1 ASSOCIATION GENETICS OF PITCH CANKER RESISTANCE IN LOBLOLLY PINE (Pinus t aeda L.) By TANIA QUESADA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGRE E OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010
2 2010 Tania Quesada
3 To my parents, my sister and Paul
4 ACKNOWLEDGMENTS This research is the product of the contributions of many people who, in one way or another, made this project possible I therefore wish to acknowledge my advisor, Dr. John M. Davis for his vision and mentorship throughout this project, Dr. Dudley Huber for his patience and guidance in quantitative genetics as well as Dr. Matias Kirst, Dr. Lauren McIntyre and D r. Jeffrey Rollins for their advice and service as members of my committee. Special thanks to Chris Dervinis for his extraordinary help and advice in the laboratory, Xiaobo Li and Vikneswaran Gopal for their helpful discussions, as well as Dr. George Casella and Jessica Li for statistical support. Special thanks to the members of the Forest Genomics Labs: Kathy Smith, Cynthia Silva, Gustavo Ramirez, Philip Bocock, Brianna Miles, Derek Drost, Barbara Kahn, Evandro Novaes, Claire Anderson, Alison Morse, Ca therine Benedict, Ryan Brown, Katie Termer, Emmarita Golden, Anne Mwaniki, Luis Osorio, Patricio Munoz, Juan Acosta and Daniel Lambert for braving the heat in the greenhouse to arrange, inoculate and measure the plants used in this project. I also wish to acknowledge Patrick Cumbie and Dr. Barry Goldfarb from North Carolina State University for providing the plant material used in this project as well as Andrew Eckert, Jill Wegrzyn and Dr. David Neale of the University of Califorina at Davis for providing the genotypic information for the association analyses. I also hold special gratitude to the University of Costa Rica for my formation prior to my doctoral studies, and their support throughout; especially to Dr. Ana Espinoza and her group for stimulating my interest in research. Finally, special thanks to my parents Alicia and Carlos and my sister Ixel for their unconditional support throughout my life and particularly to my loving husband Paul, for walking with me the rest of the way
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ...................................................................................................... 4 LIST OF TABLES ................................................................................................................ 8 ABSTRACT ........................................................................................................................ 11 CHAPTER 1 LITERATURE REVIEW AND PROJECT OVERVIEW .............................................. 13 Quantitative Disease Resistance in Plants ................................................................ 16 Plant Responses to Necrotrophic Pathogens ............................................................ 19 Pitch Canker Disease ................................................................................................. 22 Association Mapping .................................................................................................. 25 Importance of Loblolly Pine ........................................................................................ 29 Loblolly Pine Resources for this Study ...................................................................... 31 Populations ........................................................................................................... 31 Genotyping and Association Platforms ............................................................... 32 Appendices ................................................................................................................. 33 Project Objectives ....................................................................................................... 34 2 ASSOCIATION MAPPING OF QUANTITATIVE DISEASE RESISTANCE IN A NATURAL POPULATION OF LOBLOLLY PINE ( PINUS TAEDA L.) ...................... 38 Introduction ................................................................................................................. 38 Materials and Methods ............................................................................................... 41 Plant Material ....................................................................................................... 41 Experimental Designs .......................................................................................... 41 Fungal Inocululm .................................................................................................. 42 Phenotyping ......................................................................................................... 42 Genotyping ........................................................................................................... 44 Association Analyses ........................................................................................... 45 Tests for Linkage Disequilibrium and Departure from Hardy -Weinberg Equilibrium ........................................................................................................ 47
6 Estimating SNP Effects ........................................................................................ 47 BLAST Analyses .................................................................................................. 48 Results ........................................................................................................................ 50 Discussion ................................................................................................................... 53 3 VALIDATION OF SIGNIFICANT ASSOCIATIONS TO PITCH CANKER RESISTANCE USING A POPULATION WITH KNOWN PEDIGREE AND REAL -TIME QUANTITATIVE POLYMERASE CHAIN REACTION .......................... 67 Introduction ................................................................................................................. 67 Materials and Methods ............................................................................................... 70 Plant Material ....................................................................................................... 70 Expe rimental Design and Inoculation Experiments ............................................ 71 Genetic Parameter Estimation from Phenotypes ................................................ 72 Genotyping ........................................................................................................... 73 Pre -Processing Of SNP Genotypic Data............................................................. 73 Association Analyses ........................................................................................... 75 Estimation of SNP effects .................................................................................... 78 Blast Analyses of Candidate Loci ........................................................................ 79 Evaluation of Candidate Loci in Response to Pathogen Challenge................... 79 Results ........................................................................................................................ 81 Significant Associations Observed on a Population with Known Pedigree Verify That Resistance to Pitch Canker Involves Multiple Genes with Small Effects .................................................................................................... 81 A Combined Dataset Comprised of Individuals from Two Populations Confirms Identified Significant SNPs. .............................................................. 82 Transcripts of Diseas e Resistance Candidate Genes Respond to Pathogen Challenge. ......................................................................................................... 84 Discussion ................................................................................................................... 85 Supplementary Data ................................................................................................. 100 4 CONCLUSIONS ........................................................................................................ 111 Contribution to Current Knowledge on Pitch Canker Resistance ........................... 112 Contribution to Ass ociation Genetics in Loblolly Pine ............................................. 113 Contribution to Current Knowledge on Quantitative Disease Resistance .............. 115 Challenges and Consi derations for Future Research ............................................. 116
7 APPENDIX A COMPARATIVE ANALYSIS OF THE TRANSCRIPTOMES OF POPULUS TRICHOCARPA AND ARABIDOPSIS THALIANA SUGGESTS EXTENSIVE EVOLUTION OF GENE EXPRESSION REGULATIO N IN ANGIOSPERMS ........ 120 Introduction ............................................................................................................... 120 Materials and Methods ............................................................................................. 123 Plant Material ..................................................................................................... 123 Real -Time PCR .................................................................................................. 123 Poplar Whole -Genome Oligonucleotide Microarrays ....................................... 124 Microarray Data Analysis ................................................................................... 124 Test for Random Distribution of Expressed Genes .......................................... 126 GO Annotation of Expressed Genes. ................................................................ 127 Results ...................................................................................................................... 127 Constitutive and organ -specific expressed genes ............................................ 127 Pairwi se Quantitative Differences in Expression Levels Among Vegetative organs ............................................................................................................. 130 Node and internode-preferred genes. ........................................................ 130 Young leaf pref erred genes. ....................................................................... 130 Mature leaf -preferred genes. ...................................................................... 131 Root preferred genes. ................................................................................. 131 Non -Random Distribution of Expressed Genes in the Genome ....................... 132 Is the Origin of the Woody Habit in the Salicaceae Due to Novel, Unique Genes? ............................................................................................................ 133 Expression of Genes Implicated in Essential Plant Processes ........................ 136 Discussion ................................................................................................................. 138 B SCREENING FOR FUSIFORM RUST IN RO OTED CUTTINGS OF LOBLOLLY PINE INOCULATED WITH TWO UREDINIAL CULTURES OF CRONARTIUM QUERCUUM AND THEIR F1 HYBRID .................................................................... 149 LIST OF REFERENCES ................................................................................................. 157 BIOGRAPHICAL SKETCH .............................................................................................. 180
8 LIST OF TABLES Table page 2 -1 Cl onal lesion length measurements at 4, 8, and 12 weeks after inoculation. ...... 58 2 -2 Clonal repeatability obtained for the first and second inoculation experiments .. 59 2 -3 List of the SNPs with significant ass ociation to pitch canker resistance .............. 60 2 -4 SNPs significant for association with pitch canker resistance and best hits based on BLASTx search ...................................................................................... 61 2 -5 SNPs significant for association with pitch canker resistance using pre processing based on effects of ind ividual SNPs on clonal variance .................... 63 3 -1 SNPs significant for assoc iations to pitch canker resistance in the CCLONES population and the putative functions of underlying genes usin BLASTx analyses .................................................................................................................. 92 3 -2 Effects of SNPs significant for associations to pitch canker resistance observed in the CCLONES population .................................................................. 93 3 -3 SNPs with significant associations for pitch canker resistance obtained in a combined dataset of two populations. ................................................................... 94 3 -4 SNPs with significant associations for pitch canker resistance observed in NCSU, CCLONES and a combined dataset ....................................................... 104 A-1 Summary of pairwise c omparisons among five poplar vegetative organs showing preferentially expressed genes according to main categories. ............ 143 B-1 Possible outcomes of pathogen challenge experiments depending on the host and pathogen genotypes. ............................................................................ 153 B-2 Number of loblolly pine cuttings and controls showing galls at six months after inoculation with Cronartium quercuum f.sp. fusiforme ............................... 153 B-3 Number of galls per genotype obtained for each Cqf inoculum at six months after inoculation .................................................................................................... 154
9 LIST OF FIGURES Figure page 1 -1 Examples of loblolly pine rooted cuttings showing resistance and susceptibility to the necrotrophic fungus Fusarium circinatum ............................. 36 1 -2 Examp le of an association analysis ....................................................................... 37 2 -1 Geographical distribution of loblolly pine acce ssions sampled for this study ..... 65 2 -2 Distribution of BLUP clonal es timates for pitch canker lesion length ................... 66 3 -1 SNP discovery and genotyping platform ............................................................... 96 3 -2 Significant SN Ps observed in the asso ciation NCSU population, CCLONES population and the combined dataset ................................................................... 97 3 -3 LSmeans of relative fold change values depicting changes in transcript abundance after pathogen challenge. ................................................................... 98 3 -4 Relative fold change values showing changes in transcript abundance in individual clones at 0, 1, 3, and 8 days after pathogen challenge ........................ 99 3 -5 Experimental design for inoculation of loblolly pine cuttings with Fusarium circinatum spores ................................................................................................. 108 3 -6 Overlay plot of the proportion of sha red genotypes for full -sib, half -sib or unrelated pairwise comparisons between individuals from a subset of CCLONES families and the NCSU population. ................................................... 109 3 -7 Venn diagrams showing the number of SNPs selected in NCSU and CCLONES populations, as well as the combined dataset using two preprocessing methods ............................................................................................. 110 A-1 Poplar organs sampled for microarray analysis .................................................. 145 A-2 Organ-specific expression in eaf, stem and root organs .................................... 145 A-3 Proportion of organ-specific genes in each Gene Ontology functional category ................................................................................................................ 146
10 A-4 Runs tests based on run lengths for p oplar vegetative tissues .......................... 147 A-5 Rank correlation between all A. thaliana and Populus orthologs ....................... 148 A-6 Gene ontology categories with an over -representation of genes expressed in a conserved manner ............................................................................................ 148 B-1 Life cycle of the biotrophic fungus Cronartium q uercuum f.sp. fusiforme causal agent of fusiform rust ................................................................................ 156 B-2 Gall production in new sh oots at the time of inoculation .................................... 156
11 Abstract of Dissertation Presente d to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ASSOCIATION GENETICS OF PITCH CANKER RESISTANCE IN LOBLOLLY PINE (Pinus t aeda L.) By Tania Quesada Au gust 2010 Chair: John M. Davis Major: Plant Molecular and Cellular Biology Quantitative disease resistance usually involves many genes with small effects whereas major gene resistance is simple and involves single genes with large effects. In this study I aim ed to quantify the genetic diversity of disease resistance in loblolly pine and provide a better understanding of the basis of quantitative disease resistance using association analyses. Pitch canker is a disease that affects pine species and is in cited by the necrotrophic fungus Fusarium circinatum Nirenberg et ODonnell causing resinous lesions, seedling mortality, and crown dieback. Resistance to the disease is heritable and quantitative. In this study, I performed a ssociation studies on 498 unrelated loblolly pine genotypes using 3,938 SNP markers where 10 candidate genes with small effects were found that could be involved in pitch canker resistance. I then validated the results of association analysis on a population with a known pedigree and on a combined data set of both populations, where three of the 10 significant SNPs previously detected showed significant associations. Finally, I show that t ranscript abundance of these three
12 genes changed significantly after pathogen challenge and may possibly provide information on the mechanisms underlying pathogen recognition and response The use of an association platform that uses genotypic information of SNPs distributed across the loblolly pine genome rather than a candidate gene approach allow ed the detection of significant SNPs with unknown function. This allowed the detection of SNPs that may be taxonomically unique or that have no previously described function which would have otherwise been overlooked using a candidate gene approach. Altho ugh the functional effects of the candidate genes found need to be assessed, i n the long term, this work may be useful as these candidate genes c ould be used to assess the implications of these discoveries for durable disease resistance breeding. This wou ld provide a better understanding of the mechanisms of quantitative disease resistance in loblolly pine as well as host pathoge n coevolution.
13 CHAPTER 1 LITERATURE REVIEW AND PROJECT OVERVIEW Disease resistance in plants involves a highly coordinated network of genes and gene products that must function together to provide defense against a pathogen. Most of what is currently known about disease resistance and plant -pathogen interactions has been obtained from model angiosperms such as Arabidopsis maize rice, and tobacco (DANGL and JONES 2001; DONG 1998; GLAZEBROOK 2005; 2003) ; In woody species, mechanisms of plant response to pathogens have been mainly studied in model tree species such as eucalyptus and poplar (FREEMAN et al. 2008; MAJOR et al. 2010) whereas studies in gymnosperms have lagged due to a lack of genomic resources as comprehensive as the aforementioned angiosperms (JOHNK et al. 2005; KAYIHAN et al. 2005; KEELING and BOHLMANN 2006; LI et al. 2006b) Association studies are valuable approaches to understand the complex interactions that comprise the quantitative responses to disease in species such as loblolly pine ( Pinus taeda L). Here, I aim to quantify the genetic diversity of disease resistance in loblolly pine and to provide a better understanding of the basis of resistance to pitch canker disease using association analyses. In the long term, this knowledge could contribute to the genetic improvement of loblolly pine through the identification of genes that can be used as markers to select disease resistant genotypes for breeding, and could also provide important information on the evolution and biology of host -pa thogen interactions in gymnosperms. Loblolly pine ( Pinus taeda L.) has large native outcrossing populations across the Southeastern United States (B aker and L angdon 1990) This, along with rapid linkage disequilibrium decay (B rown et al. 2004) et al., 2004) and abundant genotypic data
14 (Eckert et al. 2010) make loblolly pine a good candidate species for association detection and validation (N eale and S avolainen 2004) This species has also been the object of genetic improvement over the past 50 years that includes the participation of universities and cooperatives. This effort has generated considerable amounts of phenotypic data useful for association studies (E mhart et al. 2006; E mhart et al. 2007; L i 2009; W hite and D uryea 1998) Because loblolly pine breeding populations have undergone a few generations of selection, the species still retains most of its natural genetic variability, an advantage for association studies (N eale and S avolainen 2004; S avolainen and P yhajarvi 2007) in which populations with little or no population structure are ideal for association discovery and validation. Studies on loblolly pine have also shown that this species h as substantial levels of nucleotide variation, about one SNP per 63 base pairs (between 3 to 41 SNPs per locus) and low linkage disequilibrium (Brown et al. 2004) Furthermore, loblolly pine can be clonally propagated through hedging and rooting, which allows evaluating clonal replicates in different environments for the detection of genotype by environment interactions. Plant Pathogen Interactions Plants have evolved an array of mechanisms to protect themselves aga inst pathogens and predators. These can include passive defenses thatprovide pre-formed physical or chemical barrier s against pathogen entry through the production of waxes and reinforcement of the cell wall as well as toxic compounds (DONOFRIO and DELANEY 2001; NIMCHUK et al. 2003; PERSSON et al. 2009; VORWERK et al. 2004). Other mechanisms of defense against pathogens are considered active and may involve the induction of metabolites that are toxic or deterr ent. The most thoroughly studied mechanisms are those in which single genes play a major role in triggering resistance
15 responses through recognition of pathogen-secreted proteins (effectors) that trigger a series of events leading to resistance. The path ogen effectors are the product of avirulence ( avr ) genes which were identified after bacteria carrying such genes failed to induce disease in host plants carrying a corresponding resistance gene, in a gene-for gene interaction model (FLOR 1971) These effectors were first identified in soybean cultivars challenged with the bacterial pathogen Pseudomonas syringae (STASKAWICZ et al. 1984), and si nce then multiple avr genes have been identified (ARNOLD et al. 2001; FILLINGHAM et al. 1992; VAN DEN ACKERVEKEN et al. 1992). The recognition event triggers a series of mechanisms that typically result in cell death surrounding the infection site, known as a hypersensitive response, as a way to contain the spread of the pathogen (SHIRASU and SCHULZE-LEFERT 2000) L ack of recognition between effector and resistance protein fails to trigger the hypersensitive response mechanism which allows the pathogen to spread, causing disease (DANGL and JONES 2001; STASKAWICZ et al. 1984; VAN DEN ACKERVEKEN et al. 1992) Major gene resistance triggered by a gene -for gene interaction can be rapidly overcome by the pathogen and is common to bacterial and fungal pathogens that are biotrophic, that is, they obtain their nutrients from live tissue. In contrast, plant resistance to necrotrophic pathogens usually involves a different mechanism since the outcome of cell death due to hypersensitive response would be more favorable t o the pathogen than to the host (GLAZEBROOK 2005) Therefore, resistance to necrotrophic pathogens is quite distinct from the well -characterized hypersensitive response to biotrophic pathogens and will be addressed the following section.
16 Q uantitative Disease Resistance i n Plants Many economically important traits in plants such as height, yield, and wood quality are quantitative, that is, they are the result of the action of multiple genes and their interaction with the environment (LYNCH and WALSH 1998) In the case of quantitative traits Mendelian se gregation ratios are not observed in segregating families as phenotypic variation is caused by segregation at different loci, each with small effects that are also affected by the environment (DOERGE 2002; MACKAY 20 01) In the context of disease resistance, there are two main types of resistance that have been observed. The first was mentioned above qualitative resistance typically is conditioned by the action of a single gene that contributes to the entirety of the phenotype. In the case of major gene resistance, the plant with the resistance gene is able to recognize pathogen proteins or products and trigger a hypersensitive response mechanism (DANGL and JONES 2001) This resistance gene is therefore inherited in a Mendelian fashion and is not always influenced by environmental effects. In contrast, b ecause multiple genes have different partial effects on the phenotype, quantitative disease resistance is usually incomplete, and exhibits a gradient of response (POLAND et al. 2009) Resistance is often measured as a quantification of the infected tissue at a given time point after inoculation, and varies according to pathogen and host. For example, measurements can be recorded as number of infected spikelets per single spike (PUMPHREY et al. 2007) lesion lengths (KAYIHAN et al. 2005) or proportion of infected tissue area (LUBBERSTEDT et al. 1998) The mechanisms underlying quantitative resistance are poorly understood, as studies involving quantitative traits are typically complex and the actual causal agents
17 are not easily detected (POLAND et al. 2009) In spite of the challenges, quantitative resistant loci have been identified in many crop species such as rice (BALLINI et al. 2008) wheat (OTTO et al. 2002; SHEN et al. 2 003) and maize (LUBBERSTEDT et al. 1998) which highlights the importance of quantitative resistance in agriculture and the efforts to reduce the economic impact caused by losses due to necrotrophic pathogens Although the mechanisms underlying quantitative resistance loci are poorly understood, s everal hypotheses have been proposed (POLAND et al. 2009) For example quantitative disease resistance may be c onditioned by genes that regulate plant architecture or development (BRADLEY et al. 2003) or that quantitative resistance loci could represent mutati ons or different alleles of genes involved in basal defense or that quantitative resistance loci could code for genes involved in the production of secondary metabolites that prevent pathogen attack or can counteract or mitigate the effects of pathogen -de rived compounds (COLMENARES et al. 2002; KLIEBENSTEIN et al. 2005; POLAND et al. 2009). In addition, quantitative resistance loci can be involved in signal transduction that can trigger the activation of defense m echanisms or may be weakened major R genes, possibly due to a pathogen strain overcoming resistance (POLAND et al. 2009) Studies on partial resistance to rice blast ( Magnaporthe grisea), however, d o not support this last hypothesis (BALLINI et al. 2008) al though evidence of the presence of co localized quantitative resistance loci and R genes has been observed (WISSER et al. 2005; XIAO et al. 2007) Finally, quantitative resistance loci could be a set of taxonomically unique genes that lack similarity to any previously reported defense genes. These hypotheses are not mutually exclusive so that a given resistance mechanisms may harbor one or more genes that fit any of these hypotheses.
18 It has been considered that qu antitative resistance is more durable and is effective on a broader spectrum of pathogen genotypes than qualitative resistance (HAMON et al. 2010; KOU and WANG 2010) This is a desirable trait for plant breeding s trategies (LACOMBE et al. 2010) since it increases the time required for the pathogen to overcome resistance and also protects the plant against different pathogen genotypes Most quantitative trait loci (QTLs) involved in disease resistance are minor QTLs which ex plain less than 10% of the phenotypic variation (KOU and WANG 2010; SUN et al. 2010) However, a mixture of major and minor QTL is also common (SUN et al. 2010; ZHANG et al. 2010) Some major QTLs that have been detected in wheat and rice ha ve also been isolated by mapbased cloning (FU et al. 2009; FUKUOKA et al. 2009; KRATTINGER et al. 2009) Minor QTLs, in contrast, have been successfully isolated using candidate gene approaches and validation of functional analysis (KOU and WANG 2010) The genetic architecture of quantitative disease resistance is still poorly understood. Evidence from studies i n crops like maize (MOREIRA et al. 2009; POZAR et al. 2009; WISSER et al. 2006) rice (TABIEN et al. 2002) wheat (LIANG et al. 2006) barley (BILGIC et al. 2006) and other crops (LE CLERC et al. 2009) suggest that resistance is due to the action of multiple genes, mostly with small effects. In maize, about 437 quantiative trait loci have been identified for disease resistance, versus 17 resistance genes (R genes) and 25 R -gene analogs (WISSER et a l. 2006) A survey of multiple plant species for quantitative disease resistance have shown that there are multiple genes that determine resistance, and that QTLs involved in that resistance explained a low proportion of the phenotypic variance, with few exceptions (KOVER and CAICEDO
19 2001) Even though there were QTL that explained up to 87% of the phenotypic variance, the majority (67%) explained less than 20% of the phenotypic variance, showing that most ge nes have small effects (KOVER and CAICEDO 2001) QTLs for disease resistance were also found spread across the genome, suggesting that there are no large chromosomal regions that are solely responsible for quantitative disease resistance (KOVER and CAICEDO 2001) Clustering of disease resistance QTLs have been reported (WISSER et al. 2006) but they may reflect QTLs for r esistance to different pathogen genotypes since t he number of resistance QTLs detected after inoculation of a given population with a single pathogen isolate is relatively low In other words, there is some evidence for genetic interactions in quantitative disease resistance but the magnitude of effect on symptom development is correspondingly small. Plant Responses to Necrotrophic Pathogens Fungal pathogens trigger very diverse responses in their hosts depending on their biology. Necrotrophic fungi are not obligate parasites ; t hey commonly obtain nutrients from dead host tissue that eventually leads to the death of the host or directly kill the host using toxic compounds or degrading enzymes (VAN KAN 2006) The host plant response to this type of pathogen is considered to be a general response mechanism that involves the action of multiple genes (vs. a specific genetic int eraction) triggering responses quite distinct from the hypersensitive response, such as localized cell death, which would be advantageous to the pathogen (GOVRIN and LEVINE 2000; MAYER et al. 2001)
20 The way plant s recognize and respond to necrotrophic pathogens is still poorly understood. Unlike their biotrophic counterparts, necrotrophs do not exhibit a direct recognition mechanism mediated by host resistant gene products that recognize specific pathogen effecto rs (JONES and DANGL 2006) Necrotrophs typically release toxins, h ydrolytic enzymes such as polygalacturonases, that damage the cell wall, and other compounds whose functions are not well understood These are believed to be involved in triggering host mechanisms of pathogen recognition responses (FERRARI et al. 2007) The recognition of necrotrophic pathogen compounds appears to trigger the host response via the jasmonic acid/ethylene dependent pathway (GLAZEBROOK 2005; THOMMA et al. 19 98) based on inference from genetic analysis of Arabidopsis Plant r esponses to necrotrophic pathogens and insect attack may share common features in that they both trigger the production of a variety of compounds known as oxylipins or jasmonates. These compounds are known to participate in the regulation of defense signaling, coordinating diverse responses (FARMER et al. 2003) It has been observed that the production of jasmonic acid (JA) causes different types of responses depending on the type of stress (BECKERS and SPOEL 2006; REYMOND et al. 2000) The active form of JA, JA Ile, binds to the F box protein COI1, which is the receptor for JA. This forms a complex th at targets repressors of JA -responsive genes for ubiquitin proteasome mediated degradation, which in turn activates the JA -responsive genes (BECKERS and SPOEL 2006; CHUNG et al. 2009; YAN et al. 2009) The jasmoni c acid regulated pathway is usually, but not always, antagonistic to the salicylic acid (SA) pathway, which mediates the hypersensitive response as well as systemic acquired resistance commonly triggered by biotrophic pathogens (GLAZEBROOK 2005) Cross -talk
21 between the two pathways has been shown to occur through modulation of the transcription factor WRKY70 and the protein NPR1 (GLAZEBROOK 2005; LEYVA-BACA et al. 2007; LI e t al. 2004; SPOEL et al. 2003) This cross-talk signaling between the SA and JA pathways, as well as ethylene and abscisic acid (ABA) may tailor the responses according to the type of stress or pathogen (ADIE et a l. 2007; BECKERS and SPOEL 2006; GLAZEBROOK 2005) Other transcription factors, such as ERF, WRKY and MYB have been shown to play a role in defense against necrotrophs,in Arabidopsis (BERROCALLOBO et al. 2002; LAI et al. 2008; MENGISTE et al. 2003; ZHENG et al. 2006) Studies on plant responses to necrotrophi c fungi have shown that multiple genes are expressed after to infection. Differential gene expression studies between resistant and susceptible cultivars o f Brassica napus infected with Sclerotinia sclerotiorum showed a dynamic response after pathogen challenge (ZHAO et al. 2009) Among the differentially expressed genes are those involved in defense response such as lectins and protease inhibitors, hormone biosynthesis, secondary metabolism like sulfotransferases and cynnamoyl -CoA reductase, and carbon metabolism, like glyceraldehyde3 -phosphate, hexokinase and gluc ose -6 phosphate dehydrogenase, among others (ZHAO et al. 2009) Proteomics studies in Arabidopsis thaliana challenged with the necrotroph Alternaria brassicicola showed that certain proteins, like NADPH dehydrogenase, g lucose 6 -phosphate isomerase, the pathogenesis -related protein PR4, ATP synthase, and a lectin family protein, among others, were differentially regulated (MUKHERJEE et al. 2010). The induction or repression of the different genes involved in pathogen response may modulate different lev els of transcript abundance, thus contributing to the quantitative nature of disease resistance. Alternatively, the
22 complexity of the transcriptome response may be due to being triggered by several resistance genes that are each involved in signal transduc tion and therefore have large downstream consequences on gene expression. Pitch Canker Disease Pitch canker is one of the most economically important diseases affecting most pine species (CAREY et al. 2005; ENEBAK and STANOSZ 2003; SAKAMOTO and GORDON 2006) This disease was first described in 1946 (HEPTING and ROTH 1946) and is incited by the necrotrophic fungus Fusarium circinatum Nirenberg et ODonnell In itially thought to affect older trees, it was later determined that this disease can affect susceptible pines at any age (BARNARD and BLAKESLEE 1980) Pitch canker produces resinous lesi ons in trunk, shoots and branches, whereas in young seedlings and shoots, the lesions show purple discoloration (Fig.1 1). This disease can also affect reproductive structures, causing a reduction in cone size and seed viability and quality (BARROWS-BROADDUS and DWINELL 1985) Infected seeds can also cause significant losses in nurseries (BARNARD and BLAKESLEE 2006) by affecting germination, or transferring the fungu s to young seedlings. In addigion, high mortality and decreased growth rates are observed in seedlings and young stands exhibit crown dieback if infection occurred in succulent shoots (BARROWS-BROADDUS and DWINELL 1983; BARROWS-BROADDUS and DWINELL 1985; DWINELL et al. 1985) Fungal spores are disseminated by wind and penetrate plant tissue through open wounds. Spore dissemination has also been attributed to insects, such as weevils, beetles and moths, which may carry spores that are transmitted to the wounds, or that cause wounds that later, become infected (CORRELL et al. 1991; DWINELL et al. 1985)
23 Pitch canker is thought to be endemic to the southeastern United State s (BARNARD and BLAKESLEE 2006) and has a broad host range among the pine species, though all are not equally susceptible (BARROWS-BROADDUS and DWINELL 1983) This disease is episodic, characterized by periods of outbreak, wh ere incidence levels have been above 90% in slash pine (BLAKESLEE and OAK 1979). Histology of wounded tissue infected with F. circinatum has shown that resistance mechanisms involve the formation of several layers of regenerative parenchyma, sloughing off of infected cortex and lignification of pith parenchyma (BARROWS-BROADDUS and DWINELL 1983) Among highly susceptible species are: Monterrey pine ( Pinus radiata) and Virginia pine ( P. virginiana ), whereas slash pine ( P. elliotii) is considered to have intermediate resistance, and loblolly pine ( P. taeda ) and pond pine ( P. serotina) are more tolerant to pitch canker (BARROWS-BROADDUS and DWINELL 1983; CORRELL et al. 1991; KUHLMAN and CADE 1985) The term tolerance is sometimes used as a general surrogate for resistance, although the terms differ in that tolerant trees appear unaffected in years following an outbreak, whereas intolerant trees are stunted and have significantly reduced value. Although endemic to the southeastern United States, and presumably the Mexican center of origin for southern pines, pitch canker h as spread across the globe to countries such as South Africa (VILJOEN et al. 1994) Korea (LEE et al. 2000) Chile ( WINGFIELD et al. 2001) and Japan (MURAMOTO and DWINELL 1990) These cases of pitch canker infection are related to the development of the timber and pulp industry in these countries, as they have been detected in nurseries or in tree plantations.
24 Differences in resistance and tolerance to the pathogen have been also observed among host species (BARROWS-BROADDUS and DWINELL 1984; ENEBAK and STANOSZ 2003; HODGE and DVORAK 2000; KAYIHAN et al. 2005; STORER et al. 1999) For example, i n slash pine plantations affected by pitc h canker during severe outbreaks, disease-free individuals were found among infected trees (ROCKWOOD et al. 1988) suggesting that resistance to the disease is under genetic control. Inoculation experiments on clonally propagated slash pines followed by evaluations of presence or absence of disease showed high family -level heritabilities, ranging from 0.57 to 0.86, validating previous hypotheses that resistance to pitch canker has a g enetic component and can be used in breeding strategies (ROCKWOOD et al. 1988). Other s tudies on full sib and half -sib families in Virginia pine showed differences in susceptibility to the disease under field and greenhouse conditions (BARROWS-BROADDUS and DWINELL 1984) Variation in virulence using different F. circinatum isolates is not well understood but is likely to be low Barrows -Broaddus and Dwinell (1984) used two different isolates to infect full -sib and half -sib families of Virginia pine, but no significant interactions were detected. M ore recently, studies on P. patula using different pathogen isolates showed evidence of pathogen x isolate interactions but these effects were confounded with block effects (HODGE and DVORAK 2007) In the studies proposed here, the pathogen was restricted to a single clonal genotype to eliminate variation due to pathogen genotype. Changes in gene expression in pine after infection with F. circinatum indicated that host responses involve the induction of multiple genes that encode proteins such as
25 chitinases, peroxidases, antimicrobial peptides, and transporter proteins, among others (MORSE et al. 2004) More recent studies involving over 60 families of loblolly pine reported that pitch canker resist ance was quantitative and heritable (KAYIHAN et al. 2005) suggesting that resistance to pitch canker is a complex trait that may involve multiple genes. The work proposed in this proje ct aims to leverage the existing knowledge that pitch canker disease is heritable, and identify significant genetic marker associations with pitch canker resistance Association Mapping As mentioned previously, quantitative traits involve the contribution of multiple genes with different, and often small effects r. These genes may be found in different regions of the genome, and their effects, when small, are commonly diluted due to background variation (FALCONER and MACKAY 1996) Quantitative trait locus (QTL) mapping is based on linkage disequilibrium between marker alleles and the QTL alleles, and is typically based on crosses between lines that have large or extre me differences for a trait of interest or are based on segregating populations (DOERGE 2002; FALCONER and MACKAY 1996) These parental lines are usually highly inbred to be as homogeneous as possible for most trai ts and are crossed to produce a hybrid F1 generation, which is then backcrossed to one of the parental lines (HALL et al. 2010) QTL mapping has been the main approach for the identification and dissection of complex traits in plants, commonly aimed tow ards the detection of loci that contribute to agronomically important characters, such as response to environmental stresses (YANG et al. 2010a) seed or grain quality (LIU et al. ; SHI et al. 2009; YANG et al. 2010b) and disease resistance (LU et al. 2009) The practical outcome is that markers linked to the
26 trait can then be used to select individuals carrying the desirable trait s without the need to wait for the appearance of the trait, reducing the time traditionally needed to select and achieve an improved crop variety. In addition, transgenic technology could be used to directly introgress desirable alleles after major genes a ffecting quantitative traits are identified (FALCONER and MACKAY 1996; REN et al. 2005; ZHAO et al. 2007b) In spite of being a widely used approach for identifying candidate genes underlying quantitative traits in crop species, QTL mapping is a challenging forward genetic march to identify such genes in loblolly pine. Fine mapping specific QTL in any particular family segregating for desirable alleles in this species results counterproductive using classic QTL ana lyses Loblolly pine has long generation time s requiring many years to perform the necessary crosses for QTL mapping. In addition, given the large populations of loblolly pine background variation is significant since the populations are not akin to r ecombinant inbreds Furthermore, the large physical size (~20 Gb) of the loblolly pine genome leads to uncertainty with respect to genetic: physical distance ratios within any particular genomic segment. It seems reasonable to consider association mapping as a more useful approach to identify genes underlying quantitative trait variation Association g enetics is an appealing alternative to family -based QTL mapping for dissection of complex traits and was first applied to identify genes contributing to hu man diseases (HODGE 1993; KEREM et al. 1989; RISCH and MERIKANGAS 1996) This approach links molecular markers with a quantitative phenotype, such that marker alleles significant for association with the phenotype will be in higher frequency than that expected for segregating background alleles (Fig. 1 -2) (RISCH and MERIKANGAS 1996)
27 In contrast to QTL mapping, association genetics uses historical recombination events to identify relat ionships between phenotypes and genetic markers based on linkage disequilibrium (LD) decay (FLINT-GARCIA et al. 2003; THEVENON et al. 2007) Association mapping also allows the analysis of a large number of alleles per locus, depending on the molecular markers used. Additionally, because LD decay generally occurs rapidly in unstructured populations, high resolution can be obtained from association mapping (FLINT-GARCIA et al. 2005; NEALE and SAVOLAINEN 2004) With the recent progress in high-throughput sequencing using next generation technologies (METZKER 2010) bi allelic variant s that occur in large numbers in the genome, making these single nucleotide polymorphisms (SN Ps) molecular tools of choice for genotyping are possible to identify (HALL et al. 2010) There are important aspects that must be taken into consideration when performing association analyses. For example, o ne ch allenge of association mapping is that the observed LD may not be necessarily due to physical linkage. LD depends largely on population substructure and mechanisms such as selection, genetic drift, and population admixture, among others (FALCONER and MACKAY 1996; FLINT-GARCIA et al. 2003) Therefore, population substructure effects should be considered when performing association analyses (MALOSETTI et al. 2007; WEI et al. 20 06) Another aspect to consider is that the use of large number of markers, especially when determining genomewide associations, may result in increase of type I error rate due to multiple testing (CARLSON et al. 2004) Different approaches have been developed to correct for these iss ues (BALDING 2006; DE BAKKER et al. 2005; HAVILL and DYER 2005; STOREY and TIBSHIRANI 2003). Finally, another important issue is that, as in QTL
28 analysis, many identified loci have small effects; however, the prop ortion of phenotypic variance explained by the QTL tends to be overestimated if the number of individuals is low. This is known as the Beavis effect (BEAVIS 1998; XU 2003) where simulation studies on back -crossed populations (XU 2003) showed that this effect is high (QTL is overestimated) when populations are small (<100 progeny), QTL are slightly overestimated on medium -sized populations (500 progeny) and QTL effects are close to their actual magnitude in larger populations (>1000 progeny). Although association studies have bee n widely used in analyses of human (CLERGET-DARPOUX 1982; EASTON et al. 2007; KEREM et al. 1989; LEE et al. 2007) and livestock (LEYVA-BACA et al. 2007; MARTINEZ et al. 2006 ) diseases, they have only been recently incorporated into plant breeding (ZHANG et al. 2005) Association mapping has been performed to identify linkage between DNA markers and disease resistance in crops such as sugarcane (WEI et al. 2006) maize (FLINT-GARCIA et al. 2005; WEBER et al. 2009; YU and BUCKLER 2006) potato (MALOSETTI et al. 2007) wheat (JING et al. 2007) and rice (WU et al. 2004) In the last few years, there has been a large increase in the number of association studies in forest trees as well where traits such as wood composition (GONZALEZMARTINEZ et al. 2007; LI 2009) drought tolerance (GONZALEZMARTINEZ et al. 2008) cold hardiness (ECKERT et al. 2009a) and growth (LI 2009) have been evaluated. These studies have revealed important information on the genetic structure underlying these traits, as well as identifying potential candidate genes and their estimated effects on the phenotypic variation. Association analyses on pitch canker resistance was performed in this study to enrich understanding of complex traits
29 in loblolly pine, and to generate novel information on genetic architecture and genes underlying quantitative disease resistance in gymnosperms. Importance of Loblolly Pine Loblolly pine is a member of the Pinaceae family and is native to the Southeastern United States. This species is also one of the most economically important forest species in this area (BAKER and LANGDON 1990) occupying 45 percent of the commercial forest land (SCHULTZ 1997) The geographical distribution of loblolly pine ranges from southern New Jersey to central Florida and from the east coast to eastern Texas. It is the dominant forest species on over 11 million ha and constitutes over half of the standing pine volume (BAKER and LANGDON 1990) Loblolly pine occupies over 54 million acres of total forest land area in the United States territory, and constitutes over 26% of the forest land area in the southeastern United States (SMITH et al. 2004) Of the forest area corresponding to loblolly pine, 5.7 million acres are destined for industrial purposes, such as pulp, timber and paper production. Because of its importance in the timber industry, e fforts have been made to improve loblolly pine yield and quality. Multiple studies have aimed towards improving the performance of different traits of interest such as disease resistance (KAYIHAN et al. 2005; KUBISI AK et al. 2004; MORSE et al. 2004) wood quality (GONZALEZMARTINEZ et al. 2007; LI 2009) growth (EMHART et al. 2006) and drought tolerance (GONZALEZMARTINEZ et al. 2006) Several diseases affect pine species, but the ones that are most important in terms of incidence and cause of economic loss are pitch canker and fusiform rust. Studies aimed towards a better characterization of these disea ses, their biology (DWINELL et al. 1985; SCHMIDT 2003) their genetic architecture (KAYIHAN et al.
30 2005) and host pathogen response have been performed (ENEBAK and STANOSZ 2003; WILCOX et al. 1996) Loblolly pine is one of the best characterized conifers. Phenotypic information is available for different complex traits, such as disease resistance (KAYIHAN et al. 2005; KUBISIAK et al. 2005) crown architecture (EMHART et al. 2007) growth rate (EMHART et al. 2006; MAIER et al. 2002) cold hardiness (BURR et al. 1990) wood properties (LI 2009; YU et al. 2005) and drought tolerance (GONZALEZMARTINEZ et al. 2006) In addition, there are a large number of molecular markers available to identify and map such traits. The use of molecular markers has contributed to new breeding strategies through the development of loblolly pine genetic maps (KOMULAINEN et al. 2003) and comparative studies with other pine species (KRUTOVSKY et al. 2004; LISTON et al. 1999) More recently, QTL and association studies have contributed to understanding som e of the quantitative traits that are of interest for loblolly pine improvement (BROWN 2003; ECKERT et al. 2010; GONZALEZMARTINEZ et al. 2008; GONZALEZ-MARTINEZ et al. 2007; LI 2009) This has made loblolly pine one of the best -characterized gymnosperms and a model species for genetic studies (LEVYADUN 2000) With the loblolly pine genome sequencing project under way, along with current knowledge on the genomic and physiological traits in loblolly pine, much more will be learned about this species to facilitate research aimed towards its genetic improvement and conifer germplasm conservation.
31 Loblolly Pine Resources for t his Study Populations Two loblolly pine populations are being analyzed in this study. The first population consists of 498 individuals collected from wild populations by the North Carolina State University (NCSU) Cooperative Tree Improvement Program (NCTIP). These individuals are assumed unrelated with unknown population s tructure. These plants were clonally propagated as rooted cuttings and then transferred from Raleigh, North Carolina to the greenhouse facilities at the University of Florida, Gainesville, FL, where they were kept for inoculation experiments. This population is referred to as the NCSU population. This population is an ideal population for association analyses, as relatedness among individuals is very low and the population is highly diverse, reflecting the genetic structure of naturally occurring pine populations. Rapid decay in linkage disequilibrium is thus expected, such that significant markers associated to the phenotype are likely to be physically close to the causal genes influencing the trait. The second population consists of recently mated indi viduals where the family information is known. Plants from this population were provided by members of the Cooperative Forest Genetics Research program at the University of Florida and the North Carolina State University -Industry Cooperative Tree Improvem ent Program Propagation and distribution was coordinated by the Forest Biology Research Cooperative at UF Individuals from this population are mainly progeny of 32 parental genotypes obtained from a circular mating design, with some off -diagonal crossi ng, for a total of ~1400 genotypes, 668 of which were genotype d This population is referred to as the CCLONES population based on its moniker within the Forest Biology Research
32 Cooperative, and it serves as a platform to detect associations in a family b ased study population. Because of the mating structure of the CCLONES population and the relatedness among individuals, a slow decay in linkage disequilibrium is expected. This allows for the detection of broad chromosomal regions where genetic associati ons are likely to occur, and can be used to validate the results from the association analyses. Genotyping a nd Association Platforms Loblolly pine has a large, publicly available expressed sequence tag (EST) database, which has enabled the discovery of s ingle nucleotide polymorphisms (SNPs) in about 35,000 EST sequences. Through collaboration with Dr. David Neales group at the University of California at Davis, it was possible to obtain genotypic data on 3,938 SNPs for nearly all individuals from the N CSU and CCLONES populations. About 23,000 unique SNP markers were identified from the EST sequences, and 7,535 unique ESTs were then re-sequenced using 18 loblolly pine haploid megagametophytes (ECKERT et al. 2010) After re -sequencing, 7,216 SNP markers were chosen for genotyping using the Illumima Infinium platform (Illumina, San Diego, CA, USA). Genotype calling was performed on BeadStudio v. 188.8.131.52 (Illumina), where genotypes were selected based on quality of reads, signal intensity and clustering of genotypic classes. A total of 3,938 SNP genotypes were selected for the NCSU population and 2,182 for the CCLONES p opulation to be used in association analyses. A key feature of the SNP genotyping resource is that genes were selected without regard to their annotation, and as such, there is no bias for identifying genes previously shown to play a role in disease resist ance.
33 The association platform used in this study was developed through collaborations with Dr. George Casella and his students in the Department of by the Statistics department at the University of Florida, and uses a Bayesian approach to incorporate a si multaneous solution for associations based on SNP effects, population structure, and missing data (GOPAL et al. 2009; LI 2008) This program, BAMD (Bayesian Association with Missing Data) was developed using R software and is available free online at CRAN ( http://cran.r project.org/ ). Appendices The work presented in this document contains two main chapters showing the results of association testing. The first one is focused on the genetic architecture and association genetics for resistance to pitch canker in the natural (NCSU) p opulation of loblolly pine, whereas the second chapter discusses the validation of significant associations observed in the population with known structure (CCLONES) and on a combined dataset composed of the two populations used in this study. In addition to these chapters, I am including additional data in the form of appendices. The first appendix consists of a manuscript that was published in New Phytologist in 2008 with data obtained from microarray analyses in poplar ( Populus trichocarpa). This was the result from an initial training prior to the research included in this dissertation, in preparation for manipulating large datasets. In this section, I analyzed differences in gene expression across the genome and in different tissues of Populus tricho carpa which contributed to the publication of this paper. The second appendix involves the results of an effort to perform a screening of loblolly pine cuttings from the NCSU population for fusiform rust disease (causal agent
34 is Cronartium quercuum f.sp. fusiforme). The plan endorsed by the Advisory Committee at my proposal defense, which was prior to the actual screen, was to screen the NCSU population for both pitch canker and fusiform rust resistance and analyze both traits for association in parallel. For rust screening, plants were inoculated with three inoculum types: one of which contained an avirulence (avr) gene that interacts with a resistance gene (Fr1) that was previously mapped with RAPD markers in a parent within the CCLONES population; one l acked the avirulence gene, and the hybrid segregating for avirulence The goal was to map the avirulence gene and other avirulence loci, but the percentage of diseased plants was too low to have sufficient power for analyses; therefore, the results are l imited to the initial screen. A collection of pycnial spores was established from the galls of diseased plants, with aims towards future studies on the characterization of this fungal pathogen. Project Objectives In this project, I use association geneti cs to provide a better understanding of the genetic architecture of loblolly pine resistance to the fungal pathogen Fusarium circinatum Nirenberg et ODonnell causal agent of pitch canker disease. In the experiments described in Chapter 2, I hypothesize d that significant associations with resistance to pitch canker can be obtained and that resistance involves multiple genes. I test this hypothesis using 400 SNP markers on a population of 404 unrelated loblolly pine genotypes using a program that utilize s a Bayesian approach to incorporate a simultaneous solution for SNP effects, population structure and imputation of missing data. I also estimate the effects of the SNPs with significant
35 associations and infer the putative function of the corresponding c andidate genes using sequence alignments to all known protein sequences available to date. In the experiments described in Chapter 3, I aim to validate the significant associations obtained previously using a population of known pedigree and a combined d ataset of the two populations included in this study. I also analyzed the changes in transcript abundance of the validated genes to test the hypothesis that such genes are pathogen responsive. To test this hypothesis, I performed real -time PCR analyses t o follow the changes in transcript abundance on a number of extremely susceptible and extremely resistant clones before and at different time points after pathogen challenge. In C hapter 4 I summarize and discuss the findings of this study, and provide suggestions for future studies that can be conducted to better understand the mechanisms of quantitative disease resistance as well as other quantitative traits.
36 Figure 11 Examples of loblolly pine rooted cuttings showing resistance and susceptibilit y to the necrotrophic fungus Fusarium circinatum causal agent of pitch canker disease in this species as well as other pine species. Resistant Susceptible
37 Figure 12 Example of an association analysis. A quantitative trait (i.e. lesion length) is the result of the inter action of multiple genes. In this representation, a QTL is observed to contribute to the trait and is evidenced by differences in allelic distributions between the two phenotypes. Genetic analysis using molecular markers (i.e. SNPs) show that individuals of a given phenotype (i.e. shoots with longer lesions phenotype ) are enriched in some marker alleles with respect to the other (i.e. short lesions ). This is suggestive of an association between a marker and the phenotypic trait. (Figure adapted from Rafa lski (2010) and Jannink et al (JANNINK et al. 2001) Aa A T C C A G T A T T G A C G T A G C C A G T A T T G A C G T A T C C A G T C T T G A C G T A T C C A G T A T T G A C G T A T C C A G T A T T G A C G T A T C C A G T A T T G A C G G A G C T A G T C T T G A T G G A G C T A G T C T T G A C G G A G C T A G T C T T G A T G G A G C T A G T C T T G A T G G A G C T A G T C T T G A T G T A G C T A G T A T T G A T G G Phenotype SNP markers QTL allele distributions SNPsIndividuals Aa Aa A T C C A G T A T T G A C G T A G C C A G T A T T G A C G T A T C C A G T C T T G A C G T A T C C A G T A T T G A C G T A T C C A G T A T T G A C G T A T C C A G T A T T G A C G G A G C T A G T C T T G A T G G A G C T A G T C T T G A C G G A G C T A G T C T T G A T G G A G C T A G T C T T G A T G G A G C T A G T C T T G A T G T A G C T A G T A T T G A T G G Phenotype SNP markers QTL allele distributions SNPsIndividuals
38 CHAPTER 2 ASSOCIATION MAPPING OF QUANTITATIVE DISEASE RESISTANCE I N A NATURAL POPULATION O F LOBLOLLY PINE ( PINUS TAEDA L.)1 Introduction Genetic interactions between host and pathogen populations result in abundant natural variation in the genes involved in host disease resistance. Most of the studies leading to identification and cloning of disease resistance genes are focused on major gene disease resistance (DANGL and JONES 2001; JOHAL and BRIGGS 1992; JONES and DANGL 2006) In cases where resistance is associated with single gene s genetic effects are large in magnitude and detection is straightforward. In contrast, quantitative disease resistance is typically conditioned by many genes with relatively small effects. Quantitative resistance is generally considered to be more durable but also more difficult to investigate relative to major gene resistance, since the effects of individual genes are small and phenotyping ex periments must be performed with high levels of precision. As a consequence, the genes and mechanisms of quantitative disease resistance are poorly understood. Interactions between plants and necrotrophic pathogens often exhibit quantitative resistance (BALINT-KURTI et al. 2008; POLAND et al. 2009) Pitch canker disease of loblolly pine and other pine species is incited by the necrotrophic pathogen Fusarium circinatum Nirenberg et ODonnell and is manifest ed as res inous lesions in stems and branches (CAREY et al. 2005; DWINELL et al. 1985; ENEBAK and STANOSZ 2003; SAKAMOTO and GORDON 2006) There is evidence for 1 Reprinted with per mission from Quesada et al., 2010. Genetics doi: 0.1534/genetics.110.117549
39 heritable resistance to pitch canker in loblolly pine (KAYIHAN et al. 2005) as well as other pine species (HODGE and DVORAK 2000; HODGE and DVORAK 2007) In this manuscript we report the first population wid e phenotypic screen of a clonally propagated population of loblolly pine for association testing (ECKERT et al. 2010) Clonal propagation of this population enabled precise phenotyping, which was required to obtain the resolution needed to identify candidates for quantitative disease resistance loci. Pine species in general exhibit high levels of nucleotide variation and low linkage disequilibrium (LD) (BROWN et al. 2004) An association genetic approach relies on the premise that historical, u nrecorded recombination events over many generations have reduced LD between markers and quantitative trait loci such that only those marker -trait pairs that are tightly linked remain detectable; this may enable fine mapping to identify genes underlying quantitative variation (FLINT-GARCIA et al. 2003; NEALE and SAVOLAINEN 2004) Association -based approaches have been used to identify candidate genes underlying traits in plants (INOSTROZA et al. 2009; STICH et al. 2008; STRACKE et al. 2009; WANG et al. 2008; YAHIAOUI et al. 2008; ZHAO et al. 2007a) based in part on applications in humans (D ALFONSO et al. 2002; EASTON et al. 2007; LEE et al. 2007; MCGUFFIN et al. 2003) livestock (CHARLIER et al. 2008; GODDARD and HAYES 2009; MARTINEZ et al. 2006) and Drosophila (JIANG et al. 2009; KENNINGTON et al. 2007; NORRY et al. 2007) Recent association studies in tree species have evaluated single candidate genes, or a modest number of candidate genes for association (ECKERT et al. 2009a; GONZALEZMARTINEZ et al. 2008 ; GONZALEZ-MARTINEZ et al. 2007; INGVARSSON et al. 2008; THUMMA et al. 2005) Association mapping has been used to identify disease
40 resistance genes in several crop species including sugarcane, maize, barley and potato (FLINT-GARCIA et al. 2005; INOSTROZA et al. 2009; MALOSETTI et al. 2007; MURRAY et al. 2009; STICH et al. 2008; WEI et al. 2006; YU and BUCKLER 2006) The population analyzed in this study was genotyped at 3,938 SNP loci that were selected without re gard to the functional annotation of ESTs from which they were derived. Thus, we reasoned that the status of any particular marker as a candidate disease resistance gene would be determined by association testing, as opposed to previous studies in which markers were typically evaluated based on their presumed roles in disease resistance in other species. Several different, but not mutually exclusive hypotheses have been proposed regarding the genetic origins of quantitative resistance (POLAND et al. 2009) providing a useful framework for understanding evolution of resistance to necrotrophic pathogens. These six hypotheses proposed by Poland et al (2009) predict that q uantitative disease resistance is conditioned by : 1) genes regulating morphological and developmental phenotypes; 2) mutations in genes involved in basal defense causing small, incremental levels of resistance; 3) components of chemical warfare, through the action of genes producing antibiotic or antifungal compounds; 4) genes involved in defense signal transduction pathways ; 5) weak forms of defeated R genes; and /or 6) genes not yet known to be involved in diseas e resistance. In this study, our main objective was to evaluate the genetic architecture of pitch canker disease resistance: to quantify the extent to which genes contribute to variation in the disease phenotype, to evaluate the hypothesis that disease re sistance was quantitative, and to identify candidate genes for resistance as well as quantify their
41 magnitude of effect. In the process of identifying candidate genes for resistance we were also able to evaluate support for hypotheses recently put forth by Poland et al. (2009) regarding the biological roles and origins of quantitative resistance genes. Materials a nd Methods Plant Material Loblolly pine ( Pinus taeda L.) material was propagated by juvenile stem cuttings (LEBUDE et al. 2004) at the North Carolina State University Horticultural Field Laboratory, Raleigh, NC The material was obtained from repeatedly -hedged stock plants representing a sample of 498 genotypes collected as wild selections of the NCSU Cooperative Tree Improvement Program (NCTIP), supplemented by a few unrelated gen otypes from controlled crosses from the NCTIP and the Western Gulf Forest Tree Improvement Program (Fig. 2 1). Depending on propagation efficiency and availability, one to four cuttings of each clone were transferred to the greenhouse facilities at the Un iversity of Florida, Gainesville, FL. The plants were placed on Ebb -n -Flow benches and subirrigated twice daily with a Peters Professional Fertilizer (10 2010; adjusted to 2 mM ammonium nitrate) supplemented with iron (Sequestrene; adjusted to 0.037 mM elemental iron). The cuttings were then hedged to stimulate flushing and were placed in the experimental design. Experimental Designs Two inoculation experiments were performed. An initial inoculation experiment consisted of up to four replicates of the entire population of 498 genotypes, placed in a randomized incomplete block design with 21 rows and 22 columns per replicate. A subset of genotypes from this first experiment was selected based on response to
42 pathogen challenge. This subset was comprised of the 50 most susceptible and the 50 most resistant genotypes. The selected plants were hedged and transferred to onegallon pots and placed in a 9 x 9 partially balanced lattice design. Shoots selected for inoculation in the second experiment were individually identified. Fungal Inocululm Fusarium circinatum strain S45 was cultured in PDA (potato dextrose agar) medium for 10 to 15 days, as described by Young et al (2006) Microconidia representing clonally derived spores of a single genetic isolate, were then harvested by flooding the culture plates with 5 ml of sterile distilled water and collecting the spore suspension with a pipette onto a glass beaker. The concentration of microconidia was estimated using a hemacytometer and dilutions were made until a final concentration of 500 spores/l (YOUNG et al. 2006) was obtained. Phenotyping One to five shoots per plant were selected for inoculation and the tips were cut off to allow fungal penetration. Plants were sprayed with the spore solution described above using a manual pressure spray pump. For re-inoculation experiments, selected genotypes from the resistant and susceptible tails were transferred to 1-gallon pots and hedged twice to induce mul tiple shoot growth. Plants were wounded at the shoot tip and 2 l of inoculum (500 spores/l) were manually placed on the wound with a pipettor. After inoculation, all plants were placed overnight in a humid chamber, constructed by sealing flood benches i n clear plastic sheeting material. The following morning, the plastic was removed.
43 Lesion length measurements were taken at 4, 8, and 12 weeks after inoculation, using a digital caliper. Lesion lengths were recorded, in millimeters, from the shoot tip (w ound site) to the lowest point where necrosis was observed. To keep data collection consistent, lesion lengths of a given block were recorded by the same person in all three measurements. Estimates of clonal values were obtained using Best Linear Unbiased Predictions (BLUP) in ASReml (GILMOUR et al. 2006) with t he following model for the initial inoculation experiment: yijklmno = + Repi+ clonej + rep*cloneij + ram(clone*rep)ijk + tray(rep)il + row(rep)im + col(rep)in + eijklmno (2 -1) Where: yijklmno is the oth lesion loglength observation for the kth r amet within the jth clone in the mth row and the nth column of the lth tray within the ith replicate for each time point. is an overall mean. R epi is the fixed effect replication i = 1-4. clonej is the random variable clone ~ NID (0, 2 clone). rep*clonei j is the random variable rep by clone ~ NID (0, 2 clone*rep). ram(clone*rep)ijk is the random variable ramet within clone by rep ~ NID (0, 2 ram (clone*rep)). tray(rep)il is the random variable tray within replicate ~ NID (0, 2 tray(rep)). row(rep)im is th e random variable row within replicate ~NID (0, 2 row(rep)). col(rep)in is the random variable column within replicate ~NID (0, 2 col(rep)). eijklmo is the random variable error within the experiment ~ NID (0, 2 e).
44 The random variables rep*cloneij, tray(r ep)il and col(rep)in were later excluded from the model because of zero variance. The genotypes were ranked according to their clonal BLUP estimates and the 50 most susceptible and resistant genotypes (tails) were selected. For the second inoculation exper iment the BLUPs were obtained using the following model, with the same variables as described above: yijmno = + Repi+ clonej + rep*cloneij + row(rep)im + col(rep)in + eijmno (2 -2 ) Clonal repeatability was estimated using the following formula: (2 -3 ) For the re -inoculation experiment, 2 ram was omitted from the above formula. The variances of clone, ram et and residual effects were used to estimate the phenotypic standard deviation, as the square root of the sum of these three values. Genotyping Genotyping of single nucleotide polymorphisms (SNPs) was performed using the Illumina InfiniumTM assay (Illumina, San Diego, CA). Similar, yet lower throughput, platforms have been shown to work well within the large and complex genome of conifers (ECKERT et al. 2009b) The discovery, selection and genotyping of these SNPs are described in Eckert et al (2010) In brief, SNPs were detected and genotyped for 7 508 resequenced amplicons obtained from all available unique EST contigs representing all pine ESTs known to date using an InfiniumTM genotyping chip. EST sequences were utilized without regard to gene annotation. In total, ~22,000 SNPs were discovered, of which 7 216 were chosen for genotyping. Results were analyzed u sing the BeadStudio ver. 184.108.40.206 software (Illumina), and 3 938 SNPs were selected rclone = 2 clone 2 clone2 ram2 e
45 based on the quality and reliability of reads as well as frequency of polymorphism across genotypes in the association population (i.e., common variants were selected). Genotypic data of the 3,938 SNP markers were available for 404 of the 498 clones screened for pitch canker resistance. Association Analyses Patterns of population structure within this association population were assessed using 23 nuclear single sequence repeat markers in conjunction with STRUCTURE ver. 2.2 (PRITCHARD 2000) The association analyses performed in this study were done with a cluster number of five ( K = 5). This value was the minimal value of K at which the log -probability of the data leveled, and membership coefficients (i.e. qvalues) illustrated geographical trends for most clusters (ECKERT et al. 2010) Membership coefficients for these clusters were also in agreement with previous research, which identified significant structure (FST = 0.020.04) between samples spanning the Mississippi River Valley (AL-RABAB'AH 2002; SCHMIDTLING 1999) Prior to testing for significa nt associations, SNPs were pre -selected based on their significance for additive effects. A test for significance of SNP effect was performed by an analysis of variance on all 3,938 SNPs, using R software version 2.8.1 (RDEVELOPMENTCORETEAM 2005) A complete model, consisting of SNP, replicate and interaction effects was compared to a reduced model with only replicate effects. The formulas for the analysis of variance are shown below: Complete model: Y ij = + SNPk + Repj + SNP*repjk + eij (2 -4 ) Reduced model: Yij = + Repj + eij (2 -5) Where:
46 Yij is the ith log-transformed mean lesion length for the for the jth replicate. is the overall mean SNPk is the fixed effect SNP k = 1 to 3,93 8 Repj is the fixed effect replicate j = 1 to 4 eij 2 etail) Pvalues for each individual SNP were obtained and ranked according to level of significance. The 400 SNPs with lowest P values were used to test for significant associations in th e entire population. Significant associations were identified using the BAMD (Bayesian Association with Missing Data) program developed in R software (RDEVELOPMENTCORETEAM 2005) which incorporates a sim ultaneous solution for SNP effects and population structure. BAMD also accounts forthe missing SNP data by performing multiple imputations for the missing SNPs based on the observed SNPs as well as the observed phenotypes,under the assumption that the SNP data are missing at random. (GOPAL et al. 2009; LI 2008) Unlike other Bayesian marker software, this program assumes a common variance across SNPs for additive effects, and is available free online at CRAN ( http://cran.r project.org/ ). The association model was the following: y = X + Z (2 -6) where y is the vector of clonal least -square means for the trait (mean logtransformed coefficient for population structure effects, Z is th 2 e). A total of 50,000 iterations were performed on the program, of which the last 20,000 were kept. Mean
47 SNP effects and 95% confidence intervals were obtained from the BAMD output, estimated from the gamma values of the last 20,000 iterations. This generated a 95% confidence interval of effect for each SNP that either did, or did not, include a value of zero. The 95% confidence interval reflects SNP effe cts calculated across all values (i.e., imputed multiple times) for missing SNP data points. SNPs were considered significant if they did not include a value of zero in the 95% confidence interval. Tests for Linkage Disequilibrium and Departure f rom Hardy -Weinberg Equilibrium All 3,938 SNPs were tested for linkage disequilibrium and departure from Hardy Weinberg equilibrium using the SAS PROC ALLELE procedure (SAS version 9.1, SAS Institute Inc., Cary, NC, USA.). SNPs were considered significant for either test at a fal se discovery rate (FDR) of 5% Estimating S NP Effects The effects of the significant SNPs on the clonal variances were determined by evaluating the model used to obtain the BLUPs, incorporating all significant SNPs as random effects and the n re running a reduced model without SNP effects. A chi -square test was performed on the difference between the 2Log Likelihood values from the two models to determine whether the effect of the SNPs was significant. The percentage of clonal variance exp lained by each individual SNP was obtained using the following formula: 2 Clone_Red 2 Clone_SNPx2 Clone_Red)*100, (2 -7 ) 2 Clone_Red is the clonal variance of the reduced model (without SNP effects), and 2 Clone_SNPx is the clonal variance obtained by including each individual SNP (x = 1 to 10) separately as a random variable in the model.
48 A similar approach was used to determine the percentage of the phenotypic variance accounted by the effect of each individual SNP on the clonal variance. This was obtained by using the following formula: 2 Clone_Red 2 Clone_SNPx2 Phenotypic)*100, (2 -8 ) 2 Clone_Red is the clonal variance of the reduced model (without SNP effect), 2 Clone_SNPx is the clonal variance obtained by including each SNP (x = 1 to 10) separately as a random variable in the model, 2 Phenotypic is the phenotypic variance obtained by summing all variance components other than environmental corrections from the reduced model. The phenotypic standard deviation was obtained by taking the square root of the sum of all variance comp onents other than environmental corrections from the reduced model. The percentage of phenotypic standard deviation represented by each SNP was obtained by dividing the absolute value of the mean SNP effect from the association output by the phenotypic st andard deviation and multiplied by 100, as shown below: %Std.DevPhenotypic = 100*(Mean SNP effect/Std.DevPhenotypic) (2 -9 ) B LAST Analyses Sequences flanking SNPs as well as the corresponding EST contig sequences were obtained from the Dendrome database (http://dendrome.ucdavis.edu/interface ), for each SNP that showed significant associations to pitch canker resistance. A BLASTx search was performed against the entire NCBI non-redundant protein datab ase (http://blast.ncbi.nlm.nih.gov/Blast.cgi ) to determine whether the sequences encoded proteins with known function. Hits with expect values lower than E 10 were selected,
49 otherwise they were considered as no-hits. The best hits were used as reference for interpretation of putative biological functions of the EST sequences from which the SNPs were obtained. To determine whether a SNP was located in a coding region, the same cDNA sequences used for BLASTx were used as a BLASTn query against the NCBI database (http://blast.ncbi.nlm.nih.gov/Blast.cgi ). The pine genomic DNA sequences with highest similarity to the query sequence were used as guidelines to determine the location of the SNP. The EST contig sequence, genomic DNA sequence and the two versions of the SNP flanking sequences (each version with the corresponding nucleotide substitution), were aligned using ClustalW2 (http://www.ebi.ac.uk/Tools/clustalw2/index.html ). The alignment of all four sequences was suggestive of the SNP being in a coding region. To further verify whether the SNPs were located in a coding region, the EST cont ig sequence and the BLASTn best hit sequence were translated using ExPASy (http://ca.expasy.org/tools/dna.html ). The translated sequences were compared to the protein sequence of the BLASTx best hit. The BLASTx best hit was also used as reference for the strand orientation of the translated EST contig sequence. BLASTx results of the EST contig sequence and BLASTn best hit sequences were also compared. In cases where the BLASTn best hit yielded no hits wi th the BLASTx results but its corresponding contig sequence did, was evidence that the SNP could be located in a non-coding region, possibly the 3UTR. Translation of the SNP flanking sequences and alignment with the translated EST contig and BLASTn best hit sequence allowed determining whether the SNP caused a synonymous or non-synonymous substitution.
50 Results The Distribution of Clo nal Predicted Values Validates the Quantitative Nature o f Pitch Canker Resistance The clonal predicted values for lesion length obtained using BLUP showed a continuous distribution, characteristic of quantitative traits (Fig. 2 -2). This supports previous observations on the nature of pitch canker resistance in loblolly pine (KAYIHAN et al. 2005) The 50 clones with the most extreme phenotypes at each end of the distribution (highly susceptible and highly resistant) that had three or more ramets were re -inoculated and the lesion length values were compared to those from the original population. Clonal Lesion L engths Progressively Increased by Measurement Period a nd Showed Signif icant Differences bet ween Extreme Phenotypes Mean lesion lengths observed for the population in the first experiment increased fro m 5.75 mm at 4 weeks after inoculation to 9.5 mm at 12 weeks after inoculation. When measured in the resistant and susceptible tails, the respective mean lesion lengths ranged between 2.86 and 4.57 mm, and between 7.05 and 14.01 mm (Table 2 1). High level s of variation were apparent within the population and in both tails, as shown by large values for the standard deviation. Such variation is likely due to a highly unbalanced experimental design, because the number of ramets and of available shoots for in oculation varied among genotypes. For this reason, we used best linear unbiased predicted values for selection of clones with extreme phenotypes, as this approach adjusts for the variable number of observations.
51 An analysis of variance was conducted to test for significant differences among the susceptible and resistant genotypes after the second inoculation. For this test, the results from the third measurement (12 weeks after inoculation) were used. The results showed that the differences among the susceptible and resistant genotypes were significant (P<0.0001). Clonal Repeat ability Values Were Consistent with Those Fr om a Population With Known Pedigree Clonal repeatability is a measure of heritability commonly estimated for populations where the family structure is unknown. Clonal repeatability values ranged from 0.21 to 0.28 in the first experiment, and from 0.35 to 0.38 in the second inoculation (Table 2 2). The results suggest that nearly 30% of the variation on the disease resistance trait in th e first inoculation can be attributed to genetic effects, whereas in the second inoculation experiment, genetic effects account for nearly 40% of the phenotypic variation. The increase of 10% in clonal repeatability observed from one experiment to the nex t could be due to better growth conditions and tissue uniformity of plant material and more effective inoculation procedures that caused clonal measurements to be less variable across replicates. Such repeatability values were consistent with the broad se nse heritabilities reported by Kayihan et al. (2005) Associations Suggest Pitch Canker Resistance Involves Many Genes With Small Effects Out of 3,938 SNPs, ten were si gnificant at a 95% Bayesian confidence interval 2 3). The
52 phenotypic standard deviation was 0.82 log mm and was estimated as the square root of the sum of the variances of clone, ramet and residual effects. The percentage of the phenotypic standard deviation affected by a given SNP ranged between 4.78 and 7.21 (Table 2 3), suggesting that there are no major genes that are involved in pitch canker resistance, but rather resistance coul d be due to the action of several genes with small effects. The effect of all significant SNPs on the clonal variance was estimated by running a full model (with all SNPs included as random variables in the model), as well as a reduced model (without SNPs in the model). The percentage of clonal variance, the genetic component of the phenotypic variance, accounted by all the SNPs together was 13.19%. A chi-square test was performed to determine the significance of the effect of SNPs on the clonal variance. The observed chi -square value was 18.26, resulting in a pvalue, at 0.05% significance and 10 degrees of freedom, of 0.05. Thus, the SNPs appear to have a significant, although not large, effect on the clonal variance. The individual SNP effects on the clonal variance ranged from 0.29% to 3.83% (Table 2 -3). The sum of all individual SNP values accounts for 14.6% (data not shown) which is close to the 13.19% accounted by the effect of all SNPs together in the clonal variance. In terms of the effect of t he SNPs on the phenotypic variance, these are very low, with the highest being 0.98%. Overall, the sum of the SNP effects on the phenotypic variance account for about 3.74%. To evaluate potential consequences of preprocessing, we used the 400 SNPS with th e greatest effect on the clonal variance as a preprocessing criterion. This identified 18 significant SNPs, of which 5 were identical to those described above (Table 2 5 ).
53 Annotation o f Genes Containing Significant SNP s BLASTx analysis of the contig EST sequences was performed against the complete NCBI database for those SNPs that showed significant associations with the disease resistance phenotype. Out of the 10 EST sequences from the corresponding significant SNPs, two gave no hits and one resulted in an unknown protein when a maximum expected value of E -10 was used (Table 2 4 ). The remaining EST sequences showed similarity to known proteins, such as lectin -like protein kinase, geranylgeranyl transferase beta I subunit, DELLA protein, hexokinase, plas tid hexose transporter and blue copper protein (Table 2 4). The flanking sequences of 8 out of the 10 significant SNPs were located to a coding region. The remaining two appear to be in the 3 UTR end, based on sequence alignments with the EST contig and their corresponding pine genomic DNA (Table 2 4). Six of the significant SNPs result in non -synonymous substitutions ; and although some amino acid changes observed appear minor (V to A substitution), others may cause major amino acid changes or truncation of the coding sequence, which may result in more dramatic changes to protein structure (Table 2 -4). In addition, only one of the 10 significant SNPs showed departure from Hardy Weinberg equilibrium (Table 2 4). Discussion In this study we exploited vegetative propagation to quantify the extent to which genetic factors condition disease resistance, and to enable the precision required to detect quantitative disease resistance genes that exert small effects on the phenotype. Lesion lengths increased among ti me points with significant differences between two
54 groups of clones that showed extreme phenotypes (tails). High standard deviation values were suggestive of high levels of variation within the population, a reflection of its high genetic diversity; but also due to differences in the number of observations for each genotype. When this occurs, variances on entries with extreme high or low number of observations have a tendency to be over estimated (BEAVIS 1998; GORIN G et al. 2001) Therefore, clonal values were adjusted for different numbers of observations using best linear unbiased predictions (BLUP). This provided more reliable clonal values for the experiment and allowed a more unbiased detection of the extreme phenotypes. Genetic resistance to necrotrophic pathogens is frequently found to have a quantitative basis, although exceptions to this general rule have been noted in crops (e.g., (JOHAL AND BRIGGS 1992) Pitch canker resistance in loblolly pine appears to be quantitative based on the observed continuous distribution of resistance phenotypes within a large family -based population (KAYIHAN et al. 2005) and the results reported in the present study Quantitative traits typically are defined by relatively small contributions from several genes, or by one or two genes with large effect and several additional genes with small effects (FLINT and MACKAY 2009) Our detection of ten loci associated with disease resistance that collectively account for approximately 15% of the clonal variance is consistent with an infinitessimal model in which all of the clonal variance could be explained by many genes with small effects, similar to flowering traits in maize (BUCKLER et al. 2009) However it should be noted that the proportion of all genes in the pine genome marked by SNPs in the present study is not known, since the total number of genes encoded in the pine genome is not known. Perhaps major genes for pitch canker resistance exist but remain undetected in this study, as well as
55 potentially severe alleles in loci that could account for large fractions of the rem aining clonal variance that is not currently explained by SNPs. Thus the evidence for lack of major genes reported in this study, which is consistent with results of other studies (ECKERT et al. 2009a; GONZALEZMART INEZ et al. 2008; GONZALEZMARTINEZ et al. 2007) should not be taken as evidence that quantitative disease resistance is conditioned entirely by genes that exert minor effects. The results from BLASTx analyses showing that three out of the 10 SNPs signif icant for associations corresponded to unknown or predicted proteins suggest s that such sequences could correspond to taxonomically restricted genes that have not been detected in other plants or share similarity with genes whose function is yet unknown Given the observation that these genes may lack detectable orthologs in angiosperms this illustrates the value of testing all possible loci for association with phenotypes of interest these loci would not have been detected had we restricted the pool o f tested SNPs to those annotated with roles in disease resistance (MORSE et al. 2004) Most loci associated with pitch canker disease resistance were related to known genes, many of which ha d supporting evidence of possible involvement, directly or indirectly, in disease resistance or stress response. We interpret these associations in the context of hypotheses recently proposed by Poland et al. (2009) to explain the genetic basis of quantitative disease resistance. These are not expected to be mutually exclusive, and our observations regarding the nature of the genes containing significant SNPs associated with pitch canker resistance suggest that different genes support several of those hypotheses. DELLA proteins and geranylgeranyl transfe rases are
56 both involved in modulating the salicylic acid, and the jasmonic acid/ethylene pathways (COURDAVAULT et al. 2009; GORITSCHNIG et al. 2008; LLORENTE et al. 2008; NAVARRO et al. 2008a; NAVARRO et al. 2008b) supporting the hypotheses that quantitative resistance loci are involved in defense signal transduction Similarly, hexokinases and hexose transporters could also support this hypothesis, if their role in plant pathogen response were mediated by their r oles in sugar signaling and sensing (HERBERS et al. 1996; HERBERS et al. 1995; YOSHIDA et al. 2002) .The role of blue copper proteins in redox reactions (NERSISSIAN ET AL. 19 98) known to be involved in detoxifying pathogen produced phytotoxins, could be interpreted as supporting the chemical warfare hypothesis, or as supporting the hypothesis of developmental phenotypes given a potential role for blue copper proteins in lign in formation (LOOPSTRA and SEDEROFF 1995) T he lectin -like protein kinase support s the hypothesis that mutations in g enes involved in basal defense occur through putative recognition of pathogen elicitors (KANZAKI et al. 2008) Finally, those SNPs within unknown or unclassified genes favor the hypothesis that quantitative resis tance genes represent a set of genes that have not previously been associated with disease resistance and are therefore not ann otated with any known function (POLAND et al. 2009) The results of this study raise important and unresolved questions regarding durability of quantitative resistance. Evidence that was obtained for significant provenance X isolate interactions between Fusarium circinatum and Pinus patula, but not Pinus tecunumanii (HODGE and DVORAK 2007) suggest s specificity in pitch canker disease resistance Specific interactions would imply that subsets of quantitative res istance loci may have been overcome by the pathogen during coevolution with Pinus
57 patula. In this context, it would be informative to inoculate the loblolly pine genotypes in the extreme tails with diverse isolates of the pitch canker fungus in order to t est the hypothesis that subsets of quantitative resistance loci may be involved in isolate -specific resistance in this host species as well. Genetic variation in Fusarium is conditioned by sexual reproduction (BRITZ et al. 2005; COVERT et al. 1999) as well as horizontal transfer of chromosomes that confer pathogenicity (MA et al. 2010) This increases the importance of distinguishing whether resistance loci are associated wi th general (i.e., more durable) or specific (i.e., less durable) interactions to inform breeding and selection in genetic improvement programs aimed at increasing disease resistance (POLAND et al. 2009)
58 Table 2 1. Clonal lesion length measurements increased with time period in the two inoculation experiments and were significantly different (p< 0.001) among tails in the second inoculation. Measurements were performed at 4, 8, and 12 weeks after inoculation in the association population and in the re-inoculated resistant and susceptible tails. Time post inoculation (weeks) 1st Inoculation (Association population) 2nd inoculation (Tails) Resistant Susceptible 4 8 12 4 8 12 4 8 12 Mean (mm ) 5.75 7.93 9.50 2.86 3.81 4.57 7.05 12.15 14.01 Standard 2.63 5.01 7.56 1.10 1.31 3.00 3.84 7.72 9.57 Median 5.37 6.67 7.27 2.69 3.38 3.87 6.31 10.05 10.75 N 498 45 47
59 Table 2 2. Clonal repeatability, a measure of heritability, was obtained from the variances for clone, ramet and residual in the first inoculation experiment and from the variances for clone and residual in the second inoculation experiment. Respective repeatability values totaled 0.28 in the first inoculation and 0. 38 in the second inoculation at 12 weeks after inoculation. Variances are shown in mm2. Variances (mm2) Lesion Loglength Clone Ramet Residual Repeatability First inoculation 4 weeks 0.11 0.098 0.32 0.21 8 weeks 0.16 0.077 0.41 0.25 12 weeks 0.21 0.069 0.47 0.28 Second inoculation 4 weeks 0.29 -0.54 0.35 8 weeks 0.34 -0.53 0.39 12 weeks 0.35 -0.56 0.38
60 Table 2 3. List of the SNPs with significant association to pitch canker resistance ( <0.05) and their effects on genotypic (clonal) and phenotypic (lesion length) variation. Mean SNP effects on the phenotype were estimated from the last 20,000 iterations in the BAMD program, along with the corresponding 95% confidence intervals The contribution of the SN P to the phenotype is shown as the p ercentage of the phenotypic standard deviation, whereas genotypic effects are indicated as the percent difference in the clonal variance. The percentage of the phenotypic variance due to the SNP effect in clonal variance is an indicator of the genetic contribution of the SNP to the phenotype. SNP_ID Allele A Allele B Mean (log mm) 95% Confidence Interval (log mm) % Phenotypic Standard Deviation % Diff in clonal variance 2 Phenotypic due to SNP effect in clonal variance 0_15227_01_160 T C 0.040 [0.002, 0.078] 4.775 0.924 0.236 0_15382_01_104 G A 0.061 [0.007, 0.115] 7.210 3.832 0.980 0_2234_01_128 G T 0.048 [0.009, 0.087] 5.697 1.745 0.446 0_6323_01_248 G C 0.045 [0.002, 0.088] 5.307 0.889 0.227 0_9288_01_372 A G 0.048 [0.011, 0.085] 5.662 0.288 0. 074 1_3327_01_116 A G 0.054 [0.005, 0.103] 6.383 2.187 0.559 2_4484_02_622 T C 0.057 [0.004, 0.110] 6.738 1.101 0.282 2_6181_02_400 T G 0.057 [0.005, 0.110] 6.761 1.129 0.289 2_8946_02_437 G C 0.056 [0.003, 0.107] 6.560 1.031 0.264 CL4336Contig1_01_180 T C 0.053 [0.001, 0.106] 6.277 1.519 0.389
61 Table 2 4. SNPs significant for association with pitch canker resistance and best hits based on BLASTx search using the contig sequence as query. Predicted SNP location and effect on amino acid sequence are also shown based on sequence alignments with genomic DNA sequences. SNP_ID Best h it (Expect < 1e 10) Best h it (no cutoff) Predicted SNP location Effect on a.a. sequence # SNPs in LD* 0_15227_01_159 L ectin like protein kinase L ectin like protein kinase (Expect = 7e 27) Coding region Synonymous substitution 0 0_15382_01_99 G eranylgeranyl transferase type I beta subunit G eranylgeranyl transferase type I beta subunit (Expect = 2e 30) Coding region V to A change 0 0_2234_01_128 P utative long chain acyl CoA synthetase P utative long chain acyl CoA synthetase (Expect = 5e 63) Coding region D to Y change 0 0_6323_01_240 DELLA protein DELLA protein (Expect = 3e 59) Coding region Synonymous substitution 0 0_9288_01_370 No hits found Predicted protein Populus tr ichocarpa (Expect = 4e 06) Coding region Synonymous substitution 0 1_3327_01_113 No hits found Unnamed protein product [Vitis vinifera] (Expect = 0.23) Coding region C to Y change 8** 2_4484_02_622 P lastid hexose transporter P lastid hexose transporter (Expect = 8e 64) Coding region C to Y change 0 2_6181_02_400 H exokinase H exokinase (Expect = 2e 31) Non coding, Putative 3'UTR S to R change 0
62 Table 2 4. Continued SNP_ID Best h it (Expect < 1e 10) Best h it (no cutoff) Predicted SNP location Effect o n a.a. sequence # SNPs in LD* 2_8946_02_435 Cucumber peeling cupredoxin Cucumber peeling cupredoxin (Expect = 3e 10) Non coding, Putative 3'UTR S to Stop 1 CL4336Contig1_ 01_180 Unknown [Picea sitchensis] Unknown [Picea sitchensis] (Expect = 2e 72) Co ding region Synonymous substitution 1 Tests for departure from Hardy Weinberg equilibrium and linkage disequilibrium were performed for all pairwise combinations of the 3,938 SNPs available for this study. None of the significant SNPs detected were in LD with each other. ** Also significant for departure from Hardy Weinberg equilibrium *** NA, not applicable
63 Table 2 5 SNPs significant for association with pitch canker resistance using preprocessing based on effects of individual SNPs on clon al variance. The best hits based on BLASTx search using the contig sequence as query correspond to the sequence with highest similarity to the query sequence. Best known hit corresponds to the hit with highest similarity to the query sequence and that also has a previously described known function. E values are shown in parentheses. Putative function of the best known hit is also shown based on gene ontology data. The shaded cells correspond to SNPs significant for association using the ANOVA pre-processin g. Contig Best Hit (Expect < 1E 10) Best known hit (no cutoff) Putative function 0_11598 hypothetical protein OsJ_03610 [Oryza sativa Japonica Group] (1E 12) RRP4; exonuclease [Arabidopsis thaliana] (9E 11) RNA binding / exonuclease 0_11724 conserved hyp othetical protein [Ricinus communis] (9E 33) conserved hypothetical protein [Ricinus communis] (9E 33) -0_15382 "geranylgeranyl transferase type I beta subunit, putative [Ricinus communis]" (2E 30) "geranylgeranyl transferase type I beta subunit, putati ve [Ricinus communis]" (2E 30) protein prenylation, cell signaling, 0_1583 unknown [Picea sitchensis] (8E 57) unknown [Picea sitchensis] (8E 57) -0_2092 unknown [Picea sitchensis] (4E 36) putative protein kinase [Arabidopsis thaliana] (2E 30) kinase activity, protein amino acid phosphorylation 0_4285 amino acid carrier [Zea mays] (5E 22) amino acid carrier [Zea mays] (5E 22) Transmembrane amino acid transporter protein 0_9288 No Hits Found No Hits Found -0_9534 putative acid phosphatase [Pinus pin aster] (1E 49) putative acid phosphatase [Pinus pinaster] (1E 49) acid phosphatase activity 1_3327 No Hits Found No Hits Found -2_4484 unknown [Picea sitchensis] (5E 64) PREDICTED: plastid hexose transporter [Vitis vinifera] (8E 60) sugar transport an d sensing
64 Table 2 5 Continued Contig Best Hit (Expect < 1E 10) Best known hit (no cutoff) Putative function 2_717 unknown [Picea sitchensis] (7E 18) unknown [Picea sitchensis] (7E 18) -2_945 unknown [Picea sitchensis] (1E 122) alpha tubulin [Physcomitrella patens] (1E 122) microtubulebased movement, protein polymerization C L1468Contig1 unknown [Medicago truncatula] (1E 70) unknown [Medicago truncatula] (1E 70) -CL4277Contig1 unknown [Picea sitchensis] (5E 13) unknown [Picea sitchensis] (5E 13) -CL4336Contig1 unknown [Picea sitchensis] (2E 72) nucleic acid binding prote in, putative [Ricinus communis] (6E 34) nucleic acid binding, oxidoreductase activity UMN_1022 PREDICTED: hypothetical protein [Vitis vinifera] (9E 7) PREDICTED: hypothetical protein [Vitis vinifera] (9E 7) -UMN_1397 eukaryotic translation initiation factor 3 subunit 7 [Zea mays] (8E 26) eukaryotic translation initiation factor 3 subunit 7 [Zea mays] (8E 26) eukaryotic translation initiation factor UMN_4383 unknown [Picea sitchensis] (1E 36) arginine methyltransferase [Populus trichocarpa] (4E 80) S adenosyl methionine dependent methyltransferase activity
65 Figure 21. Geographical distribution of loblolly pine accessions sampled for this study. Size of the dots denotes the number of accessions collected in a par ticular county, as follows: 1 5, 6 -10, 11 -15, 16+ accessions. Bar line equals 200 km Gulf of Mexico Atlantic Ocean
66 Figure 22. Distribution of BLUP clonal estimates (in standard units) for pitch canker lesion length (log-transformed), highlighting the 50 most resistant and susceptible clones Inserts show phenotypes of resistant and susceptible genotypes. Resistant Susceptible
67 CHAPTER 3 VALIDATION OF SIGNIFICANT ASSOCIATIONS T O PITCH CANKER RESIS TANCE USING A POPULATION W ITH KNOWN PEDIGREE AND REAL -TIME QUANTITATIVE POLYMERASE CHAIN REACTION Introduction Studie s on plant pathogen interactions in crops and model species have helped understand the mechanisms underlying disease resistance in plants (BALINT-KURTI et al. 2008; DANGL and JONES 2001; JONES and DANGL 2006; KOVER and CAICEDO 2001) In the context of host pathogen interactions and disease resistance mechanisms, quantitative resistance typically involves several genes with small effects (FLINT and MACKAY 2009; GLAZEBROOK 200 5) al though major genes have also been identified (DANGL and JONES 2001; JOHAL and BRIGGS 1992) Quantitative resistance is also considered to be more durable in that it requires a longer time for the pathogen t o overcome the resistance as the effects of multiple genes must be surpassed, in contrast with single -gene resistance mechanisms (KOVER and CAICEDO 2001; POLAND et al. 2009; SALVAUDON et al. 2005) In spite of th e importance of understanding quantitative disease resistance, r esearch focused on identifying the genes that underpin such trait is not an easy task. Because the goal is to identify small gene effects, disease screening experiments are best performed in well -replicated and randomized study designs to facilitate removing variation due to environmental noise during phenotyping. In addition, the reproducibility of candidate gene effects in different environments and genetic backgrounds is considered highly desirable for use in breeding strategies. Esperiments to identify and validate quantitative disease resistance genes require much larger populations and
68 more robust study designs, compared to studies to detect major gene effects in which single genes acco unt for mos t of the phenotypic expression. Loblolly pine ( Pinus taeda L. ) genotypes vary in their resistance to pitch canker disease which is incited by the necrotrophic fungus Fusarium circinatum Nirenberg et ODonnell causes resinous lesions in stems an d branches (DWINELL et al. 1985; ENEBAK and STANOSZ 2003; SAKAMOTO and GORDON 2006) and is one of the major causes of seedling mortality in nurseries (CAREY et al. 2005) Previous studies have shown that resistance to pitch canker in loblolly pine, as well as in other pine species is quantitative and heritable (HODGE and DVORAK 2000; KAYIHAN et al. 2005) and involves several genes with small effects (QUESADA et al. 2010) In addition, it has been observed that resistance to pitch canker in other pine species, such as Pinus patula and Pinus tecunumanii, varies according to site of origin and pathogen isolate although the effect of isol ate was technically confounded with the replication effect (HODGE and DVORAK 2007) Identifying the genes that underpin pitch canker resi stance is critical for conifer breeding programs. Association mapping has been a useful approach to identify candidate genes contributing to quantitative traits in diverse organisms, such as humans (CIRULLI and GOL DSTEIN 2010; D ALFONSO et al. 2002; EASTON et al. 2007; LEE et al. 2007; PILLAI et al. 2009) Drosophila (JIANG et al. 2009; JUMBOLUCIONI et al. 2010; KENNINGTON et al. 2007) livestock (C harlier et al. 2008; G oddard and H ayes 2009; M artinez et al. 2006) and plants like maize (BUCKLER et al. 2009; WANG et al. 2007; YU and BUCKLER 2006) Arabidopsis (ARANZANA et al. 2005) pine (ECKERT et al. 2010; GONZALEZMARTINEZ et al. 2008; GONZALEZMARTINEZ et al. 2007) barley (INOSTROZA et al. 2009; STRACKE et al.
69 2009) and potato (LI et al. 2008; MALOSETTI et al. 2007) Because association mapping relies on the evolutionary history and the recombination events across generations, specie with large out -crossing populations and large effective population sizes are good models, as linkage disequilibrium is low between genetic markers and the loci of the quantitative trait of interest (JANNINK et al. 2001; NEALE and SAVOLAINEN 2004) The d etection of marker trait associations in areas of low linkage disequilibrium would allow fine mapping (i.e., direct detection) of causal SNPs P ine species exhibit high levels of genetic variation and low linkage disequilibrium (NEALE and SAVOLAINEN 2004) which are advantageous to identify significant marker associations with quantitative traits (BROWN et al. 2004; LEVYADUN 2000) Replicable marker -trait associations are important to determine whether statistically significant markers are within or tightly linked to any causative genes that ar e also biologically significant and can thereby serve as markers for breeding and genetic improvement strategies. Such associations also provide insight into the mechanisms of disease resistance, since the annotation of the genes should provide clues as to the biochemical roles of the gene products in the host and potentially indicate other novel targets of marker assisted selection In the approach to genotyping chosen in our studies, the SNPs in the genotyping platform were selected without regard to annotation, similar to a blind genomewide association analysis, w ith the advantage that associations can identify genes with previously unexpected roles in disease resistance. A previous association study in an unstructured loblolly population identified 1 0 candidate SNPs associated with pitch canker resistance (QUESADA et al. 2010) using
70 3,938 SNP markers (ECKERT et al. 2010) To determine whether these SNPs are reproducible, SNP -trait associations were performed in a different loblolly pine population that had 2,182 genotyped SNPs, of which 1,919 were in common with the 3,938 SNPs previously analyzed in the NCSU population (QUESADA et al. 2010) This new population (CCLONES) consisted of the resulting progeny of a circular mating design (KAYIHAN et al. 2005) To improve resolution and increase power, a combined dataset consisting of NCSU and CCLONES was analyzed for 1,919 SNPs in common between the two populations Functional validation was performed to determine if these associations were implicated in the causal genes. P athogen regulation of the genes harboring the significant SNPs was also evaluated. Results showed that three of the significant SNPs reported in the NCSU population were also significant in the combined analyses. Functional validation revealed that the genes containing these SNPs show changes in transcript abundance after pathogen challenge. Materials and Methods Plant Material Two populations of l oblolly pine ( Pinus taeda L.) co nsisting of clonally -propagated rooted cuttings were used for the inoculation experiments. We used a population (NCSU population) composed of 498 genotypes collected from wild accessions, as well as a few unrelated genotypes from controlled crosses from t he NCSU Cooperative Tree Improvement Program (NCTIP) and the Western Gulf Forest Tree Improvement Program. R ooted cuttings were transferred from the North Carolina State University (NCSU) Horticultural Field Laboratory, Raleigh, NC, to the greenhouse faci lities at the University of Florida, Gainesville, FL and kept as reported by Quesada et al. (2010 ).
71 A validation population with known pedigree (CCLONES population) was also screened and used for association analyses. This population consisted of rooted cuttings of 1065 clones resulting from the crossing of 32 parents following a circular mating design (KAYIHAN et al. 2005) The plants were kept at the greenhouse complex of the USDA Forest Service Resistance Screening Center in Bent Creek, NC and were fertilized weekly with Miracle-Gro 153015, and pruned twice to induce shoot formation for inoculation (KAYIHAN et al. 2005) Experimental Design a nd Inoculation Experiments Rooted cuttings of the NCSU population were arranged in a randomized incomplete block design with 21 rows and 22 columns. The experiment included four replicates although not all clones were repr esented in all replicates (Fig 3 5 ). Up to five cuttings from the CCLONES population were placed on trays in an incomplete block design with one cutting per clone per replicate, although not all clones were represented in all five replicates (KAYIHAN et al. 2005) Inoculation experiments for both populations were performed using clonally propagated macroconidia (CCLONES) or microconidia (NCSU) of Fusarium circinatum isolate S45, as pre viously described (KAYIHAN et al. 2005; QUESADA et al. 2010; YOUNG et al. 2006) The inoculation of the NCSU population material was done in the fall of 2007 in the facilities of the University of Florida, Gainesv ille, Florida whereast he inoculation of the CCLONES population occurred at the USDA Forest Service Resistance Screening Center (RSC) in Bent Creek, NC, during the fall of 2002. Lesion length measurements were taken at 12 weeks after inoculation, measur ing the distance in millimeters from the excision site of the inoculated shoots to the end of the lesion.
72 Genetic Parameter Estimation from Phenotypes Variance components and genetic parameters for the CCLONES population were obtained from log -transformed lesion length measurements using ASReml (GILMOUR et al. 2006) The following mixed linear model was used: Yijklm = + Ri + t(r)ij + gcak + gcal + scakl + c(family)klm + r*fikl + ijklm (3 -1) where: Yijklm is the mth observation of the klth cross between mother k and father l in the jth tray of ith rep. overall population mean. R i is the fixed effect replication, i=1 5. t(r)ij is the random variable tray incomplete bl 2t ), j=1 21. gca k is the random variable female general combining ability (GCA) ~NID(0, 2gca) k=1 -26 gca l 2gca ) l=1 27 Given that male and female variances were pooled, this re sulted in 32 parents. scakl 2 sca). c(family)klm 2 c(family)). r*fikl 2 r*f)). ijklm 2 ). In the case of the NCSU population, variance components and genetic parameters were also obtained using ASReml a s previously described (QUESADA et al. 2010)
73 Genotyping The genotyping of single nucleotide polymorphisms (SNPs) was performed separatel y for each population using the Illumina InfiniumTM assay (Illumina, San Diego, CA). SNPs were detected and genotyped for 7508 resequenced amplicons obtained from all available unique EST contigs representing all pine ESTs known to date using an InfiniumT M genotyping chip (ECKERT et al. 2009b; ECKERT et al. 2010) In total, ~22,000 SNPs were discovered, of which 7216 were chosen for genotyping (ECKERT et al. 2010) (Eckert et al., 2010) Results were provided by Dr. David Neales group at UCDavis and analyzed using the BeadStudio ver. 220.127.116.11 software (Illumina), where SNPs were selected based on the quality and reliability of reads, pol ymorphism across clones and minor homozygote genotypic frequency of 2.5%. This resulted in 3 938 SNPs selected from the NCSU population and 2182 from the CCLONES population. For the combined analysis, genotypic data of 1919 SNP markers were available for 1074 clones from the two populations screened for pitch canker resistance (Figure 3 1). Pre Processing Of SNP Genotypic Data Prior to testing for significant associations, SNPs were filtered based on two different methods: significance for SNP additive effects and significance of SNP effects in explaining the clonal variance. To test the significance of SNP additive effect s, an analysis of variance was performed on all SNPs of the NCSU and CCLONES populations separately A complete model, consisting of SNP, replicate and interaction effects was compared to a reduced model with only replicate effects using the following equations : Complete model: Y ij = + Sk + Rj + S*rjk + ij (3 -2)
74 Reduced model: Yij = + Rj + ij (3 -3) Where: Yij is the ith log-transformed mean lesion length for the for the jth replicate. is the overall mean Sk is the fixed effect SNP k = 1 to 3,938 Rj is the fixed effect replicate j = 1 to 4 eij 2 etail) This model was fit in R. SNP effects on clonal variance were tested to evaluate nonadditive effects using a mixed model approach on all SNPs on both populations separately. The tested complete and reduced models were as follows: Complete model : Y ij = + Rj + sk + fl + c(f)lm + ijjklmn (3 -4) Reduced model : Y ij = + Rj + fl + c(f)lm + ij jklmn (3 -5) Yij is the ith log-transformed mean lesion length for the for the jth replicate. is the overall mean Rj is the fixed effect replicate j = 1 to 4 sk is the random effect S NP ~NID (0, 2 snp) c(f)lm is the random effect clone within family ~ NID(0, 2 clone*fam) fl is the random effect family ~ NID(0, 2 fam) eij is the random variable e rror within the experiment ~ 2 etail) In the case of the NCSU population, the effect family was not included in the model, as individuals are assumed unrelated. This model was fit in SAS.
75 Pvalues for each individual SNP were ranked according to level of significance. For CCLONES and NCSU, the 400 SNPs with lowest P values obtained from each preprocessing method were tested separately for significant associations in each population. In the case of the combined dataset, only SNPs obtained from the preprocessing method based on significance of SNP additive effects was used. To obtain t he combined rankings of each SNP across both populations, the 220 SNPs with lowest Pvalues from each population were selected. This resulted in a total of 417 SNPs from both populations that were used for the association analyses. The results of the ass ociation testing after preprocessing for SNPs with largest effect on the clonal variance are presented as Supplemental Information at the end of this chapter. Association Analyses Patterns of population structure within the NCSU population were assessed us ing 23 nuclear single sequence repeat markers in conjunction with STRUCTURE ver. 2.2 (PRITCHARD 2000) The association analyses performed in this study were done with a cluster numbe r of five (K = 5). This value was the minimal value of K at which the logprobability of the data leveled, and membership coefficients (i.e. qvalues) illustrated geographical trends for most clusters (ECKERT et al. 2010) Membership coefficients for these clusters were also in agreement with previous research which identified significant structure (FST = 0.02-0.04) between samples spanning the Mississippi River Valley (ALRABAB'AH 2002; SCHMIDTLING 1999) Significant associations we re identified using the following association model: (3 -6)
76 where y is the vector of clonal least -square means for the trait (mean log-transformed e coefficient for SNP additive effects with a single variance 2 e). This analysis was performed using the BAMD (Bayesian Association with Missing Data) program developed in R software (RDEVELOPMENTCORETEAM 2005) This program which incorporates a simultaneous solution for SNP effects, population structure and imputation of missing SNP data (GOPAL et al. 2009; LI 2008) and is available free online at CRAN ( http://cran.r project.org/ ). A total of 50,000 iterations were performed on the program, of which the last 20,000 were kept. Mean SNP effects and 95% confidence intervals were obtained from the BAMD output, using t he gamma values of the last 20,000 iterations. The above a ssociation analys i s was first performed on NCSU and CCLONES separately and the data from the structure and SNP effects matrices were concatenated for the combined analys i s. Because the populations had different structures and relationships among clones, some adjustments were made to construct the matrix for running in BAMD. The association model for NCSU consisted of a population structure matrix (X matrix) of 5 groups a Z matrix, which included t he genotypic data for 3,938 SNPs, and an error identity matrix, given that the individuals of the NCSU population were assumed unrelated. In the CCLONES population, the association model included 6 groups in the X matrix, 2,182 SNP genotypes in the Z mat rix and an error matrix consisting of a
77 consanguinity matrix containing the genetic relatedness across all pairwise comparisons among clones. For the combined dataset, the matrices for the NCSU and CCLONES association matrices were concatenated. This res ulted in that the X matrix consisted of the sum of the groups for each population (11 groups), with no shared population structure between the clones from CCLONES and NCSU. The Z matrix was composed of those SNPs that were common to both populations, which consisted of available genotypic data for 1,919 SNPs. For this combined dataset, the error matrix consisted of a block diagonal identity and consanguinity matrices with zero matrices off -diagonal. Because it was uncertain whether there were any relatedn ess between clones in the two populations, the SNP genotypic data for each clone was concatenated and pairwise comparisons of the resulting sequences were performed to estimate the relationships among all clones. This analysis revealed the proportion of s hared alleles across clones. A subset of this output was evaluated in full -sib and half -sib families, as well as unrelated individuals from the validation population and in a subset of individuals from the association population. The proportion of shared alleles was plotted according to degree of relatedness (full -sib, half -sib, and unrelated), showing that the levels of shared alleles in the NCSU population were comparable to tho se in the CCLONES population ( Fig. 3 -6 ). The number of clones that had high levels of shared alleles (i.e. comparable to full -sibs or half -sibs) was very low, suggesting that the consanguinity matrix and identity matrices can be joined and that estimations of relatedness between both populations do not seem necessary for this anal ysis. Therefore, any degree of
78 relatedness between a clone from the association population an d one from the validation population was always considered zero. Estimation of SNP effects SNP effects were estimated as described previously (QUESADA et al. 2010) Briefly t he the model used to obtain the BLUPs was used incorporating al l significant SNPs as random effects and then re -running a reduced model without SNP effects. T he difference between the 2Log Likelihood values from the two models was determined statistically significant using a chi -square test. The percentage of clonal variance explained by each individual SNP was obtained using the following formula: 2 Clone_Red 2 Clone_SNPx2 Clone_Red)*100, (3 -7) 2 Clone_Red is the clonal variance of the reduced model (without SNP effects), and 2 Clone_SNPx is the clonal variance obtained by including each individual SNP (x = 1 to 10) separately as a random variable in the model. T he percentage of the phenotypic variance accounted by the effect of each individual SNP on the clonal variance was estimated using a similar formula: 2 Clone_Red 2 Clone_SNPx2 Phenotypic)*100, (3 -8) 2 Clone_Red is the clonal variance of the reduced model (without SNP effect), 2 Clone_SNPx is the clonal variance obtained by including each SNP (x = 1 to 10) 2 Phenotypic is the phenotypic variance obtained by summing all variance components other than environmental corrections from the reduced model. The phenotypic standard deviation was obtained by taking the square root of the sum of all variance components other than environmental corrections from the r educed
79 model. The percentage of phenotypic standard deviation represented by each SNP was obtained as shown : %Std.DevPhenotypic = 100*(Mean SNP effect/Std.DevPhenotypic), (3 -9) where the mean SNP effect was obtained from BAMD. Blast Analyses o f Candi date Loci Sequences of 50 nucleotides upstream and downstream of the significant SNPs and corresponding EST contig sequences were obtained from the Dendrome database (http://dendrome.ucdavis.edu/interfac e ). A BLASTx search was performed against the entire NCBI non-redundant protein database ( http://blast.ncbi.nlm.nih.gov/Blast.cgi ) to determine whether the sequences encoded proteins with known func tion. A threshold of E 5 expect ed values was used to determine whether a sequence was similar to one previously identified. The best hits were used as reference for interpretation of putative biological functions of the EST sequences from which the SNPs were obtained. In cases where the best hits corresponded to unknown, hypothetical or predicted proteins, the best hit with a function al annotation within the threshold was considered as reference. Evaluation of Candidate Loci in Response t o Pathogen Chall enge A time -course experiment was performed using the genotypes from the NCSU population that showed extreme phenotypic response to F. circinatum challenge. The genotypes were selected according to their rankings as extremely susceptible or extremely res istant, excluding any clones that were from similar geographical regions. The plants were arranged in a randomized complete block design consisting of 12 genotypes (6 susceptible a nd 6 resistant ) arranged in 4 rows and 3 columns for each
80 replicate for a total of 4 replicates. Prior to inoculation, up to four shoot samples per plant were collected, pooled in 1.5 ml tubes and placed in liquid nitrogen, for use as uninoculated controls. Inoculations with F. circinatum microconidia (500 spores/ul suspended i n distilled water) were then conducted by clipping the tips of each shoot and placing 2 ul of spore solution directly into the wound. The plants were placed overnight in high humidity to facilitate spore germination. Up to 15 shoots per plant were inocul ated, corresponding to a maximum of 5 shoots per time point. Inoculated shoots were collected at 1, 3 and 8 days after inoculation. The shoots were pooled and placed in 1.5 ml tubes in liquid nitrogen and stored along with the controls at -80 C prior to RNA extraction. Total RNA was extracted using the CTAB method (CHANG 1993) and purified using the Qiagen RNeasy extraction kit. cDNA was synthesized using oligo-dT primers and M MLV reverse transcriptase (Invitrogen ) using 0.2 ug of DNAse-treated RNA. R eal -time PCR was performed with primers designed for the significant candidate genes common to both association analyses and for the candidate genes with unknown function observed from the Assoc iation Population. PCR reactions were prepared using 2 ul of a 1:10 dilution of cDN A, 3.125 ul of Brilliant II SYBR Green QPCR Master Mix (Stratagene) and 0.075 ul of each of the forward and reverse primers, for a reaction volume of 6.25 ul. The amplification program consisted of a denaturing step of 5 minutes at 95 C, 40 cycles of: d enaturation at 95 C for 30 seconds, annealing at 55 C for 30 seconds and extension at 72 C for 30 seconds, and a final extension step at 72 C for 5 minutes. Transcript abundance was obtained using the software MxPro and was quantified using the comparati ve Ct method (LIVAK and SCHMITTGEN 2001)
81 Results Significant Associations Observed on a Population w ith Known P edigree Verify That Resistance t o Pitch Canker Involves Multiple Genes w ith Small Effects. A previous association study using 3,938 SNP markers on an unstructured population of 404 genotypes revealed that ten significant SNPs with small effects were associated to pitch canke r resistance (QUESADA et al. 2010) The association analysis was repeated in a population with known pedigree using 2,182 SNP markers and 668 genotypes (Fig. 3 -1). The 2,182 SNP markers were pre-processed using based on the significance of additive effects (differences in the mean phenotypic values between each homozygous genotyp e). The 400 SNPs with greater additive effects were selected for association analyses using BAMD. Results revealed 9 SNPs showing significant association with pitch canker resistance (Table 3 -1). BLASTx analyses against the NCBI database of the contig s equences containing these SNPs show that all these sequences have highest similarity (best hit) to predicted or unknown proteins or have no similarity to any of the genes in the database at a cutoff value of 1xE5. To determine if the contigs used as quer ies were similar to other genes with known function we considered whether there were other sequences that had less sequence similarity but had a known function. These sequences were labeled as best known hits and helped determine the putative function o f the identified contig sequences harboring the significant SNPs (candidate genes). In this population, the candidate genes included sequence similarity to a GRAS family transcription factor, a putative glucan endo 1,3-beta-glucosidase precursor, and acid phosphatase 1 precursor, and an enhanced disease susceptibility (EDS1) gene (Table 3 1).
82 SNP effects were determined as reported previously (QUESADA et al. 2010) and showed that the mean effects ranged between 0.1131 and 0.1952 log mm, which translates to about 1.1 to 1.2 mm length of the lesion induced by F. circinatum The per centage of phenotypic standard deviation is an indicator of how much of the phenotypic standard deviation is due to SNP effects, and ranged between 7.661 and 13.228 (Table 3 -2). The effect of the SNP on the clonal variance ranged from values near zero to 10.7%. The near zero values observed in two of the SNPs may be due to large differences in genotypic frequencies between the homozygous classes (data not shown), which suggests little contribution to the clonal variance. The SNP effects observed here were small but were overall slightly higher than those observed in an unstructured population (QUESADA et al. 2010) It is important to note that the significant SNPs observed here were different to those observed by Quesada et al. (2010) possibly because not all SNPs previously analyzed in the unstructured population were represented after pre processing using the data from this population wit h known pedigree. These results, however, validate previous observations that there are multiple genes with small effects involved in quantitative resistance to pitch canker. A Combined Dataset Comprised o f Individuals from Two Populations Confirms Identified Significant S NP s. A combined dataset comprised of two populations used previously in association analyses was used to increase the power of detection of significant SNPs. These two populations are the association population with unknown structured analyzed by Quesada et al (2010) and the population with kn own pedigree analyzed in this study. Genotypic information for each population had been obtained separately, so only the 1,919 SNPs common to both populations were used in this study (Fig. 3 1). A total of
83 417 SNPs were selected for association analyses and consisted of the top 250 SNPs obtained from among the 400 selected after pre processing for each individual population. Association analyses using the combined population revealed 25 significant SNPs at p<0.05 (Table 3 -3). Of these, two were signifi cant in the unstructured population and in the population with known pedigree (Fig. 3 -2). An additional SNP that was significant in the unstructured population was also significant in the combined population at p=0.06. The genes containing these three SN Ps from the association population were later used to test for changes in transcript abundance after pathogen challenge. A BLASTx analysis of the contig sequences (cutoff value of 1XE5) containing the significant SNPs observed in the combined dataset reve aled that the majority (17 SNPs) had highest similarity to unknown or predicted genes. A smaller number of significant SNPs (6) had no similarity to any of the sequences in the NCBI database, whereas only three SNPs had highest similarity to genes with kn own function (Table 3 3). To determine the putative functions of the genes with unknown function, sequences within the cutoff value that did not have the highest similarity to the query sequences were considered. These included transcription factors, prot eins involved in signaling, DNA binding proteins and cell metabolism, among others (Table 3 3). Some of these proteins, such as a lectin-like protein kinase ( 0_15227_01_159 ) or an ABA-responsive protein ( 2_4841_01_597) constitute known components that par ticipate in disease or stress response; others may participate indirectly or be the result of the effect of pathogen challenge. These results validate some of the significant SNPs previously observed in each of the two populations studied individually and show that a large
84 number of genes participate in response to pathogen challenge. The larger number of significant SNPs obtained in the combined dataset with respect to each population analyzed separately could be the result of increased power of detection as more individuals were included. Transcripts of Disease Resistance Candidate Genes Respond to P athogen C hallenge. Three candidate genes that showed significant associations with pitch canker resistance in the NCSU population were analyzed to determine whether their transcripts were pathogen responsive (Fig. 3 -2a). Changes in transcript abundance were evaluated on a subset of highly susceptible and resistant genotypes (tails) across four time points: Before inoculation (0 days), and 1, 3 and 8 days aft er inoculation (DAI). Quantitative real -time PCR data indicated that, although there were no significant differences between the resistant and susceptible tails, all candidate genes showed changes in transcript abundance in at least one of the time points suggesting that these genes were pathogen responsive (Fig. 3 -3 ). The chitinase transcript previously shown to be pathogen responsive (MORSE et al. 2004) was used here as a positive control for transcript regulation, not for association with pitch canker resistance. Transcripts of all three candidate genes show ed significant shifts in abundance in response to pathogen challenge in both resistant and susceptible tails A candidate gene similar to a lectin -like protein kinase showed no significant differences in transcript abundance between resistant and suscepti ble genotypes, but was characterized by reduced expression at 1 DAI, compared to its levels before inoculation. Transcript abundance then recovered at 3 and 8 DAI in the resistant clones and increase in expression at 8 DAI in the susceptible tail. An app arently taxonomically
85 restricted gene (i.e., no similarity to any known sequence ) showed a significant increase in transcript abundance at 3 DAI, and the patterns of transcript abundance were similar between the susceptible and resistant tails (Fig. 3 3 ). Transcripts of a predicted hexose transporter increased at 3 and 8 DAI (Fig. 3 3 ). Changes in transcript abundance of resistant and susceptible clones were considered individually within their corresponding group. When transcript abundance was observed in the susceptible clones, the response to pathogen challenge across time appeared more homogeneous among clones (Fig. 3 4), suggesting that possible changes in gene expression are consequence of similar response mechanisms. In contrast, the clones in th e resistant tails showed more dynamic levels of transcript abundance, not only across clones, but also across time points. C hanges in transcript abundance in the lectinlike protein kinase (0_15227), for example, showed a number of clones with higher tra nscript levels prior to inoculation, which decreased upon exposure to the pathogen. However, other clones showed low transcript levels prior to inoculation, but these later increased after 3 and 8 days of pathogen challenge. While it is difficult to draw overarching conclusions, these results imply that resistant clones may exhibit temporally diverse responses to pathogen challenge, some of which might be triggered or regulated indirectly by other genes, thus reflected in the variability of the response. Discussion In a previous association study involving an unstructured population of loblolly pine (QUESADA et al. 2010) we found that resistance to pitch canker involves multiple genes with small effects, validating the quantitative nature of this trait (KAYIHAN e t al. 2005)
86 Association studies in a population with known pedigree (CCLONES) also revealed that multiple genes with small effects are involved in resistance to pitch canker in loblolly pine. However, though small, the average SNP effects were nearly t hree times as large as the average SNP effects observed previously in the association (NCSU) population. Heritability values observed for both populations were considered high (KAYIHAN et al. 2005; QUESADA et al. 2010) The presence of multiple genes with small effects contributing to the phenotype suggests that such effects are expected to be additive, which highlights the importance of identifying the causative genes, particularly for incorporation in breeding strategies Population structure is one of the factors that may influence the detection of a SNP and the estimation of its effects. In the case of the NCSU population, the effect of population structure is limited primarily to geographical provenances, where shared alleles are more likely to be due to state than to descent. In the CCLONES p opulation, family structure is an extra factor in population structure that may not be fully adjusted by the consanguinity matrix. Another factor that may inflate apparent SNP effects in CCLONES relative to the NCSU population is the comparatively slow dec ay of LD in CCLONES due to small number of recombination events between causative and linked loci. It is commonly observed in association studies that more structured populations tend to overestimate the number of significant SNPs and their effects (BEAVIS 1998) The detection of multiple significant SNPs and the estimated magnitude of their effects in this study, as well as in the study performed by Quesada et al ., (2010) supports the infinitesimal model theory (BUCKLER et al. 2009; FLINT and MACKAY 2009) in which quantitative traits typically involve the a ction of a very large quantity of genes
87 with very small effects. This type of genetic architecture is common among plant responses to necrotrophic pathogens, often complex and involving a large number of genes (BAL INT-KURTI et al. 2008; BUCKLER et al. 2009; ROWE and KLIEBENSTEIN 2008; WISSER et al. 2006) Genomewide studies on the effects of genes involved in quantitative traits on model organisms, such as maize have shown that the abundance of genes with large e ffects is very low (WISSER et al. 2006) It is important to mention that the of SNPs evaluated in this study were selected based on quality of reads and were chosen for intermediate frequency, thus constituting a subset of the total number of possible SNP markers that c an be potentially tested in the pine genome (Fig. 3 1). Therefore, the hypothesis that one could discover genes with large effects, and/or discover severe alleles with large effect within loci already genotyped here, should not be discarded. Association studies in other Pinaceae have also reported lack of evidence for major genes (ECKERT et al. 2009a; GONZALEZMARTINEZ et al. 2008; GONZALEZ-MARTINEZ et al. 2007) but such observations might change as increased gen ome coverage is incorporated in future studies. The NCSU and CCLONES populations, when analyzed separately, identified nonidentical sets of significant SNPs, possibly due to several factors : First, there is a difference in the number of SNPs that segregate in the two populations, with NCSU having nearly twice the number of SNPs considered as high quality calls (and therefore used in association testing) than CCLONES (3,938 vs. 2,182), and this could have affected the outcome of the pre processing and asso ciation analyses. Second, differences in plant growth conditions (i.e. location, watering regimes, and inoculation periods) could have had an effect on the phenotypes in each population. Although the
88 pathogen genotype was identical in the two studies, th e genotype by (macro) environment component (i.e., the underlying resistance loci that affected symptom expression in one screen differently than the other screen) was not explicitly modeled here. Our focus on the SNPs common to both studies presumably identified those that underpin resistance in both screens. The goal of a combined NCSU and CCLONES population analysis linked by common SNPs was to increase power to detect significant associations. Using this combined dataset, we were able to confirm a number of significant SNPs from each of the individual populations. The validation of these SNPs gives support to the possibility of these SNPs being true associations, corresponding to genes that are likely involved in response to pathogen challenge. The changes in transcript abundance of three of the significant SNPs identified in the association (NCSU) population and validated in the combined dataset supported the hypothesis that these genes are pathogen responsive. Additionally, we observed that patterns of change in transcript abundance in the resistant clones were more dynamic than those in the susceptible clones, suggesting that there may be multiple pathways by which resistant genotypes respond to pathogen challenge. There is evidence that genetic bac kground may affect the ability of resistance genes to confer resistance to biotrophic and necrotrophic pathogens. For example, rice resistance to bacterial blight incited by Xanthomonas oryzae depends on the presence of multiple genes that alter the effect iveness of the resistance gene Xa3/Xa26 (ZHOU et al. 2009) The vast majority of studies that evaluate differential expression of defense -related genes in situations where resistance i s quantitative focus on contrasts among a small number of susceptible and resistant lines (GHOSE et al. 2008; KONG et al. 2007; ZHAO et al. 2009)
89 Our results suggest that comparison of many lines reveals temporal complexity in host transcriptional responses that preclude simple categorization as resistant and susceptible response types. BLASTx analyses of contig sequences containing significant and validated SNPs revealed a lectin -like protein kinase (KANZAKI et al. 2008; NISHIGUCHI et al. 2002) a hexose transporter (HAUSLER et al. 2000; WEBER et al. 2000) and an apparently taxonomically restricted protein. Lectin like protein kinases are a class of proteins that have a similar structure to other receptor like kinases that typically contain en extracellular domain is similar to legume l ectins (HERVE et al. 1996) Although biological func tion of LRKs is still obscure, it has been suggested that they could be involved in carbohydrate binding or in the recognition of hormone ligands, like auxins or cytokinins (BARRE et al. 2002) LRKs may also function as linkers between t he plasma membrane and cell wall in Arabidopsis (GOUGET et al. 2006) and have been shown to be abundant in response to mechani cal wounding (NISHIGUCHI et al. 2002) Recently, a LRK from Nicotiana benthamiana was shown to interact with the Phythophthora infestans INF1 elicitor, suggesting that it might be part of a receptor complex that recognizes the pathogen elicitor, triggering a downstream hypersensitive response (KANZAKI et al. 2008) Whether a similar function may hold true for the candidate gene detected in this stud y remains to be determined. A possible hypothesis could be that a RLK might be involved in pathogen recognition, but may trigger different signals involved in resistance to necrotrophic pathogens. The putative role in disease resistance of another of the candidate genes detected in this study the hexose transporter, is not clear. The candidate gene sequence had
90 high similarity to a hexose transporter described by Weber et al. (2000) where it appears to mediate the export of glucose from the chloroplast as a result of starch breakdown during the night Sugar transporters appear to participate in plant microorganism interactions (HALL and WILLIAMS 2000), where induction of monosaccharide transporters such as AtST P4 (MST) in Arabidopsis upon necrotrophic and biotrophic fungal infections have been reported (FOTOPOULOS et al. 2003; HALL and WILLIAMS 2000; TRUERNIT et al. 1996) It appears that in the case of AtSTP4(MST) such induction occurs in response to higher demand for carbohydrate by cells under stress (TRUERNIT et al. 1996) though the metabolic pathways involved are unknown. The role of a hexose transporter in response to pat hogen challenge remains to be tested but could involve the possibility of a response to stress due to pathogen infection. Our association analyses had been previously performed using a preprocessing method that was based on additive effects, which assum es that heterozygotes have intermediate phenotypic values with respect to the corresponding homozygous classes. However, this situation is not always the case for some genes, which may exhibit non additive effects, such as dominance. We therefore assayed an alternative approach where SNPs were pre processed based on their effects on clonal variance. Our observations showed that this alternative method may yield a different set of associations, but also validates a number of SNPs that were previously significant using the pre-processing methods based on additive effects. Though many studies on quantitative trait loci (QTL) are focused on their additive effects, there are a few examples of animal breeding studies describing QTLs with nonadditive effects (KUEHN
91 2007; VAN LAERE et al. 2003) Therefore, the use of this pre -processing method could help detect SNPs linked to quantitative traits with non additive effects. It is important to mention that both preproces sing methods are not mutually exclusive and their combined use could help increase the possibility of candidate gene detection. Pitch canker affects a broad range of pine species with different levels of susceptibility (ENEBAK and STANOSZ 2003; HODGE and DVORAK 2000; STORER et al. 1999) Studies on Pinus patula have shown evidence of provenance x isolate interactions, suggesting specificity in pitch canker resistance (HODGE and DVORAK 2007) The evaluation of multiple fungal isolates on the loblolly pine population would provide insights on whether there is evidence for isolate-specific resistance l oci (Poland et al. 2009) in the pitch canker pathosystem. These isolate-specific resistance loci, if they exist, would be more easily overcome by the pathogen population and are therefore important to identify in order to guide deployment of families or cl ones with durable resistance.
92 Table 3 1. SNPs significant for associations to pitch canker resistance obtained in a population with known pedigree. The p utative functions of genes with high similarity to the contig sequences containing these SNPs were obtained by BLASTx analyses against the entire NCBI database. SNPs were considered significant at 95% confidence. Sequences with highest similarity to the query sequences were considered best hits at a threshold e value of 1E 5. If a sequence with less er similarity within the threshold had a known function, it was included as a "best known hit". Number in parentheses correspond to the expect value (evalue). SNP_ID Best Hit Best Known Hit 0_12940_01_316 predicted protein [Populus trichocarpa] (1E 42) predicted protein [Populus trichocarpa] (1E 42) 0_2222_01_85 hypothetical protein [Vitis vinifera] (2E -63) GRAS family transcription factor [Populus trichocarpa] (4E 61) 2_4655_02_198 unknown [Picea sitchensis] (1E -34) Glucan endo-1,3 beta glucosidase pr ecursor, putative [Ricinus communis] (3E 32) 2_9087_01_114 No Hits Found No Hits Found CL1765Contig1_04_583 predicted protein [Physcomitrella patens subsp. patens] (5E 45) Acid phosphatase 1 precursor, putative [Ricinus communis] (3E 42) CL61Contig1_05 _102 unknown [Picea sitchensis] (4E -92) EDS1 (enhanced disease susceptibility 1) [Nicotiana tabacum] (3E 33) UMN_2993_01_167 No Hits Found No Hits Found UMN_3264_02_312 No Hits Found No Hits Found UMN_5166_01_35 No Hits Found No Hits Found
93 Table 3 2 Effects of SNPs significant for associations to pitch canker resistance observed in the CCLONES population SNP effects are shown as mean SNP eff ect on the phenotype (log mm), percentage of a phenotypic standar d deviation attibuted to the SNP, effect o f the SNP on the change in clonal variance and percentage of phenotypic variance due to the effect of the SNP on the clonal variance. SNP_ID Allele A Allele B Mean (log mm) 95% Confidence Interval % Phenotypic Standard Deviation (log mm) % Diff in clonal variance 2 Phenotypic due to SNP effect in clonal variance Genotypic Frequency (AA,AB,BB) n 0_12940_01_316 T C 0.1131 [0.0044, 0.2217] 7.661 3.92E 06 1.9E 06 0.09, 0.35, 0.56 622 0_2222_01_85 T C 0.1306 [0.0122, 0.2490] 8.848 0.812 0.373 0.58, 0.33, 0.09 622 2_4655_02_198 C T 0.1439 [0.0081, 0.2798] 9.752 2.071 0.951 0.61, 0.32, 0.07 624 2_9087_01_114 C G 0.1777 [0.0647, 0.2906] 12.039 5.750 2.640 0.39, 0.5, 0.11 622 CL1765Contig1_04 _583 G A 0.1952 [0.0596, 0.3309] 13.228 2.436 1.118 0.76, 0.2, 0 .04 625 CL61Contig1_05_ 102 G T 0.1611 [0.0304, 0.2918] 10.916 2.800 1.285 0.34, 0.48, 0.18 542 UMN_2993_01_ 167 T G 0.1578 [0.0338, 0.2817] 10.690 4.19E 06 1.92E 06 0.41, 0.46, 0.13 621 UMN_3264_02_ 312 G A 0.1139 [0.0013, 0.2264] 7.715 0.284 0.130 0.1 4, 0.05, 0.81 571 UMN_5166_01_35 A C 0.1489 [0.0352, 0.2626] 10.089 10.703 4.914 0.47, 0.4, 0.12 614
94 Table 3 3. SNPs with significant associations for pitch canker resistance obtained in a combined dataset of two populations and the putative function of the genes containing them. SNPs were considered significant at 0.05 significance. SNP_ID Best Hit Best Known Hit 0_10373_01_711 PREDICTED: hypothetical protein [Vitis vinifera] (3E 61) root hair defective 3 GTP binding (RHD3) family protein [Arabidops is thaliana] (3E 59) 0_11424_01_73 unknown [Picea sitchensis] (5E 69) unknown [Picea sitchensis] (5E 69) 0_11563_01_41 unknown [Picea sitchensis] (5E 14) unknown [Picea sitchensis] (5E 14) 0_12862_01_98 unknown [Picea sitchensis] (1E 62) unknown [Pic ea sitchensis] (1E 62) 0_13657_02_45 PREDICTED: hypothetical protein [Vitis vinifera] (3E 57) putative tubulin folding cofactor C [Oryza sativa Japonica Group] (9E 54) 0_14789_01_110 unknown [Picea sitchensis] (6E 63) unknown [Picea sitchensis] (6E 63) 0_1583_01_61 unknown [Picea sitchensis] (8E 57) unknown [Picea sitchensis] (8E 57) 0_16084_01_134 No Hits Found No Hits Found 0_1682_01_580 unknown [Picea sitchensis] (2E 17) inwardly rectifying potassium channel subunit [Daucus carota] (9E 10) 0_173 83_01_149 hypothetical protein [Vitis vinifera] (1E 123) ATP dependent RNA and DNA helicase, putative [Ricinus communis] (1E 121) 0_4803_01_331 alpha amylase like protein [Arabidopsis thaliana] (6E 28) alpha amylase like protein [Arabidopsis thaliana] (6E 28) 0_5832_01_96 unknown [Picea sitchensis] (5E 29) ripening regulated protein DDTFR18 [Lycopersicon esculentum] (8E 26) 0_8523_01_224 No Hits Found No Hits Found 2_10170_01_821 unknown [Picea sitchensis] (1E 100) unknown [Picea sitchensis] (1E 100) 2_2810_01_71 No Hits Found No Hits Found 2_4484_02_622 unknown [Picea sitchensis] (5E 64) PREDICTED: plastid hexose transporter [Vitis vinifera] (8E 60)
95 Table 3 3 Continued SNP_ID Best Hit Best Known Hit 2_4841_01_597 unknown [Picea sitchensis] (8 E 96) GRAM domain containing protein / ABA responsive protein related [Arabidopsis thaliana] (4E 58) 2_6283_01_41 PREDICTED: hypothetical protein isoform 2 [Vitis vinifera] (6E 23) oxidoreductase, 2OG Fe(II) oxygenase family protein [Arabidopsis thaliana] (3E 22) 2_6461_01_722 hypothetical protein OsJ_13244 [Oryza sativa Japonica Group] (2E 13) hypothetical protein OsJ_13244 [Oryza sativa Japonica Group] (2E 13) 2_6470_01_26 PREDICTED: hypothetical protein [Vitis vinifera] (1E 52) protein phosphatase 2C family protein / PP2C family protein [Arabidopsis thaliana] (1E 52) 2_9087_01_114 No Hits Found No Hits Found CL14Contig4_03_193 chlorophyll a/b binding protein [Pinus thunbergii] (1E 138) chlorophyll a/b binding protein [Pinus thunbergii] (1E 138) CL43 78Contig1_02_107 SCARECROW [Pinus sylvestris] (1E 105) SCARECROW [Pinus sylvestris] (1E 105) UMN_2505_01_238 No Hits Found Protein regulator of cytokinesis, putative [Ricinus communis] (3E 07) UMN_5166_01_35 No Hits Found No Hits Found 0_15227_01_159* h ypothetical protein OsI_29868 [Oryza sativa Indica Group] (7E 37) lectin like protein kinase [Populus nigra] (1E 24) 0.06
96 Figure 31. SNP discovery and genotyping platform ( based on Eckert et al., (2010) ) showing the resulting num ber of SNPs used both loblolly pine populations. The overlapping SNPs (1919) were used for association analyses using a combined dataset of both populations. 40,000 pine EST contigs 20,500 unique EST contigs Remove duplicates 14,000 contigs with successfully designed primers Primer design 7,900 validated primer pairs Primer validation 7,424 amplicons (representing 6,924 unigenes ) Re sequencing 2019 1919 263 NCSU population: 3,938 SNP genotypes for 498 clones (404 used for association studies for pitch canker resistance) CCLONES population: 2182 SNP genotypes for 965 clones (668 used for association analysis for pitch canker resistance Genome coverageSelection SNP genotypes ** Good quality, welldefined clusters, not monomorphic minor homozygous class with more than 15 individuals (CCLONES only).
97 Figure 32. Significant SNPs observed in the association (NCSU) population ( A) or validation (CCLONES ) pop ulation (B ) and the combined dataset, using the differences in additive effects as pre-processing method, and showing common SNPs validated in both populations. The transcript levels of the contigs containing these common SNPs were analyzed in a pathogen challenge experiment on susceptible and resistant clones. 23 2 8Combined dataset (CCLONES + NCSU) Association population (NCSU) 23 2 7Combined dataset (CCLONES + NCSU) Validation population (CCLONES)A. B.
98 Figure 33. LSm eans of relative fold change values depicting changes in transcript a bundance after pathogen challeng e. Three different genes that contained SNPs significant for a ssociation with pitch canker resistance and a control gene (chitinase), known to be pathogen-responsive, were analyzed on resistant and susceptible genotypes prior to inoculation (0 DAI), and at 1,3 and 8 days after inoculation (DAI). 0 4 8 12 16 20 Chitinase 0 0.2 0.4 0.6 0.8 1 0 DAI 1 DAI 3 DAI 8 DAI Lectin like protein kinase (0_15227) 0 0.2 0.4 0.6 0.8 1 Unknown gene (0_1583) 0 2 4 6 8 10 0 DAI 1 DAI 3 DAI 8 DAI Predicted hexose transporter (2_4484)Resistant Susceptible Resistant SusceptibleLSMeans Rel. Fold Change ( 2ddCt)
99 Figure 34. Rela tive fold change values showing changes in transcript abundance in individual clones at 0, 1, 3, and 8 days after pathogen challenge. The clones were grouped in resistant and susceptible classes for comparison. Plants were inoculated with a solution of F usarium circinatum microspores (1000 spores/ microliter). 0.4 0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 1 3 8 Lectin like protein kinase (0_15227)Resistant 0 1 3 8 Susceptible 20 10 0 10 20 30 40 50 0 1 3 8 Chitinase 0 1 3 8 0.5 0 0.5 1 1.5 0 1 3 8 D f ili ih Fi ii i p Unknown (0_1583) 0 1 3 8 4 0 4 8 12 16 0 1 3 8 Predicted hexose transporter (2_4484) 121C 250C 307A 352A 448C 502C LegendsResistant 15A 277A 471B 485A 527B 636C SusceptibleRelative fold change (2ddCt)0 1 3 8 0 1 3 8Days after inoculation
100 Supplementary Data The association analyses conducted had involved a preprocessing method consisting of selecting the SNPs based on the differences in additive effects. This method assumes heter ozygotes have intermediate values between the two homozygous classes. An alternative approach is to select the SNPs for association studies based on their effects on clonal variance. Because our populations are composed of clonally propagated individuals the measure of genetic variation consists of the variance among clones (LYNCH and WALSH 1998) This approach will allow the detection of SNPs that have stronger genetic ef fects and do not exclude those in which the heterozygotes may not have intermediate values between homozygotes (overdominance, for example). To test the significance of SNP effects on clonal variance, a mixed model approach was performed on all SNPs on b oth populations separately, using SAS software (SAS version 9.2). The tested complete and reduced models were as follows: Complete model : Y ij = + Repj + snpk + faml + clone(fam)lm + eij jklmn (3 -10 ) Reduced model : Y ij = + Repj + faml + clone(fam )lm + eij jklmn (3 -11 ) Yij is the ith log-transformed mean lesion length for the for the jth replicate. is the overall mean Repj is the fixed effect replicate j = 1 to 4 snpk is the random effect SNP ~NID (0, 2 snp) clone(fam)lm is the ran dom effect clone within family ~ NID(0, 2 clone*fam) faml is the random effect family ~ NID(0, 2 fam) eij is the random variable e rror within the experiment ~ 2 etail)
101 Because the NCSU population is presumed unrelated, all family components were omitted from the model. This pre processing method tested for significance of SNP effects on the clonal variance using a mixed model approach. As in the previous case, the 400 SNPs with greatest effects were tested for significant associations. Using this pre-processing method, we obtained 18 and 8 significant SNPs in the association (NCSU) and validation (CCLONES) populations, respectively. When we performed the association analysis on the combined dataset, we observed 20 SNP s. In addition, four of these significant SNPs (2_4655_02_ 198 and 0_18461_03_ 229 from CCLONES, and UMN 139701416 and 2 -4484 02622 from NCSU) were also significant in the combined analysis ( Table 3 4 ). In general, we observed different sets of signif icant SNPs on a same population using the two pre-processing methods. Ten significant SNPs were observed in the NCSU population using preprocessing based on additive effects, whereas 18 were observed using the differences in clonal variance as pre-proces sing methods, and both populations had five significant SNPs in common ( Table 3 4 ). In the case of CCLONES, where 9 and 8 SNPs were significant with the pre-processing based on additive effects and differences in clonal variance, respectively, only one SN P (2_9087_01_114) was significant in both sets. The combined dataset revealed 25 significant SNPs using the additive effects pre processing and 20 significant SNPs when differences in clonal variance were used to select the SNPs for association analyses. Of these, four SNPs were significant in both groups ( Table 3-4 ).
102 Both preprocessing methods produced different sets of 400 SNPs that were used in association analyses. When both sets were compared for each population, it was observed that of the 400 S NPs selected using each pre processing method, 256 SNPs were common in the association population whereas only 109 were common to both pre processing methods in the validation population. The comparison of both pre processing methods in the combined datas ets revealed 184 shared SNPs ( Figure 3-7 ). The number of shared SNPs between pre processing methods could have influenced the outcome of common significant SNPs obtained from the association analyses. BLASTx analyses of contig sequences containing the sig nificant SNPs showed that a large number of SNPs had greatest similarity to genes with unknown function. The significant SNPs obtained on the CCLONES population revealed that three of the sequences had no similarity to any of the sequences in the database, whereas the remaining five were similar to predicted hypothetical or unknown proteins ( Table 3-4 ). When additional sequences with known function were included, among them were a putative transcription factor, a putative Ocs element -binding factor and a flavonoid 3 hydroxylase. In the case of the association population (NCSU), 14 significant SNPs had similarity with unknown or predicted genes or did not have hits with any sequence in the database (QUESADA et al. 2010) When best known hits were considered, several defense and stress response protein sequences, such as geranylgeranyl transferase, lectin -like protein kinase and DELLA proteins showed similarity to the query sequences used. These data are included again in Table 3 4 as reference.
103 The combined dataset generated significant SNPs from contigs with similarity to mostly u nknown or predicted proteins. All but two (an RNA -dependent RNA polymerase and a eukaryonic translation initiation factor subunit ) were either unknown or had no hits ( Table 3 -4 ). When the best known hits were considered, the sequences with highest simila rity corresponded to proteins involved in cell signaling, cell wall biosynthesis, protein synthesis and metabolism, disease response, or nucleic acid binding. Overall, unknown or predicted proteins as well as unique sequences (no hits) constituted about 80 % of all contig sequences that contained the significant SNPs from the association analyses. These percentages decreased to approximately 50% as other sequences with less similarity, but with known function, were taken into consideration. Both preprocessing methods revealed different groups of significant SNPs, mainly depending on the type of effects tested. The presence of common SNPs that were significant after association analyses were performed provides support to these SNPs in their putative roles in resistance to pitch canker. It is of importance that these preprocessing methods are not mutually exclusive and may be used as tools to detect significant SNPs under different criteria.
104 Table 3 4 SN Ps with significant associations for pitch canke r resistance observed in the association (NCSU) and validation (CCLONES) populations as well as on the combined dataset. Association analyses were per formed using SNPs selected based on the differences in clonal variance observed for every SNP. SNPs were considered significant at 95% confidence. Putative functions of the contig sequences containing each SNP were obtained using BLASTx analyses against the entire NCBI database. SNPs marked with an asterisk (*) were also significant in association analyses using pre-processing based on additive effects. Population SNP_ID Best Hit Best Known Hit Validation (CCLONES) population 0_10830_01_358 No Hits Found No Hits Found 0_13673_01_164 No Hits Found No Hits Found 0_18461_03_229 PREDICTED: hypothetical pro tein [Vitis vinifera] (5E 40) putative transcription factor X1 [Oryza sativa Japonica Group] (7E 40) 0_7512_01_312 PREDICTED: hypothetical protein [Vitis vinifera] (1E 18) Ocs element binding factor, putative [Ricinus communis] (1E 15) 0_8089_01_393 unknown [Picea sitchensis] (6E 63) flavonoid 3' hydroxylase [Vitis amurensis] (4E 38) 0_9379_01_192 unknown [Picea sitchensis] (1E 20) unknown [Picea sitchensis] (1E 20) 2_4655_02_198 unknown [Picea sitchensis] (1E 34) Glucan endo 1,3 betaglucosidase pr ecursor, putative [Ricinus communis] (3E 32) 2_9087_01_114* No Hits Found No Hits Found Association (NCSU) population 0_11598_01_82 hypothetical protein OsJ_03610 [Oryza sativa Japonica Group] (1E 12) ATRRP4; exonuclease [Arabidopsis thaliana] (9E 11) 0_11724_01_364 conserved hypothetical protein [Ricinus communis] (9E 33) conserved hypothetical protein [Ricinus communis] (9E 33) 0_15382_01_99* geranylgeranyl transferase type I beta subunit, putative [Ricinus communis] (2E 30) geranylgeranyl transfer ase type I beta subunit, putative [Ricinus communis] (2E 30) 0_1583_01_61 unknown [Picea sitchensis] (8E 57) unknown [Picea sitchensis] (8E 57)
105 Table 3 4 Continued Population SNP_ID Best Hit Best Known Hit Association (NCSU) population 0_2092_01_215 unknown [Picea sitchensis] (4E 36) putative protein kinase [Arabidopsis thaliana] (2E 30) 0_4285_01_447 amino acid carrier [Zea mays] (5E 22) amino acid carrier [Zea mays] (5E 22) 0_9288_01_370* No Hits Found No Hits Found 0_9534_01_589 putative acid phosphatase [Pinus pinaster] (1E 49) putative acid phosphatase [Pinus pinaster] (1E 49) 1_3327_01_113* No Hits Found No Hits Found 2_4484_02_622* unknown [Picea sitchensis] (5E 64) PREDICTED: plastid hexose transporter [Vitis vinifera] (8E 60) 2_717 _01_374 unknown [Picea sitchensis] (7E 18) unknown [Picea sitchensis] (7E 18) 2_945_01_77 unknown [Picea sitchensis] (1E 122) unknown [Picea sitchensis] (1E 122) CL1468Contig1_01_184 unknown [Medicago truncatula] (1E 70) unknown [Medicago truncatula] ( 1E 70) CL4277Contig1_03_163 unknown [Picea sitchensis] (5E 13) unknown [Picea sitchensis] (5E 13) CL4336Contig1_01_180* unknown [Picea sitchensis] (2E 72) nucleic acid binding protein, putative [Ricinus communis] (6E 34) UMN_1022_01_636 No Hits Found PREDICTED: hypothetical protein [Vitis vinifera] (1E 7) UMN_1397_01_416 eukaryotic translation initiation factor 3 subunit 7 [Zea mays] (8E 26) eukaryotic translation initiation factor 3 subunit 7 [Zea mays] (8E 26) UMN_4383_01_143 unknown [Picea sitc hensis] (1E 96) arginine methyltransferease [Populus trichocarpa] (4E 80)
106 Table 3 4 Continued Population SNP_ID Best Hit Best Known Hit Combined dataset (NCSU + CCLONES) 0_10048_01_60 rna dependent RNA polymerase [Populus trichocarpa] (1E 23) rna depe ndent RNA polymerase [Populus trichocarpa] (1E 23) 0_12862_01_98* unknown [Picea sitchensis] (1E 22) unknown [Picea sitchensis] (1E 22) 0_12940_01_316 predicted protein [Populus trichocarpa] 1E 42) predicted protein [Populus trichocarpa] 1E 42) 0_137 65_01_65 No Hits Found No Hits Found 0_15790_01_65 No Hits Found No Hits Found 0_16084_01_134* No Hits Found No Hits Found 0_17123_02_380 unknown [Picea sitchensis] (2E 13) TIR/P loop/LRR [Pinus taeda] (1E 11) 0_18461_03_229 PREDICTED: hypothetical protein [Vitis vinifera] (5E 40) putative transcription factor X1 [Oryza sativa Japonica Group] (7E 40) 0_18789_01_316 unknown [Picea sitchensis] (2E 76) PREDICTED: similar to Tetratricopeptide like helical [Vitis vinifera] (2E 73) 0_18837_02_194 puta tive protein [Arabidopsis thaliana] (8E 08) MAP kinase activating protein like [Oryza sativa Japonica Group] (9E 07) 1_3327_01_113 No Hits Found No Hits Found 2_4484_02_622* unknown [Picea sitchensis] (5E 64) PREDICTED: plastid hexose transporter [Viti s vinifera] (8E 60) 2_4655_02_198 unknown [Picea sitchensis] (1E 34) Glucan endo 1,3 betaglucosidase precursor, putative [Ricinus communis] (3E 32) 2_4934_03_307 predicted protein [Physcomitrella patens subsp. patens] (4E 32) COMM domain containing protein 4 [Salmo salar] (6E 19)
107 Table 3 4 Continued Population SNP_ID Best Hit Best Known Hit Combined dataset (NCSU + CCLONES) 0_10048_01_60 unknown [Picea sitchensis] (5E 24) Putative bZIP transcription factor [Oryza sativa (japonica cultivar group )] (3E 10) 2_6470_01_26* PREDICTED: hypothetical protein [Vitis vinifera] (1E 52) protein phosphatase 2C family protein / PP2C family protein [Arabidopsis thaliana] (1E 52) 2_718_03_144 No Hits Found No Hits Found CL996Contig1_03_66 predicted protei n [Populus trichocarpa] gb|EEE88822.1| predicted protein [Populus trichocarpa] (4E 47) alpha D xylosidase [Tropaeolum majus] (3E 45) UMN_1397_01_416 eukaryotic translation initiation factor 3 subunit 7 [Zea mays] (8E 26) eukaryotic translation initiation factor 3 subunit 7 [Zea mays] (8E 26) UMN_2429_01_66 No Hits Found No Hits Found
108 Figure 35 Experimental design for inoculation of loblolly pine cuttings with Fusarium circinatum spores. The experiment consisted of four replicates, laid out in the greenhouse ebbn -flow benches (one bench per replicate). Each replicate had 21 rows and 22 columns, placed on racks. Up to three columns were placed in one rack, as shown above, and spacing between racks were such that adjacent columns, whether within a rack or between a rack, were evenly spaced. 6 12 Benches on greenhouse Rack position on bench Plant (red dot) position on rack. North * Row 1, Column 1 position
109 Figure 36 Overlay plot of the proportion of shared genotypes for full-sib (red), half -sib (blue ) or unrelated (green) pairwise comparisons between individuals from a subset of CCLONES families and the NCSU population.
110 Figure 37 Venn diagrams showing the number of SNPs selected in the association (A.) and validation ( B.) populations, as well as the combined dataset (C .) using two pre processing methods and the shared SNPs between the two methods. The set containing SNPs obtained by the preprocessing method based on additive effects is shown in grey, whereas the set with SNPs obtained through pre-processing based on the differences in clonal variance is shown in white. 144 256 144 291 109 291 224 184 233 A. B C.Association (NCSU) population Validation (CCLONES) population Combined dataset (NCSU + CCLONES)
111 CHAPTER 4 CONCLUSIONS In this project, the overall goal was to better understand the genetic architecture of pitch canker resistance in loblolly pine and identify genes associated with this trait. The mechanisms of quantitative disease resistance are poorly understood; therefore, knowl edge of the genetic architecture is a first step to determine how resistance is achieved. With respect to resistance to pitch canker, a previous study (KAYIHAN et al. 2005) had shown in a population with known pedigree that this trait is quantitative and heritable. In the first section of this work (Chapter 2), I performed inoculation experiments on a population with unknown structure (NCSU population). The results obtained validated t hose observed by Kayihan et al. (2005) by which resistance to pitch canker is quantitative and heritable. Also in the first section of this work, I performed associati on analyses on 404 clones of the NCSU population using 400 SNPs selected from a total of 3,938 based on their additive effects. This was done to test the hypothesis that significant associations for pitch canker resistance could be detected in an unstruct ured population. Association analyses revealed that pitch canker resistance involves the action of multiple genes with small effects, as 10 significant SNPs were observed, each producing small changes in the clonal and phenotypic variances. Similar case s of quantitative disease resistance have also shown that multiple genes with small effects are involved (FLINT and MACKAY 2009; GLAZEBROOK 2005) and the presence of major genes is quite rare (DANGL and JONES 2001; JOHAL and BRIGGS 1992) In the second part of this work (Chapter 3), I performed association analyses on a population with known pedigree (CCLONES population) to validate the observations
112 previously obtained th at pitch canker resistance involves many genes with small effect. Association analyses yielded 8 significant SNPs, none of which had major effects, thus validating the results obtained on the NCSU population. In addition, a combined dataset consisting of both populations was also used for association analyses. Increasing the number of individuals in the association analysis should increase the power of detection of significant associations. This analysis yielded 25 significant SNPs, of which three were also significant in the NCSU population at (or near) 95% confidence in the CCLONES population. Also in the second part of this work, it was hypothesized that if the genes containing the significant SNPs from the association analyses were involved in diseas e resistance, they would be pathogen responsive To test this, I examined whether there were significant differences in transcript abundance over timebetween resistant and susceptible genotypes. The three candidate genes containing the significant SNPs co mmon to the NCSU population and the combined dataset were selected for quantitative real time PCR analysis. Although no significant differences were observed between resistant and susceptible genotypes, it was shown that the genes containing significant S NPs were pathogen responsive. Based on the results of this work, I summarize my findings as follows: Contribution to Current Knowledge on Pitch Canker Resistance Pitch canker affects different pine species with several degrees of severity (BARROWS-BROADDUS and DWINELL 1983; CORRELL et al. 1991; KUHLMAN and CADE 1985) and also there are differences in tolerance within a same species (BARROWSBROADDUS and DWINELL 1984; ENEBA K and STANOSZ 2003; HODGE and DVORAK 2000;
113 KAYIHAN et al. 2005; STORER et al. 1999) Differences in susceptibility among full -sib and half -sib families suggested that resistance to pitch canker might have a genetic component (BARROWS-BROADDUS and DWINELL 1984) later shown ing that this trait is quantitative and heritable (KAYIHAN et al. 2005) The results presented in this work validate the quantitative nature of pitch canker resistance, where multiple genes with small effects appear to contribute to this trait, given the number of significant associations found. The susceptible and resistant clones identified in the NCSU population could also be taken into consideration as part of a genetic pool to design breeding strategies aimed to improve the quality of current stands of loblolly pine. Contribution to Association Genetics i n Loblolly Pine This study involves the detection of marker -trait associations in a natural population of loblolly pine where individuals are assumed unrelated. I performed association analyses on 404 loblolly pine genotypes using 3,938 SNP markers selected from all available EST markers (ECKERT et al. 2010) rather than from a candidate gene approach (GONZALEZMARTINEZ et al. 2008 ; GONZALEZMARTINEZ et al. 2007) This more general approach can be comparable to a genomewide analysis with a subset of markers which allows the detection of new putative candidate genes that have not been previously described, for example, taxonomical ly restricted genes that may be only found in gymnosperms or in the Pinaceae. To the best of my knowledge, the magnitude of the number of genotypes and markers used in this study is one of the largest reported for an association analysis only surpassed b y association studies in maize, where 8590 markers and 553 elite inbred lines were used (BELO et al. 2008)
114 The use of an unstructured population obtained from stands within the natural range of loblolly pine offers a unique perspective that contrasts with domesticated species. Domesticated model species, such as maize or rice have undergone multiple generations of selection and breeding for a set of desirable traits (LI et al. 2006b) (LI et al. 2006a; VOLLBRECHT and SIGMON 2005) thus losing a significant portion of their natural genetic variation. In contrast, loblolly pine retains most of its natural diversity, allowing the marker -trait associations to be tightly linked, as linkage disequilibrium decays rapidly (GONZALEZMARTINEZ et al. 2007; NEALE and SAVOLAINEN 2004) The use of the BAMD software for association analyses allowed the simultaneous solution of all SNPs using a Bayesian approach (GOPAL et al. 2009; LI 2008) This approach incorporates multiple imputations for missing genoty pic data and also solves the problem of multicollinearity which is created when large numbers of markers are used with relatively fewer observational units; for example, the use of thousands of SNPs and only a few hundred clones (GOPAL et al. 2 009; LI 2009) BAMD also adjusts for population structure and SNP effects as genetic components of the phenotype, and adjusts for family relationships as part of the environmental effects in an additional matrix (GOPAL et al. 2009; LI 2008) This work adds to a series of association analyses that have been performed in l oblolly pine and other gymnosperms on traits, such as carbon isotope discrimination and wood property traits in loblolly pine (GONZALEZMARTINEZ et al. 2008; GONZALEZMARTINEZ et al. 2007) cold hardiness in Dougla s fir (ECKERT et al. 2009a) and environmental conditions in loblolly pine (ECKERT et al. 2010) Because the loblolly pine association analyses were performed on the same populations, an interesting approach
115 using multi trait analyses could contribute to current knowledge of the b iological and physiological role of associated genes, as well as to the applied selection of desirable traits. Contribution t o Cur rent Knowledge on Quantitative Disease Resistance Quantitative disease resistance typically involves multiple genes with sma ll effects (Kover and Caicedo, 2001; Poland et al., 2009; (FLINT and MACKAY 2009; GLAZEBROOK 2005) with few exceptions (FU et al. 2009; FUKUOKA et al. 2009; KRATTINGER et a l. 2009) as commonly occurs with plant resistance to necrotrophic pathogens (GLAZEBROOK 2005) In the case of loblolly pine resi stance to pitch canker, this trait was shown to be quantitative (KAYIHAN et al. 2005) and was demonstrated in this study to involve multiple genes with small effects using association analyses in an unstructured population (NCSU). Further association analyses on a population with known pedigree (CCLONES) validated the previous observations on pitch canker resistance in the NCSU population. Three of the significant SNPs that were observed in the NCSU population were also significant in a combined dataset comprised of the NCSU and CCLONES population. The putative function of the genes containing these three SNPs was inferred based on BLASTx analyses against the entire NCBI database. One of the genes had no known function, one corresponded to a lectin-like protein kinase and the third was a hexose transporter. The gene with no known function illustrates the utility of evaluating SNPs in all genes regardless of their annotation, since this gene has now been implicated for the first time in disease resistance. Lectin -like protein kinases are known to participate in plant basal defense, conferring resistance to plant pathogens (CHEN et al. 2006; KANZA KI et al. 2008; NISHIGUCHI et al. 2002)
116 apparently through putative interactions with pathogen elicitors (KANZAKI et al. 2008) Since they are typically involved in signal transduction as well as recognition, it seem reasonable that the lectin -like protein kinase may recognize one or more Fusarium effectors and initiate a signaling cascade leading to downstream gene expression. Its own promoter may be a negatively regulated target of the transcriptional cascade, since its transcript is reduced after inoculation. Hexose transporters are involved in carbohydrate metabolism, which may suggest that allelic differences at this locus exert differential effects on host carbon metabolite availability for Fusarium during d isease development. In addition, hexose transporters may participate in pathogen response through sugar sensing and signaling through hexokinase (HERBERS et al. 1996; HERBERS et al. 1995; YOSHIDA et al. 2002) whic h may represent a genetic link between sugar availability and pathogen -induced transcriptional cascades in the host All three of the genes containing these three significant SNPs were pathogen responsive, as evidenced by changes in transcript abundance using quantitative real time PCR. These observations implicate these three genes as playing roles in disease resistance or their involvement in pathogen response because the majority of genes are not typically pathogen responsive about 1-2% of transcri pts are normally responsive to pathogens in whole-transcriptome surveys. Future studies are required to dissect the basis of the transcript regulation and to perform transgenic tests of their function in disease resistance. Challenges and Considerations f o r Future Research More markers are needed. In this work, 3,938 SNP markers that segregated and were high quality, were used to perform association analyses for pitch canker resistance
117 in loblolly pine. Despite the large number of markers, which randomly cover the entire linkage map of loblolly pine, they represent only a fraction of the available 23,000 SNP markers across all populations of this species (ECKERT et al. 2010) Furthermore, the complete number of genes in loblolly pine is not known, nor is the number of allelic variants. Because of these uncertainties the results of this work do sample the entire genome and therefore do not provide a complete perspective of the mechanisms involved; however, they provide valuable information on the genetic architecture and on the putative genes associated with pitch canker resistance. In this study, no genes with large or intermediate effects were found. Although the results presented here agree with what has been previously reported for resistance to necrotrophic pathogens, it is possible that major genes were not detected simply because there were no SNPs markers represe nted in this study. However, the current new sequencing technologies (BRASLAVSKY et al. 2003; MARGULIES et al. 2005; SHENDURE et al. 2005) and Applied Biosystems SOLiDTM technology that have become available in t he past few years obtaining better genome coverage will soon be attained. Moreover, the sequencing of the loblolly pine genome is currently under way which would not only open the possibility of genome wide association mapping, but also it will be possi ble to perform comparative analyses with available sequenced angiosperm genomes. This could provide insight on evolutionary aspects of disease resistance between the taxa, along with other traits of interest. To further characterize this pathosystem, dif ferent pathogen isolates should be tested, and field validation is required This study constitutes an initial approach to study disease resistance in a large, diverse population, as well as on a population with
118 known pedigree. The results shown here wer e generated under controlled greenhouse conditions, using a single fungal isolate for both populations. However, field trials may be necessary to validate the results observed in this study. Currently, there are field plantations of replicates of the CCLONES population and, more recently, stands of the NCSU population have also been planted in locations such as Florida, Georgia and Texas (G.F. Peter, personal communication). Although the purpose of these field trials is mainly to collect data on other tr aits such as height, diameter and plant architecture, it would be relatively straightforward to identify possible pitch canker outbreaks and validate the susceptible and resistant clones identified in this study. Additionally, evaluations under greenhouse conditions using different pathogen isolates could be used to obtain rank correlations of the current susceptible and resistant genotypes. Transgenic validation should be feasible. Reverse genetic analysis of these candidates will be quite interesting. Although time -consuming, technically difficult and beyond the scope of this work, a collaboration established with ArborGen (M. Kirst, personal communication) should enable the construction of transgenes with constitutive promoters and RNAi constructs that would be expected to function as strong alleles. This may enable phenotypic validation of gene function. Association testing should be extended. Finally, the genotypic data currently available for the CCLONES and NCSU populations, as well as the implem entation of BAMD are part of a useful platform to conduct association studies on other traits that may be genetically correlated with pitch canker resistance, and for other species that segregate for resistance to pitch canker. There is a growing quantit y of phenotypic data collected on traits like height, diameter, crown architecture, wood composition, and
119 disease resistance, within cooperatives focused on loblolly pine. Association analyses can be a comparative platform to identify the basis of potential pleiotropic effects of pitch canker resistance genes within loblolly pine, specifically to evaluate if genes for pitch canker resistance are also involved in other traits. Furthermore, because pitch canker is a disease of many different conifer species, the identification of SNPs associated with resistance outside of loblolly pine (where there is even greater unrecorded historical recombination than within the species) would be a powerful validation of causative loci.
120 APPENDIX A COMPARATIVE ANALYSIS OF THE TRANSCRIPTOMES O F POPULUS TRICHOCARPA AND ARAB IDOPSIS THALIANA SUG GESTS EXTENSIVE EVOLUTION OF GENE EXPRESSION REGULATION IN ANGIOSPERMS2 I ntroduction The sequencing of the first woody perennial plant species, Populus trichocarpa (Torr. & Gray ex Brays haw) (TUSKAN et al. 2006) creates opportunities for novel comparative genomic studies in plants Poplar is a model -species for tree genetic and genomic research(BRADSHAW et al. 2000; BRUNNER 2004; TAYLOR 2002) because of its relatively small genome size (480 Mbp), ease of genetic transformation and vegetative propagation, and abundant natural genetic variation. With the release of the first draft of the genome sequence, novel genomic tools such as whole genome microarrays have become available, providing the first opportunity for analysis of the transcriptome of a woody species and comparison to the well -studied plant model Arabidopsis thaliana. The relative phylogenetic prox imity to A. thaliana makes Populus valuable to compare the plant architecture, development and life history of a woody perennial relative to an annual herbaceous species. Populus and A. thaliana share a large common set of genes (~ 90%, (TUSKAN et al. 2006) ), suggesting that transcriptional regulation plays a significant role in the morphological and developmental differences that distinguish the two species. Differential regulation of gene expression, rather than the creation of novel transcriptional units, has been implicated in the wide diversity observed in animals (BALTIMORE 2001; KING and WILSON 1975; LEVINE and TJIAN 2003) The same mechanism may be important in plants, judging from the high sequence 2 Reprinted with permission from Quesada et al., 2008 New Phytologist; 180: 408 420
121 similarity and contrasting developmental and morphological traits between distantly related plant species such as A. thaliana and the conifer Pinus taeda (KIRST et al. 2003) Gene duplication, cis -regulatory element s and protein interaction complexes that modulate gene expression may create the opportunity for the differential regulation of a common set of genes and, as a consequence, the evolution of the woody, perennial habit in Populus Populus and other perennial woody plants have improved supportive and soluteconductive vegetative structures that arise through secondary growth of the cambial meristem. The aerial support of leaves by the woody stem creates a competitive advantage for light, while the perennial gr owth of the root system may condition a greater potential to explore soil for water and nutrients. Perennial species also undergo intermittent periods of cambial and shoot apical dormancy associated with seasonal and other environmental condition changes, presumably as a stress avoidance adaptation. Shifts in programs of gene transcription have been implicated in the development of the woody stem, including cell differentiation and lignin biosynthesis (PAUX 2005; SCHRADER 2004) auxin -stimulated cell signaling (MOYLE et al. 2002) ; synthesis, transport and remodeling of structural carbohydrates (ASPEBORG et al. 2005; MELLEROWICZ 2001; SAMUELS 2002) and programmed cell death (NEILL 2005) However, the transcription regulation of genes implicated in essential physiologic al processes would be expected to remain conserved. Genomewide assessment of the transcriptome could aid in explai ning the molecular basis of the variation in plant growth, development, environmental response and, ultimately, adaptation and evolution (DOEBLEY and LUKENS 1998; PURUGGANAN
122 2000; TAUTZ 2000; WRAY et al. 2003) Gene expression analyses using whole-genome microarrays provides a time defined snapshot of genes that are expressed in specific plant organs and growth stages. Recently, microarray based whole genome surveys of genes expressed in A. thaliana and rice have become available (MA 2005; MA et al. 2005; SCHMID et al. 2005) permitting a comparison of the expres sion of orthologs on a whole genome scale. Thus, the analysis of the Populus vegetative transcriptome could provide evidence of what genes are expressed throughout the genome, and define those with significant role s in processes unique to trees, such as th e development of woodiness and the perennial habit. A comparative analysis could also define the orthologous genes for which transcript abundance has diverged dramatically among species (implying functionalization) as well as those genes on which selection has acted to maintain transcript abundance (implying physiological relevance) in angiosperms. Here we report a detailed whole-genome survey of the genes transcribed in the vegetative organs of the woody plant P. trichocarpa. A compendium of genes expresse d in five vegetative organs of Populus was created by analyzing their expression in whole genome microarrays representing the majority of the 45,555 predicted transcriptional units. The analysis identified the woody stem as the vegetative organ with the gr eatest variety of expressed genes, but also the one with the highest proportion of uncharacterized transcripts. New statistical approaches were developed and implemented to determine whether adjacently expressed genes exhibit significan t deviations from random chance. Comparisons of expression between Populus and Arabidopsis orthologs showed very little conservation in expression based on rank
123 correlations. However, exceptions may identify conserved physiological mechanisms in plant species. Materials and M ethods Plant Material Greenwood cuttings of P. trichocarpa reference genotype Nisqually 1 were rooted in a misthouse for two weeks. Four r ooted cuttings (cloned biological replicates) were planted in separate pots in a greenhouse equipped with an ebband-f low flood bench system with daily supply of Peters Professional 20 -10 -20 water -soluble fertilizer diluted to a final concentration of 4 mM nitrogen. After 45 days, whole -roots (R), young leaves (YL) leaf plastochrone index (LPI) 05, mature leaves (ML) LPI 6-9, nodes (N) and internodes (IN) were collected from each of the four biological replicates, and immediately frozen in liquid nitrogen. RNA was extracted using standard methods (CHANG 1993) DNAse -treated and purified in RNAeasy Qiagen columns (Valencia, CA). The P. tr ichoca rpa P. deltoides hybrid genotype H1111 used for a comparison of transcript abundance between poplar species was grown under the same conditions as those described above. Mature leaves, whole-roots and stems (nodes and internodes) were collected 45 days after rooting. Real -Time PCR Total RNA was treated with RNAseusing a mixture of 500ng oligo dT, 100ng random primers, and M MLV RT (Invitrogen). Gene expression was analyzed using the SYBR Green kit (Stratagene), in a Mx3000P thermo -cycler (Stratagene). A total of 0.5 l of the synthesized cDNA and 0.075 l of a
124 0.25 M solution of each primer were used for each 25 l real -time PCR react ion. Primers were designed using NetPrimer (Premier Biosoft International) and synthesized (Invitrogen). Reaction were carried out with annealing, extension, and melting temperatures of 55 C, 72 C, and 95 C, respectively. A melting curve was generated to c heck the specificity of the amplified fragments. Changes in gene expression relative to the geometric mean (VANDE SOMPELE et al. 2002) of three control genes (ACT, UBQ, and UBQ_L) (BRUNNER et al. 2004) were determined using the program DART -PCRv1.0 (PEIRSON et al. 2003) Poplar Whole -G en ome Oligonucleotide M icroarrays This study was based on hybridizations to whole genome microarrays containing features representing 42,364 predicted transcriptional units from the P. trichocarpa nuclear genome. All transcriptional units were represented by three 60-mer probes, designed by NimbleGen (Madison, WI) in collaboration with Oak Ridge National Laboratory and were synthesized using maskless lithography. cRNA was synthesized fr om total RNA extracted from individual plants. Labeling, hybridization and scanning were carried out by NimbleGen (Madison, WI) using standard procedures. Microarray Data Analysis The data were analyzed using a two-step strategy previously outlined by Chu and colleagues (CHU et al. 2002) Data from the hybridizations of f our biological replicates were used f or identification of genes expressed in each organ. E ach vegetative organ was analyzed separately. Initially, the signal intensity detected in each probe, in each microarray, was log2 transformed. The data was not background corrected. After inspection of signal distribution (box plots) across all microarrays, log2 signal intensities
125 were microarray -centered to zero. A mixed model analysis of variance (ANOVA) was applied to estimate relative transcript levels for each gene with PROC M IXED in SAS (SAS Institute, Cary, NC) using the model yij= +Gi+P(G)j(i)+ ij that included the sample mean gene (Gi) as a fixed effect and probe nested within gene ( P(G)j(i)) as a random effect Probe was included in the model to account for general effe cts (for instance, melting temperature and potential of forming secondary structures) that may contribute for differences in signal detected among the three probes representing a gene, and are associated with specific probe properties. Residual plots indic ated that residual 2) was met across all microarrays. Least -square means were calculated for each gene and for the negative control probeset (20 probes). P airwise comparisons ( one-sided t tests) were carried to evaluat e if the least -square mean ( estimated transcript level ) of each gene was significantly higher than that of the negative control. P values were adjusted for false discovery rate (BENJAMINI and HOCHBERG 1995) with modifications (STOREY and TIBSHIRANI 2003) Genes were considered expressed above background and were placed in a binary scale a value of 1 was given if they had a Q value below 0.01 (FDR <1%). Genes with a Q value above or equal to 0.01 (FDR described above were carried out using the SAS and JMP software (SAS I nstitute, Cary, NC). Analysis carried with other poplar genotypes followed the same procedures. For contrasting the transcript abundance between vegetative organs, each gene was analyzed individually using SAS (SAS Institute, Cary, NC) in a mixed ANOVA mod el yij= +Ti+Pj+ ij that included the plant organs node, internode, young and mature leaf and root (Ti) as fixed effect and probe (Pj) as random effect and the sample mean
126 We further filtered this list to identify genes up or down-regulated among organs (FDR < 1%), and differentially regulated by at least 2 -fold. Gene expression data is deposited in the Gene Expression Omnibus Database under the Accession Numbers: GSM146141 GSM146299; Series: GSE6422, and Platform: GPL2618. Test for Random Distributio n of Expressed G enes A new statistical test was developed to determine whether expressed genes were randomly distributed across each of the 19 poplar linkage groups. E xpressed and assembled genes were assigned a value of 1 and nonexpressed genes were given a value of 0 (unassembled genes were not considered for these analyses). A search was then carried out through the sequence of 0s and 1s to identify patterns, or runs of expressed genes, using a runs test as first proposed by OBrien and Dyck (1985) A run was defined as a succession of the same digit, bordered by different digits The l ength of a run was defined by the number of digits in that run. T he null distribution was obtained using a bootstrap approac h. A set of 25000 independent data sets were generated under the null hypothesis, wher e each data set is a sequence with a random pattern of 0s and 1s with length of sequence and proportion p of 1s identical to t he observed data T he 2statistic (OBRIEN and DYCK 1985) was calculated for each random dataset and compared to the 2 value of the observed data, generat ing a p value for the test of no co -regulation. To define the extent of clusters of co expressed genes we also eval uated the run lengths. For each data set generated under the null hypothesis we calculate the number of runs of length 1 (i.e. one gene expressed flanked by nonexpressed genes) the
127 number of runs of length 2 (i.e. two consecutive genes flanked by non exp ressed genes) up to the longest run in the data set. This provided a null distribution for each run length. F rom the observed data, the number of runs of each length was calculated to assess whether the observed value of the run length deviated from what was to be expected under the null hypothesis. Finally, for each run l ength, if the null hypothesis was rejected, the position of the runs of that length was defined by using a sliding window to detect the exact position of the non -randomly expressed genes in the sequence. GO Annotation of Expressed G enes. The Gene Ontology functional classification was obtained from the Populus Genome Portal at JGI ( http://genome.jgi psf.org/Poptr1/Poptr1.home.html ), and from the A. thaliana GO database ( http://www.arabidopsis .org). Results Constitutive and organspecific expressed genes To what extent are genes dedicated to specific organ types during development of a woody plant ? To evaluate this we initially defined the set of genes expressed in five major organs of the tree species P. trichocarpa stem nodes and internodes, whole roots and young and mature leaves ( Fig. 1) by contrasting the signal intensity det ected across each of 42,364 predicted transcriptional units to a set of seven negative -control genes (20 negative -control probes) Each plant organ was represented by four biological replicates collected from the P. trichocarpa reference genotype Nisqually 1. All analyses were carried out on whole genome microarrays representing 93% of predicted transcriptional units from the P. trichocarpa genome, each
128 represented by three independent 60-mer probes based on the sequence of the reference genotype Nisqually -1. Expression was detected for 22,616 transcription units (1% false discovery rate [FDR]), 53% of the genes represented in the microarray (Supplemental Table I ). The specificity of the microarray in detecting organ-specific expression and discriminating in dividual members of genes families was validated for a set of transcriptional units by real time quantitative PCR (RT -QPCR) (Supplemental Fig. 1 ). The highest diversity in expressed genes was detected in stem nodes, where expression evidence was detected for 21,081 transcriptional units. Stems (nodes and/or internodes) contained the largest proportion of organ-specific genes i.e. genes expressed exclusively in a particular organ. Transcripts for 3,468 genes were detected exclusively in stems, 811 in leav es (young and/or mature) and 332 in roots (Fig. 2) Organ-specific genes were classified into Gene Ontology functional categories (GO, www.geneontology.org) and the frequency of genes in each category, and each maj or plant organ (stem, leaves and roots) was calculated (for example, 175 of 3,468 genes specif ically expressed in stems were categorized as ce ll organization and biogenesis, a frequency of 0.05 [175/3,468]). Next the frequency of organ-specific genes in each GO class was compared among the different organs to identify categories over or under -represented (Fig. 3) As expected, leaves revealed a higher proportion of tissue specific genes assigned to the chloroplast and plastid cellular component, largely involved in carbon fixation However, if the number (rather than the proportion) of organspecific genes in each GO category is considered, the stems actually displayed a larger set of genes expressed exclusively in the chloroplast. Because the stem of juv enile
129 poplar trees is photosynthetically active this may suggest that different chloroplast genes are transcribed in stems and leaves. Roots had a much higher proportion of organ-specific genes dedicated to the biological process of responding to external biotic and abiotic stimuli 27% of root -specific genes versus and stress Therefore, the relatively limited number of root -specific genes (332) appears to be largely dedicated to condition root system -specific responses to belo wground external stimuli encountered by perennial plants during their extended life cycle. Nodes and internodes significantly exceeded leaves and roots in the number of genes classified as both biological process unknown and cellular component unknown, suggesting there is a comparatively poor understanding of the genes that govern basic physiological and molecular mechanisms of wood development and vegetative bud dormancy relative to other vegetative organs. The same trend was observed when organ-specif ic genes were classified according to annotation. Nodes had the lowest proportion of annotated genes (31.9%) and the highest proportion of unknown genes (37.6%), when compared to mature leaves (41.4% and 28.3%) and roots (49.1% and 17.9%) (Supplemental Fig 2 ) A large fraction of the transcriptome 14,555 transcriptional units was detected in all three main vegetative organs (stems, roots and leaves). Because the role of gene expression in organ identity may not only be evident from the presence or absence of transcripts, but also through differential quantitative regulation of the expressed gene (i.e. quantitative vs. qualitative measure), we evaluated whether genes were also equally expressed across vegetative organs. A series of F -tests were carried out to contrast the transcript levels of each constitutively expressed gene among nodes,
130 internodes, roots and young and mature leaves. Only 1/3 (4,954 out of 14,555) of expressed genes were differentially regulated among the five organs (FDR 1%). Therefore there appears to be a core set of expressed at similar levels across the various vegetative organs. Pairwise Quantitative Differences in Expression Levels Among V egetative organs Which genes are differentially regul ated during development of the vegetative plant body of Populus ? Here we compared organ -preferred expression of gene s among vegetative organs (Table I). Below we describe the main features and unique characteristics of each vegetative organ. Node and inter node -preferred genes. A relatively small set of genes (83) was detected as node preferred in the contrast with internodes, potentially reflecting the presence of uniqu e meristematic structures (i.e. axillary buds) in this organ. In general, node preferred expression was detected for several genes involved in the phenylpropanoid biosynthesis pathway, such as ferulate5 -hydrolase, pinoresinol -lariciresinol reductase, cinnamyl alcohol dehydrogenase, caffeoyl -CoA 3-O -methyltransferase, and 4-coumarate:CoA liga se. Several genes encoding xyloglucan endotransglycosylase and the fasciclinlike arabinogalactan proteins FLA11 and FLA12 were detected in higher levels in internodes, compared to the other non woody organs. Young leaf preferred genes. Young leaves were enriched for transcripts related to pathogen defense, such as germin -like proteins, pathogenesis -related proteins and glycosyl hydrolases such as chitinase, when compared to mature leaves. Overall young leaves also had higher
131 expression of genes involved in lipid metabolism (e.g. lipases, lipid hydrolases and lipid transfer proteins). Leaves also showed a higher abundance of RNAs for photosynthesis -related genes, particularly when compared to roots. The same was not observed in the comparison of young leaves to poplar stem expression (IN+N), most likely because poplar stem tissues external to the cambium (bark) contain phot osynthetically active cells Mature leaf -preferred genes. Mature leaves differed significantly from young leaves particularly for gene s encoding for metalloproteinases, and enzymes involved in cell wall biosynthesis and isoprenoid metabolism. Several highly expressed mature leaf preferred genes did not have putative homologues in plants. Genes related to carbon metabolism (e.g. starch bi osynthesis) and sugar transporters were also preferentially expressed in mature leaves compared to young leaves. As expected, mature leaves present preferential expression of genes involved in the photosynthesis machinery and the citric acid and carbon fix ation cycles, when compared to roots. When compared to nodes and internodes, mature leaves presented a higher mRNA abundance for genes involved in defense response, oligosaccharide biosynthesis (galactinol synthase, fructose bisphosphate aldolase), photoas similate response, the electron transport chain, photosynthesis -related genes, and citric tricarboxylic acid and carbon fixation cycles. Root -preferred genes. mRNA abundance profiles of root -preferred genes were consistent with the expected physiological role of roots and contrasts were similar regardless of the organ to which the comparison was made. Metal -binding proteins, such as metallothioneins,
132 iron transport proteins, metal ion transporters and copper binding proteins were consistently preferentiall y expressed in roots, as well as specific disease resistance proteins, water channel proteins and dehydration-induced proteins. Nitrate transporters are also preferentially expressed in roots in comparison to the other organs that were analyzed. Non -Random D istribution of Expressed Genes in the G enome Are expressed genes distributed non-randomly in the genome, suggesting epigenetic mechanisms of transcription regulation? Co expression of adjacent genes could be influenced at the level of chromatin architec ture; however objective criteria for declaring significance of adjacently expressed genes are needed. The phenomenon has been reported previously, but studies have typically focused on identifying correlations among the expression levels of contiguous gene s in order to identify patterns of co regulation (COHEN 2000; SPELLMAN and RUBIN 2002; ZHAN et al. 2006) We approached the problem by testing whether expressed genes were observed in clusters, considering the presence or absence of transcripts (qualitative measure) rather than its level (quantitative measure) and that of its neighbor(s). This statistical approach uses a runs test based on run lengths (OBRIEN and DYCK 1985) adapted for plant genome analysis. Initially, a binomial system was established where genes expressed in any given vegetative organ were assigned a value of 1 whereas nonexpressed genes were assigned a value of 0. We detected several genomic regions with large numbers of expressed genes adjacent to each other, including up to 15 consecutively expressed genes in young leaves. To evaluate the statistical si gnificance of these runs, null distributions were generated for runs of 1 to 13 adjacent expressed genes, for every
133 linkage group and organ. The statistical significance of the observed data was assessed by comparing it to the null distributions generated for each organ and linkage group Most of the runs of short length, such as a single gene flanked by non expressed genes, showed significant departure from the null distribution (p<0.01), occurring less frequently than due to chance alone (Fig 4, red) In contrast, larger run lengths occurred more frequently than expected by chance (Fig. 4, green) Our results reveal islands of genes for which there is a statistically significant tendency for co expression We carried out a similar analysis to identify pot ential chromatin domains that would be specific to a given vegetative organ. The binomial system was defined so that a gene expressed in one organ but not another was recorded as 1, while if expressed or not expressed in both organs it was given a value of 0. Although runs of up to 6 organ specific genes were detected in some instances, none departed significantly from what would have been expected by chance. Therefore, although chromatin domains can be identified in the poplar genome, they do not appear to be associated with the specific plant organs we analyzed. Is the Origin of the Woody H abit in the Salicaceae Due to Novel, Unique G enes? Among the 42,364 genes evaluated in the microarray, 5,674 (13%) had no similarity to A. thaliana genes (Evalue 3) and 3,636 (9%) had no identifiable homolog, for any species. We evaluated the pattern of expression of these predicted genes that appear to be unique to poplar and assessed whether if there was evidence of expression and/or bias towards woody org ans. Evidence of transcription could be identified for more than a third (1,321) of the genes, with the majority (945) being detected in all five organs. Within the genes that appear to be unique to poplar, the
134 small fraction that is organ -specific (147) i s highly enriched for genes expressed in stems 98 genes are detected only in nodes or internodes, compared to 32 in leaves and 17 in roots. For the majority (72/98) of those poplar unique genes expressed exclusively in stems, the difference in signal int ensity relative to the controls is relatively small (< 2 -fold), although differences of up to 3.3-fold could be detected. Extensive Diversification of Transcription R egulation in Populus and Arabidopsis O rthologs The availability of whole -transcriptome mic roarrays from P. trichocarpa and A. thaliana offers one of the first opportunities to examine how genome wide gene expression regulation has evolved in angiosperms A conserved expression pattern derived from a common ancestor (i.e. ortholog) could suggest that the transcriptional units are under balancing selection, because of conserved mechanisms of regulation. Gene expression regulation of paralogs i.e. genes derived from duplication events after lineage separation diverged to a great extent in Popul us and may have played a significant role in the establishment of the woody habit in the Salicaceae (TUSKAN et al. 2006) Here we compare expression patterns between A. thaliana and Populus orthologs in correspondi ng organs, to evaluate the extent of transcript abundance conservation in these plant transcriptomes. Arabidopsis -Populus orthologs were identified using an Inparanoid analysis (OBRIEN and DYCK 1985; REMM et al. 200 1) carried out as part of the initial analysis of the popl ar genome sequence (TUSKAN et al. 2006) The analysis focused on 4188 Inparanoid clusters of orthologous genes with a single member from each Arabidopsis and Populus and gene expression information available for both species (Supplemental Table I). The most comprehensive atlas of the
135 A. thaliana transcriptome, AtGenExpress (SCHMID et al. 2005) was utilized as a reference for organ and developmental gene expression. AtGenExpress provides transcriptional information for a broad range of plant organs, including stem nodes and internodes, mature and young leaves and roots, measured under several development and growth conditions. We focused on gene expression measured from A. thaliana plants harvested at 9 and 17 days, grown in soil. In cases where multiple sample types were collected for an organ (f or instance, fully expanded mature rosette leaves #2, #4 and #6 were analyzed separately), they were compared individually to the Populus transcriptome. We initially c ompared the qualitative patterns of expression (i.e. presence or absence of transcripts) of Populus genes relative to the A. thaliana orthologs in internodes, nodes, leaves (young and mature) and root organs Where both genes (i.e. the A. thaliana and Populus orthologs) were identified as either expressed or nonexpressed in the two species, i n each organ, the pattern was considered to be in agreement. Out of the 4188 pairs of orthologs, the lowest similarity in the expression pattern was detected in roots and young leaves (60% and 58% of genes expressed similarly, respectively). Better agreeme nt was detected between the orthologs expressed in mature leaves and internodes (69%), and stem nodes (76%). In all cases, this proportion was higher (6-13%) than expected by chance alone (Supplemental Fig. 3A). The proportion of orthologs expected to have the same expression pattern by chance was estimated by summing the product of the frequency of Populus and A. thaliana expressed genes to the product of the frequency of nonexpressed in the two species. The fraction of orthologous genes with the same pat tern of expression in each
136 organ is significantly higher ( p value < 0.0001) relative to what would be expected by chance for all organs evaluated, based on a chi -square test. Nonetheless, extensive diversification of gene expression regulation appears to h ave occurred between A thaliana and Populus orthologs. Expression of Genes Implicated in Essential Plant P rocesses We used rank correlation of orthologs expressed in the two species to evaluate the extent of conservation in relative transcript abundance. This analysis no longer assigns a presence (1) or absence (0) value to each gene, but whether the relative transcript abundance of a given expressed gene is conserved or not between A thaliana and Populus Therefore this analysis focuses exclusively on g enes expressed in both species in each organ. Gene transcript abundance was ranked in the two species, in each organ, and a Spearman correlation was estimated. For all plant organs, the estimated rank correlation indicates positive but limited conservation in the quantitative expression patterns (r2 < 1 8 %) suggesting that there is significant remodeling of the transcription networks in the two species (Supplemental Fig. 4) The highest conservation in transcriptional pattern was observed in young and mature leaves, where the expression of a larger number of genes appears to remain highly consistent between the two species The gene expression data distribution in both species is similar in each of the five plant organs (Supplemental Fig. 5). To further contr ast the Arabidopsis and Populus transcriptomes, we narrowed the comparative analysis to genes that are expressed in multiple plant organs in both species. Genes broadly expressed may be under stronger selection pressure because mutations that lead to diffe rential transcription regulation only need to be negative in
137 one organ to be removed by negative selection (KHAITOVICH et al. 2006) Therefore, genes expressed in a large number of organs might be less divergent between species. We compared rank correlations in a subset of the orthologs expressed in all five organs in both A. thaliana and Populus and contrasted them to the correlations detected previously, including all orthologs. As predicted, the quantitative pattern of expression between the two species is more conserved for this subset of genes (Fig. 5) suggesting that, indeed, transcript abundance may be more conserved in genes that are expressed in a broad spectrum of organs in plants, compared to those that are organ-specific. Finally, we identified the functional categories of the orthologs expressed at similar levels in the two species. For each organ we selected orthologs with the smallest transcript abundance rank differences (upper 10%) between the two species. Next we compared the frequency at w hich they were observed in each Gene Ontology category, relative to the entire set of orthologs expressed in both species (Fig. 6). Several GO cellular component categories are enriched for these conserved genes, particularly in young and mature leaves. In these organs, genes associated with the chloroplast and plastids, as well as other intracellular and cytoplasm components, are over -represented. These genes include those encoding subunits of the photosynthesis I and II complex and carbohydrate metabolism In contrast, genes implicated in categories related to gene expression regulation appear to show little conservation between A. thaliana and Populus The eight genes more highly conserved in the two species, across all five vegetative organs, are describ ed in the Supplemental Table II. Most of these genes are implicated in the endomembrane system and stress response, but encompass a relatively broad spectrum of functions.
138 Discussion The genus Populus encompasses the most important short rotation woody spe cies in North America for biomass production and plant based carbon sequestration strategies. P trichocarpa is the first woody perennial plant species to have a sequenced genome (TUSKAN et al. 2006) providing the foundation for understanding developmental properties of evolutionary interest in trees such as secondary growth, dormancy and plant architecture. Here we generated the first compendium of expressed genes in a woody perennial, utilizing a microarray platform that includes the majority of predicted genes. W e assessed the genes expressed in five vegetative organs of the reference genotype Nisqually -1 and detected evidence of transcription for approximately half of the 45,555 predicted transcriptional units previously less than 1/3 of the gene models showed evidence of expression based on EST data (TUSKAN et al. 2006) Our study does not address whether these genes are being actively translated into proteins. A subset of the genes for which we inferred transcription activity may be regulated post transcriptionally, or may not be translated. Nonetheless, studies in other eukaryotes show that most of the mRNA captured in microarray analysis is associated with polyribo somes, suggesting translation (ARAVA et al. 2003) This study also identified segments of the poplar genome where there is a significantly larger than expected number of expressed genes, suggesting evidence of domains or regions th at are transcriptionally active in certain plant organs. Factors such as histone distribution and modification could contribute to transcriptional activation in these regions. While there is currently no further experimental evidence to support that the genome structure is dissimilar in these segments from other parts of the genome, experiments such as
139 chromatin immunoprecipitation coupled to tiling microarrays (ChIP -chip), supported by the availability of the poplar genome sequence, could be used to test t hese hypothesis. The comparison among P. trichocarpa organs indicates that stem nodes contain transcripts from the highest number of genes in the genome. Nodes are presumed to have elaborated during the evolution of perenniality in woody plants like Populu s (GROOVER 2005) Nodes comprise most tissues represented in the stem internodes, but differ in t hat a dormant vegetative meristem represented by the lateral axillary bud is also present. Despite the similarity between the two organs, nodes had a strikingly higher diversity of genes being actively transcribed; in contrast, all internode transcribed ge nes were also detected in nodes at a 2-fold change threshold. Part of the diversity of transcripts may also be due to suppression of mRNA degradation in dormant vegetative meristems. Transcriptional richness of the node m ay be necessary for the dormant sho ot meristem (bud) to acquire novel vegetative or reproductive functions in subsequent growth periods For example, t he poplar dormant cambium has a surprisingly large number of genes (~ 1,600) that are up -regulated when compared to the active cambium (SCHRADER 2004) suggesting that these genes are transcriptionally active in the dormant state. Similarly, nitrogen -starved plants entering eco dormancy yielded ~4X more up-regulated transcripts compared to rapidly growing plants grown in adequate nitrogen levels (COOKE 2005) Epigenetic mechanisms may play a role in maintaining this transcriptionally active state in tissues of perennial species and have been proposed to play a role in controlling the timing of vegetative-to floral transition in buds (BOHLENIUS et al. 2006) Genomic DNA in undifferentiated or juvenile tissues of perennial woody plants is generally undermethylated when compared to older,
140 differentiated tissues (BITONTI et al. 2002; FRAGA et al. 2002a; FR AGA et al. 2002b) If maintenance of a dormant state in perennial plants requires broad transcriptional activation, then as growth commences after the dormant period, selective repression of transcriptionally active regulons may be a mechanism by which gr owth and development is initiated and maintained. The statistical tools applied in this manuscript should be useful for identifying such domains to test these hypotheses. We also describe the first comparative genomic analysis between the transcriptome of the model tree species P. trichocarpa to that of the model plant A. thaliana Both species belong to distinct clades within th e Eudicotyledenous angiosperms Populus is part of the Eurosid I clade, while A. thaliana occurs in the Eurosid II clade. Despite the differences, sequencing of the P. trichocarpa genome showed that both species share a substantial number of genes almost 90% of Populus predicted genes are homologs to A. thaliana genes (TUSKAN et al. 2006) To what extent is the Populus transcriptome comparable to that of A. thaliana? In animal systems, it h as been argued that because of the high levels of gene sequence similarity among closely related species, most of the developmental and morphological div ersity must have been created by evolution at the level of transcript ion regulation. Similarly, our data suggests that gene expression regulation has evolved to a large extent between A. thaliana and Populus as we detect very weak similarity between quant itative patterns of expression. Although they are both angiosperms, P. trichocarpa shares limited morphological similarity with A. thaliana Poplars are woody perennials with an indeterminate growth habit, while A. thaliana is an herbaceous annual with a b asal rosette of vegetative tissues. The distinct patterns of gene expression between
141 orthologs despite a large set of shared genes support s the hypothesis that transcription regulation directs expression of novel traits that are important for adaptation and evolution of plants. These results have implications for gene functional annotation of genes in woody species, as comparative sequence analysis to model organisms has been largely used to make indirect inferences. Extensive divergence in gene functio n, inferred from low conservation in quantitative gene expression patterns, suggests that the use of A. thaliana as a model for functional genomics of woody species like Populus may be limited. Still, we identify a small set of genes that appear to maintai n highly consistent expression levels in the two lineages, despite 100 million years of separation. These genes may be under some form of selection to maintain transcript abundance at physiologically relevant levels in diverse plant lineages, based perhaps on the levels of transcripts required for production of enzymes in core plant processes. A compendium establishing the genes expressed throughout development, in distinct organs and tissues, is the first step for a comprehensive functional characterizatio n of the P. trichocarpa genes. This study presents a first comprehensive contribution towards this goal and will assist in efforts to define target genes for genetic modification and candidates for genetic control of complex traits. The challenge ahead is highlighted by our observation that the most elaborated organs of trees the stem nodes contain the highest proportion of unknown predicted genes, and that function may not be immediately inferred from A. thaliana or other models. The path forward shoul d take advantage of the tremendous nucleotide diversity of poplars and attempt to
142 link genotype with phenotype, providing the information needed to assign function to genes whose role is still largely undefined.
143 Table A -1. Summary of pairwise comparisons among five poplar (Nisqually 1) vegetative organs showing preferentially expressed gene s according to main categories. Tissues with preferentially expressed genes Nodepreferred Internode preferred Young Leaf preferred Mature Leaf preferred Root pref erred Tissue compared Node n o significant preferred genes observed (N=0) disease response, lipid transfer prot. light harvesting chlorophyll binding prot. dehydrationinduced prot. photosynthesis related prot. (N=622) disease resistance, pectinesteras e, oligosaccharide synthesis, photoassimilateresponsive prot. photosynthesis related prot. (N=1591) i ron transport, water channel prot. defense response, peroxidases (N=549) Internode l ipase/hydrolases, disease response prot., lipid biosynthesis prot. methyltransferases, (N=83) lipase/hydrolases, pathogenesis related prot., steroid sulfotransferase, dehydration induced prot. (N=708) chloroplast membrane prot., ripeningrelated prot., disease resistance prot. auxin induced prot., ribosomal prot. (N =1861) i ron transport, water channel prot. defense response, dehydration induced prot. (N=470) Young Leaf l ignin biosynthesis, disease resistance, peptide transporter, cellulose synthase, sucrose synthase, unknown proteins (N=737) b ark storage protein lignin biosynthesis, phenylpropanoid pathway prot., fasciclin like arabinogalactan prot. (FLA11, FLA12) (N 680) metalloproteinase, stress response, photoassimilateresponsive prot. sugar transport, cell wall biosynthesis related prot., starch synthase (N=312) metallothionein, iron transport, bark storage protein, disease resistance, water channel prot., dehydration induced prot. (N=1078)
144 Table A -1 C ontinued Tissues with preferentially expressed genes Nodepreferred Internode preferred Young Leaf preferred Mature Leaf preferred Root preferred Tissue compared Mature Leaf lipase/hydrolases, disease resistance prot., vegetative storage prot., meristem related homeobox like prot., sucrose synthase (N=1008) unknown proteins, transposable elemen ts, NADH dehydrogenase subunits (N=660) lipase/hydrolases, pathogenesis related prot., gibberellin regulated prot. pectinesterase, ethyleneresponsive element (N=253) iron transport protein, dehydration induced prot. disease response, water channel prot. metallothionein (N=897) Root lipase/hydrolases, disease resistance prot., calreticuliln, fatty acid biosynthesis, vegetative storage prot. (N=231) vegetative storage prot., fasciclin like arabinogalactan prot. (FLA11, FLA12) (N=99) lipase/hydrol ase, disease resistance prot. early light induced prot., auxinregulated prot., photosynthesis related prot. (N=1787) photosynthesis related prot., auxin regulated prot. Carbonic anhydrase, oxidative stress response, early light induced prot. (N=1565)
145 Figure A1. Poplar organs sampled for the microarray analysis included whole-roots, young leaves (leaf plastochron index 0 -5), mature leaves (leaf plastochron index 6-9), stem nodes and internodes. Figure A2. Organ-specific expression. Genes expre ssed exclusively in leaf (YL+ML), stem (IN+N) and root organs, and combinations of organs. Expressed genes were defined based on a false-discovery rate of 1%. 14555 3468 332 1336 165 1818 942 Root Stem Leaf
146 Figure A3. Proportion of organ -specific genes in each Gene Ontolog y functional category. A gene was considered specific to leaf (YL+ML, black bars), stem (N+IN, grey bars) or root (R, white bars) when transcripts were detected in that organ and not in any other. 0.00 0.10 0.20 0.30 0.40 0.50 biological process unknown cell organization and biogenesis developmental processes DNA or RNA metabolism electron transport or energy pathways other biological processes other cellular processes other metabolic processes other physiological processes protein metabolism response to abiotic or biotic stimulus response to stress signal transduction transcription transport cellular component unknown cell wall chloroplast cytosol ER extracellular Golgi apparatus mitochondria nucleus other cellular components other cytoplasmic components other intracellular components other membranes plasma membrane plastid ribosome DNA or RNA binding hydrolase activity kinase activity molecular function unknown nucleic acid binding nucleotide binding other binding other enzyme activity other molecular functions protein binding receptor binding or activity structural molecule activity transcription factor activity transferase activity transporter activity GO cellular component GO molecular function GO biological process
147 Figure A4. Runs tests based on run lengths for poplar vegetative tissues. Runs tests were performed for up to 13 run lengths for all linkage groups in young leaf, internode, mature leaf, node and root. Shaded areas indicate those linkage groups in which the run lengths obtained were non-random, either less freq uent (green) or more frequent (red) than expected by chance. Linkage group 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13 RUN LENGTH RUN LENGTH RUN LENGTH RUN LENGTH LG 5 LG 6 LG 7 LG 1 LG 2 LG 3 LG 4 LG 8 LG 10 LG 11 LG 12 LG 9 LG 13 LG 14 LG 19 LG 15 LG 16 LG 17 LG 18 ROOT YOUNG LEAF INTERNODE MATURE LEAF NODE RUN LENGTH
148 Figure A5. Rank correlation between all A. thaliana and Populus orthologs expressed in nodes (N), internodes (IN), roots (R), and mature (ML) and young leaves (YL) (black bar), and including only orthologs expressed in all five organs (i.e. broadly expressed genes), in both species (white bars). Figure A6. Gene ontology categories with an over -representation of genes expressed in a conserved manner. The Y axis indicates the difference in the frequency of orthologs with the lowest transcript abundance rank differences between A. thaliana and P. trichocarpa relative to the overall frequency of all orthologs in that category, for each organ. Elevated values suggest selection may be acting on several genes in the category to maintain conserved transcript abundance in these species.
149 APPENDIX B SCREENING FOR FUSIFO RM RUST IN ROOTED CUTTINGS OF LOBLOLLY P INE INOCULATED WITH TWO UREDINIAL CULTURES O F CRONARTIUM QUERCUUM AND THEIR F1 HYBRID Fusiform r ust is a disease incited by the biotrophic basidiomycete fungus Cronartium quercuum (Cqf). It appears to have originated in the southeastern United States and attacks different pine species (SCHMIDT 2003) The disease affects primarily young pine plantations, causing losses mainly due to mortality of the young plants. In addition, deformations due to stem galls may weaken the trees and decrease the economic value of the wood (SCHMIDT 2003) The life cycle of Cqf is complex and involves two different hosts, pine and oak, producing five different types of spores. Dika ryonic (N+N) aeciospores are produced in pine galls and transported by wind to oak leaves. They then germinate and produce local lesions in which urediospores (N+N) are produced. The urediospores are considered the repeating stage of the fungus, as they ca n re-infect other oak leaves. Later, karyogamy occurs and telial structures are formed which contain diploid (2N) teliospores. These, under favorable conditions, undergo meiosis to form haploid (1N) basidiospores. The basidiospores are then transported by wind to pine shoots where they infect needles or succulent stem tissues and cause branch and stem galls. Infection in pine only occurs from the basidiospores that come from infected oak leaves and cannot be transmitted from pine to pine (SCHMIDT 2003) Several months after infection, p ycnial droplets are secreted from the gall surface. These droplets contain haploid pycniospores (1N) (PHELPS and CZABATOR 1978) Later during infection, aeciospores are produced from the galls and the cycle continues (Figure B1).
150 Because of the economic impact of fusiform rust on loblolly pine populations, efforts to manage the disease have been made since the 1950s through cooperative tree improvement programs in Florida, Georgia, North Carolina and Texas (SCHMIDT 2003) that resulted in the selection of rust resistant families. Research on the biology of the pathogen and the interactions with its host has led to the identificat ion of differentially expressed genes in pine and Cqf (MYBURG et al. 2006; WARREN and COVERT 2004) as well as resistance genes, such as FR1 (WILCOX et al. 1996) Models for the detection of additional putative resistance genes have also been developed (LI et al. 2006b) Progress in the development of techniques for disease screening and the development of cultures derived from single urediospores has provided material for identification of additional rust resistance genes (WILCOX et al. 1996) and fungal avirulence genes (KUBISIAK et al. 2005) Furthermore, the use of Cqf specific microsatelite markers have aided in the genotyping of fungal lines (KUBISIAK et al. 2004) It was expected that the availability of more than 500 genotypes of loblolly pine would allow further progress in un derstanding the diversity and host pathogen interactions between Cqf and its host. The objectives of this section were to quantify the genetic variation in loblolly pine for resistance to fusiform rust ( Cronartium quercuum f.sp. fusiforme) incited by two single uredinial cultures of t he pathogen and their F1 hybrid and to map the avirulence gene and other avirulence loci through the segreg ation of markers in pycnial drops. The hypothesis is that s pecific resistance genes could be identified in a populatio n level screen based on differential interaction with pathotypes. Identif ying specific resistance genes can be validated by their genetic correspondence with Avr loci in the pathogen
151 A total of 512 unrelated genotypes were used. Rooted cuttings of these genotypes were propagated at North Carolina State University, Raleigh, NC and then transported to the University of Florida where they were allowed to flush and were later hedged. Four weeks after hedging, the ramets were transported to the Disease Resis tance Screening Center in Asheville, North Carolina, for inoculation with Cqf. Each ramet was inoculated with basidiospores (100,000 spores/ml) derived from the single uredinial cultures SC20-21 and NC2 -36 and their F1 hybrid culture P2. After inoculation the plants were kept in Asheville, North Caroline for 8 weeks and then transported back to the greenhouse facilities at the University of Florida. They were placed in ebband-flow benches in a randomized incomplete block design consisting of 30 columns and 16 rows The presence of galls was recorded for each genotype at 6 months after inoculation. It was expected that, if an individual has a rust resistance gene (FR1 or other FRn, where n represents any number) that interacts differentially with SC2021 and NC2 36 no gall would be observed after inoculation with one culture, whereas galls would be observed after inoculation with the other culture (Table B 1) This would be evidence that t he cultures harbor distinct Avr genes. In these cases, I expect gall s after inoculation with P2. The possible outcomes of the pathogen challenge experiments are shown in Table B1. The number of galled genotypes was extremely low, as less than 5% of the cuttings inoculated with a given fungal culture had galls (Table B -2) These represented 109 out of the 512 different clones inoculated. With such low numbers, there was not sufficient power to perform the analyses and establish any clear differential interaction between spore culture and host genotype. For some genotypes like 108A or 523B
152 (Table B 3), g alls were observed when inoculated with one of the single uredinial cultures and the F1 hybrid but not when inoculated with the other uredinial culture, suggesting a case of differential interaction. However, the number o f biological replicates represented was very low in all cases; therefore, these results do not have sufficient power to obtain a more robust outcome. During the course of the experiment, it was noted that there was a high variation among clones in the t ime for shoot induction after hedging, as some clones produced shoots more rapidly than others. Because basidiospores from oak infect succulent shoots (SCHMIDT 2003) the presence of this tissue at the moment of inoculation increases the chance of infection and galls production in the susceptible genotypes, as was observed in the number of galls obtained among clones having succulent shoots with respect to the clones that lacked new shoot growth at the time of inoculation (Figure B 2) Since the different clones had succulent shoots at different stages (or were lacking altogether) at the time of inoculation, the time needed for shoot induction would be a factor to consider for optimizing the hedging protocol to obtain a more uniform material. In addition to the gall scores obtained, pycnial drops were collected from all galled material. These pycnia represent haploid material that can be used for genotyping and obtain a characterization of the variety of fungal material. Furthermore, the available genotypic information from SNP marke rs for most of the genotypes in this study could be used to perform linkage disequilibrium analyses aimed to map possible candidate resistance genes in future experiments
153 Table B -1 Possible outcomes of pathogen challenge experiments depending on the ho st and pathogen genotypes. Host genotype Pathogen genotype Possible outcome Frn/ Avr n /Avr n No galls Avr n /a vr n Gall a vr n /a vr n Gall frn/frn Avrn/Avrn Gall Avr n /a vr n Gall a vr n /a vr n Gall Table B -2. Number of loblolly pine cuttings and controls showing galls at six months after inoculation with Cronartium quercuum f.sp. fusiforme. The numbers correspond to the total cuttings present in the four biological replicates (approximately 480 plants per replicate). Controls (10 5 seedlings) NCSU cuttin gs NC P2 SC NC P2 SC Gall 88 69 70 91 42 60 No gall 17 31 42 1724 1778 1748 Total Inoculated 105 100 112 1815 1820 1808 % Cuttings with galls 83.81 69.00 62.50 5.01 2.31 3.32
154 Table B -3. Number of galls per genotype obtained for each Cqf inoculum at six months after inoculation. The numbers shown per inoculum correspond to individual cuttings out of a total of 4 cuttings (1 cutting per replicate) for the majority of clones. NC corresponds to uredinial culture NC236, SC corresponds to SC2021, an d P2 corresponds to the F1 hybrid between these two cultures. Inoculum Clone NC P2 SC Total 101A 0 1 0 1 102A 1 0 0 1 108A 3 1 0 4 117B 2 2 3 7 118B 1 0 0 1 120A 0 1 0 1 137B 0 0 1 1 142B 0 0 2 2 147A 1 0 0 1 149B 2 0 2 4 153B 1 0 0 1 155B 0 1 0 1 158A 0 0 1 1 171B 1 0 0 1 172C 0 0 1 1 183B 0 0 1 1 186B 0 2 0 2 187A 0 0 1 1 192C 2 0 1 3 1B 3 0 0 3 209A 1 0 0 1 226C 1 1 0 2 230A 1 0 0 1 231A 1 1 1 3 235A 1 0 0 1 238A 0 0 1 1 239A 0 1 0 1 242C 0 1 0 1 24B 1 0 0 1 254C 0 1 0 1 Inoculum Clone NC P2 SC Total 257B 3 1 2 6 260B 1 0 0 1 262A 1 0 0 1 265A 2 2 1 5 268A 1 1 0 2 269A 1 0 0 1 277A 2 0 0 2 278A 1 1 1 3 279B 2 0 0 2 283C 1 0 0 1 28C 1 1 0 2 298B 1 0 0 1 303C 0 0 1 1 306A 0 0 1 1 310C 0 0 1 1 318B 0 1 0 1 31A 1 0 0 1 320C 1 1 1 3 323B 0 1 0 1 327A 0 0 1 1 331B 0 0 1 1 339B 2 2 2 6 341C 2 0 1 3 343B 1 1 1 3 348C 0 0 1 1 35A 1 0 0 1 360B 1 0 0 1 361B 1 2 0 3 368B 2 1 1 4 378B 0 0 2 2
155 Table B -3 Continued Inoculum Clone NC P2 SC Total 379B 1 0 0 1 395A 1 0 0 1 401B 2 0 0 2 406A 0 1 1 2 41C 1 1 0 2 422B 2 0 0 2 430C 1 1 0 2 434B 0 1 0 1 436A 0 1 1 2 442C 0 0 1 1 485A 1 2 0 3 487C 0 0 2 2 492C 1 0 0 1 514A 1 0 0 1 515B 1 0 0 1 519B 0 1 1 2 520B 1 0 1 2 523B 0 2 2 4 524B 1 0 0 1 531A 1 0 0 1 545A 1 0 1 2 549A 2 1 0 3 554A 0 0 2 2 560B 1 0 0 1 561A 1 0 1 2 Inoculum Clone NC P2 SC Total 565C 1 0 0 1 571A 2 0 1 3 572C 0 0 1 1 577B 2 1 1 4 57A 0 0 1 1 5A 1 0 0 1 600A 1 0 1 2 609A 0 0 1 1 612C 0 1 0 1 62A 1 0 1 2 634C 1 1 0 2 637B 0 0 1 1 640B 0 0 1 1 66A 1 0 0 1 670A 0 0 2 2 676B 1 0 1 2 690B 1 0 0 1 695A 0 0 1 1 69A 1 0 1 2 79C 1 0 0 1 89C 1 0 0 1 92A 1 0 0 1 94C 1 0 0 1 98A 2 0 0 2 10 5 88 69 70 227
156 Figure B 1 Life cycle of the biotrophic fungus Cronartium quercuum f.sp. fusiforme, causal agent of fusiform rust, showing different spore types. Spore ploidy is shown in parentheses. Figure B 2 Gall production in new shoots at the time of inoculation. The presence or absence of new shoots w as recorded for all clones at the time of inoculation and the number of clones with galls was documented for all fungal cultures combined. A total of 512 clones were inoculated. Gall production in clones with new shoots at time of inoculation 282 103 121 6 # clones with shoots at time of inoculation with no galls # clones with shoots that developed galls # clones without shoots at time of inoculation # clones without shoots that developed galls
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180 BIOGRAPHICAL SKETCH Tania Quesada was born in San Jose, Costa Rica. After graduating high school she spent a year abroad in Quebec, Canada, where she attended College St. Maurice as an exchange student. She later attended the University of Costa Rica where she earned her Bachelors degree in Biology in 1997. Throughout her undergraduate studies, T ania was a student assistant in the Rice Biotechnology Program at the Center for Research in Cellular and Molecular Biology, under the direction of Dr. Ana M. Espinoza. During that time, Tania was involved in research on rice tissue culture and genetic tr ansformation using bi olistics. Before starting her m asters studies at the University of Costa Rica, she spent six months abroad in Tsukuba, Japan, in a training course in Plant Genetic Resources, where she was involved in a project that studied the genet ic diversity of wild rice species using microsatellite markers, a skill she applied during her Masters and posterior work at the University of Costa Rica. During her Masters studies, Tania researched the genetic diversity of the wild rice species Oryza latifolia in Costa Rica. She received her Masters degree in Agricultural Sciences and Natural Resources with a major in Biotechnology in 2001 and remained as a researcher at the Center for Research in Cellular and Molecular Biology at the University of C osta Rica. In 2005, Tania joined the Ph.D. program in Plant Molecular and Cellular Biology at UF, where she has been conducting association studies for pitch canker resistance in loblolly pine, under the direction of Dr. John M. Davis.