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Photoperiodic competency for dormancy induction in Populus balsamifera Zhang, Li 2013

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PHOTOPERIODIC COMPETENCY FOR DORMANCY INDUCTION IN POPULUS BALSAMIFERA L.  by Li Zhang  B.Sc., Nanjing Forestry University, 2010  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2013  © Li Zhang, 2013  Abstract  Bud dormancy is an important overwintering mechanism for woody perennials and is induced in most species during late summer by short days (SD) and/or low temperature. Adaptively, however, it is important for new growth not to respond to similar photoperiods in early spring. To investigate this matter, two growth chamber experiments were conducted on four genotypes of balsam poplar (Populus balsamifera L.) originating from two latitudes. Rooted cuttings were moved into SD conditions at weekly intervals after flushing at either 15°C or 20°C. Plant heights were measured every other day. Plants were harvested weekly and total RNA was extracted for quantitative reverse transcription polymerase chain reaction (qRT-PCR) on genes known to be involved in dormancy induction, and for transcriptome sequencing followed by qRT-PCR validation to identify additional genes marking photoperiodic competency. Height growth cessation (HGC) data showed that before a certain age, no matter how soon plants were transferred to SD conditions, they continued to grow until they became competent to respond to photoperiod. The different genotypes became competent at different times (18-40 days since flush), indicating possible genetic variation in this trait. Once competency was attained, it took plants 7-20 days under 20°C to cease height growth under SD, depending on genotype, experiment and time since competency acquisition. Leaf number data revealed that competency acquisition and the transition from preformed leaf emergence to neoformed leaf production, are two independent processes. Temperature did not appear to influence the development of competency, though it increased the speed of height growth cessation. RT-qPCR results revealed three promising gene markers for competency: Potri.017G051100 (G6), Potri.001G222000 (G7) and CONSTANS 2. Increased expression for G6 and G7 was observed post-competency relative to pre-competency, and changes in expression varied between leaves and stem tissue. In contrast, CONSTANS 2 mRNA peaked 32 days after bud flush, coinciding with competency acquisition. This research offers new insight into the molecular mechanisms that underlie the acquisition of photoperiodic competency, which, in a warming climate, may cause phenological mismatch in deciduous boreal tree species, causing premature HGC and loss of productivity.  ii  Preface  The dissertation is original unpublished work conducted by the author, Li Zhang. All experiments, data collection were performed by the author. Dr. R. D. Guy conceived and supervised the study. Transcriptome sequencing was performed by the Michael Smith Genome Science Centre, Vancouver, Canada (http://www.bccrc.ca/dept/cmsgsc). Charles Hefer oversaw the in silico preparation of the RNA-seq data. The thesis was written by Li Zhang with editorial assistance from the supervisory committee.  iii  Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iii Table of Contents ......................................................................................................................... iv List of Tables ................................................................................................................................ vi List of Figures .............................................................................................................................. vii List of Abbreviations ................................................................................................................... ix Acknowledgements ........................................................................................................................x Chapter 1: Introduction ...............................................................................................................1 1.1  Dormancy and its environmental control .................................................................. 1  1.2  Dormancy molecular control and the circadian clock .............................................. 3  1.3  Photoperiodic competency and its investigation....................................................... 6  1.4  Molecular methods.................................................................................................... 8  1.5  Populus species as model plants ............................................................................. 11  1.6  Research objectives ................................................................................................. 12  Chapter 2: Materials and Methods ...........................................................................................13 2.1  Materials ................................................................................................................. 13  2.2  Methods................................................................................................................... 14  2.2.1  Experiment I........................................................................................................ 14  Plant growth measurements and sampling .................................................. 14  Total RNA extraction and DNase treatment ............................................... 16  RNA-seq and data analysis ......................................................................... 16  iv  2.2.2  Experiment II ...................................................................................................... 17  cDNA conversion........................................................................................ 17  Primer design and optimization .................................................................. 17  RT-qPCR assay ........................................................................................... 18  Calculations and statistics ........................................................................... 19  Chapter 3: Results.......................................................................................................................21 3.1  Growth chamber data .............................................................................................. 21  3.2  RNA sequencing data ............................................................................................. 34  3.3  RT-qPCR data ......................................................................................................... 36  Chapter 4: Discussion .................................................................................................................42 4.1  Variation in competency development and speed of HGC ..................................... 42  4.2  Climate change and tree adaptation ........................................................................ 43  4.3  Candidate gene markers for competency ................................................................ 45  4.4  Limitations of the present work .............................................................................. 49  Chapter 5: Conclusions ..............................................................................................................50 Bibliography .................................................................................................................................51 Appendices ....................................................................................................................................62 Appendix A Galaxy work flow ........................................................................................... 62 Appendix B Supplementary tables...................................................................................... 66 Appendix C RT-qPCR efficiency ....................................................................................... 86  v  List of Tables  Table 3.1 Summary of the mean number of leaves at different developmental stages and the timing of competency acquisition for four Populus balsamifera L. genotypes grown at either 15°C or 20°C..................................................................................................... 33 Table 3.2 Candidate gene list narrowed down from RNA-seq data for RT-qPCR................. 35 Table 4.1 Expression level summary for G6 and G7 in stems (s), leaves (l), and pooled stems and leaves (s+l) respectively from day 25 to day 53, plus control.. ........................... 47 Table B.1 Primer sequences for RT-qPCR. ............................................................................ 66 Table B.2 Complete list of significant genes from RNA-seq. ................................................ 67  vi  List of Figures  Figure 1.1 The core oscillator made up by CCA1, LHY, and TOC1 in a transcriptional positive/negative loop. CCA1 and LHY bind to TOC1 and repress its transcription......... 4 Figure 2.1 The range and AgCanBaP provenance collections of Populus balsamifera L. .......... 13 Figure 2.2 An example of how break points were located, in this case for day of height growth cessation (HGC), using a two-segment linear curve-fitting program ................... 15 Figure 3.1 Relationship between the age at HGC and the age at transfer to short day (SD) conditions when grown at 20°C in Experiment I (2011).. ................................................ 23 Figure 3.2 The relationship between the age at HGC and the age at transfer to short days (SD) when grown at 15°C in Experiment I (2011).. ......................................................... 24 Figure 3.3 The relationship between the age at HGC and the age at transfer to short days (SD) when grown at 20°C in Experiment II (2012).......................................................... 25 Figure 3.4 Shoot age at competency for different genotypes in different treatments.. ................. 26 Figure 3.5 HGC speed for different genotypes in different treatments.. ...................................... 27 Figure 3.6 The relationship between leaf number and shoot age following bud flush for M-2, M-13, F-12 and F-15 grown at either 15°C or 20°C.. ....................................................... 30 Figure 3.7 The final number of leaves, after HGC, on stems transferred to SD at different shoot ages.. ........................................................................................................................ 32 Figure 3.8 Time taken to lammas after transfer from short days (SD) back into long days (LD)................................................................................................................................... 34 Figure 3.9 Over-represented GO categories during competency acquisition.. ............................. 36  vii  Figure 3.10 Expression levels of GI5, ELF3 and CO2 in leaves of M-13 sampled from the 2011 (Experiment I) growth chamber experiment.. .......................................................... 38 Figure 3.11 Expression levels for G2, G5, G6, G7, G8, G11 and G12 at 18, 25 and 53 days after bud flush.. ................................................................................................................. 39 Figure 3.12 Relative expression of G6 and G7 in leaves, stems, and pooled leaves and stems of M-13 sampled from the 2011 growth chamber experiment (Experiment I)................. 41 Figure C.1 Amplification and standard curves for candidate genes and reference genes ............ 88  viii  List of Abbreviations  ACT: ACTIN CO: CONSTANS CRY: cryptochrome  CT: threshold cycle DD: 24hr darkness ELF3: EARLY FLOWERING 3 FT: FLOWERING LOCUS T GA: gibberellic acid GI: GIGANTEA HGC: height growth cessation LHY: LATE ELONGATED HYPOCOTYL LL: 24hr light PHYA: phytochrome A RT-qPCR: quantitative reverse transcription polymerase chain reaction SD: short days  TUA: -TUBULIN  ix  Acknowledgements  I would like to express my appreciation to all who have helped me in my master program. First and foremost, I’d like to thank my supervisor Dr. Rob Guy for his guidance and assistance through the whole experimental process as well as his patience and editing feedback during the thesis writing. I also owe thanks to my committee members Dr. Shawn Mansfield and Dr. Carol Ritland. They offered me access to their labs to conduct molecular experiments, and suggestions and knowledge on the genetic aspects of my topic. Thanks to Dr. Colette Breuil for allowing me to use her RT-qPCR machine. And thanks to all technicians and students from all aforementioned three labs who provided me with training. Thanks to Limin Liao, Linda Quamme, and Natalie Ryan for their assistance in my lab work. Thanks to Lee Kalcsits and Mina Momayyezi for their help with writing. Thanks to Dr. Carl Douglas and his lab members for their assistance in submitting my samples for RNA-seq. Thanks to Charles Hefer for his contribution in genome analysis. Thanks to Dr. Pia Smets for training me to use growth chambers. Thank you to Dr. Raju Soolanayakanahally and the AAFC Agroforestry Development Centre at Indian Head, SK for providing me with experimental materials. The work reported in this thesis was supported by an NSERC Discovery Grant to my supervisor. I would also like to thank my parents, roommates, lab mates and friends for their support in my life over the last few years, enabling me to quickly adjust to a new environment in Canada.  x  Chapter 1: Introduction 1.1  Dormancy and its environmental control Dormancy is generally defined as “absence of visible growth in any plant structure  containing a meristem” (Lang et al. 1987). Winter dormancy is an important survival mechanism that synchronizes freezing and dehydration tolerance with unfavorable conditions (Olsen et al. 1997). Woody perennials in temperate zones alternate between periods of growth and winter dormancy as environmental conditions change with the annual cycle of the seasons. Lang et al. (1987) divided dormancy into eco-, para- and endodormancy, which is a widely accepted conceptual framework, though debate exists (Horvath et al. 2003). Ecodormancy is caused by limitations in environmental factors, and growth resumes when the conditions again become favorable; in paradormancy, growth inhibition arises from another part of the plant outside the dormant meristem; while endodormancy is caused by internal factors residing within the meristem itself. For a terminal shoot meristem, dormancy is a continuum process initiated with induction (paradormancy), followed by maintenance (endodormancy), and ending up with release (followed by ecodormancy, if conditions remain cold). In most woody species, a shortened photoperiod is the main environmental signal that triggers growth arrest, bud formation, and simultaneously, some degree of drought and cold resistance in most woody plants (Rinne et al. 1998, Welling et al. 1997). Subsequently, cooler temperatures promote increased cold tolerance. If days remain short, buds become endodormant, characterized by an inability to resume growth even when exposed to growth-inductive conditions. This is an important strategy to avoid resuming growth before spring frost (Howell and Weiser 1970). A chilling requirement must normally be satisfied to release trees from endodormancy into ecodormancy, where bud break will occur after a specific heat sum is accumulated (Farmer 1968, Heide 1993, Rinne et al. 1997). Bud break may also occur during paradormancy (i.e., before endodormancy is achieved) if day length or other conditions are suitable. This is known as “lammas growth” or “second flushing”, and is represented by a temporary resumption of height growth without cold exposure before a second and final terminal bud is developed (Olszyk et al. 1998). Lammas growth takes 1  advantage of favorable conditions in late summer, adding substantially to height growth (Cannell and Johnstone 1978, Kaya et al. 1994). However, a second flush is a “double-edged sword” ─ plants may be exposed to the risk of early fall frost because of a delayed hardening of lammas shoots (Cannell and Johnstone 1978). A few species (e.g., Picea sitchensis (Bong.) Carrière and Pseudotsuga menziesii (Mirb.) Franco) are known to undergo lammas before reaching deep endodormancy in nature when environmental conditions are permissive (Cannell and Johnstone 1978, Kaya et al. 1994). Both environmental and genetic controls are involved in lammas shoot formation. The tendency for a second flush decreases with age and with less favorable environmental conditions (Roth and Newton 1996). It is well established that the reduction in day-length below a critical photoperiod is sensed by leaves (Wareing 1956). For decades, it has been clear that some sort of signal from the leaves is transported to the shoot apical meristems (Yang et al. 2007), where it invokes height growth cessation. Corbesier et al. (2007) established that the translocated signal is a reduction in FLOWERING LOCUS T (FT) protein. Height growth cessation (HGC) is followed by a transition from leaf formation to bud scale formation and then the production of leaf primordia within the bud (Owens and Molder 1976, Rohde et al. 2002). The primordia expand rapidly at bud break and grow into what are called preformed leaves. Neoformed leaves develop later; these are derived from new primordia not already present within the bud. Preformed leaves typically show distinct morphological characteristics compared to neoformed leaves. Typically, northern ecotypes have a longer critical photoperiod and initiate dormancy earlier than southern ecotypes (Howe et al. 1995, Junttila 1982). The clinal variation of critical photoperiod with latitude of origin enables temperate and boreal trees to maximize growth, but minimize the risk of frost damage (Hall et al. 2007, Luquez et al. 2008). Although low temperature alone is also reported to be sufficient to induce dormancy, its inductive effect has only been shown in a limited number of species (e.g., Rosaceae) (Heide and Prestrud 2005, Wareing 1956). Møhlmann et al. (2005) successfully induced dormancy in Populus with low night temperatures under long day-conditions, but the depth of the dormancy was questionable. Cool temperatures have been shown to induce dormancy in northern ecotypes of red-osier dogwood (Cornus sericea L.), bypassing the requirement of short-day induction (Svendsen et al. 2006). More typically, temperature appears to affect the depth of dormancy and 2  the rate of dormancy acquisition (Fennell et al. 2005, Heide 2003, Kalcsits et al. 2009, Westergaard and Eriksen 1997). For example, growth cessation and cold acclimation were observed to occur earlier in hybrid poplar exposed to short photoperiod with exposure to warmer nights (Kalcsits et al. 2009). 1.2  Dormancy molecular control and the circadian clock Bud dormancy has been extensively studied. However, the molecular mechanisms  underlying the establishment of dormancy are still poorly understood. Ruttink et al. (2007) published a molecular timetable covering dormancy induction and bud formation in shoot apical meristems of poplars, describing changes in specific regulatory and marker genes, metabolites and hormones. Significant breakthroughs have been achieved with the identification of roles for some regulatory genes and responsive molecules such as CONSTANS (CO), FLOWERING LOCUS T (FT), GIGANTEA (GI), EARLY FLOWERING 3 (ELF3), LATE ELONGATED HYPOCOTYL (LHY), abscisic acid and gibberellic acid (GA), associated with the whole dormancy cycle (Böhlenius et al. 2006, Horvath et al. 2008, Salomé and McClung 2005). The timing of bud dormancy in perennial trees is believed to be controlled by the endogenous circadian clock (Ibáñez et al. 2010a) made up by an input, an oscillator and an output pathway (Harmer 2009). The oscillator works to regulate the timing of outputs which include metabolic and physiological processes, according to environmental inputs including photoperiod perceived by phytochromes (PHY) and cryptochromes (CRY), as well as temperature stimuli (Devlin 2002, Howe et al. 1996, Kozarewa et al. 2010). The circadian oscillator consisting of a positive element (TOC1) and two negative elements (CCA1 and LHY) is based on an interconnected feedback loop (Harmer 2009) (Fig. 1.1).  3  Figure 1.1 The core oscillator made up by CCA1, LHY, and TOC1 in a transcriptional positive/negative loop. CCA1 and LHY bind to TOC1 and repress its transcription. In turn, TOC1 promotes transcription of CCA1 and LHY. GI is required to induce a high level expression of CCA1 and LHY (Salomé and McClung 2005). However the placement of GI in the oscillator is debatable. The circadian oscillator is entrained by light mediated by phytochromes (PHY) and cryptochomes (CRY). ELF3 is a negative regulator of light input. After entrainment, the oscillator controls the expression of downstream CO gene which positively regulates the abundance of FT (Imaizumi et al. 2005). CO is activated by GI through the degradation of CO repressor. (Adapted from Salomé and McClung 2005)  For flowering, a CO/FT regulatory module is known to be involved in day length detection (Suárez-López et al. 2001, Valverde et al. 2004). Functioning of the module is based on the theory of coincidence between the daily CO peak and the light period. Under long days, CO transcription is active before dusk, leading to abundant CO proteins which promote expression of FT. Flowering is stimulated as a result of the subsequent FT peak. Under short days, the accumulation of CO mRNA after dusk does not give rise to accumulated CO protein, because the CO protein is labile in the absence of light. FLOWERING LOCUSTT expression does not peak and hence flowering is prevented (Searle and Coupland 2004). CONSTANS mediates between the circadian clock and the activation of FT in the presence of light (Yanovsky and Kay 2002). Plants expressing CO by the constitutive 35s promoter (35s::CO plants) entrained under long days were transferred to 24 h darkness (DD) and 24 h light (LL) conditions respectively. Elevated FT mRNA abundance was seen in LL condition, whereas FT was degraded gradually in DD condition even though CO mRNA was constantly produced.  4  Mutation of circadian components can lead to an altered CO expression level or pattern (Ibáñez et al. 2010a, Suárez-López et al. 2001). Late flowering was reported for gi (loss-offunction) and lhy (gain-of-function) mutants, which stemmed from a lower FT level caused by a lower CO level. Flowering was accelerated in elf3 mutants that corresponded to an increase in expression of CO (Suárez-López et al. 2001). However, other components like TIMING OF CAB EXPRESSION 1 can affect flowering time without changing CO transcript abundance, but by changing the phase angle of its expression. Appropriate timing of the CO peak is critical for the sensing of day length in plants (Yanovsky and Kay 2002). The role of phytochromes in light sensing is supported by Kozarewa et al. (2010) in an experiment where deficiency in PHYA rendered transgenic hybrid aspen super-sensitive to short days, and timing of bud set was advanced. Overexpression of oat PHYA in hybrid aspen inhibited bud formation in response to short days (Olsen et al. 1997), which could be overcome by the treatment of short days, low night temperature and GA biosynthesis-repressor combined, leading to bud set and cold hardiness development (Møhlmann et al. 2005). New evidence shows that photoreceptors have an additional function of stabilizing CO proteins (Valverde et al. 2004). For example, cry and phyA mutations dramatically reduced CO mRNA level. Correspondingly, FT mRNA decreased to the same extent. The CO/FT day-length discrimination module also functions in the regulation of dormancy timing. The transmitted signal responsible for height growth cessation and bud development is known to be a reduction in the level of FT, a phloem-mobile protein (Corbesier et al. 2007) that controls dormancy induction in angiosperm (Böhlenius et al. 2006) and gymnosperm trees (Gyllenstrand et al. 2007). High levels of Populus trichocarpa FT ortholog (PtFT1) expression in poplar results in uncontrolled growth regardless of day-length, while a deficiency in PtFT1 significantly advances growth arrest (Böhlenius et al. 2006). Recently, a functional differentiation between FT1 and FT2, products from duplication of an original FT gene, is reported to exist in poplars: FT1 promotes reproductive onset in response to winter temperatures, whereas FT2 maintains vegetative growth under permissive photoperiods (Hsu et al. 2011).  5  1.3  Photoperiodic competency and its investigation Most plants must reach a certain age or size, and pass through a transition in meristem  activity from juvenile phase to adult phase, before they become competent to respond to a critical photoperiod for flowering (Lawson and Poethig 1995). There is some likelihood that the competence concept in flowering also applies to bud dormancy, since, as described above, these developmental processes clearly share components of the same photoperiodic timekeeping mechanism. Soolanayakanahally et al. (2013) proposed that balsam poplar would not set bud until shoots acquire photoperiodic competency to respond to the critical day-length. Genotypes from different latitudinal origins were moved to an inductive short-photoperiod chamber at regular intervals upon flushing. On average, they acquired competency about 35 days after bud burst, but it’s unknown whether there is any genetic variation in this trait. The annual acquisition of competence to set bud, its physiological control and adaptive value, are under-explored topics in plant biology. Usually, in indeterminate woody species, shoots respond to the critical short photoperiod in late summer or fall, presumably after competency is achieved. However, by advancing bud burst, global warming may expose temperate and boreal trees to the risk of responding to the critical short photoperiod in spring instead of fall (Soolanayakanahally et al. 2013). Earlier fulfillment of the heat sum requirement forces an early flush (Hunter and Lechowicz 1992, Robert et al. 1975, Tanino et al. 2010), thus shifting the onset of competency to spring (Fig.1.2). Because critical photoperiods in high latitude trees are not much shorter than the longest day of the year (June 21), HGC can occur before the solstice and the growing period may be tremendously shortened. Another way of looking at this is that there is a “competency requirement” that would normally ensure that buds do not set prematurely, in the spring. Large shifts in bud flush phenology resulting from global warming may render this mechanism ineffective in some woody plant species (Soolanayakanahally et al. 2013).  6  Figure 1.2 Expected effects of an early spring on bud phenology at Fort MacMurray, Alberta. Fort MacMurray is close to the center of the natural range of balsam poplar. The length of day (h) at this latitude is plotted (solid black line) as a function of time of year (solar days). The dotted green line is the maximum day length on 21 June (solar day 183). The red band represents the critical photoperiod (~17 h) for the induction of height growth cessation (HGC) in typical Fort McMurray genotypes. Spring flush is achieved about 6 May (solar day 136). If competency is achieved after 35 days growth, then shoots will be responsive to photoperiod beginning 10 June (solar day 171, solid blue line). Normal HGC (denoted by the bracket on the right) will occur within 1-2 weeks after the critical photoperiod is encountered near 21 July (solar day 212).With climate warming, if mean dates of spring flush are advanced by 6 weeks, then most trees will acquire competency earlier (solar day 129, solid cyan line), and cease height growth prematurely (denoted by the bracket on the left). (Adapted from Soolanayakanahally 2010)  Genotypic effects on competency are expected since there is evidence of genetic and environmental effects on dormancy acquisition. It is likely that selective pressures for the appropriate competency requirements vary geographically. For example, just a short period of non-competence should suffice in arctic or alpine environments where bud flush does not occur until very late spring (June in the northern hemisphere). In contrast, delayed competency could be important where growth begins earlier, such as at middle latitudes or low elevations, or in areas moderated by a maritime influence. It is also important to understand how temperature 7  affects competency acquisition since temperature affects physiological development, varies geographically and changes considerably during spring. Unfortunately, the only available assay for competency is to directly test for it by placing actively growing plant material into an inductive environment; for example, a growth chamber. To investigate competency development as a physiological process (e.g., environmental effects, genetic variation, species differences, etc.) requires multiple ramets of any particular clone to be grown under long days and then moved to short days at different intervals after bud break. A competent shoot will undergo HGC within a few days, whereas an incompetent shoot will continue to elongate. Differences between genotypes or treatments should be reflected in timing of HGC. There is no outward phenotypic indication that a shoot is competent, and the lack of any other reliable indicator severely limits research progress. The groundbreaking work of Böhlenius et al. (2006), who discovered that the CO/FT module controls growth cessation in aspen, suggests a possible alternative approach. As mentioned before, a low level of CO2 (CO homolog) protein causes down regulation of PtFT1, leading to height growth cessation within a few days and bud set shortly thereafter. Interestingly, examination of data presented by Böhlenius et al. (2006) shows that PtFT1 transcript abundance was reduced in the weeks prior to induction in a tree growing outdoors, peaking not long before the critical photoperiod was experienced. This pattern suggests that it may be possible to use PtFT1 as a marker for competence. On the other hand, its rise may simply be coincidental with competency development and may not contribute to the process. Hence, other transcripts directly involved in competence development/control may be more informative. 1.4  Molecular methods Since there are no apparent effects on growth or responses during the acquisition of  competence, and because it is endogenously controlled, molecular changes should be discrete, possibly subtle, against a relatively stable background. Whole transcriptome analysis offers the prospect of identifying more precise genetic markers and a means for understanding the functional mechanisms underlying competency development.  8  RNA sequencing (RNA-seq) is a revolutionary tool permitting transcriptome analyses including the detection of alternative splice variants, gene fusion and novel transcripts, and digital measurement of expression levels for genes and transcripts (Bruno et al. 2010, Daines et al. 2011, Marioni et al. 2008, Wang et al. 2010). Compared with conventional approaches of transcriptome analysis such as miroarray, RNA-Seq offers several key advantages (Daines et al. 2011, Marioni et al. 2008, Mizuno et al. 2010, Shendure 2008): 1) no requirements for normalization when comparing expression levels across different experiments; 2) no prior knowledge of the genome sequence; 3) low or no background noise; 4) a dynamic range of expression levels over which transcripts can be detected; and 5) highly reproducible for both technical replicates and biological replicates. The Illumina Inc. Solexa sequencing system is a widely used sequencing platform generating more information with short reads (<100bp) compared with other NexGen sequencing methodologies (Neverov and Chumakov 2010). The Solexa system uses a paired-end sequencing strategy, in which short sequences are determined from both ends of a cDNA fragment, making alignment easier for short reads (Shendure 2008). Solexa’s Genome Analyzer II has been used to successfully sequence transcriptomes of many species, including Candida albicans, Oryza sativa, Arabidopsis thaliana, Drosophila melanogaster, Aspergillus oryzae, and Caenorhabditis elegans (Bruno et al. 2010, Chen et al. 2010, Daines et al. 2011, Hillier et al. 2009, Mizuno et al. 2010, Wang et al. 2010). However, complete dormancy-related transcriptomes have not been reported thus far for poplar, and most of the information available that is available deals with actual dormancy induction and/or development, and not readiness (i.e., competency) to become dormant. Analysis of RNA-seq data remains a challenge statistically due to the large amount of information generated per lane (Wang et al. 2009). Fortunately, a series of software applications are available which non-bioinformaticians can easily employ. Sequenced reads are first aligned to a reference genome with TopHat. Mapped reads are then fed to Cufflinks to generate a transcriptome assembly. These assemblies are usually merged together for further analysis by Cuffmerge, part of the Cufflinks package. Cuffdiff quantifies expression levels by reads per kilo base gene and million reads (RPKM), and tests the statistical significance of observed changes (Mizrachi et al. 2010, Mizuno et al. 2010, Trapnell et al. 2010, Trapnell et al. 2012). Galaxy 9  (Trapnell et al. 2010, Trapnell et al. 2012) is a newly developed open web-based platform for genome analysis combining all those aforementioned tools. Changes in candidate gene transcripts identified via RNAseq are often validated by using reverse transcription (RT) followed by polymerase chain reaction (PCR). This has been a proven tool for validating high resolution transcriptome analyses with high reproducibility, sensitivity and accuracy (Marioni et al. 2008, Mizuno et al. 2010, Wang et al. 2010, Trapnell et al. 2010). There are two common methods of presenting RT-PCR data: relative expression and absolute transcript abundance. Absolute expression calculates the exact copy number from a standard curve (Pfaffl 2004, Schmittgen and Livak 2008).However for this thesis, relative quantification is sufficient to confirm expression changes revealed by transcriptome analysis. Theoretically when using the ideal 2-fold amplification rule, the amount of PCR product is determined by the amount of starting target (template) and number of amplification cycles. As amplification reactions proceed, product accumulates and the fluorescence signal increases (Applied Biosystems Inc. 2008, Bio-Rad Laboratories Inc. 2006). The number of PCR cycles a reaction takes to reach a designated signal level is called the threshold cycle (CT). The larger the template copy number, the fewer amplification cycles are required, hence, the smaller the CT value. By knowing the CT value of each sample for a given target gene, the relative initial abundance of the target in each sample before PCR amplification can be deduced, which forms the basis for relative RT-qPCR quantification. Mathematical models have been developed to calculate the relative expression ratios of a target versus chosen housekeeping genes (reference genes) relative to a calibrator (usually control samples). The PCR efficiency-corrected model proposed by Pfaffl (2001) achieves high reproducibility and accuracy because efficiency differences between targets and housekeeping genes are accounted for. However, quantification by this model strongly relies on valid reference genes for data normalization. Some of the most frequently used reference transcripts are 18S rRNA, glyceraldehyde-3-phosphate dehydrogenase, polyubiquitin, actin (ACT), elongation factor1-alpha , and -tubulin (TUA) (Brunner et al. 2004, Caldana et al. 2007, Czechowski et al. 2005, Gutierrez et al. 2008). However, studies have shown that these putative reference genes can vary in expression level over different tissue types, developmental stages and environmental conditions (Brunner et al. 2004, Gutierrez et al. 2008). Using these traditional housekeeping 10  genes as reference genes without appropriate validation undermines reliability of results obtained from RT-qPCR. Brunner et al. (2004) recommended single factor analysis of variance (ANOVA) and linear regression to verify expression stability of reference genes in poplar. Engagement of more than one reference gene is recommended for transcript expression analyses (Czechowski et al. 2005). The reliability of results is also influenced by RNA quality, primer specificity, overall PCR efficiency, etc. (Applied Biosystems Inc. 2001, Bustin et al. 2009, Dušanić et al. 2012, Pfaffl 2004). 1.5  Populus species as model plants The genus Populus (poplar) has ~29 recognized species and is a major component of  boreal and temperate forests in the Northern Hemisphere (Eckenwalder 1996). Extant poplars are commonly classified into six sections: Aigeiros (cottonwoods), Populus (aspens), Tacamahaca (balsam poplars), Leucoides (big leaf poplars), Turanga (subtropical poplars) and Abaso (Mexican poplars). The section Tacamahaca includes some 12 species, including Populus balsamifera L. (balsam poplar). Balsam poplar extends across northern North America from Newfoundland (the Atlantic coast) to Alaska (the Pacific coast), skirting the greater portion of British Columbia (Halliday and Brown 1943). Soon after the retreat of the continental ice sheet some 12-15 thousand years ago, the abundant light windborne seeds and an ability to recover quickly from disturbances enabled balsam poplar to recolonize much of the ice-freed area. Balsam poplars are usually found on moist, rich, and low-lying ground (e.g., river valleys), growing in pure stands or mixed with willows, alders and other boreal species (Farrar and Evert 1997). As a consequence of their natural attributes and available genomic resources (Tuskan et al, 2006), poplars have become model systems for tree molecular biology (Brunner et al. 2003, Cronk 2005, Taylor. 2002). They grow rapidly and can easily be transformed, regenerated, and vegetatively propagated. High levels of genetic and ecological diversity in Populus, resulting from wide geographic distributions and interspecific crosses, facilitate new insights into the molecular mechanisms underlying tree adaptation (Cronk 2005). Populus trichocarpa Torr. & Gray (which is synonymous with P. balsamifera L. ssp. trichocarpa (Torr. & Gray) Brayshaw) was the first tree species to have its genome entirely sequenced (Tuskan et al. 2006). A wide 11  range of poplar genomic resources is publicly available, and many of these can be directly accessed via GenBank and the worldwide web. Poplars belong to the Rosids, and thus are well positioned for comparative analysis with other intensively studied and sequenced eudicot genomes (Brunner et al. 2003). 1.6  Research objectives This purpose of this thesis was to lay a foundation for identifying differentially expressed  genes associated with photoperiodic competency, and ultimately to approach an understanding of the environmental, physiological and molecular regulation of competency in dormancy. The objectives were to: 1. perform a global transcriptome survey to identify candidate gene trancripts for a qRT-PCR assay to detect competency in Populus balsamifera. 2. test whether the acquisition of competence is dependent on time per se, or on developmental stage (e.g., leaf number). 3. determine whether “spring” temperatures could influence the timing of competence development. 4. ascertain whether there is clonal variation in the timing of competency acquisition. 5. ascertain whether “competency acquisition” coincides with the transition from preformed to neoformed leaves.  12  Chapter 2: Materials and Methods 2.1  Materials The genetic material used in this study consisted of four genotypes of balsam poplar from  two populations in the AgCanBaP Populus balsamifera provenance collection (Fig. 1.2): Fort McMurray 12 & 15 (F-12, F-15) and Minnedosa 2 & 13 (M-2, M-13). These particular genotypes were chosen because previous work had indicated that they have a critical photoperiod between 16 and 20 hours (Soolanayakanahally et al. 2013).  Figure 2.1 The range and AgCanBaP provenance collections of Populus balsamifera L. Genotypes used in the present study are from Fort McMurray and Minnedosa (marked by red diamonds). (Adapted from Soolanayakanahally 2010)  13  2.2  Methods  2.2.1  Experiment I Plant growth measurements and sampling For Experiment I, dormant cuttings were collected in the winter of 2010 from Indian  Head, Saskatchewan, and stored at 5°C in sealed plastic bags to ensure adequate chilling had been provided. In February 2011, the cuttings were cut into single-node segments approximately 6 cm in length. The stem segments were then rooted in 965 mL D40H plastic DeepotsTM (Stuewe & Sons, Tangent, Oregon) filled with a mixture of Sunshine-2 (Sun Gro Horticulture, Vancouver, Canada) growing mix (60%), peat (30%) and vermiculite (10%). In total, 66 ramets per genotype were started under long-day (LD), high temperature (HT) conditions (20h day, 20°C) in Conviron E15 (Winnipeg, Canada) growth chambers at the University of British Columbia, Forest Sciences Centre. Photosynthetic photon flux density was maintained around 360 μmol m-2s-1 by fluorescent lamps and incandescent bulbs (1:1). An extra set of age control plants (three replicates) for the last harvest of M-13 was started 28 days later. Plants were watered to keep soil moist before bud flush, and watered every day after bud flush. After rooting, plants were fertilized twice a week with modified Hoagland’s solution (½ strength micronutrients and ¼ strength macronutrients) at a pH adjusted to 5.8–6.3 (Hoagland and Arnon 1950). Four days after flush, 24 ramets per genotype were transferred to another growth chamber to continue growth under long days (LD) at low temperature (LT) (i.e., 20h day, 15°C). Three ramets per genotype were also moved to a third chamber providing inductive 16 hour short day (SD) conditions at 20°C (HT). The remaining ramets were kept under LD/HT conditions. Beginning three weeks after flush, three replicates from each of the four genotypes were moved to the SD/HT chamber from both LD/LT and LD/HT chambers at regular weekly intervals. There were seven transfers altogether, leaving three replicates per genotype for LD controls under both HT and LT conditions. Young shoots with ~4 fully unfolded leaves were harvested (N = 3) from each genotype from the LD/HT chamber at approximately 18, 25, 32, 39, 46 and 53 days (d) after bud flush. Harvest timing was approximate because of asynchronous bud flush for individual ramets for 14  each genotype. Plant material was collected around 2pm and flash frozen in liquid nitrogen and stored at -80°C until RNA extraction. Shoot development (phenological stage) was monitored using a scale of 0 - 10 for phenology according to Soolanayakanahally et al. (2013). Bud flush was recorded as the mean date of stage 2 (= bud open with visible green tip) and stage 4 (= very small leaves with visible petiole). Height and leaf number of each ramet in all treatments were recorded every two days throughout the experiment. Dates of HGC were indicated by break points in growth curves detected by two-segment linear regression in GraphPad Prism 6 (Fig. 2.2). Examination of these data helped narrow the choice of tissue samples for transcriptome analysis. To test for effects of temperature and genotype on competency acquisition and speed of HGC after competency development, two way ANOVA, and Kruskal-Wallis one way ANOVA on ranks and Mann-Whitney rank sum tests were used when assumptions of normality and homogeneity of variance could not be met.  Figure 2.2 An example of how break points were located, in this case for day of height growth cessation (HGC), using a two-segment linear curve-fitting program.  15  Total RNA extraction and DNase treatment Total RNA was separately isolated from the leaves and remaining shoots (i.e., adjacent  elongating stem tissue, including the shoot apex; hereafter called “stem”), by grinding in liquid nitrogen using a modified 100mg protocol based on Kolosova et al. (2004). Extracted total RNA was quantified on a NanoDrop 2000cTM Spectrophotometer (Thermo scientific Inc. Wilmington, DE USA). The quality was checked on 1.5% agarose gel with 1× TBE. Genomic DNAcontaminated RNA was then treated with TURBOTM DNase (Ambion, Inc. California, USA) according to the manufacturer’s protocol. Resulting RNA was aliquoted and stored at -80°C for later use.  RNA-seq and data analysis RNA samples of M-13 from 18, 25, 46 and 53 days were chosen for transcriptome  sequencing to pretest differential expression of genes before and after the acquisition of competency for dormancy induction. RNA quality was assayed on an Agilent 2100 BioanalyzerTM (Agilent Technologies, Inc. Santa Clara, CA). Only RNA samples with RNA Integrity Number (RIN) above 7 were accepted for submission. Qualifying 18 and 25 day RNA samples were combined to constitute a “pre-competency” sample (total 15 µg), as were 46 and 53 day RNA samples for a “post-competency” sample. RNA-seq (including the library construction) was conducted by the Michael Smith Genome Science Centre, Vancouver, Canada (http://www.bccrc.ca/dept/cmsgsc), using a HiSeqTM 2000 sequencing system (Illumina, Inc. San Diego, CA) with a paired-end sequencing strategy. In total, there were 156,183,891 pairs of 75bp reads produced from the two samples multiplexed in one sequencing lane. Resulting RNA-seq data were analyzed on a web-based Galaxy platform (http://west.zoology.ubc.ca:8081/root) according to the workflow in Appendix A. Further screening for candidate genes of interest from the RNA-seq results was undertaken by first ruling out genes whose expression levels of both pre- and post-competency were below 1, then cutting off genes with a less than 2-fold change in expression. An annotation analysis for the rest of genes on the list was run against the Populus trichocarpa v3.0 annotation data file. A gene ontology analysis (GO) was also conducted to look for systematic changes between the two phases. GO analysis was performed using BiNGO 2.44 (Maere et al. 2005). A 16  custom annotation file was made using the Biomart tool available from the phytozome website (http://www.phytozome.net/poplar.php), and an ontology file was downloaded from the website (http://www.geneontology.org/). File format was referred to http://www.psb.ugent.be/cbd/papers/BiNGO/Customize.html. Emphasis was placed on genes with higher fold changes and higher expression levels during either phase. And genes believed to be involved in gene regulation and signal transduction pathway were favoured. 2.2.2  Experiment II A follow-up growth chamber experiment (M-13 and M-2 at 20°C) was conducted  beginning in January 2012. Methods were similar to Experiment I except that two more replicates were added to each treatment and the inductive short photoperiod was reduced to 15h from 16h to prevent second flushing, which was observed in some ramets in Experiment I. One week after all transfers were completed, plants were moved back into long day (20h) conditions to assess depth of dormancy. Date of lammas (i.e., a second flush of growth) was recorded. In contrast to Experiment I, RNA was extracted from whole young shoots. Most RT-qPCR experiments were conducted after the follow-up growth chamber experiment, except for qPCR assays on CO2, GI5, and ELF3 transcripts, which were conducted before the follow-up growth chamber experiment while waiting for RNA-seq results.  cDNA conversion A two-step RT-PCR was performed. Total RNA was converted to cDNA with an oligo  d(T)23 primer using ProtoScriptTM AMV (New England Biolabs, Ipswitch, MA, USA) first strand cDNA synthesis kit.  Primer design and optimization Thirteen promising candidate genes were chosen from the narrowed-down RNA-seq list  based on overall expression levels, fold-changes and reference to relevant literature. Corresponding sequences were obtained from the phytozome website according to their transcript ID. Specific primers (Table B.1) were designed with the online program Primer 3.0 (Rozen and Skaletsky 2000) using primer picking conditions including predicted range of 17  melting temperatures (57-65°C), GC content 40%-70%, primer length 18-27 nucleotides, and PCR amplicon length 80-250 nucleotides. A blast search was then performed on Primer Blast against a Populus trichocarpa mRNA library to check primer specificity (http://www.ncbi.nlm.nih.gov/tools/primer-blast/). Intron spanning and intron flanking primers were favored if possible to avoid genomic DNA contamination. After receiving primers from Eurofins MWG Operon Inc., a 100 pmol/L stock solution was made and diluted 10-fold to obtain a working concentration. Optical annealing temperatures were assessed on a S100TM Thermal Cycler (Bio-Rad Inc. Hercules, CA, USA) with gradient temperatures for each primer pair set to: 94°C for 5min; 45 cycles of 94°C for 50s, 50°C for 50s, 72°C for 50s; and then 72°C for 5min. Specificity of amplification products was accessed with a 1.5% agarose gel on a minimum of three cDNA sources and a negative control for each primer pair.  RT-qPCR assay Reverse transcriptase qPCR was performed to validate RNA-Seq analysis and to help  further narrow-down the gene marker choices. Real time RT-PCR was conducted on either a DNA Engine Opticon 2 (MJ Research, Waltham, MA, USA) or a CFX96TM Real-Time PCR Detection System (Applied Biosystems Inc., Foster City, CA, USA) using SYBR Green fluorescence to detect sdDNA synthesis in optical 96-well plates. The following protocol was applied: 95°C for 15min; 40 cycles of 95°C for 15s, 60°C for 50s, 72°C for 30s; and then 72°C for 10min. The fluorescence signal was monitored at the end of each cycle and melting curve analysis was conducted from 65°C to 95°C, with data capture every 0.2°C during a 1s hold. Each reaction of 20µL volume contained 90ng cDNA and 2× master mix 10µL (DyNAmo HS SYBR green kit). Each candidate gene was tested across different time points with three biological replicates for each time point and three technical replicates for each biological replicate. A series of 4-fold dilutions was included for both target genes and reference genes to construct standard curves in order to evaluate the PCR assay and calculate amplification efficiency. A sample maximization approach was followed whereby all samples were analyzed for a given gene in the same run, to avoid run-to-run variation. Otherwise, inter-run calibration was performed to ensure consistency among different runs. Controls with either no RT or no template were also included to test for sample contamination.  18  Data were analyzed by Option Monitor analysis software version 2.02 (MJ Research) or CFXTM Manager Software. Baseline range was set in log-view to ensure a start cycle after background noise and an end cycle before the signal shift due to amplification across the entire reaction plate. The setting of 3-15 cycles was usually adopted if no unexpected amplification curve distortion occurred. A threshold line was manually adjusted such that, on a baselinesubtracted graph of the logarithmic increase in fluorescence versus cycle number, it surpassed background noise and intersected the fluorescence traces at a point where cDNA began to be amplified exponentially. On the other hand, the standard curve was also considered so that the position of the threshold line provided a perfect fit of the experimental data into a regression line.  Calculations and statistics PCR efficiency was automatically calculated by the qPCR machine using the following  formula:  (  )  Two reference genes (TUA and ACT) were used in the analysis. Reference gene expression levels were calculated by:  √ where, n is the number of reference genes (in this case n=2) and Eref is the amplification efficiency plus 1 (i.e., Eref = Efficiency + 1) of the reference gene(s). The relative expression level of each candidate gene at time point n was expressed as a ratio compared to time point 1 normalized to the expression of the reference genes using:  where, Ecan is the amplification efficiency plus 1 of the candidate gene. ∆CTcan(tp1-tpn) and ∆CTref(tp1-tpn) are the differences in CT value of each time point compared to time point 1. 19  Data from low efficiency (out of the range of 85-110%) reactions were used with discretion. Consistency across replicates (standard deviation < 0.4) was required. Reoptimization of the qPCR assay was required depending on circumstances. All statistics were done in GraphPad Prism 5/6. Two way analysis of variance (ANOVA) was used to validate stability of expression of the reference genes. One way ANOVA followed by a Tukey’s multiple comparisons test was applied to test whether the expression level changed over time for RTqPCR (P = 0.05). Data were ln transformed where necessary to meet assumptions of normality and homogeneity of variance.  20  Chapter 3: Results 3.1  Growth chamber data The experimental data indicated that before a certain time point, earlier transfer from LD  to SD did not result in earlier HGC. For example, in Experiment I at 20°C (Fig. 3.1), the controls and the next two transfers of the M-13 clone stopped growing simultaneously at ~50 days after bud flush even though the controls were placed into short-day (SD) just 4 days after flushing and the next two transfers occurred at ~18 days and ~25 days, respectively. Later transfers then resulted in later HGC. The transition from a more-or-less stable timing for HGC under SD to a timing dependent on the age at transfer corresponds to the acquisition of competency. This particular break-point, where detectable, defines the boundary between pre- and postcompetency and varied among genotypes (P= 0.0279; Fig. 3.4). The break-point representing competency achievement was very distinct in some cases (M-2 and M-13 at 20°C; Fig. 3.1), but less distinct or ambiguous in others (M-2 and M-13 at 15°C; Fig. 3.2). In Experiment I it took M13 about 32 days to reach competency when grown at 20°C. M-13 was moved forward for transcriptome analysis, because samples from this clone under these treatment conditions covered the whole process of competency acquisition (delimited by the red arrow in Fig. 3.1). The time necessary for HGC to occur after transfer varied among genotypes (P<0.0001; Fig. 3.5). HGC occurred most rapidly in F-12, within about 7 days after transfer once competency was achieved. In some situations, the speed of HGC seemed relatively constant (e.g., M-13 at 20°C; Fig. 3.1). In most cases, however, it gradually increased, as indicated by a narrowing of the gap between the observed shoot age at HGC and the 1:1 line (e.g., F-12 at 20°C; Fig. 3.1). This pattern was also generally evident in all circumstances where a transition between pre- and post-competency could not be detected (Fig 3.1, 3.2 and 3.3), suggesting a much more gradual transition between states in these cases. Comparison of Fig. 3.1 and Fig. 3.3 reveals differences between the two parallel experiments in 2011 and 2012. Height growth cessation of controls at 20°C, for both M-2 and M-13, was advanced from 40d to 36d and 52d to 46d after flushing, respectively, in 2012 versus 2011. Likewise, the shoot age at competency was advanced from 18d to 15d and 19d to 14d for 21  M-2 and M-13, respectively. The speed of HGC was also faster; after competency was achieved, it took fewer days for M-2 (from 13 days to 11 days) and M-13 (20 days to 13 days) to cease height growth in 2012. Lower temperature (Fig. 3.2) caused non-uniform growth among individual ramets leading to greater errors for curve-fitting compared to higher temperature (Fig. 3.1). Growth at either 15°C or 20°C did not have a consistent effect on the timing of competency development. Higher temperature seemed to advance competency achievement in some cases (e.g., M-13 in 2011; Fig. 3.4), but delayed competency in other cases (e.g., F-12; Fig. 3.4). However, plants grown at 20°C responded to short days slightly faster than those grown at 15°C (P<0.0001; Fig. 3.5). The time range was reduced from 7-20 days for competent poplars to cease height growth under 15°C, to 10-24 days under 20°C. There was also a significant genotype×temperature interaction effect (P=0.0014) on HGC speed.  22  F -1 2 2 0  C  F -1 5 2 0  C 100  Shoot age at HG C  Shoot age at HG C  100 80 60 40 20  80 60 40 20 0  0 0  20  40  60  80  0  100  20  40  60  80  100  S h o o t a g e a t tr a n s fe r/h a r v e s t  S h o o t a g e a t h a rv e s t/tr a n s fe r  T ra n s fe r H a rv e s t  M -1 3 2 0  C  M -2 2 0  C  100 Shoot age at HG C  Shoot age at HG C  100 80 60 40 20  80 60 40 20 0  0 0  20  40  60  80  S h o o t a g e a t tr a n s fe r/h a r v e s t  100  0  20  40  60  80  100  S h o o t a g e a t tr a n s fe r/h a r v e s t  Figure 3.1 Relationship between the age at HGC and the age at transfer to short day (SD) conditions when grown at 20°C in Experiment I (2011). The age at bud flush corresponds to 0. Height growth cessation cannot occur below the 1:1 line. The distance to any point above the 1:1 line is the time required for HGC after transfer to SD. The point of inflection (most obvious in M-13 at ~30 days) corresponds to the age of competency acquisition (indicated by the arrow). There were three biological replicates for each transfer, and all of these are individually shown except for those whose HGC could not be objectively determined by GraphPad Prism 6. Ages at which samples were harvested for molecular analysis are shown on the x axis.  23  Figure 3.2 The relationship between the age at HGC and the age at transfer to short days (SD) when grown at 15°C in Experiment I (2011). Other details as in Fig. 3.1 (except that no samples were harvested for molecular analysis in this treatment).  24  Figure 3.3 The relationship between the age at HGC and the age at transfer to short days (SD) when grown at 20°C in Experiment II (2012). See details in Fig. 3.1.  25  Figure 3.4 Shoot age at competency for different genotypes in different treatments. Competency acquisition was identified by the breakpoints in Fig. 3.1-3.3 after applying a two-segmental curve fitting program. Standard error is presented.  26  Figure 3.5 HGC speed for different genotypes in different treatments. The speed of HGC is inversely related to the number of days it takes for height growth cessation, after transfer of a competent plant into short day conditions. Bars present mean numbers of days with standard error.  With the exception of F-12 and F-15 under 20°C, genotypes in both years were slow to start producing new leaves (Fig. 3.4A and B), coinciding with the transition from preformed leaves to neoformed leaves. According to leaf count data in Table 3.1, between 4 and 7 preformed leaves were produced from each bud. This agreed with counts of leaf primordia present within buds prior to flushing, as determined under a dissecting microscope. There were 6-7, 5, 5-6, and 3-4 preformed leaves in buds of F-12, F-15, M-2 and M-13, respectively. The leaf number at competency (between 8 and 13 in Experiment I at 20°C) was greater than the number of preformed leaves, indicating competency acquisition was normally achieved after the emergence of several neoformed leaves (Table 3.1). The later the plants were transferred to SD, the more neoformed leaves they produced before HGC (Fig. 3. 5). The number of leaves at competency was not significantly correlated with the number of preformed leaves. Between genotypes, the range in minimum leaf number at HGC was from 17 to 19. The difference between the minimum final leaf number at HGC and the leaf number at competency confirmed the fact that plants did not stop growing immediately under an inductive photoperiod after 27  competency acquisition. Because of earlier competency acquisition and HGC in Experiment II (2012), there were fewer leaves at the end of the experiment and at competency acquisition for M-2 and M-13 compared to the same genotypes in Experiment I (2011).  28  A  Leaf num ber  F -1 2 1 5  C  F -1 5 1 5  C  35  35  30  30  25  25  20  20  15  15  10  10  5  5  0  0 0  20  40  60  80  100  0  20  Leaf num ber  35  35  30  30  25  25  20  20  15  15  10  10  5  5  80  100  80  100  0  0 0  Leaf num ber  60  M -1 3 1 5  C  M -2 1 5  C  20  40  60  80  0  100  20  40  60  S h o o t a g e s in c e f lu s h  S h o o t a g e s in c e f lu s h  F -1 2 2 0  C  F -1 5 2 0  C  35  35  30  30  25  25  20  20  15  15  10  10  5  5  0  0  0  20  40  60  80  100  0  20  40  60  80  100  80  100  M -1 3 2 0  C  M -2 2 0  C  Leaf num ber  40  35  35  30  30  25  25  20  20  15  15  10  10  5  5  0  0  0  20  40  60  80  S h o o t a g e s in c e f lu s h  100  0  20  40  60  S h o o t a g e s in c e f lu s h  29  Figure 3.6 The relationship between leaf number and shoot age following bud flush for M-2, M-13, F-12 and F-15 grown at either 15°C or 20°C. Average leaf number on growing stems (i.e., before HGC) is plotted for each genotype and the different temperature treatments. Means with standard deviations (N=3) are shown. A) Data from Experiment I (2011). B) Data from Experiment II (2012). In B, the horizontal line indicates the approximate number of preformed leaves recorded after casual observation.  30  A  F -1 2  1 5 °C  25  20  15  2 0 °C  30  F in a l le a f n u m b e r  30 F in a l le a f n u m b e r  F -1 5  2 0 °C  25  20  15  10  10 0  10  20  30  40  50  60  0  70  10  20  30  40  50  60  70  S h o o t a g e a t tra n s fe r  S h o o t a g e a t tra n s fe r  2 0 °C  M -2  15 C  30  F in a l le a f n u m b e r  30 F in a l le a f n u m b e r  20 C  M -1 3  1 5 °C  25  20  15  10  25  20  15  10 0  10  20  30  40  50  S h o o t a g e a t tra n s fe r  60  70  0  10  20  30  40  50  60  70  S h o o t a g e a t tra n s fe r  31  Figure 3.7 The final number of leaves, after HGC, on stems transferred to SD at different shoot ages. A) data from Experiment I (2011). B) data from Experiment II (2012). In A, F-15 under 15°C was excluded because of low quality of data.  32  Table 3.1 Summary of the mean number of leaves at different developmental stages and the timing of competency acquisition for four Populus balsamifera L. genotypes grown at either 15°C or 20°C. Refer to Fig. 3.4 and 3.5 for the calculation of shoot age at competency acquisition and HGC speed. The number of preformed leaves was obtained by fitting a two-segmental nonlinear curve (with slope1 constrained to 0) to Fig. 3.4 and then taking the y-intercept. The timing of competency acquisition was determined as the breakpoint in age at HGC (Fig. 3.1 and Fig. 3.2) using the same curve fitting program. Standard error was given by the program. Leaf number at competency was averaged from all plants in one treatment when competency was reached. The minimum number of leaves (from data on controls) was extracted as means from Fig. 3.5 Standard error was also calculated.  Treatments  Shoot age at competency acquisition  HGC speed  The number of leaves at competency  The number of preformed leaves  F-12 15°C (2011)  The minimum number of leaves at HGC  21.4±2.6  10.3±0.4  8.5±0.2  6.9±0.2  F-15 15°C (2011)  21.0±9.9  24.5±1.4  6.4±0.3  6.4±0.8  M-2 15°C (2011)  27.2±4.9  21.1±.08  8.1±0.4  5.2±0.4  M-13 15°C (2011)  40.3±3.3  22.7±2.5  10.1±0.3  4.5±0.3  F-12 20°C (2011)  27.8±2.1  7.0±0.7  13.4±0.3  17.0±0.6  F-15 20°C (2011)  23.3±2.0  16.0±0.4  7.6±0.2  17.0±0.6  M-2 20°C (2011)  22.4±1.8  13.5±0.7  9.3±0.2  5.5±0.4  18.0±1.0  M-13 20°C (2011)  31.6±1.8  20.0±0.8  10.2±0.2  4.4±0.2  19.3±0.3  M-2 20°C (2012)  17.8±5.9  14.0±0.9  7.7±0.1  7.3±0.1  15.0±0.3  M-13 20°C (2012)  25.71±1.8  14.6±0.8  6.6±0.1  5.8±0.1  14.6±0.2  33  After height growth cessation under SD conditions, lammas growth (a flush of growth after initial bud set) occurred in plants that were transferred to long-day (LD), growth-inductive conditions. The number of days taken for lammas growth displayed an exponential relationship with the number of days since HGC (Fig. 3.6). Lammas growth occurred in all transfers after being transferred to LD except for the controls and the earliest transfer to SD. Even after almost 40 days since HGC, balsam poplar was not quite completely dormant in this particular experiment. However, the controls and the earliest transfers, which were under SD for about 43 days since HGC, did not resume growth prior to the termination of the experiment at 69 days of LD treatment.  Figure 3.8 Time taken to lammas after transfer from short days (SD) back into long days (LD). On April 17, 2012, all dormant plants from Fig. 3.3 (first transfers to seventh transfers plus controls) were moved into a LD treatment. Date of lammas was recorded for each individual until June 25, 2012.  3.2  RNA sequencing data Using cuffdiff analysis, a total of 504 candidate genes (Table B.2) were detected with  significant changes in expression level between pre- and post-competency. Accounting for all criteria mentioned in the methods, the list was reduced to 13 candidate genes for RT-qPCR (Table 3.2).  34  Table 3.2 Candidate gene list narrowed down from RNA-seq data for RT-qPCR. Gene transcripts marked with an asterisk were successfully assayed by RTqPCR.  Gene ID  Pre-competency expression level  Post-competency expression level  log2 fold change  G1  Potri.018G079100  30.22  0.13  -7.82  G2*  Potri.004G148500  2.88  0.10  -4.84  Clast3-related  G3  Potri.006G243600  116.24  9.33  -3.64  BURP domain-containing protein  G4  Potri.013G136400  162.60  23.73  -2.78  O-methyltransferase family protein  G5*  Potri.005G113700  48.11  8.51  -2.50  2OG-Fe(II) oxygenase superfamily  G6*  Potri.017G051100  8.05  46.78  2.54  G7*  Potri.001G222000  1.42  423.54  8.22  MLP-like protein 31 Pathogenesis-related thaumatin superfamily protein  G8*  Potri.005G169200  0.00  104.34  1.80e+308  G9  Potri.018G122700  11.10  2.09  -2.41  Inorganic H pyrophosphatase family protein  G10  Potri.015G144800  1.11  19.03  4.10  PAR1 protein  G11*  Potri.T161500  35.06  4.48  -2.97  Ribosomal protein L11 family protein  G12*  Potri.014G006000  3.04  0.26  -3.56  Heptahelical protein 4  G13  Potri.014G103000  0.20  1.49  2.91  Homeobox 7  Annotation  35  GO categories over-represented during competency acquisition were representative of vigorous growth. Dominant activities were grouped into two main network systems (Fig. 3.7). In the left network system, gene categories significantly enriched (p < 0.05) were associated with cellular activities (indicated by cytoplasm in Fig. 3.7) such as metabolic reactions, cell division, and calcium ion-dependent signaling activities. In the right network system, gene categories significantly enriched were related to metabolic processes such as translation, protein metabolic activities, catalytic activities, and biosynthetic reactions.  Figure 3.9 Over-represented GO categories during competency acquisition. All genes with significant changes in expression level from pre-competency to post-competency phase were considered for the enrichment analysis. Note that circle size is proportional to the number of genes in each category. Colors shaded according to significance level (white – no significance; yellow – meets threshold false discovery rate of 0.05; deepening towards orange indicates the false discovery rate falling progressively below 0.05).  3.3  RT-qPCR data A total 18 pairs of primers were designed (Table B.1); two were for reference genes,  13 were from RNA-seq (Table 3.2), and the remaining three were from literature review. 36  Quantitative PCR assays were successful for 10 candidate genes and 2 reference genes. Primer specificity was assessed; a single peak in the melt curve indicated specific amplification and PCR efficiency was also validated (Appendix C). The two reference genes, ACT and TUA, were stably expressed among different time points within a defined tissue type, but there were differences in expression level between tissue types (not shown). The three initial candidate genes (GI5, ELF3, and CO2) chosen from the literature (Böhlenius 2007, Fowler et al. 1999, Yu et al. 2008) were examined via RT-qPCR. GI5 expression did not change over time or with the shift from incompetent to competent (Fig. 3.8). Abundance of both CO2 and ELF3 peaked at 32d, coinciding with the acquisition of competency (30d). In particular, a strong difference as high as three times between 32d and the other time points made CO2 stand out as a potential gene marker for competency. A significant five-fold decline during the transition to competency was also detected via RNAseq (Table B.2). Primers were successful in transcript amplification for just seven of the 13 candidate genes from the RNA-seq results (Fig. 3.9). Ability to assess the consistency between the RNA-seq results and qPCR data was limited because of the lack of comparable samples in the two analyses (i.e., RNA-seq: between pooled 18d and 25d and pooled 46d and 53d samples; PT-qPCR: three replicate samples at each of 18d, 25d and 53d). However, in all candidate genes, with the exception of G8, changes in expression were approximately equal to RNA-seq results. According to RNA-seq data (Table 3.2), G2, G5, G11, and G12 displayed a decrease in expression from pre-competency to post-competency while G6, G7, and G8 showed the opposite expression pattern . When the expression of G8 at 18 and 25 days after flush is combined from the qRT-PCR (Fig 3.9), the directional change in expression is not consistent with the increase in expression for G8 from the RNA-seq analysis (from 0 prior to competency acquisition to 104.34 afterwards). G6 and G7 stood out as promising gene markers because of distinct changes between pre-competency and postcompetency phases. G6 increased its expression level gradually from 18d to 53d, consistent with the degree of change in expression demonstrated by RNA-seq analysis. The change in expression for G7 from qPCR also matched that from RNA-seq, with a constant expression level between 18d and 25d followed by an increase in expression at 53d. 37  Figure 3.10 Expression levels of GI5, ELF3 and CO2 in leaves of M-13 sampled from the 2011 (Experiment I) growth chamber experiment. Expression level at each time point was normalized to a reference gene (ACT) and compared to that at 25d. Mean expression level of three biological replicates with standard deviation is shown. One way ANOVA followed by multiple-comparson testing (Tukey method) was performed on data shown in each panel. Data were ln transformed where necessary to meet assumptions of normality and homogeneity of variance. Where present, significant differences between means are indicated by letters above each bar (90% confidence interval was applied). Black bars represent the growth phase prior to competency acquisition (pre-competency), while grey bars represent the phase after competency acquisition (post-competency).  38  Figure 3.11 Expression levels for G2, G5, G6, G7, G8, G11 and G12 at 18, 25 and 53 days after bud flush. Plants were sampled from the 2012 growth chamber experiment (Experiment II). RNA was extracted from pooled leaf and stem tissue. Three biological replicates for each time point were pooled after RNA isolation for RT-qPCR. There were three technical replicates for each time point. Standard error bars are based on technical replicates and do not indicate biological differences. Expression level at each time point was normalized to two reference genes ACT and TUA, and compared to that at 18d. See details in Fig. 3.8.  39  To verify if trends in G6 and G7 were repeatable in samples collected from Experiment I in 2011, more time points and tissue types were included for further RT-qPCR assay (Fig. 3.10). No tissue was sampled on 18d, and shoot RNA quality of 32d and 39d was not good enough, so these samples were excluded from RT-qPCR. G7 leaf, G6 pooled stem and leaf RNA, and G7 pooled stem and leaf RNA showed significant changes in expression level (P value = 0.0077, 0.0196 and 0.0372, respectively). For G6 in pooled stem and leaf RNA, abundance significantly increased from 25d to 46d/53d. No significant change was detected between 25d and control, or between 46d and 53d. For G7 in leaf tissue, a significant increase was observed from 25d to 32d/39d/46d/53d. The 25d sample and the control were similar to each other, as were samples taken at 32d, 39d, 46d, and 53d. G7 in pooled stem and leaf RNA showed a higher expression level at 53d compared to 25d or the control, which were not different. Expression at 46d appeared similar to 53d but was not significantly different from 25d or the control. In general, similarity between the 25d and control samples confirmed the reliability of the results. Both G6 and G7 responded differently across different tissue types. G6 showed significant changes in expression levels at different time points in the combination of stems and leaves, but not in stem tissue nor in leaf tissue alone, indicating stems and leaves together may give more consistent results. In contrast, G7 could be a competency marker both in the combination of stems and leaves, and pure leaf tissues. The difference in expression between pre-competency and post-competency was more pronounced in RNA from leaves alone compared to RNA pooled from stems and leaves. This suggests that the effect of leaves was reduced, in part, by the stem, resulting in a smaller difference between the stem and leaf tissues. There was consistency in pattern reflected by RNA pooled from leaf and stem tissues in 2011 and RNA extracted from whole young shoots in 2012, although the fold-change was not the same (e.g., Fig. 3.9 and Fig. 3.10).  40  Figure 3.12 Relative expression of G6 and G7 in leaves, stems, and pooled leaves and stems of M-13 sampled from the 2011 growth chamber experiment (Experiment I). RNA was extracted separately from leaves and stems. PCR reactions were run on stem, leaf and pooled leaf and stem RNA respectively. Three time points were included with three biological replicates for each time point and three technical replicates for each biological replicate. Mean expression level based on three biological replicates with standard deviation is shown. Where present, significant differences between means are indicated by letters above each bar (90% confidence interval was applied). See details in Fig.3.8.  41  Chapter 4: Discussion 4.1  Variation in competency development and speed of HGC There was variation in the shoot age at competency development and speed of HGC  among the different genotypes. A similar growth chamber study was conducted by Soolanayakanahally et al. (2013) to identify the timing of competency development. They reported that the average time needed for controls (always under an inductive photoperiod of 16h) of four genotypes each from six populations, to acquire competency, was 39d. The possibility of genotypic variation in the time to competency acquisition was suggested but no data were available until now. Additionally, Soolanayakanahally et al. (2013) did not test for a relationship between competency development and latitude of origin, which, because of low sample sizes (only 2 genotypes per population), remains unresolved here. Those authors did report that it took approximately 4 days for fully competent shoots to stop height growth after being transferred to 16 hour photoperiod from a 20 hour photoperiod. However, variation among genotypes was not included in the analysis. Genotypic variation in time needed to achieve competency was clearly observed in the present study (see Table 3.1 and Fig 3.1), and, likewise, genotypic variation in the subsequent speed of HGC. Ideally, to provide more concrete evidence of genotypic variation in these traits, more repetition and a wider range of geographic origin of plants will be required. Although warmer temperature resulted in an increased speed of HGC, temperature did not show a consistent effect on the timing of competency development, suggesting that chronological age is more important than developmental age. In other words, leaf number is not a reliable indicator of competency achievement. This is in contrast to whole plant development, where leaf number can be a reliable index of maturity class (reviewed by Chase and Nanda 1967). Differences in shoot age at competency acquisition and the speed of HGC were not only observed among various genotypes, but also between the different experiments conducted in different years. The same growth chamber conditions were used in Experiment II (2012) as in Experiment I (2011) except that the SD treatment was reduced to 15h from 16h. The shoot age at competency development was shorter in 2012, as well as the period 42  between competency acquisition and HGC. The differences between experiments may reflect differences in the growth or storage of the stock material, or they may indicate that the critical photoperiod interacts with competency development such that shorter photoperiods are effective at a younger shoot age. Furthermore, competency was achieved abruptly in 2011 (Fig. 3.1), but more gradually in 2012 (Fig. 3.3) implying competency development may be either a discrete occurrence or a more gradual process. In keeping with the latter interpretation, although M-13 responded to the SD signal at an earlier age in 2012, subsequent HGC occurred more slowly at first but became progressively more rapid as shoots aged (i.e., approaching the 1:1 line in Fig. 3.3 instead of paralleling it as in Fig. 3.1). Howe et al. (1995) have previously reported that shorter photoperiods accelerated bud set in Populus trichocarpa. It is logical that plants would evolve towards an increased speed of HGC when facing a reduced photoperiod (or a greater age) in order to survive severe conditions. The strength of the inductive SD signal that ultimately results in dormancy deserves more research attention. The slightly advanced HGC in 2012 resulted in reduced height growth (data not shown). Furthermore, the minimum final leaf number at HGC for both M-2 and M-13 also decreased by 3-4 leaves from 2011 to 2012 (Table 3.1). Fewer leaves were present at competency and fewer new leaves developed after transfer of competent plants to SD conditions, consistent with earlier competency acquisition and faster HGC in 2012 compared to 2011. Therefore, earlier HGC can result from advanced competency acquisition, a faster rate of HGC after induction, or both. 4.2  Climate change and tree adaptation Numerous reviews have suggested that there should be a positive effect of global  warming on plant growth (Cleland et al. 2007, Ibáñez1et al. 2010b, Jeong et al 2011, Jeong et al. 2013, Körner and Basler 2010). A longer green cover is expected because of accelerated bud burst and an expected delay in autumn phenology, like bud set and leaf senescence. Date of budburst is well known to depend on spring weather conditions, but there has been much less work on leaf senescence, and particularly bud set, which is not so obvious. Because bud set and leaf senescence are at least under partial if not major photoperiodic control in most 43  temperate and boreal species, climate change should have less effect on the timing of these events. A newly developed model predicts that, by the end of the current century, climate change in the eastern United States will lead to earlier budburst by up to 17 days (Jeong et al. 2013). Positive effects on even spring growth, however, may not be sustained under extreme climate change. In a proxy for such, Soolanayakanahally et al. (2013) grew a full range of Populus balsamifera in two common gardens at similar latitudes but with different annual temperature regimes. As expected, spring phenology was recorded much earlier in the milder common garden, by 44 days. However, most trees planted at this location also unexpectedly set bud during the spring and were not able to take advantage of favorable late season conditions. Earlier flush in spring resulted in earlier competency acquisition, leading to HGC well before the summer solstice when day length was still increasing, which was the case especially for genotypes originating from middle and high latitude. Middle latitude populations tended to resume growth by forming lammas shoots while most high latitude populations remained fully dormant until the following spring. Therefore, earlier achievement of competency negated and, indeed, reversed the benefit of an extended growing period. The low latitude, southern provenance populations were less affected by climate differences between the gardens, but photoperiods were also not much different from their provenances of origin. Middle latitude populations were able to partially accommodate the change in climate by plastic phenology of lammas growth. When grown in a climate that is milder than the southern range of the species, but with much shorter summer days, northern populations are the ones that are most impacted. More research on the interaction between competency development and the effects of an earlier spring, un-confounded by changes in photoperiodic regime, is needed to predict impacts of global warming on boreal trees growing in situ. As implied above, lammas growth can be viewed as an adaptive strategy to correct and recover from a premature bud set. This phenomenon will be important in the context of global warming and should be well characterized and dissected. As shown in Fig. 3.6, in balsam poplar, dormancy progressively deepens with the length of time since HGC, which is also supported by Olsen (2003). Roughly speaking, the same numbers of days were required to lammas under LD conditions as were previously spent under SD conditions. For example, 44  plants kept under SD for 35 days after HGC required ~30 days to lammas after transfer to LD conditions. Presumably, at some time greater than 40 days in SD after HGC, plants will reach a deep dormancy status in which lammas cannot be triggered by long days at all until a chilling requirement is also met. Therefore, it is possible that the first transfer and the controls might never have been observed to lammas if the experiment in Fig 3.6 were prolonged beyond 69 days of LD treatment. Even if plants in these two treatments did eventually lammas, it would not be relevant to a normal field situation. The period without any height growth (more than 92 days) would extend beyond any realistic time for suitable growth conditions to reoccur within a single year. The fundamental biological mechanism that underlies lammas still remains unclear. Other unresolved questions include: What is the selection pressure? How do plants make a decision when to lammas so that they can use the extended growing season and avoid the risk of late hardening at the same time? How does second flush affect the timing of a final bud set? Are lammased plants immediately competent to set bud or do they lose photoperiodic competency for a time? Does lammas render plants more sensitive to short days to increase the speed of HGC? 4.3  Candidate gene markers for competency I initially proposed CO (CONSTANS) as a candidate for RT-qPCR because of its  known involvement in day-length detection and its close relationship with FT (FLOWERING LOCUS T). RT-qPCR results confirmed its potential as a molecular marker due to the coincidence between the appearance of the CO2 peak and the occurrence of competency acquisition. However, capture of the CO2 peak can only mark the point of competency acquisition; its expression level at other times, based on RT-qPCR, does not appear promising as an indicator that a plant is or is not in the competent state. In contrast, CO2 was on the significant gene list distinguishing between pre- and post-competency by RNA-seq. Unfortunately, these two analyses were not fully comparable since there were no common sampling times or tissue types (qPCR: leaf tissues over 25d, 32d, 39d, 46d, and 53d; RNA-seq: pooled leaf and stem tissues between pre-competency 18d and 25d, and post competency 46d and 53d). Despite this reservation, the independent identification of CO2 as 45  a candidate gene for competency to respond to short photoperiod suggests that further investigation would be useful. CONSTANS (specifically CO2) is well characterized as a switch diurnally turning on the expression of FT during exposure to long photoperiods (Böhlenius et al. 2006, SuarezLopez et al. 2001, Searle and Coupland 2004, Valverde et al. 2004, Yanovsky and Kay 2002). However, the diurnal peak of expression is given so much attention that its relative abundance over weeks or months has been rarely studied. The peak expression of FT varies seasonally as well as annually (Böhlenius et al. 2006). In the study by Böhlenius et al. (2006), within a year, Populus displayed the highest peak of FT during the floral initiation period against the background of a diurnal peak pattern, whereas no peak was detected after short day induction. Across different years, a gradual increase in FT was detected as Populus approached flowering competency. Given this variation, then, what is the molecular mechanism that regulates the long-term expression level of FT? Can CO2 be involved in adjusting FT expression level by altering its own abundance? These questions are worth further study. Based on the results presented here, transcripts for G6 (MLP-like protein 31) or G7 (a pathogenesis-related thaumatin superfamily protein) may be better markers of a state of competency than CO2. The advantage of either transcript is that any sampling date should be effective, when data are expressed relative to an appropriate reference gene. A possible disadvantage is that their functions are unknown. To the best of my knowledge, studies on either protein in poplars are nonexistent and to date we only know that they may be involved in pathogenesis and defense-related processes. MLP-like protein 31 is similar to a major latex protein (MLP) homologue only found in plants. The MLPs have been characterized as members of the Bet v 1 protein superfamily functioning in binding and metabolism of large compounds like lipids (Radauer et al. 2008). The expression of an MLP from cotton is activated by fungal elicitor and salt stress (Chen and Dai 2010). An MLP is also associated with peach flower and fruit development in addition to abiotic and biotic stress responses (Ruperti et al. 2002). G7 is presumed to code for a protein belonging to the thaumatin family, which includes a protein widely used as a sweetener first isolated from the fruit of Thaumatococcus daniellii. A thaumatin-like protein is also highly expressed in ripening 46  cherry fruits (Fil-Lycaon et al. 1996). The thaumatin protein induced by various agents like pathogens and ethylene is a pathogenesis-related (PR) protein ubiquitous in plants, though its precise biological role is unknown (Herrera-Estrella et al. 1992, Fil-Lycaon et al. 1996). The PR thaumatins are extensively reported to demonstrate antifungal activities in different species (Vigers et al. 1992). Several thaumatin-like proteins were characterized as allergens in fruits and pollens (Breiteneder 2004). I proposed G6 and G7 for RT-qPCR test because of their significant increase in abundance from before competency development to after competency acquisition, as indicated by RNA-seq. The expression pattern change was validated by the RT-qPCR testing. Their reliability as potential gene markers was further supported by a repeatable expression change from pre-competency to post-competency between samples in 2011 and 2012. Both of these genes responded differently across different tissue types (Table 4.1). Table 4.1 Expression level summary for G6 and G7 in stems (s), leaves (l), and pooled stems and leaves (s+l) respectively from day 25 to day 53, plus control. The control was harvested on the same day as the 53d sample but from material planted at a later date to achieve an age of just 25 days. Standard deviation is given for biological replicates. Data for s and s+l tests at 32d and 39d are missing because of poor quality RNA.  G6  G7  Tissue types s  25d  32d  39d  32.67 ± 26.72  10.26 ±5.90  1.00 ± 0.99  l  1.00 ± 0.84  s+l s l s+l  1.00 ± 1.13 1.00 ± 0.93 1.00 ± 0.25 1.00 ± 0.40  3.23 ± 1.05  3.38 ± 0.87  46d  53d  Control  5.21 ± 3.67  3.47 ± 0.80 25.97 ± 25.50 8.87 ± 3.90 1.45 ± 0.62 6.21 ± 4.25 4.11 ± 1.23  1.52 ± 0.43 12.3 ± 11.00 2.31 ± 1.07 1.09 ± 0.59 1.57 ± 0.95 1.29 ± 0.31  15.13 ± 3.85 7.93 ± 2.23 2.68 ± 3.35 4.20 ± 1.39 3.48 ± 1.70  Although levels of the other gene transcripts explored here did not show a consistent pattern with the timing of competency development, they might still have the potential to mark competency and/or be involved in competency acquisition. Because most clock genes are time-sensitive, the scheduling of daily harvests could affect experimental results. For  47  example, GI transcript levels show a strong diurnal rhythm, typically peaking 8-10 h after dawn (Fowler et al. 1999). GIGANTEA, on the one hand, is under circadian clock control. After entrainment under long days, GI mRNA sustained a rhythmic cycle in both LL (24hr light) and DD (24hr darkness) (Fowler et al. 1999). The rhythmic pattern of GI expression was impaired in toc1 mutants, cca1 mutants and the double mutants (Fowler et al. 1999, Mizoguchi et al. 2002, Mizoguchi et al. 2005). On the other hand, GI also affects the rhythm of the circadian clock. Oscillator component genes LHY and CCA1 are both down regulated in gi-3 mutants (loss of function) in LD and SD (Park et al. 1999). Mizoguchi et al. (2005) also reported COLD CIRCADIAN REGULATED2 (a reporter gene regulated by the circadian clock) mRNA was unable to oscillate with a proper amplitude or period length in 35S:GI (gain of function) and gi-3 mutants. It’s not possible to propose a functional hierarchy for the action of these genes, but rather they influence each other’s expression. The role of GI in promoting flowering by activating CO which stimulates FT expression has been extensively studied in Arabidopsis thaliana (Fowler et al. 1999, Más 2005, Mizoguchi et al. 2002, Mizoguchi et al. 2005, Park et al. 1999, Yu et al. 2008). Loss of function in GI suppresses flowering, while gain of function in GI causes flowering to become insensitive to day-length. Assays of these mutants revealed decreased CO and FT abundance in gi, and increased CO and FT abundance in 35:GI mutants. Manipulation of GI levels can correct flowering at the wrong time caused by mutations in CO and FT (Mizoguchi et al. 2005). The late flowering phenotype of gi mutants can be rescued by overexpressing CO or FT (Fowler et al. 1999). In summary, GI acts upstream of CO and FT in a straightforward linear arrangement of GI-CO-FT from the oscillator to the flowering output pathway. New evidence proves that the same functional hierarchy applies to dormancy except that an enhanced GI level inhibits dormancy development (Böhlenius 2007). GIGANTEA is also regulated by ELF3 other than the oscillator genes. Flowering is inhibited under SD because GI is degraded in the presence of abundant ELF3. The suppression of flowering is released in elf3 mutants because they have higher levels of GI compared to wild type (Fowler et al. 1999, Yu et al. 2008). 48  4.4  Limitations of the present work There are some aspects to be improved in this study. First, some F-12 in 2011 were  scorched by lights in the growth chamber because of rapid height growth. The quality of height data might be compromised, though seriously damaged ramets were removed from data collection. Second, leaves occasionally suffered from accidental mechanical injury from being moved around. This may have impacted leaf counts in cases where they were lost from the plant. Third, RT-qPCR tests on FT failed because of poor primer design. The interpretation of CO2 data might have been more informative if corresponding FT expression data were available for the same time points. Fourth, RNA-seq data was not completely trustworthy because of a lack of replication. RNA was extracted from separate stems and leaves in 2011. When stem and leaf RNA from the different time points were pooled together, pre-competency and postcompetency samples might not have exactly same relative amounts of stem/leaf RNA due to pipetting mistakes. Fifth, individual variation existed among replicates. Especially in 2011, the larger plants were often chosen first to be transferred from LD to SD, leaving weaker ramets for later transfer arbitrarily, especially under 15°C conditions where growth was less vigorous. Lastly, time points of samples used for RNA-seq and PT-qPCR were not perfectly corresponding and consistent. As a result, data from the two analyses were not absolutely comparable, limiting cross-validation.  49  Chapter 5: Conclusions This study opens a window into the understanding of competency from a molecular perspective. A few conclusions can be drawn from the phenological growth chamber experiment and subsequent molecular work.   Different genotypes became competent at different times (e.g., M-13 was the last to acquire competency in both years). The boundary between pre- and post-competency was ambiguous in some cases, suggesting that competency development might be a gradual process. Genotypes also differed in the speed of response between perceiving the SD signal and completing HGC. In some cases, the speed increased with shoot age, which deserves future investigation.    A 5°C temperature difference had no clear effect on competency acquisition, suggesting that time is more important than developmental age. A greater range in temperature might yield greater effects. Although the warmer temperature (20°C) did not advance competency acquisition, it did speed up HGC noticeably.    The difference in results between the 2011 and 2012 growth chamber experiments suggests that photoperiod may influence competency acquisition as well as the speed of HGC. The shorter day-length plants are exposed to, the faster they may appear to become competent and develop terminal buds. Accordingly, final leaf number and height are reduced. Alternatively, the critical photoperiod may be developmentally plastic to some degree. To test either hypothesis, various day-length treatments in different growth chamber should be done in the same experiment.    CO2, G6 and G7 are strong candidates of gene markers for photoperiodic competence. They may or may not be directly involved in regulating competency development. 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Both the reference genome and annotation model were imported from Phytozome website using Bulk data tool (www.phytozome.net).  Run some quality control checks on the raw data for bias in sequence and low quality data. Per Base Sequence Quality helps to determine if quality trimming is necessary, usually 20 or 30 are good thresholds. Use Per Base Sequence Content and Kmer Content to determine if adapter trimming is necessary. NGS: QC and manipulation → Fastqc: Fastq QC.  Trim poor quality bases and / or adapter sequences from data. NGS: QC and manipulation → Cutadapt.  Align or map reads to the reference genome using TopHat. TopHat accepts files in Sanger format, and outputs splice junctions and accepted hits. NGS: RNA Analysis → Tophat 2. The following is a list of index setting for TopHat we adopted following parameter setting: Is this library mate-paired? Paired-end RNA-Seq FASTQ file, forward reads: Output dataset from Fastq QC step RNA-Seq FASTQ file, reverse reads: Output dataset from Fastq QC step Mean Inner Distance between Mate Pairs: 150 Std. Dev for Distance between Mate Pairs: 20 Report discordant pair alignments? No Will you select a reference genome from your history or use a built-in index? Use one from the history Select the reference genome  62  TopHat settings to use Full parameter list (Using default parameters may not give meaningful results ) Library Type: FR Unstranded Transcriptome mismatches: 2 Genome read mismatches: 2 Final read mismatches: 2 Use bowtie -n mode? No Anchor length (at least 3): 8 Maximum number of mismatches that can appear in the anchor region of spliced alignment: 0 The minimum intron length: 70 The maximum intron length: 10000 Allow indel search? Yes Max insertion length: 3 Max deletion length: 3 Maximum number of alignments to be allowed: 20 Minimum intron length that may be found during split-segment (default) search: 50 Maximum intron length that may be found during split-segment (default) search: 500000 Number of mismatches allowed in each segment alignment for reads mapped independently 2 Minimum length of read segments: 25 Use Own Junctions? No Use Coverage Search? No Use Microexon Search? No Do Fusion Search? No Set Bowtie2 settings? No  Visualize the data in Galaxy Trackster Visualization → New Track Browser. Create the visualization using the appropriate reference build and add datasets (accepted hits BAM dataset and the splice junctions dataset  63  from Tophat and also the gene model GTF file) to your visualization by clicking on the Add Datasets to Visualization button.  Assemble transcripts and estimate their relative abundance in fragments per kilobase of exon per million fragments mapped (FPKM) on Cufflinks. Cufflinks takes SAM alignments as input, and produces transcripts and genes assembly. NGS: RNA Analysis → Cufflinks. The following is a list of index applied: SAM or BAM file of aligned RNA-Seq reads: Output dataset TopHat accepted hits Max Intron Length: 300000 Min Isoform Fraction: 0.1 Pre MRNA Fraction: 0.15 Perform quartile normalization: No Use Reference Annotation? Use reference annotations (If choose No - de novo assembly; Use Reference - Tells Cufflinks to use the supplied reference annotation to estimate isoform expression. It will not assemble novel transcripts, and the program will ignore alignments not fit into the gene model; Use Reference As Guide - Tells Cufflinks to use the supplied reference annotation (GFF) to guide assembly. Output will include all reference transcripts as well as any novel genes and isoforms). Reference Annotation: Gene models (a GTF file) Perform Bias Correction: Yes Reference sequence data: History, the reference genome file Set Parameters for Paired-end Reads? (not recommended): No Repeat the same process for the second sample.  Find differentially expressed genes/transcripts by Cuffdiff. Cuffdiff detects significant changes in gene and transcript expression, splicing, and promoter use. It takes output files of Cufflinks and Cuffcompare as well as aligned TopHat accepted hits. NGS: RNA Analysis → Cuffdiff. Parameters applied in this step are listed as follows: Transcripts: Gene models Perform replicate analysis: No (In our case there was no replicate for the pretest)  64  SAM or BAM file of aligned RNA-Seq reads: TopHat accepted hits for pre-competency sample SAM or BAM file of aligned RNA-Seq reads: TopHat accepted hits for post-competency sample False Discovery Rate: 0.05 (False discovery rate: threshold for determining what gets flagged as "significant") Min Alignment Count: 10 (threshold that determines how many reads a transcripts requires to be given an "OK" status instead of "NOTEST") Perform quartile normalization: Yes Perform Bias Correction: Yes Reference sequence data: History Using reference file: Gene models Set Parameters for Paired-end Reads? (not recommended): No  Extract significant differential expression data to an Excel file. Filter and Sort → Filter Condition: c14 == 'yes' or c14 == 'significant'  65  Appendix B Supplementary tables Table B.1 Primer sequences for RT-qPCR.  ID G1  Potri.018G079100  G2  Potri.004G148500  G3  Potri.006G243600  G4  Potri.013G136400  G5  Potri.005G113700  G6  Potri.017G051100  G7  Potri.001G222000  G8  Potri.005G169200  G9  Potri.018G122700  G10  Potri.015G144800  G11  Potri.T161500  G12  Potri.014G006000  G13  Potri.014G103000  TUA4  Potri.001G004600  18S  Potri.010G138100  UBQ11  Potri.017G036800  ACT11  Forward  Reverse  CCACCCGGTTGAAACTT CCT CTCGTGCAACAACGATC TCC ACTCTGTGCTGCCTAAC ACT GAGGAAGGAGGCTTCT CTCG CAAGATCGTGGAGGCA TGTG ACCAGCGTTCCTGCTGA AAA TTTGGCATTTAACCAGC CGC GCTGTAAATTGGAGCCA GTCT TGTGCTGAGATTCAAAA CGCC GCTCTCCTCAGTGCTAC GAT CGGACCCCTCGGACTTT CT TATGGCCTTCGACTTGA GAGC GCGTTGGCGATACAGTT GC AGAGAGACTTTGAGTGT CCGA AGAGTCAAACGTAGGA AACGAGA TTCCACCAGACCAGCAG AGA CACACTGGAGTGATGGT TGG  GGAAAATGGAGTGGG GAGCA CCAGCCGTTCACAACA ATCA GGCTTCCCTTTTCCTGC ATC GCCAACCTTTTCCCTCG GAT TCTTGAACTGCCTCTCC CTG TCTTCCATAACCTTGGC GGC AGCTTGAGGACACTGC TTCT TTCAAGTACTGCTGGC CAAC ATGGGTCATAGGTGCA AGGC TTTCACTGCCTTCACTG GTCC GATTCTGGACGGTGAG CTTGA CAGCTCTGAATCCTCA CTCGT ATTCTCCGATTCGCCCT CAC CGGTGAAGTGTCACTA GGCA CTTCAAGGCCCATCAC TGGT TTAGAAACCACCGCGG AGAC ATTGGCCTTGGGGTTA AGAG  Product size 178 217 133 190 204 188 109 113 166 182 107 140 114 196 171 128 228  66  Table B.2 Complete list of significant genes from RNA-seq.  Gene  Locus  log2-fold change  p-value  Potri.001G038200  Chr01:2701073-2702136  -3.67  2.87E-05  Potri.001G162600  Chr01:13596151-13598490  -3.84  1.08E-07  Potri.001G179400  Chr01:15510047-15514738  -3.24  0.0002905  Potri.001G222000  Chr01:22980398-22980697  8.22  0  Potri.001G236700  Chr01:24815369-24817412  -5.69  0.00078768  Potri.001G319800  Chr01:32449716-32451961  -3.01  3.23E-05  Potri.001G360500  Chr01:37104621-37110977  4.85  3.43E-11  Potri.001G366500  Chr01:37934049-37935458  1.93  0.00011446  Potri.001G388900  Chr01:40683040-40685189  -2.61  2.89E-06  Potri.001G395400  Chr01:41491990-41493525  3.81  3.38E-06  Potri.001G436100  Chr01:46757505-46762148  -2.91  0.0007077  Potri.001G039200  Chr01:2835063-2838974  -2.73  0.00017893  Potri.001G054800  Chr01:4151090-4154512  3.16  5.89E-06  Potri.001G056700  Chr01:4281270-4283377  -4.93  2.15E-10  Potri.001G107800  Chr01:8563784-8564939  2.42  4.46E-07  Potri.001G152500  Chr01:12587165-12588716  3.21  3.16E-08  Potri.001G162500  Chr01:13586466-13588886  -2.36  6.03E-07  Potri.001G173100  Chr01:14678607-14697262  -4.47  5.09E-06  Potri.001G173300  Chr01:14678607-14697262  -4.47  5.09E-06  Potri.001G200700  Chr01:19601827-19603210  1.70  0.00029624  Potri.001G240600  Chr01:25156424-25158091  -2.85  4.99E-06  Potri.001G278400  Chr01:28445072-28448792  -2.13  2.93E-06  Potri.001G289000  Chr01:29440350-29446451  -4.62  4.52E-06  Potri.001G291700  Chr01:29704719-29708557  -3.74  6.58E-06  Potri.001G320800  Chr01:32566529-32567917  2.27  0.00035905  67  Gene  Locus  log2-fold change  p-value  Potri.001G362600  Chr01:37518817-37520588  -5.88  1.85E-05  Potri.001G363100  Chr01:37599605-37601208  -4.12  0.00056855  Potri.001G363900  Chr01:37661151-37662333  -4.72  2.03E-05  Potri.001G386900  Chr01:40300219-40304160  -2.27  0.00051723  Potri.001G395700  Chr01:41584676-41626633  -2.27  5.90E-05  Potri.001G416800  Chr01:44144817-44148920  2.04  0.0001376  Potri.001G423400  Chr01:44861615-44866452  -4.45  2.13E-14  Potri.001G430900  Chr01:46135631-46140604  4.86  2.90E-06  Potri.001G445700  Chr01:47840091-47845171  3.84  0.00051948  Potri.001G447700  Chr01:48058092-48131926  -2.58  0.0001882  Potri.001G454000  Chr01:48853975-48864488  -2.80  0.00017628  -  Chr01:29038952-29039607  -2.28  0.00014072  -  Chr01:29046340-29046598  -2.39  0.00080482  -  Chr01:30310783-30311558  7.64  1.44E-06  -  Chr01:30311861-30313673  8.53  5.45E-08  -  Chr01:33788344-33790801  8.08  2.61E-10  -  Chr01:33791108-33792995  7.26  7.41E-13  -  Chr01:49720922-49722413  4.56  2.42E-05  Potri.002G185200  Chr02:14484580-14491316  -1.54  0.00069723  Potri.002G195400  Chr02:15638805-15640145  4.15  0.00015528  Potri.002G205200  Chr02:16807505-16808868  -4.94  7.03E-06  Potri.002G206400  Chr02:16981685-16985419  -2.14  0.00077606  Potri.002G206700  Chr02:17094660-17095552  -2.58  4.89E-05  Potri.002G214800  Chr02:19697129-19699991  -3.91  7.84E-07  Potri.002G215400  Chr02:19775342-19778506  -2.67  0.00018953  Potri.002G220200  Chr02:20614317-20618028  -1.97  7.77E-05  Potri.002G223200  Chr02:21049787-21052078  -3.64  0.00014609  Potri.002G240300  Chr02:23299303-23301215  1.95  0.00032738  68  Gene  Locus  log2-fold change  p-value  Potri.002G257900  Chr02:24651362-24656525  1.88  3.22E-05  Potri.002G015100  Chr02:875719-877295  -1.86  2.54E-05  -  Chr02:3718963-3719785  -3.87  0.00068626  Potri.002G156200  Chr02:11753710-11756227  -2.98  0.00067915  Potri.002G173900  Chr02:13269789-13271810  1.86  2.59E-05  Potri.002G191000  Chr02:15129250-15133634  2.18  1.81E-05  Potri.002G194600  Chr02:15561608-15566474  -2.31  0.00037211  Potri.002G220400  Chr02:20639834-20642411  -4.02  9.33E-15  Potri.002G244800  Chr02:23654815-23660422  -2.61  4.62E-05  Potri.002G245000  Chr02:23660733-23672658  -3.77  0.00011378  Potri.003G057000  Chr03:8470484-8473569  -3.68  2.81E-07  Potri.003G057100  Chr03:8498964-8500680  -2.64  1.55E-07  Potri.003G057200  Chr03:8506915-8508553  -1.79  8.20E-05  Potri.003G065300  Chr03:9391715-9392648  -5.68  6.18E-07  Potri.003G111300  Chr03:13472254-13473284  -1.99  3.05E-05  Potri.003G142800  Chr03:15938627-15941990  2.79  0.00013881  Potri.003G153800  Chr03:16701321-16706575  4.55  6.33E-05  Potri.003G185500  Chr03:19108154-19110950  -2.96  0.00054723  Potri.003G205900  Chr03:20675991-20678056  -1.78  0.00010798  Potri.003G007700  Chr03:604839-648732  -4.92  7.85E-05  Potri.003G023800  Chr03:2810835-2814196  -2.75  3.54E-05  Potri.003G037600  Chr03:4876616-4879451  5.66  6.31E-12  Potri.003G040000  Chr03:5291781-5308853  2.27  0.00039993  Potri.003G040500  Chr03:5398492-5399047  -2.94  0.00026641  Potri.003G066600  Chr03:9498608-9500508  -4.02  1.11E-15  Potri.003G066800  Chr03:9517442-9519541  -2.56  6.33E-08  Potri.003G083200  Chr03:11077049-11078785  4.24  4.28E-10  Potri.003G102600  Chr03:12807847-12813611  -4.11  3.46E-06  69  Gene  Locus  log2-fold change  p-value  Potri.003G119100  Chr03:14189778-14191428  2.73  5.49E-09  Potri.004G023500  Chr04:1546719-1554195  5.01  1.28E-07  Potri.004G054400  Chr04:4262848-4265391  -3.03  2.15E-06  Potri.004G063300  Chr04:5219326-5230911  -4.27  9.18E-11  Potri.004G063400  Chr04:5219326-5230911  -4.27  9.18E-11  Potri.004G074900  Chr04:6251955-6256365  -1.62  0.00074046  Potri.004G075500  Chr04:6293929-6298674  -2.74  8.18E-07  Potri.004G086500  Chr04:7250938-7252044  2.17  1.34E-06  Potri.004G096100,Potri.004G0 96200  Chr04:8262796-8289843  -3.14  0.00014155  Potri.004G096300  Chr04:8297867-8299315  -5.80  4.16E-09  Potri.004G108300  Chr04:9586816-9588886  -5.12  0.00019478  Potri.004G140800  Chr04:16399187-16402333  -1.51  0.00082397  Potri.004G148500  Chr04:17123073-17126295  -4.84  1.06E-05  Potri.004G185000,Potri.004G1 85100  Chr04:20097780-20110318  2.30  6.03E-05  Potri.004G012100  Chr04:780648-783359  -3.68  1.31E-05  Potri.004G014800  Chr04:953801-955542  -4.57  0  Potri.004G019900  Chr04:1318565-1321938  2.24  3.73E-06  Potri.004G029400  Chr04:2150761-2151391  -2.40  5.60E-05  Potri.004G040600  Chr04:3103236-3104922  -2.02  3.66E-05  Potri.004G048200  Chr04:3720138-3723716  -3.70  0.00032215  Potri.004G059600  Chr04:4781273-4789617  1.77  0.00016502  Potri.004G070200  Chr04:5850033-5852367  -3.46  1.63E-05  Potri.004G108900  Chr04:9674971-9677626  -3.40  0.00031394  Potri.004G136900  Chr04:15804488-15805773  -3.79  6.80E-06  Potri.004G142900  Chr04:16645311-16646906  -3.29  7.64E-11  Potri.004G216500  Chr04:22328184-22329663  -3.15  3.62E-06  -  Chr04:584000-586168  6.74  2.70E-05  70  Gene  Locus  log2-fold change  p-value  -  Chr04:708210-710850  6.49  2.14E-09  -  Chr04:710917-711890  6.15  0.00014961  -  Chr04:11656068-11656529  6.00  0.00021621  Potri.005G081000  Chr05:5957474-5959191  -2.05  0.00024787  Potri.005G115300  Chr05:8906107-8906636  -2.53  1.24E-06  Potri.005G115600  Chr05:8919986-8920752  -4.74  2.66E-15  Potri.005G140400  Chr05:11760583-11763153  3.33  9.96E-05  Potri.005G169100  Chr05:18204625-18217290  1.94  0.00024002  Potri.005G169200  Chr05:18217392-18217726  1.79769e+308  9.75E-06  Potri.005G178900  Chr05:19501474-19506781  2.31  0.00050165  Potri.005G200200  Chr05:21558279-21562676  -1.91  6.96E-05  Potri.005G200300  Chr05:21576697-21581288  -2.61  1.77E-06  Potri.005G240700  Chr05:24679955-24680970  -1.71  0.00016335  Potri.005G072800  Chr05:5307853-5310162  2.43  0.00082157  Potri.005G110800  Chr05:8526652-8527583  -5.99  3.24E-10  Potri.005G113700  Chr05:8745013-8749721  -2.50  6.20E-08  Potri.005G151200  Chr05:13857184-13860046  -3.81  0.00048065  Potri.005G153800  Chr05:14604742-14611947  -2.19  5.10E-06  Potri.006G034100  Chr06:2311360-2312652  -1.84  0.00053249  Potri.006G037300  Chr06:2582305-2585081  -2.88  1.60E-05  Potri.006G068100  Chr06:5095170-5097638  1.56  0.0005085  Potri.006G087100  Chr06:6659277-6662363  2.76  0.0001022  Potri.006G129700  Chr06:10599573-10604745  1.68  0.00035141  Potri.006G157200  Chr06:14633111-14636780  2.15  0.00015724  Potri.006G161400  Chr06:15365864-15369555  -1.70  0.00037328  Potri.006G166700,Potri.006G1 66800  Chr06:17089084-17130449  -4.33  5.65E-08  Potri.006G172600  Chr06:18138578-18145378  -1.88  0.0002018  Potri.006G181500,Potri.006G1 81600  Chr06:19572160-19589370  -2.85  2.42E-06  71  Gene  Locus  log2-fold change  p-value  Potri.006G209000  Chr06:22421444-22424514  2.10  2.96E-05  Potri.006G214300  Chr06:22808233-22810367  3.56  2.63E-05  Potri.006G243600  Chr06:25184800-25186843  -3.64  3.55E-13  Potri.006G243700  Chr06:25193356-25195343  -3.08  5.40E-11  Potri.006G252200  Chr06:25774744-25775792  6.86  1.88E-05  Potri.006G275900  Chr06:27590300-27591910  -2.01  6.70E-06  Potri.006G022300  Chr06:1574955-1577044  -2.30  0.00078736  Potri.006G022500  Chr06:1585896-1587973  -4.78  0.00067059  Potri.006G053000  Chr06:3824063-3834966  -2.79  0.00011087  Potri.006G055800  Chr06:4081421-4084445  -3.61  0.00016099  Potri.006G055900  Chr06:4081421-4084445  -3.61  0.00016099  Potri.006G056100  Chr06:4094895-4095483  2.29  0.00026675  Potri.006G060200  Chr06:4386844-4398328  -2.96  1.98E-05  Potri.006G060300  Chr06:4386844-4398328  -2.96  1.98E-05  Potri.006G087500  Chr06:6681745-6684722  2.07  0.00060662  Potri.006G103900  Chr06:8011183-8013710  1.56  0.00059324  Potri.006G156200  Chr06:14302173-14305277  -2.67  0.00049162  Potri.006G162500  Chr06:16001350-16003882  -4.38  2.24E-08  Potri.006G163100  Chr06:16258734-16261775  -3.06  7.25E-05  Potri.006G171100  Chr06:17869235-17871813  -5.90  0  Potri.006G171200  Chr06:17889554-17892095  -4.90  9.08E-10  Potri.006G181900  Chr06:19612545-19620951  1.75  0.00036047  Potri.006G239700  Chr06:24877969-24879898  3.27  2.04E-11  -  Chr06:25984743-25985432  5.73  8.72E-09  -  Chr06:14642168-14642525  7.07  9.71E-10  -  Chr06:24873196-24873794  -4.23  4.58E-05  Potri.007G055800  Chr07:5874884-5878114  -3.25  0.00076079  -  Chr07:9976328-9977342  3.17  0.00028442  72  Gene  Locus  log2-fold change  p-value  Potri.007G087900  Chr07:11367304-11382559  -5.89  1.52E-07  Potri.007G100100  Chr07:12593954-12598515  2.14  1.53E-05  Potri.007G132400  Chr07:14647644-14649501  -1.83  0.00059502  Potri.007G016400  Chr07:1205365-1207514  2.19  0.00037019  Potri.007G108300  Chr07:13183566-13184482  -5.91  1.65E-05  Potri.007G108400  Chr07:13185909-13188451  -4.28  3.64E-05  Potri.007G137600  Chr07:14961972-14964099  -2.04  1.88E-05  Potri.007G137700  Chr07:14964874-14967373  -3.72  1.25E-06  -  Chr07:2964607-2965001  -4.94  0.00043161  Potri.008G007800  Chr08:425704-426011  -4.08  0.00033247  Potri.008G019100  Chr08:1002934-1005823  -5.63  0.00015376  Potri.008G027600  Chr08:1479936-1481167  5.94  0.00026031  Potri.008G042500  Chr08:2435899-2463515  -3.20  0.00014555  Potri.008G054000  Chr08:3189755-3192879  -3.02  2.04E-09  Potri.008G064000  Chr08:3873858-3877490  4.73  0.0004883  Potri.008G174100  Chr08:11882934-11885754  1.79  0.00047764  Potri.008G183300  Chr08:12532254-12534123  3.64  0.00065508  Potri.008G221200  Chr08:18663789-18665323  -2.62  2.25E-07  Potri.008G066200  Chr08:4013748-4026565  -2.00  0.00017076  Potri.008G092700  Chr08:5793215-5796428  3.29  0.00029516  Potri.008G131200  Chr08:8631058-8632295  -3.38  9.35E-05  Potri.008G171200  Chr08:11686823-11689100  -2.34  5.01E-06  Potri.008G202200  Chr08:14348349-14350432  2.06  7.92E-05  Potri.008G205000  Chr08:14814911-14818094  3.21  5.58E-05  Potri.008G208900  Chr08:15882415-15884799  -2.96  0.00025041  Potri.008G209000  Chr08:15887187-15891359  -4.97  0.00032405  Potri.008G225800  Chr08:19366410-19368072  -4.26  0.00014915  Potri.008G226500  Chr08:19431501-19433263  -3.62  6.77E-13  73  Gene  Locus  log2-fold change  p-value  -  Chr08:870709-871408  3.27  0.00040285  -  Chr08:10230582-10231113  9.05  6.73E-09  Potri.009G005400  Chr09:1031335-1032874  -2.69  2.27E-08  Potri.009G112500  Chr09:9594031-9594787  -2.35  2.32E-06  Potri.009G130100  Chr09:10615462-10616560  -2.03  4.64E-05  Potri.009G156900  Chr09:12193381-12195817  -2.32  0.00017972  Potri.009G012200  Chr09:2104064-2105349  4.80  1.60E-06  Potri.009G044200  Chr09:5040782-5044086  1.87  0.00026771  Potri.009G104600  Chr09:9157153-9158411  -1.88  2.42E-05  Potri.009G114600  Chr09:9710232-9711633  -4.46  1.55E-08  Potri.009G118800  Chr09:9961489-9967297  2.49  6.65E-06  -  Chr09:2267874-2268076  4.59  0.00064891  Potri.010G008200  Chr10:952683-954119  -3.76  3.98E-12  Potri.010G008600  Chr10:990949-993938  -3.32  9.47E-05  Potri.010G050300  Chr10:8083661-8099826  -2.00  0.00028512  Potri.010G050600  Chr10:8116303-8120559  -4.63  9.86E-08  Potri.010G079700  Chr10:10510238-10512379  -3.83  1.07E-05  Potri.010G109400  Chr10:12867161-12869570  2.45  2.26E-05  Potri.010G150400  Chr10:15903802-15905611  3.24  0.00047371  Potri.010G150500  Chr10:15918246-15919226  -3.04  1.69E-06  Potri.010G218700  Chr10:20472910-20475680  -3.74  4.38E-13  Potri.010G218800,Potri.010G2 19100  Chr10:20478569-20484167  -3.67  2.44E-08  Potri.010G219400  Chr10:20491614-20494359  -3.98  3.22E-06  Potri.010G230600  Chr10:21266680-21270261  -2.35  2.37E-06  Potri.010G230800  Chr10:21270362-21271219  -1.94  2.55E-05  Potri.010G000600  Chr10:61368-62350  -1.58  0.00033576  Potri.010G006000  Chr10:549420-551745  4.02  0.00025632  Potri.010G019000  Chr10:2597728-2602334  -2.10  0.00050735  74  Gene  Locus  log2-fold change  p-value  Potri.010G141400  Chr10:15298232-15304245  -2.15  0.00010853  Potri.010G141600  Chr10:15313782-15315916  1.71  0.00020642  Potri.010G193100  Chr10:18783951-18787847  5.74  1.08E-06  Potri.010G208800  Chr10:19835313-19836036  -2.76  2.79E-07  Potri.010G219500  Chr10:20495201-20498005  -2.89  5.21E-09  -  Chr10:16218735-16221438  5.11  0.00015594  -  Chr10:19523840-19526430  5.05  3.19E-05  Potri.011G031800  Chr11:2627791-2631029  3.99  0.00052166  Potri.011G060800  Chr11:5444717-5446934  -3.66  7.77E-07  Potri.011G064000  Chr11:5849013-5854308  -4.47  7.92E-07  Potri.011G068900,Potri.011G0 69200  Chr11:6451166-6467002  -2.28  0.000244  Potri.011G094800  Chr11:11522720-11525686  3.99  1.02E-05  Potri.011G142800  Chr11:16394661-16397619  4.66  6.38E-10  Potri.011G002400  Chr11:131935-135532  -4.40  0.00054792  Potri.011G009600  Chr11:725925-733345  -3.61  1.55E-07  Potri.011G049900  Chr11:4276600-4278017  -1.90  0.00062866  Potri.011G060600  Chr11:5433649-5436889  3.14  0.00040518  Potri.011G076200,Potri.011G0 76400  Chr11:7417088-7435777  -2.04  0.00040085  Potri.011G098600  Chr11:12048080-12050139  4.20  1.44E-08  Potri.011G125500  Chr11:15059615-15063690  -5.38  2.82E-13  Potri.011G151700  Chr11:17001099-17004967  -3.11  3.10E-05  Potri.011G151800  Chr11:17020006-17021901  -4.94  7.00E-06  Potri.011G152100  Chr11:17056396-17058291  -2.63  1.83E-06  Potri.011G160800  Chr11:17867838-17873993  -2.43  9.12E-08  Potri.012G000600  Chr12:77297-84169  2.14  3.46E-05  Potri.012G068800  Chr12:9143436-9146081  -2.75  3.22E-09  Chr12:15385309-15393534  -2.40  0.00012375  Chr12:2136907-2325745  -3.11  2.93E-06  Potri.012G139400,Potri.012G1 39500 Potri.012G026100,Potri.012G0 26200  75  Gene  Locus  log2-fold change  p-value  Potri.012G052300  Chr12:5128491-5130902  6.80  3.06E-12  Potri.012G109200  Chr12:13200703-13204887  -2.33  6.44E-05  -  Chr12:797369-799731  6.97  1.32E-05  Potri.013G039700  Chr13:2736002-2739104  -4.67  2.55E-05  Potri.013G052900  Chr13:3953487-3955176  -3.09  2.04E-10  Potri.013G082600  Chr13:7443396-7444329  -4.37  2.63E-09  Potri.013G100700,Potri.013G1 00800  Chr13:11267680-11349338  -5.27  8.61E-13  Potri.013G101600  Chr13:11488103-11490986  -3.28  0.00056422  Potri.013G103700,Potri.013G1 03800,Potri.013G103900  Chr13:11737982-11763778  -3.17  3.07E-06  Potri.013G136400  Chr13:14644110-14655804  -2.78  4.08E-09  Potri.013G136500  Chr13:14658424-14660051  4.02  4.30E-05  Potri.013G141900  Chr13:14855715-14856736  -1.99  6.88E-05  Potri.013G143200  Chr13:14894927-14896038  -2.51  0.00023034  Potri.013G143700  Chr13:14943100-14945064  -2.59  0.00015549  Potri.013G143800  Chr13:14947546-14950509  -1.83  0.00012197  -  Chr13:16156750-16157134  -1.79769e+308  4.04E-05  Potri.013G040000,Potri.013G0 40100  Chr13:2763458-2774931  -2.37  0.00036097  Potri.013G040600  Chr13:2821495-2825163  -2.91  1.40E-05  Potri.013G050900  Chr13:3701792-3706226  -2.29  0.00044071  Potri.013G080300  Chr13:7005588-7053221  -2.78  6.22E-07  Potri.013G082700  Chr13:7453502-7500433  1.79769e+308  1.68E-05  Potri.013G082900  Chr13:7519594-7521286  4.23  0.00024484  -  Chr13:7570875-7574480  -6.83  3.24E-06  Potri.013G106800  Chr13:12051859-12054033  -2.58  4.94E-05  Potri.013G143300  Chr13:14896122-14898256  -3.13  4.90E-08  Potri.014G006000  Chr14:680543-684364  -3.56  1.34E-05  Potri.014G034500  Chr14:2833957-2834547  3.59  1.60E-08  Potri.014G149700  Chr14:11473945-11476733  1.79769e+308  7.33E-06  76  Gene  Locus  log2-fold change  p-value  Potri.014G173400  Chr14:14044249-14048148  6.48  5.86E-05  Potri.014G183800  Chr14:15860441-15862387  5.59  0  Potri.014G191900  Chr14:17734422-17737127  -3.64  4.28E-05  Potri.014G019200  Chr14:1769749-1770498  1.66  0.00053932  Potri.014G019400,Potri.014G0 19500  Chr14:1775395-1784426  -2.33  0.00019439  Potri.014G031100  Chr14:2588072-2589038  -2.34  0.00017929  Potri.014G055100  Chr14:4303082-4304227  -3.64  1.53E-09  Potri.014G088000  Chr14:6946561-6951800  1.84  0.00013667  Potri.014G100800  Chr14:7884223-7886399  3.04  1.00E-05  Potri.014G103000  Chr14:8085841-8087890  2.91  0.00075554  Potri.014G126000  Chr14:9688121-9689989  3.64  4.39E-05  Potri.014G130500  Chr14:9945019-9952339  3.70  2.98E-06  Potri.014G173300  Chr14:14024495-14033792  4.25  4.83E-07  Potri.014G173500  Chr14:14048510-14049070  5.24  1.34E-05  Potri.014G182200  Chr14:15393652-15395181  8.21  1.81E-07  -  Chr14:15416780-15419192  -2.01  6.18E-05  Potri.014G183000  Chr14:15580011-15581376  3.73  3.86E-05  Potri.014G183500  Chr14:15764168-15766529  3.20  6.88E-06  Potri.014G197200  Chr14:18773904-18776857  4.95  5.76E-12  Potri.014G198200  Chr14:18888387-18889518  5.31  1.42E-10  -  Chr14:14534527-14535931  -1.88  0.00035371  Potri.015G023900,Potri.015G0 24000,Potri.015G024100  Chr15:1860896-1878545  -2.36  0.00024788  Potri.015G053800  Chr15:7058009-7060140  -4.16  1.74E-11  Potri.015G054100  Chr15:7100662-7102798  -3.66  1.06E-06  Potri.015G056900  Chr15:7805862-7809849  -4.00  6.08E-11  Potri.015G057000  Chr15:7816692-7820486  -2.58  1.48E-05  Potri.015G110400  Chr15:12676721-12683872  -1.95  1.25E-05  Potri.015G112300  Chr15:12782374-12784823  -3.24  6.43E-05  77  Gene  Locus  log2-fold change  p-value  Potri.015G144800  Chr15:15029758-15031082  4.10  8.93E-10  Potri.016G014100  Chr16:776140-790294  -2.55  0.00017504  Potri.016G014400  Chr16:793635-795980  -2.50  0.00044935  Potri.016G023200  Chr16:1310508-1311848  -2.88  0.00020069  Potri.016G109200  Chr16:11212292-11213501  -3.27  0.00030755  Potri.016G114300  Chr16:11822896-11824381  -2.77  1.16E-05  Potri.016G127200  Chr16:13010054-13014914  -4.93  3.36E-07  Potri.016G014100  Chr16:776140-790294  -3.29  0.00071839  Potri.016G031400  Chr16:1766770-1768956  -3.36  3.33E-06  Potri.016G094000  Chr16:8195080-8195716  -5.67  2.79E-06  Potri.016G128600  Chr16:13124620-13129320  -2.90  0.00053258  Potri.017G013800  Chr17:1151933-1154745  -1.85  8.66E-05  Potri.017G026000  Chr17:2392866-2395122  -2.06  5.06E-05  Potri.017G028100,Potri.017G0 28200  Chr17:2508149-2526145  -4.75  5.42E-11  Potri.017G029200  Chr17:2578818-2582598  -2.98  3.99E-05  Potri.017G040800  Chr17:3339801-3343268  -3.49  2.99E-05  Potri.017G051100  Chr17:4288107-4306821  2.54  3.00E-05  Potri.017G113400  Chr17:12860262-12863510  -2.40  0.00029182  Potri.017G120200  Chr17:13404002-13404712  -3.12  1.83E-06  Potri.017G002300  Chr17:122503-126301  -4.67  9.83E-12  -  Chr17:2723184-2723921  8.29  4.33E-05  Potri.017G113600  Chr17:12871957-12877635  -3.60  9.15E-10  Potri.017G138800  Chr17:14709801-14711560  2.58  1.28E-05  Potri.017G144500  Chr17:15235616-15239915  -2.17  0.00010817  -  Chr17:4672961-4674322  4.79  0.00042878  Potri.018G005000  Chr18:335449-363970  -2.66  0.00071983  Potri.018G056600  Chr18:5928027-5931887  -4.33  1.79E-08  Potri.018G056900  Chr18:5975904-5979686  -2.18  3.46E-05  78  Gene  Locus  log2-fold change  p-value  Potri.018G061300  Chr18:7284784-7288275  -5.59  7.58E-11  Potri.018G072700  Chr18:9473505-9477693  -3.83  4.53E-12  Potri.018G072900  Chr18:9539459-9544298  -2.61  0.00036678  Potri.018G073200  Chr18:9586469-9586845  -4.06  1.66E-05  Potri.018G079100  Chr18:10476942-10488677  -6.74  8.88E-16  Potri.018G109100  Chr18:13543720-13546030  -2.62  9.23E-06  Potri.018G112100  Chr18:13799129-13800671  -3.49  0.0001579  Potri.018G137300,Potri.018G1 37400  Chr18:15693286-15698250  -2.59  1.22E-06  Potri.018G140100  Chr18:15991777-15997918  -3.60  7.09E-05  Potri.018G026500  Chr18:2110393-2111676  -4.84  3.77E-15  Potri.018G071800  Chr18:9217842-9220099  -4.47  6.34E-05  Potri.018G079100  Chr18:10476942-10488677  -7.82  3.25E-06  Potri.018G088500  Chr18:11580608-11582443  -3.00  2.03E-07  Potri.018G088600  Chr18:11614065-11616043  -5.02  6.89E-10  Potri.018G088800  Chr18:11640584-11642935  -1.54  0.00075938  Potri.018G088900  Chr18:11653112-11653795  -4.25  1.17E-09  Potri.018G089200  Chr18:11697911-11699851  -3.37  2.10E-12  Potri.018G089300  Chr18:11723127-11725131  -4.73  1.67E-14  Potri.018G089400  Chr18:11737757-11739603  -3.94  7.33E-15  Potri.018G089500  Chr18:11744607-11746620  -2.39  3.25E-07  Potri.018G122700  Chr18:14630736-14635853  -2.41  2.44E-06  -  Chr18:16090204-16092744  -3.36  0.00015652  Potri.018G146600  Chr18:16578834-16579547  6.06  1.80E-12  -  Chr18:14030908-14031970  -3.29  0.00015701  Potri.019G000100  Chr19:29573-32290  -2.86  0.00022762  Potri.019G000800  Chr19:130388-137256  -2.69  5.11E-08  Potri.019G002100  Chr19:258724-260604  1.98  0.00010177  Potri.019G008500  Chr19:993602-995929  -2.17  4.03E-06  79  Gene  Locus  log2-fold change  p-value  Potri.019G008600  Chr19:1008594-1011024  -2.95  0.0001484  Potri.019G014700  Chr19:1622022-1623261  -4.19  0.00010557  -  Chr19:1797876-1806743  -1.79769e+308  2.41E-05  Potri.019G027000  Chr19:3099279-3100813  -3.37  0.00014587  Potri.019G028000  Chr19:3192472-3193492  -6.15  0.0002569  Potri.019G029200  Chr19:3268334-3275045  -4.51  4.05E-07  Potri.019G046600  Chr19:5888988-5892214  -4.51  0.00039009  Potri.019G052500  Chr19:7999615-8011882  -3.28  3.69E-05  Potri.019G052600  Chr19:8019758-8025249  -4.22  1.06E-05  Potri.019G087900  Chr19:11953432-11954654  -5.72  0.00072786  Potri.019G095700  Chr19:12681871-12687989  -2.17  1.49E-05  Potri.019G108900  Chr19:13734231-13738473  2.61  0.00061172  Potri.019G109100  Chr19:13741962-13745398  4.21  3.41E-09  Potri.019G115000  Chr19:14303676-14304001  -2.85  0.00078597  Potri.019G006700  Chr19:784552-786884  -3.58  1.34E-10  Potri.019G014900  Chr19:1637146-1640789  2.14  5.90E-05  Potri.019G028500  Chr19:3201671-3202760  -3.74  1.78E-05  Potri.019G036800  Chr19:4164421-4167175  -1.84  0.00030592  Potri.019G038200  Chr19:4326952-4332125  -2.46  2.04E-06  Potri.019G044900  Chr19:5313894-5317596  3.43  0.00046649  Potri.019G063100  Chr19:9559845-9561983  -4.31  4.84E-14  Potri.019G071000  Chr19:10490989-10494736  -1.88  0.0004549  Potri.019G075000  Chr19:10914842-10916734  -4.53  0.0001234  Potri.019G088200  Chr19:11976415-11977254  -2.42  1.17E-07  Potri.019G096800  Chr19:12747738-12748603  -3.97  1.50E-06  Potri.019G105200  Chr19:13357152-13360265  4.27  0.00056265  Potri.019G111500  Chr19:14055674-14062137  5.99  6.54E-11  Potri.019G112600  Chr19:14154166-14158758  5.41  2.67E-07  80  Gene  Locus  log2-fold change  p-value  Potri.019G112700  Chr19:14164097-14166163  5.35  6.65E-05  Potri.T170200  scaffold_1021:1000-4678  -1.69  0.00061252  Potri.T088500  scaffold_109:16832-18188  -3.42  0.00051403  Potri.T088700  scaffold_109:25220-35072  -2.54  0.00020764  Potri.T171200  scaffold_1102:105-1228  -4.15  1.88E-10  Potri.T171800  scaffold_1153:8804-10217  -4.88  7.27E-12  Potri.T172300  scaffold_1184:5264-5946  -3.53  8.57E-06  Potri.T093400  scaffold_124:45737-53643  -1.99  0.00018545  Potri.T174200  scaffold_1296:2562-5607  -3.18  0.00034576  Potri.T175000  scaffold_1378:1656-3645  -2.62  4.52E-05  Potri.T175100  scaffold_1378:6762-8260  -2.35  0.0005214  Potri.T098500  scaffold_139:20928-24097  -3.57  0.00015857  -  scaffold_139:26794-27253  -4.74  7.83E-06  Potri.T099700  scaffold_141:80127-94396  -3.05  7.61E-05  Potri.T099600  scaffold_141:68808-72184  -4.62  1.09E-07  Potri.T175500  scaffold_1426:2306-5058  -2.53  4.93E-05  Potri.T175600  scaffold_1429:4430-6954  -3.67  0.00016652  Potri.T101100,Potri.T101200  scaffold_146:3058-8062  -3.64  4.15E-06  -  scaffold_146:18267-19295  -4.82  3.69E-05  Potri.T102700  scaffold_149:9964-17838  -2.43  3.89E-05  Potri.T103900  scaffold_150:63628-66859  -1.56  0.00041423  -  scaffold_151:95443-96155  -2.42  0.00075353  Potri.T176700  scaffold_1534:1131-1335  -3.53  6.11E-05  Potri.T107500  scaffold_162:45727-48410  -4.48  0.00042335  Potri.T107600  scaffold_162:49762-50573  -5.60  5.57E-08  Potri.T111000  scaffold_170:46119-51451  -2.26  0.00042922  Potri.T113300  scaffold_178:68811-73350  -2.64  0.00021225  -  scaffold_181:73481-74434  -3.83  0.00027986  81  Gene  Locus  log2-fold change  p-value  Potri.T115200  scaffold_184:51969-54419  -4.30  2.46E-06  -  scaffold_187:57937-58560  -4.69  4.25E-06  Potri.T118400  scaffold_190:64237-66774  -4.11  0.00057233  Potri.T119200  scaffold_197:51458-53329  -4.36  2.34E-06  Potri.T118900  scaffold_197:444-3091  -3.83  9.96E-07  Potri.T001300  scaffold_20:161151-197171  -1.79  0.00019937  -  scaffold_201:87462-89267  -2.52  0.00011286  -  scaffold_2022:3881-5255  -4.58  0.00030095  Potri.T121700  scaffold_205:56606-58889  -5.63  9.73E-09  Potri.T122100  scaffold_207:25584-27336  -5.20  0.00019412  Potri.T123700,Potri.T123900  scaffold_218:19360-30649  -2.95  7.78E-06  Potri.T125500  scaffold_222:36288-39400  -3.43  1.82E-05  Potri.T125700  scaffold_222:51740-59176  -2.44  1.81E-05  Potri.T125800  scaffold_223:10785-14797  -4.56  1.85E-05  Potri.T126100  scaffold_226:34-4012  -3.00  0.00065854  Potri.T007200  scaffold_23:523190-528138  -3.68  0.00015497  Potri.T007000  scaffold_23:287406-288172  -2.89  8.06E-06  Potri.T007500  scaffold_23:721186-728505  -2.80  0.0005414  -  scaffold_2339:3986-4370  -3.43  5.40E-05  Potri.T008300  scaffold_24:154095-162649  -4.68  9.74E-06  Potri.T008400  scaffold_24:214597-220269  -2.58  0.00081873  Potri.T008700  scaffold_24:273353-274674  -4.40  0.00055504  Potri.T009000  scaffold_24:360992-364706  -7.03  2.25E-05  Potri.T009600  scaffold_24:485229-488768  -5.09  4.67E-05  -  scaffold_25:120989-121561  -4.73  5.53E-05  Potri.T131100  scaffold_252:4702-10956  -4.22  1.09E-05  Potri.T016600  scaffold_26:343391-345587  -2.69  1.95E-05  Potri.T132900  scaffold_264:19810-44719  -3.00  0.00055531  82  Gene  Locus  log2-fold change  p-value  Potri.T017700  scaffold_27:124270-127031  -4.19  4.84E-08  Potri.T133500  scaffold_270:1-1046  -3.15  6.26E-06  Potri.T134200  scaffold_273:9756-15438  -2.58  9.77E-05  Potri.T134300  scaffold_273:27337-30896  -4.14  9.54E-06  Potri.T134900  scaffold_283:3629-5991  -4.78  0.00056431  Potri.T136100,Potri.T136200  scaffold_289:46464-48829  -3.47  0.00033481  Potri.T023900  scaffold_29:459130-459907  -5.28  0.0001486  Potri.T024600  scaffold_30:2347-6896  -4.67  0.00022363  Potri.T031900  scaffold_33:413393-416966  -3.35  0.00042675  Potri.T033900  scaffold_35:432540-433739  -4.13  1.60E-05  Potri.T037400  scaffold_38:20596-27185  -2.60  0.00028842  Potri.T037600,Potri.T037800  scaffold_38:36038-45621  4.13  0.00022877  Potri.T144900  scaffold_412:322-6364  -3.76  1.70E-05  Potri.T045700  scaffold_42:221332-226438  -3.85  8.64E-07  Potri.T045800  scaffold_42:228821-236456  -4.03  7.03E-05  Potri.T046200  scaffold_42:293449-298208  -1.87  0.00062079  Potri.T145100  scaffold_422:5996-8482  -3.10  2.31E-06  Potri.T145800  scaffold_449:17129-18365  -2.41  0.00078592  Potri.T046600  scaffold_45:32536-106369  7.74  2.89E-15  Potri.T046600  scaffold_45:32536-106369  8.72  1.91E-11  Potri.T146500  scaffold_463:121-11382  -3.35  0.00012882  Potri.T147200  scaffold_479:8067-9875  -3.91  5.55E-06  Potri.T051300,Potri.T051700  scaffold_48:94004-134461  -2.95  0.00013485  Potri.T147600  scaffold_480:8578-14261  -2.34  0.00042539  Potri.T147900  scaffold_480:26309-29375  -1.75  0.00023107  Potri.T054400  scaffold_49:199944-205851  -5.43  4.86E-08  Potri.T055100  scaffold_50:18249-24945  -3.90  1.42E-07  Potri.T149500  scaffold_503:37935-39296  -2.29  0.00054854  83  Gene  Locus  log2-fold change  p-value  Potri.T149900  scaffold_514:14727-18661  -1.80  0.0007266  Potri.T058000  scaffold_53:92166-104363  -2.57  1.00E-05  Potri.T151200  scaffold_531:2620-4505  -3.54  0.00041946  Potri.T153200  scaffold_578:20039-23179  -5.32  1.03E-07  Potri.T059000  scaffold_61:12620-16896  -1.77  0.00070002  Potri.T059900  scaffold_61:61349-63486  4.90  2.89E-06  -  scaffold_611:11015-12187  -3.64  0.00010341  Potri.T156000  scaffold_645:17728-19017  -2.95  8.58E-10  Potri.T156100  scaffold_648:4677-5994  -2.95  1.71E-05  Potri.T066800  scaffold_68:161803-166618  -2.37  3.29E-06  -  scaffold_697:40580-41424  -4.05  4.19E-05  Potri.T158700  scaffold_702:102-4052  -2.99  0.00047423  Potri.T159500  scaffold_705:8203-9280  -4.10  1.29E-08  Potri.T067300  scaffold_71:19130-218103  -3.88  8.42E-05  Potri.T159800  scaffold_718:4010-5899  -2.40  7.29E-06  Potri.T159900  scaffold_718:11916-13898  -2.70  3.34E-07  Potri.T069900  scaffold_75:70009-76515  -5.21  0.0001861  Potri.T070100  scaffold_75:106395-111446  -5.95  1.41E-05  Potri.T069700  scaffold_75:29855-42107  -4.20  1.39E-06  Potri.T161200  scaffold_750:7799-10443  -3.05  2.32E-10  Potri.T161500  scaffold_752:2571-3508  -2.97  1.08E-07  Potri.T162000  scaffold_755:13195-15496  -2.70  0.0001245  Potri.T071100  scaffold_76:34365-40644  -5.03  0.00032765  -  scaffold_76:176607-177303  -4.41  1.17E-06  Potri.T162900  scaffold_782:10382-11511  -3.50  3.60E-11  Potri.T163400  scaffold_800:13301-16944  -3.36  0.00024507  -  scaffold_811:19388-19719  -5.40  2.81E-06  Potri.T076700  scaffold_82:140266-144045  -3.03  5.45E-06  84  Gene  Locus  log2-fold change  p-value  Potri.T164500  scaffold_823:12823-16434  -4.10  1.91E-07  Potri.T077700  scaffold_86:1-4452  -3.94  2.29E-06  Potri.T079200,Potri.T079300,P otri.T079400  scaffold_87:83-27683  -4.80  1.97E-08  Potri.T166700  scaffold_894:10670-14306  -4.31  0.00045805  Potri.T080800,Potri.T081000  scaffold_92:40737-66413  -3.23  2.21E-05  Potri.T082300  scaffold_93:86122-90871  -3.21  4.56E-05  Potri.T168500  scaffold_946:4616-6696  -2.43  0.00053029  Potri.T169600  scaffold_978:1470-4593  -4.57  8.20E-06  85  Appendix C RT-qPCR efficiency  86  87  Figure C.1 Amplification and standard curves for candidate genes and reference genes.  88  


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