Protein 15 / 0.555 0.0150 YY1 Transcription Factor / 0.707 0.0151 N acetylneuraminic Acid Synthase / 1.136 0.0153 Cysteine Conjugate beta Lyase 2 / Similar To Cysteine Conjugate beta Lyase 2 / 2.071 0.0154 Ntl dependent Gene 5 / 1.870 0.0157 Voltage dependent Anion Channel 1 / 0.825 0.0158 Centrobin, Centrosomal BRCA2 Interacting Protein / 0.804 0.0159 Similar To Prolyl 4 hydroxylase, Alpha I Subunit Isoform 1 Precursor / 1.486 0.0159 Leukemia Inhibit ory Factor Receptor Alpha / 1.050 0.0159 Fas / 0.890 0.0160 Similar To SH3 And Multiple Ankyrin Repeat Domains 2 / 1.031 0.0163 Ubiquitin fold Modifier 1 / 1.300 0.0164 Sterol C5 desaturase Homolog / 1.300 0.0164 Mitochondrial Carrier Homolog 2 / 0.605 0.0164 Similar To ZBTB40 Protein / 0.649 0.0165 Anaphase Promoting Complex Subunit 7 / 0.623 0.0167 Heat Shock Protein 90kDa Beta, Member 1 / 1.571 0.0172 Bone Morphogenetic Protein Receptor, Type 1b / 1.539 0.0172 T box 18 / 0.455 0.0176
157 Ankyrin Repeat And Sterile Alpha Motif Domain Containing 6 / 1.083 0.0176 Centaurin, Alpha 1 / 0.334 0.0178 Protein Interacting With PRKCA 1 / 1.254 0.0178 Serine Hydrolase like / 1.174 0.0178 Similar To Nuclear Fragile X Mental Retardation Protein I nteracting Protein 1 / 0.877 0.0181 Similar To Metaxin 1b / 0.578 0.0181 SAR1 Gene Homolog A / 0.578 0.0182 Cholinergic Receptor, Nicotinic, Beta 3 / 1.692 0.0182 Lactate Dehydrogenase B4 / 1.314 0.0184 Protocadherin 9 / 1.455 0.0187 Ubiquitin ac tivating Enzyme E1 domain Containing 1 / 0.962 0.0187 Similar To Semaphorin L; H Sema L / 1.395 0.0188 Solute Carrier Family 5, Member 8 like / 1.032 0.0188 Suppressor Of Variegation 4 20 Homolog 1 / 0.676 0.0189 Homeo Box A1a / 1.137 0.0193 ER Degra dation Enhancer, Mannosidase Alpha like 2 / 0.701 0.0196 Sterol C4 methyl Oxidase like / 1.785 0.0196 Bromodomain Containing 8 / 0.547 0.0197 Si:ch211 241j12.3 / 1.435 0.0197 Phospholipase A2, Group XIIB / 0.791 0.0197 FCF1 Small Subunit Processom e Component Homolog / 0.707 0.0198 Chloride Intracellular Channel A / 0.563 0.0199 Enah/Vasp like / 0.341 0.0199 Similar To Somatostatin Receptor Type Two / 1.541 0.0200 Hypoxia Up regulated 1 / Similar To Hypoxia Up regulated 1 2.073 0.0200 Chr omodomain Protein, Y Chromosome like / 0.598 0.0202 Signal Sequence Receptor, Gamma / 0.880 0.0207 TEA Domain Family Member 4 / 0.768 0.0207 Serine Protease Inhibitor, Kunitz Type 2 / 0.551 0.0210 Solute Carrier Family 39, Member 11 / 1.452 0.0212 Paired like Homeobox 2b / 0.641 0.0213 Arachidonate 12 lipoxygenase / 1.699 0.0213 Dystroglycan 1 / 0.973 0.0215 Nuclear Transcription Factor, X box Binding like 1 / 0.519 0.0216 Poly A Binding Protein, Cytoplasmic 1 B / 0.512 0.0217 Cleavage And P olyadenylation Factor Subunit Homolog / 0.663 0.0218 Signal Recognition Particle 9 / Hypothetical Protein LOC792110 / 1.266 0.0220 Lectin, Mannose binding, 1 / Similar To Lman1 Protein / 1.337 0.0221 Discs, Large Homolog 1, Like / 1.045 0.0222 Simila r To HCG16936 / 0.789 0.0224
158 Meis Homeobox 3 / 0.257 0.0224 Similar To TBC1 Domain Family, Member 10C / 1.859 0.0224 Pleckstrin Homology Domain Containing, Family H, Member 1 / 0.956 0.0225 Dynein, Axonemal, Light Chain 1 / 1.312 0.0226 Zinc Finger P rotein 740 / 0.552 0.0227 Transducin like 2 / 1.301 0.0229 Suppression Of Tumorigenicity 5 / 0.532 0.0230 MICAL like 1 / 0.733 0.0231 Limb Bud And Heart Development Homolog / 1.309 0.0232 Similar To MutS Homolog / 1.000 0.0233 Similar To SNX26 Prot ein / 1.085 0.0233 Caspase 3, Apoptosis related Cysteine Protease A / 1.012 0.0233 Bruno like 5, RNA Binding Protein / 0.673 0.0234 Gene Model 672, / 1.327 0.0236 Amplified In Osteosarcoma / 1.105 0.0237 ST6 N acetylgalactosaminide Alpha 2,6 sialy ltransferase / 0.453 0.0237 Im:7158669 / 0.646 0.0238 Similar To Fignl1 prov Protein / 0.708 0.0240 Optic Atrophy 3 / 0.451 0.0243 Si:dkey 34f16.1 / 0.905 0.0245 Alpha3 fucosyltransferase / 1.520 0.0245 Beta Thymosin like Protein 2 / 1.099 0.0246 Asparagine linked Glycosylation 6 Homolog / 0.976 0.0247 WD Repeat Domain, Phosphoinositide Interacting 1 / 0.670 0.0248 Tetratricopeptide Repeat Domain 30A / 0.366 0.0248 Tryptophanyl tRNA Synthetase / 1.893 0.0251 Tweety Homolog 3 / 0.837 0.0254 F box And WD Repeat Domain Containing 5 / 0.428 0.0255 Eukaryotic Translation Initiation Factor 3, Subunit E, A / Similar To Eukaryotic Translation Initiation Factor 3, Subunit 6 / 0.624 0.0256 Bone Morphogenetic Protein 2b / 1.281 0.0257 Ankyrin Repe at Domain 57 / 0.728 0.0257 Similar To Chromosome 5 Open Reading Frame 5 / Hypothetical LOC568652 / 0.959 0.0258 Protocadherin 15a / 1.017 0.0260 Similar To Peroxisomal Coenzyme A Diphosphatase NUDT7 0.801 0.0261 Interferon Regulatory Factor 2 Bind ing Protein 2 / 1.048 0.0262 Coiled coil helix coiled coil helix Domain Containing 7 / 0.660 0.0263 Similar To RIKEN CDNA 5730509K17 Gene / 0.834 0.0264 Peroxisomal Membrane Protein 3 / 1.358 0.0265 Neural Precursor Cell Expressed, Developmentally 0.980 0.0266
159 Do wn regulated Gene 4 like / GIPC PDZ Domain Containing Family, Member 1 / 0.473 0.0267 AMP Deaminase 3 / 1.025 0.0267 Phosphatidylinositol 3 kinase Catalytic Delta Polypeptide / 0.968 0.0268 Similar To SIL1 Homolog, Endoplasmic Reticulum C haperone / 1.325 0.0271 Surfeit 4 / 1.105 0.0271 Nucleolar Protein 5A / 3.006 0.0273 Protein Tyrosine Phosphatase like, Member B / 1.017 0.0275 Stromal Antigen 1 / 0.570 0.0278 Plastin 3 / 0.720 0.0278 Similar To Histocompatibility 28 / 2.357 0.02 78 CDC28 Protein Kinase Regulatory Subunit 1B / 0.389 0.0280 Male specific Lethal 2 like 1 / 0.570 0.0284 Blocked Early In Transport 1 Homolog / 0.750 0.0285 Midkine related Growth Factor B / 1.344 0.0286 Mediator Complex Subunit 17 / 1.270 0.0287 Jumonji Domain Containing 1B / 0.570 0.0290 Prolyl 4 hydroxylase, Beta Polypeptide / 0.669 0.0292 Nijmegen Breakage Syndrome 1 / Similar To Potassium Channel TSK3 / 1.003 0.0292 Low Density Lipoprotein Receptor related Protein Associated Protein 1 / 1.185 0.0293
160 Table S7 Annotated genes differentially regulated from fish exposed to on campus treated wastewater Gene Title Log(2)Expressio n Fold Probability CREB Binding Protein / 1.335 0.0001 Related RAS Viral Oncogene Homolog 2 / 0.865 0.0002 Cyclin E / 0.845 0.0002 Wu:fk63e10 / Similar To Wu:fk63e10 Protein / 2.868 0.0003 Similar To LOC494811 Protein / 13.158 0.0003 Homeo Box C6a / 1.494 0.0006 Dedicator Of Cytokinesis 6 / 1.206 0.0007 Centrobin, Centrosomal BRCA2 Interacting Protein / 1.263 0.0009 Similar To Protein Kinase Beta Like / 2.182 0.0010 Peptidylprolyl Isomerase B / 0.886 0.0011 Similar To Ribosomal L1 Domain Containing 1 / 0.943 0.0014 Similar To Wu:fi20h07 Protein / 0.989 0.0016 Guanine Nucleotide Binding Protein, Alpha O / 1.684 0.0016 Bardet Biedl Syndrome 2 / 1.014 0.0017 KIAA0284 / 0.727 0.0018 Glutamate cysteine Ligase, Catalytic Subunit / 0.881 0.0020 Vesicle associated Membrane Protein 4 / 1.002 0.0020 BCL2/adenovirus E1B Interacting Protein 3 like, 2 / 0.801 0.0021 Similar To Collagen Alpha 1(VIII) Chain Precursor / 0.822 0.0021 Glucose 6 phosphate Dehydrogenase / 1.186 0.0022 Similar To F box/LRR repeat Protein 17 / 0.618 0.0023 Similar To C Myc / 1.900 0.0023 DNA Segment, Chr 15, ERATO Doi 6 21, Expressed / 0.774 0.0029 PDZ Domain Containing 4 / 1.698 0.0038 HIV 1 Tat Interactive Protein 2 / 0.920 0.0038 CTD Phosphatase, Subunit 1 / Similar To RNA Polymerase II CTD Phosphatase / 0.814 0.0039 Formin like 2 / 1.166 0.0041 Sodium Channel, Voltage gated, Type I, Beta / 1.288 0.0041 Similar To Secreted Frizzled related Protein 1 / 1.932 0.0046 Caspase 8, Apoptosis related Cysteine Peptidase, Like 2 / Similar To Caspase 8b / 0.797 0.0047 Opsin 1, Short wave sensitive 2 / 2.555 0.0048 L eukotriene B4 12 hydroxydehydrogenase / 1.340 0.0051 Damage specific DNA Binding Protein 1, 127kDa / 0.532 0.0052 Eukaryotic Translation Initiation Factor 3, Subunit M / 1.571 0.0053 Mutated In Colorectal Cancers / 0.921 0.0054 Nucleolar Protein 5 / 0 .628 0.0054 Transient Receptor Potential Cation Channel, Subfamily M, Member 1 / 0.701 0.0056
161 SH3 domain GRB2 like Endophilin B2 / 1.317 0.0057 Heat Shock Protein 5 / Heat Shock 70kDa Protein 5 / 2.653 0.0057 Tectonic Family Member 3 / 0.684 0.0058 Protein Disulfide Isomerase Associated 4 / 2.313 0.0059 Isochorismatase Domain Containing 1 / 0.890 0.0060 Mitochondrial Intermediate Peptidase / 0.808 0.0061 Lysophosphatidylcholine Acyltransferase 1 / 1.229 0.0062 Glypican 1 / 4.277 0.0063 Car nitine Acetyltransferase / 0.992 0.0065 Southpaw / 0.810 0.0069 Pirin / 1.975 0.0069 ATPase, Ca++ Transporting, Cardiac Muscle, Fast Twitch 1 / ATPase, Ca++ Transporting, Cardiac Muscle, Fast Twitch 1 Like / 0.822 0.0071 Si:ch211 264e16.2 / Si:ch211 219a15.4 / 0.621 0.0072 RNA Binding Motif Protein 15 / 0.630 0.0074 Nuclear Receptor Coactivator 7 / 1.337 0.0074 Nuclear Receptor Subfamily 4, Group A, Member 2 / 0.737 0.0075 Fas / 1.011 0.0079 Transmembrane Protein 16E / 1.000 0.0082 Galactos idase, Alpha / 1.334 0.0082 Activin A Receptor, Type 1B / 0.907 0.0086 Protein Arginine Methyltransferase 5 / 0.732 0.0087 Si:ch211 245h14.1 / 0.892 0.0090 Similar To FLJ39237 Protein / 1.954 0.0092 Tweety Homolog 3 / 1.014 0.0094 SIN3 Homolog A, Transcription Regulator / 1.041 0.0095
162 Table S8. Complete set GO biological processes altered in fish liver from each site after 48 hours exposure (Fisher exact test) (p<0.05). GO Biological Process Category Fisher Raw p value Runoff wastewater GO: 0006350; transcription 0.00037987 GO:0008299; isoprenoid biosynthetic process 0.00038435 GO:0007050; cell cycle arrest 0.00249184 GO:0006355; regulation of transcription, dna dependent 0.00324521 GO:0007049; cell cycle 0.00740545 GO:0006512; ubiquitin cycle 0.0211385 GO:0000226; microtubule cytoskeleton organization and biogenesis 0.02185175 GO:0045449; regulation of transcription 0.02356298 GO:0006284; base excision repair 0.03877239 GO:0016310; phosphorylation 0.03877239 GO:0048268; clathrin cag e assembly 0.03877239 GO:0050930; induction of positive chemotaxis 0.03877239 GO:0008202; steroid metabolic process 0.03886353 GO:0009615; response to virus 0.03886353 GO:0045892; negative regulation of transcription, dna dependent 0.04550487 GO:00164 81; negative regulation of transcription 0.04857789 GO:0006614; srp dependent cotranslational protein targeting to membrane 0.04942271 GO:0042742; defense response to bacterium 0.04942271 Treated city wastewater GO:0008299; isoprenoid biosynthetic proc ess 9.9995E 05 GO:0006457; protein folding 0.00120326 GO:0007050; cell cycle arrest 0.00250785 GO:0006614; srp dependent cotranslational protein targeting to membrane 0.00305734 GO:0006605; protein targeting 0.00847776 GO:0001889; liver development 0. 01031075 GO:0006353; transcription termination 0.01225691 GO:0007275; multicellular organismal development 0.01403278 GO:0006350; transcription 0.01420629 GO:0006812; cation transport 0.01557871 GO:0000122; negative regulation of transcription from rn a polymerase ii promoter 0.01812137 GO:0016310; phosphorylation 0.02303717 GO:0048268; clathrin cage assembly 0.02303717 GO:0006355; regulation of transcription, dna dependent 0.02956411 GO:0042127; regulation of cell proliferation 0.02969712 GO:00070 49; cell cycle 0.03621555 GO:0006298; mismatch repair 0.03692098 GO:0009058; biosynthetic process 0.04192627 GO:0016568; chromatin modification 0.04192627 On campus w astewater GO:0005975; carbohydrate metabolic process 0.00318576
163 GO:0030154; cell dif ferentiation 0.00518866 GO:0042981; regulation of apoptosis 0.01596296 GO:0006298; mismatch repair 0.01794809 GO:0016998; cell wall catabolic process 0.01794809 GO:0043065; positive regulation of apoptosis 0.01892633 GO:0007275; multicellular organism al development 0.02001781 GO:0006289; nucleotide excision repair 0.03076366 GO:0051260; protein homooligomerization 0.03076366 GO:0006816; calcium ion transport 0.03269262 GO:0007018; microtubule based movement 0.03813759 GO:0008654; phospholipid bios ynthetic process 0.04578567
164 Table S9. Top 50 Enriched pathways processes from fathead minnows exposed 48 h to urban waters. In bold, selected processes discussed in text. Runoff waste water # Name Total Entities Overlap Percent Overlap p value 1 tra nscription, DNA dependent 2265 62 2 1.20E 37 2 regulation of transcription, DNA dependent 2872 62 2 9.95E 32 3 regulation of transcription from RNA polymerase II promoter 351 17 4 5.52E 14 4 cell cycle 604 20 3 3.47E 13 5 negative regulation of transcr iption, DNA dependent 443 17 3 2.33E 12 6 interspecies interaction between organisms 325 11 3 7.63E 08 7 isoprenoid biosynthetic process 29 5 17 9.92E 08 8 sterol biosynthetic process 30 5 16 1.19E 07 9 multicellular organismal development 1146 19 1 1. 32E 07 10 cell cycle arrest 148 8 5 1.43E 07 11 positive regulation of transcription from RNA polymerase II promoter 610 14 2 1.50E 07 12 mitotic cell cycle 316 10 3 5.65E 07 13 cholesterol biosynthetic process 42 5 11 6.82E 07 14 cell division 336 10 2 9.85E 07 15 negative regulation of neuron differentiation 55 5 9 2.68E 06 16 negative regulation of transcription from RNA polymerase II promoter 506 11 2 5.80E 06 17 positive regulation of cell cycle 35 4 11 1.12E 05 18 nervous system development 4 74 10 2 2.03E 05 19 steroid biosynthetic process 85 5 5 2.30E 05 20 response to drug 484 10 2 2.42E 05 21 positive regulation of immature T cell proliferation in thymus 3 2 66 4.66E 05 22 motor axon guidance 19 3 15 5.62E 05 23 fat cell differentiatio n 57 4 7 7.90E 05 24 DNA replication 179 6 3 8.24E 05 25 isopentenyldiphosphate biosynthetic process, mevalonate pathway 4 2 50 9.31E 05 26 hindbrain development 23 3 13 0.000102
165 27 telomere maintenance 67 4 5 0.000149 28 lipid biosynthetic process 12 8 5 3 0.000162 29 kinetochore assembly 6 2 33 0.000231 30 transcription from RNA polymerase II promoter 309 7 2 0.000241 31 base excision repair 33 3 9 0.000304 32 mitotic cell cycle spindle assembly checkpoint 34 3 8 0.000333 33 meiosis 84 4 4 0.0003 55 34 positive regulation of cell proliferation 438 8 1 0.00037 35 DNA duplex unwinding 9 2 22 0.000551 36 response to muramyl dipeptide 9 2 22 0.000551 37 negative regulation of translation 41 3 7 0.000581 38 chromatin modification 262 6 2 0.000638 39 positive regulation of transcription, DNA dependent 476 8 1 0.00064 40 centriole replication 10 2 20 0.000687 41 germ cell migration 10 2 20 0.000687 42 cell differentiation 735 10 1 0.00071 43 organ morphogenesis 180 5 2 0.000773 44 DNA topologica l change 11 2 18 0.000838 45 protein monoubiquitination 11 2 18 0.000838 46 central nervous system projection neuron axonogenesis 12 2 16 0.001003 47 protein ubiquitination involved in ubiquitin dependent protein catabolic process 50 3 6 0.00104 48 neg ative regulation of cell proliferation 399 7 1 0.001092 49 pattern specification process 115 4 3 0.001158 50 negative regulation of apoptosis 297 6 2 0.001219 Treated city wastewater 1 transcription, DNA dependent 2265 46 2 6.44E 24 2 regulation of t ranscription, DNA dependent 2872 50 1 2.81E 23 3 regulation of transcription from RNA polymerase II promoter 351 15 4 2.06E 12 4 protein folding 240 11 4 1.01E 09 5 multicellular organismal development 1146 20 1 3.45E 09 6 positive regulation of transc ription from RNA polymerase II promoter 610 15 2 4.41E 09 7 nervous system development 474 13 2 1.43E 08
166 8 negative regulation of transcription from RNA polymerase II promoter 506 13 2 3.07E 08 9 cell cycle 604 14 2 3.14E 08 10 isoprenoid biosynthetic process 29 5 17 5.64E 08 11 negative regulation of transcription, DNA dependent 443 12 2 6.08E 08 12 sterol biosynthetic process 30 5 16 6.75E 08 13 cholesterol biosynthetic process 42 5 11 3.90E 07 14 negative regulation of neuron differentiation 55 5 9 1.54E 06 15 steroid biosynthetic process 85 5 5 1.33E 05 16 cell cycle arrest 148 6 4 1.51E 05 17 gene expression 415 9 2 1.76E 05 18 response to drug 484 9 1 5.81E 05 19 cellular response to extracellular stimulus 23 3 13 7.27E 05 20 isopentenyl diphosphate biosynthetic process, mevalonate pathway 4 2 50 7.42E 05 21 lipid biosynthetic process 128 5 3 9.52E 05 22 1 aminocyclopropane 1 carboxylate biosynthetic process 5 2 40 0.000123 23 sequestering of calcium ion 5 2 40 0.000123 24 biosynthetic process 75 4 5 0.000149 25 protein targeting to ER 6 2 33 0.000185 26 midbrain development 34 3 8 0.000239 27 reciprocal meiotic recombination 35 3 8 0.000261 28 Wnt receptor signaling pathway 168 5 2 0.000338 29 chromatin modification 262 6 2 0.0003 5 30 cellular response to organic cyclic compound 40 3 7 0.000388 31 cellular zinc ion homeostasis 9 2 22 0.00044 32 regulation of cell proliferation 180 5 2 0.000463 33 centriole replication 10 2 20 0.000549 34 SRP dependent cotranslational protein t argeting to membrane 10 2 20 0.000549 35 negative regulation of cell proliferation 399 7 1 0.000561 36 positive regulation of viral transcription 47 3 6 0.000626 37 calcium ion import 11 2 18 0.00067 38 negative regulation of Wnt receptor signaling pat hway 49 3 6 0.000707 39 cellular protein metabolic process 302 6 1 0.000739
167 40 central nervous system projection neuron axonogenesis 12 2 16 0.000802 41 transcription from RNA polymerase II promoter 309 6 1 0.000832 42 somitogenesis 52 3 5 0.000842 43 negative regulation of gene expression 55 3 5 0.000992 44 cation transport 125 4 3 0.001039 45 neuron differentiation 136 4 2 0.001419 46 negative regulation of canonical Wnt receptor signaling pathway 63 3 4 0.001471 47 in utero embryonic development 243 5 2 0.001768 48 central nervous system neuron differentiation 18 2 11 0.001833 49 outflow tract morphogenesis 18 2 11 0.001833 50 response to stress 249 5 2 0.001966 On campus w astewater 1 protein folding 240 8 3 4.19E 08 2 anti apoptosis 243 7 2 8.28E 07 3 carbohydrate metabolic process 369 8 2 1.1E 06 4 multicellular organismal development 1146 12 1 4.37E 06 5 apoptosis 778 10 1 5.14E 06 6 regulation of apoptosis 263 6 2 1.99E 05 7 cell differentiation 735 9 1 2.33E 05 8 ATP catabolic pr ocess 291 6 2 3.51E 05 9 protein N linked glycosylation via asparagine 91 4 4 4.22E 05 10 metabolic process 2421 15 0 0.000125 11 negative regulation of cell growth 122 4 3 0.000132 12 induction of apoptosis 240 5 2 0.000156 13 cell cell junction orga nization 63 3 4 0.000326 14 chondrocyte development 13 2 15 0.000338 15 cellular protein metabolic process 302 5 1 0.00045 16 response to endoplasmic reticulum stress 15 2 13 0.000453 17 nervous system development 474 6 1 0.000496 18 post translationa l protein modification 187 4 2 0.000669 19 homeostatic process 20 2 10 0.000814 20 locomotory behavior 91 3 3 0.000957 21 cell junction assembly 92 3 3 0.000988 22 cell wall macromolecule catabolic process 23 2 8 0.00108 23 mismatch repair 26 2 7 0.00 1382
168 24 ossification 107 3 2 0.001527 25 response to stress 249 4 1 0.001925 26 induction of apoptosis by extracellular signals 118 3 2 0.002019 27 protein autophosphorylation 118 3 2 0.002019 28 ER associated protein catabolic process 32 2 6 0.002092 29 meiotic prophase II 1 1 100 0.002112 30 negative regulation of protein glutathionylation 1 1 100 0.002112 31 regulation of stem cell division 1 1 100 0.002112 32 zymogen granule exocytosis 1 1 100 0.002112 33 sucrose metabolic process 1 1 100 0.00 2112 34 maltose metabolic process 1 1 100 0.002112 35 galactosylceramide catabolic process 1 1 100 0.002112 36 vacuolar sequestering 1 1 100 0.002112 37 establishment or maintenance of polarity of embryonic epithelium 1 1 100 0.002112 38 establishment of neuroblast polarity 1 1 100 0.002112 39 cellular response to cobalt ion 1 1 100 0.002112 40 lobar bronchus development 1 1 100 0.002112 41 outer medullary collecting duct development 1 1 100 0.002112 42 inner medullary collecting duct development 1 1 100 0.002112 43 snRNP protein import into nucleus 1 1 100 0.002112 44 microtubule based movement 121 3 2 0.002169 45 vasculature development 33 2 6 0.002224 46 negative regulation of BMP signaling pathway 34 2 5 0.00236 47 reciprocal meiotic recomb ination 35 2 5 0.002499 48 adherens junction organization 35 2 5 0.002499 49 bone mineralization 35 2 5 0.002499 50 glucose metabolic process 134 3 2 0.002896
169 Figure S1. Water collection and fish exposure design.
170 Figure S2.Altered pathways from fish exposed to on campus w astewater effluent (Enriched sub networks). A: Protein folding process downregulated (Blue). HSP90B1: heat shock protein 90kDa beta PDIA4: protein disulfide isomerase family A, member 4. TCP1: t complex 1. CREBBP: CREB binding protein. PPIB: peptidylprolyl isomerase B (cyclophilin B). EIF2AK3: eukaryotic translation initiation factor 2 alpha kinase 3. MLEC: malectin. SIL1: SIL1 homolog, e ndoplasmic reticulum chaperone. B: DNA repair process up regulated (Red). CYP27 B1: cytochrome P450, family 27, subfamily B, polypeptide 1. G6PD: glucose 6 phosphate dehydrogenase. RAD23B: RAD23 homolog B. CREBBP: cAMP response element binding protein. CASP2: caspase 2, apoptosis related cysteine peptidase. TCP1. t complex 1. HTATIP2 : HIV 1 Tat interactive protein 2, 30kDa. MSH5. mutS homolog 5 (E. coli). PMS1 postmeiotic segregation increased 1.
171 Figure S3. General pathways diagram of genes commonly altered by PFCs in cell. In blue, genes found donwregulated in this study, in red genes found up regulated in this study and green others genes related. Blue: Downregulated genes, Red: Upregulated genes, Green: Cellular complex. IDI1: isopentenyl diphosphate delta isomerase 1; IDI2 isopentenyl diphosphate delta isomerase 2; MVK: meval onate kinase; OSBP: Oxysterol binding protein; MVD: mevalonate pyrophosphate decarboxylase; HMGCR: 3 hydroxy 3 methylglutaryl coenzyme A reductase; FDPS: farnesyl pyrophosphate synthetase; PVRL4: Poliovirus receptor like proteins; MPP5: membrane protein, p almitoylated 5 (MAGUK p55 subfamily member 5); LYSMD3 : LysM, putative peptidoglycan binding, domain containing 3; NCOA7: nuclear receptor coactivator 7; MYC v myc myelocytomatosis viral oncogene homolog; DLG1: discs, large homolog 1.
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191 BIOGRAPHICAL SKETCH Ignacio Alejandro Rodriguez Jorquera w as born in Valencia, Venezuela. It was in Venezuela, particularly i n the amazing village of Tocuyito wh ere Ignacio was able to have a permanent contact with nature. He later moved to Santiago de Chile, from where his parents, Patricio and Constanza originated It was in this huge and polluted city that he complete d h is co llege career, obtaining academi c credentials in Animal Science and Veterinary Medicine from the Universidad de Chile. This period was very critical for him, since it was in th e Universidad de Chile where together with two other students from Agronomy and E ngineering schools, he created the Environmental Network of University of Chile (RAUCH), and through which they all obtained valuable experience in project management. Several years of hard work during the summers resulted in improve ment of a protected ar ea in the Altos de Lircay Nature Preserve where he work ed Altos de Lircay is a pivotal place for Ignacio, since that is where he learned about nature, Chilean taxonomy, and the relevance of the soil, water and vegetation structure for forest ecosystems. Also, the days in the preserve taught him about the beauty of loneliness and the power of contemplation in understanding nature In this place he used to stay up to 50 days wi thout seeing a single human being. Someday just before Veterinary sch ool graduation, he was reunited in a party with Susana an old school mate. F rom that day they never separated. T hey got married and had their first daughter, Maite. Ignacio always tried to escape from the polluted and collapse d city of Santiago, and after a good job offer, he moved south with Susan a to the rainy city of Valdivia. In the south of Chile Ignacio had the opportunity to work as a project leader in CODEFF,
192 the oldest wildlife and nature conservation NGO in Chile. The projects he worked on involve d conservation issues in aquatic mammals and birds. In Valdivia he al so decided to enter g raduate school in the Universidad Austral de Chile, where he obtain ed the Master in Sciences i n water resources The stay in Valdivia also impacted his career, because while living there, a paper mill plant was accused of polluting a natural sanctuary killing hundreds of b lack n ecked swans among other wildlife T his episode motivated him to learn about Ecotoxicology. Thus, he decides to pursue a PhD degree in a good school located in some part of the world where the weather was good and the birds abundant U niversity of Florida appeared as the best choice for h im. He received his Ph.D. from the Un iversity of Florida in 2014. In his research, he was able to use genomics tools to understand the effects of water pollution on wildlife. His constant search for more suitable methods for wildlife conservation motivated him to propose the use of blood as n on destructive sampling in toxico genomics.
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