DISCOVERIES OF PATHWAY AND REGULATION OF BRANCHED-CHAIN AMINO ACID CATABOLISM IN ARABIDOPSIS THALIANA REVEALED THROUGH TRANSCRIPT AND GENETIC STUDIES By Cheng Peng A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Plant Biology - Doctor of Philosophy 2015 ABSTRACT DISCOVERIES OF PATHWAY AND REGULATION OF BRANCHED-CHAIN AMINO ACID CATABOLISM IN ARABIDOPSIS THALIANA REVEALED THROUGH TRANSCRIPT AND GENETIC STUDIES By Cheng Peng The branched-chain amino acids (BCAAs) leucine, isoleucine and valine are among nine essential amino acids that humans and other animals must obtain from their diets, and can be nutritionally limiting in plant foods. Rapid development of transcript profiling technologies has enabled research on plant metabolism that offers potential to improve crop nutritional quality by allowing researchers to apply correlative analysis in hypothesis generation followed by experimental validation. Despite genetic evidence of its importance in regulating seed amino acid levels, the full BCAA catabolic network is not completely understood in plants, and limited information is available regarding its regulation. In this study, a combination of transcript and mutant analyses was performed to study the pathway and regulation of BCAA catabolism in Arabidopsis thaliana. Transcript coexpression analyses revealed positive correlations among BCAA catabolic genes in stress, development, diurnal/circadian and light datasets. BCAA catabolism genes show coordinated oscillation in diurnal and circadian treatments, and their expression patterns are altered in clock and phytochrome B mutants, providing evidence for the regulation of BCAA catabolism by the circadian clock and light. Functional divergence is suggested by transcript profile comparison between four pairs of BCAA catabolic enzyme paralogs, and the paralogs do not increase their transcript levels upon the loss of their duplicated copies in the dark. In addition, mutants defective in putative branched-chain ketoacid dehydrogenase subunits accumulate higher levels of BCAAs in mature seeds, providing genetic evidence for their function in BCAA catabolism. BCAA catabolism genes are highly expressed during the night on a diel cycle and during prolonged darkness, and mutants undergo senescence early and over-accumulate leaf BCAAs during prolonged darkness. These results extend the previous evidence that BCAAs can be catabolized and serve as respiratory substrates at multiple steps. Furthermore, comparison of amino acid profiles between mature seeds and dark-treated leaves revealed differences in amino acid accumulation when BCAA catabolism is perturbed. Together, these results demonstrate the consequences of blocking BCAA catabolism during both normal growth conditions and under energy-limited conditions. iii ACKNOWLEDGEMENTS It has been a very pleasant and rewarding six years studying plant biology at Michigan State University. When I first came to the US in the summer of 2009, I was naïve and didn’t know what to expect. Six years later, I’ve become a young scientist equipped with knowledge and skills, and better prepared to solve problems in research and in ordinary life. Within the years, I’ve not only deepened my knowledge in science, but also picked up skills in a variety of aspects in life that will be beneficial in the future. And I know I could not have accomplished these and become a better me without help from other people along the way. First and foremost, I want to thank my advisor, Dr. Robert Last, for his guidance and understanding throughout the years. I have been privileged to join his lab and work with him. Rob is a role model as an enthusiastic scientist who is always curious about science and dedicated at work, while at the same time enjoying life. He allowed creative freedom for me to pursue my way to finish my projects, and was always there when I needed suggestions and guidance. He also taught me to work independently and to think critically. I have learned so much from him in the past six years, and he will always be someone that I look up to. Next, I want to thank my committee members - Drs. Dan Jones, Robin Buell and Sheng Yang He. They provided honest and constructive suggestions to my research that better refined my projects, encouraged me when I met obstacles, and were always willing to offer useful resources. They greatly facilitated my research. I joined the Last lab in the summer of 2010, and since then, my lab mates have been very generous and helpful. Everybody in the lab is an expert on what they are doing, and they were always willing to share their experience and patiently taught me new skills. I appreciate all their iv honest, critical and constructive suggestions for my projects during the past few years. I was lucky to join the collaboration between the Last lab and the Shiu lab. As my main collaborator, Sahra Uygun provided numerous advice and help in my research. I am proud to have such a smart and dedicated collaborator and great friend. I also want to thank Dr. Shin-Han Shiu, who is not in my committee, but knows so much about my projects, and has always been extremely supportive and encouraging for my own projects as well as the projects we are collaborating on. The highly integrated plant research community at MSU is one of the finest and largest in the country, and a lot of great plant scientists were trained here. I am so proud to be part of the Department of Plant Biology and MSU-DOE Plant Research Laboratory. I greatly appreciate their generous financial supports, their great effort in creating a collaborating and friendly research environment, and the kind and helpful faculty members and staffs. I also want to thank the Research Technology Support Facility at MSU. The Genomics core helped me prepare RNA-Seq libraries and sequenced my samples. Lijun Chen at the Mass Spectrometry core was always friendly and patient, and was extremely helpful in solving problems regarding to the LC-MS/MS. My friends at East Lansing have become my second family over the years. They are the ones that I can discuss scientific issues with, share the highs and lows in life with, exercise with, attend events with, or just simply eat meals with. My friends have always been extremely helpful and supportive. My life is so much more colorful and entertaining because of them, and I cannot imagine survive graduate school without them. At last, I want to thank my parents. They gave me the freedom to pursue my dream thousand miles away, while building a warm and supportive harbor back home. Their unconditional love and support are the driven force for my achievements today. v PREFACE This dissertation research was conducted by me under the supervision of Professor Robert L. Last in the Department of Plant Biology, at Michigan State University, between July 2010 to April 2015, except where acknowledgements and references of previous work are stated. Part of this work has been included in a manuscript submitted to Plant Physiology: Peng C, Uygun S, Shiu, S-H, Last, R L (2015) The Impact of the Branched-Chain Ketoacid Dehydrogenase Complex on Amino Acid Homeostasis in Arabidopsis. vi TABLE OF CONTENTS LIST OF TABLES ...........................................................................................................................x LIST OF FIGURES........................................................................................................................ xi KEY TO ABBREVIATIONS ...................................................................................................... xiii Chapter 1 Literature review Plant branched-chain amino acid metabolism and utilization of transcript coexpression analysis in plant metabolism ..............................................................................................................................1 1.1 Overview of plant branched-chain amino acid catabolism .........................................................2 1.1.1 The biological functions of BCAAs ..................................................................................2 1.1.2 Free BCAA levels are strictly regulated ............................................................................3 1.1.3 Plant BCAA biosynthesis and herbicide development targeting this pathway ..................4 1.1.4 Plant BCAA catabolism ....................................................................................................5 1.1.4.1 Shared early steps in BCAA catabolism...................................................................6 1.1.4.2 Later steps in BCAA catabolism……………………………………………….......8 1.1.5 Regulation of BCAA biosynthesis and catabolism………………………………..........10 1.2 Transcript coexpression analysis in functional studies in plant metabolism……………….....14 1.2.1 Overview of two major transcript profiling techniques ...................................................14 1.2.2 Utilizing transcript coexpression in hypothesis generation and testing ...........................16 1.2.3 Considerations .................................................................................................................18 1.3 Aims of this research ................................................................................................................19 APPENDIX ....................................................................................................................................20 LITERATURE CITED ..................................................................................................................30 Chapter 2 Transcript analyses of BCAA catabolism genes support the participation of putative BCKDH complex subunits in BCAA catabolism .........................................................................................43 2.1 Abstract ....................................................................................................................................44 2.2 Introduction ..............................................................................................................................46 2.3 Results ......................................................................................................................................50 2.3.1 Known and hypothesized A. thaliana BCAA catabolism genes form a coexpression module.............................................................................................................................50 2.3.2 BCAA catabolic gene transcripts oscillate in diurnal/circadian conditions .....................51 2.3.3 Altered BCAA catabolic gene transcript levels in mutants defective in components of the circadian clock and photoreceptors ...........................................................................52 2.3.4 Regulatory regions for IVD1 transcript oscillation during short day revealed by promoter mutagenesis experiments ................................................................................................53 2.3.5 BCAA catabolism gene transcripts increase during prolonged darkness ........................54 2.3.6 Whole genome transcript coexpression analyses in prolonged darkness ........................54 2.3.7 Distinct expression patterns among BCAA catabolic enzyme paralogs ..........................56 2.3.8 BCAA catabolism gene paralogs do not compensate at the transcript level in prolonged vii darkness...........................................................................................................................58 2.4 Discussion ................................................................................................................................59 2.4.1 Considerations in using transcript coexpression analysis in gene functional characterization ...............................................................................................................59 2.4.2 Regulation of BCAA catabolism by the circadian clock and light ..................................61 2.4.3 Induction of BCAA catabolism gene expression by dark................................................62 2.5 Material and methods ...............................................................................................................64 2.5.1 Plant materials and growth conditions.............................................................................64 2.5.2 Transcript coexpression analysis .....................................................................................64 2.5.3 IVD1 promoter mutagenesis experiments........................................................................65 2.5.4 RNA-Seq analysis ...........................................................................................................66 2.5.5 RNA extraction and quantitative reverse transcription-PCR analysis .............................68 2.6 Acknowledgements ..................................................................................................................69 APPENDICES ...............................................................................................................................70 APPENDIX A Yeast one hybrid screen for TFs interacting with IVD1 promoter and 5’UTR 71 APPENDIX B Figures and tables ............................................................................................72 LITERATURE CITED …………………………………………………………………………111 Chapter 3 Characterization of mutants defective in BCAA catabolism ........................................................120 3.1 Abstract ..................................................................................................................................121 3.2 Introduction ............................................................................................................................122 3.3 Results ....................................................................................................................................125 3.3.1 Double mutants of BCKDH E1α subunits accumulate increased seed free BCAAs .....125 3.3.2 BCAA catabolic mutants exhibit early senescence under prolonged darkness .............126 3.3.3 Enhanced leaf free Leu, Ile and Val accumulation during prolonged darkness ............128 3.3.4 Beyond Leu, Ile and Val: Blocking branched-chain ketoacid dehydrogenase causes broad changes in leaf and seed amino acids ..................................................................129 3.4 Discussion ..............................................................................................................................131 3.4.1 Evidence that BCAA catabolism and energy metabolism interact at multiple steps .....131 3.5 Material and methods .............................................................................................................134 3.5.1 Free amino acid analysis by LC-MS/MS ......................................................................134 3.5.2 Generation of E1B1-silenced e1b2-1 mutant.................................................................134 3.5.3 Determination of the PSII photochemical efficiency.....................................................135 3.6 Acknowledgements ................................................................................................................136 APPENDIX ..................................................................................................................................137 LITERATURE CITED ................................................................................................................164 Chapter 4 Conclusions and future perspectives ............................................................................................168 4.1 Conclusions ............................................................................................................................169 4.2 Future perspectives .................................................................................................................170 4.2.1 Exploration of the physiological role(s) of BCAA catabolism in stress conditions ......170 4.2.2 Follow-up of the TFs interacting with the IVD1 promoter and 5’UTR by Y1H ...........170 4.2.3 Evaluation of the effect of IVD1 introns on IVD1 transcript accumulation in prolonged darkness.........................................................................................................................172 4.2.4 Exploration of the interaction between the amino acid metabolic network and energy viii metabolism ....................................................................................................................172 4.3 Practical implications .............................................................................................................174 APPENDIX ..................................................................................................................................176 LITERATURE CITED ................................................................................................................178 ix LIST OF TABLES Table 1.1 Summary of acetolactate synthase inhibiting herbicides ...............................................26 Table 1.2 Summary of A. thaliana branched-chain aminotransferases (BCATs) .........................27 Table 1.3 List of putative BCKDH enzyme subunits in A. thaliana .............................................28 Table 1.4 Comparison between GeneChip and RNA-Seq ............................................................29 Table 2.1 List of genes encoding experimentally validated and computationally annotated BCAA catabolic enzymes..........................................................................................................92 Table 2.2 Pairwise Pearson’s Correlation Coefficients (PCCs) for transcripts of BCAA catabolism genes............................................................................................................93 Table 2.3 Pairwise Pearson’s Correlation Coefficients (PCCs) among eight BCAA catabolism genes with elevated transcript levels in prolonged darkness .........................................97 Table 2.4 Gene ontology enrichment analysis by BiNGO ............................................................98 Table 2.5 Functional annotation clustering by DAVID ................................................................99 Table 2.6 Primers for genotyping, qPCR and IVD1 promoter mutagenesis experiments ...........103 Table 2.7 List of TFs interacting with IVD1 promoter and 5'UTR by Y1H ...............................105 Table 3.1 Mutant seed free amino acid profiles relative to the wild type (Col-0) ......................147 Table 3.2 Mutant leaf free amino acid levels in prolonged darkness (µmol/mg FW) ................150 Table 3.3 Leaf amino acid profiles of ivd1-2 relative to wild type (Col-0) at 6d and 9d in prolonged darkness ......................................................................................................162 Table 3.4 Primers for amiRNA generation..................................................................................163 Table 4.1 List of TFs that both interact with IVD1 -180/+200bp region and coexpress with BCAA catabolism genes .............................................................................................177 x LIST OF FIGURES Figure 1.1 Structures of the three branched-chain amino acids ....................................................21 Figure 1.2 Known enzymes and allosteric regulation of BCAA biosynthesis ..............................22 Figure 1.3 Proposed A. thaliana BCAA catabolic pathway ..........................................................23 Figure 1.4 Schematic representation of the reaction mechanism of the BCKDH complex ..........25 Figure 2.1 Transcript coexpression analysis of known or proposed BCAA catabolism gene transcripts in wild type (Col-0) ....................................................................................72 Figure 2.2 Graphical representation of transcript correlation modules among the four datasets ..74 Figure 2.3 Heat map of known and proposed BCAA catabolism gene expression profiles under diurnal/circadian conditions .........................................................................................76 Figure 2.4 Expression profiles of highly coexpressed BCAA catabolism genes under short day and constant light .........................................................................................................79 Figure 2.5 Analyses on BCAA catabolism gene transcripts in A. thaliana circadian clock and photoreceptor mutants ..................................................................................................81 Figure 2.6 IVD1 promoter mutagenesis experiments ....................................................................83 Figure 2.7 Heat map of log10 (FPKM+1) transcript levels of known and proposed BCAA catabolism genes in prolonged darkness in Col-0........................................................85 Figure 2.8 Expression analysis of paralogous genes known or proposed to be involved in BCAA catabolism ....................................................................................................................86 Figure 2.9 Characterization of BCKDH complex subunit mutants...............................................88 Figure 2.10 Single mutant transcript analysis for compensation between gene paralogs .............90 Figure 3.1 Changes in free BCAA content in dry seeds of mutants relative to wild type ..........138 Figure 3.2 Transcript and mutant analyses of E1B1-silenced e1b2-1 lines ................................139 Figure 3.3 Phenotypes of BCAA mutants subjected to prolonged darkness...............................141 Figure 3.4 Phenotypes of BCAA mutants subjected to prolonged darkness - early time points 143 Figure 3.5 Relative levels of leaf free BCAAs in mutants during prolonged darkness ..............145 Figure 3.6 Heat map showing the effect of disrupting known or proposed BCAA catabolism xi genes on amino acid homeostasis ..............................................................................146 xii KEY TO ABBREVIATIONS Ala Alanine amiRNA Artificial micro RNA Arg Arginine Asn Asparagine Asp Aspartate BCAA Branched-chain amino acid bZIP Basic leucine zipper Col-0 Columbia-0 Cys Cysteine GFP Green fluorescent protein Glu Glutamate Gln Glutamine Gly Glycine His Histidine Ile Isoleucine LC-MS/MS Liquid chromatography - tandem mass spectrometry Leu Leucine Lys Lysine Met Methionine mRNA Messenger RNA MS Mass spectrometry xiii PCR Polymerase chain reaction Phe Phenylalanine Pro Proline PSII Photosystem II qRT-PCR Quantitative reverse transcriptase PCR RNA Ribonucelic acid RNA-Seq RNA sequencing Ser Serine TF Transcription factor Thr Threonine Trp Tryptophan Tyr Tyrosine UTR Untranslated region Val Valine Y1H Yeast one hybrid xiv Chapter 1 Literature review Plant branched-chain amino acid metabolism and utilization of transcript coexpression analysis in plant metabolism 1 1.1 Overview of plant branched-chain amino acid catabolism 1.1.1 The biological functions of BCAAs The branched-chain amino acids (BCAAs, Figure 1.1) Leucine (Leu), Isoleucine (Ile) and Valine (Val) are among nine amino acids essential for humans and other animals because they cannot be synthesized de novo (Harper et al., 1984). Plants synthesize BCAAs and are the main source of these essential nutrients in the diets of humans and agriculturally important animals. However, the BCAA contents in plant foods are insufficient to meet dietary requirements, making genetic improvement in increased BCAA levels in plants a desiring target for metabolic engineering (Angelovici et al., 2013). Unfortunately, few attempts in optimization of nutritional values in plants have been successful, largely due to other unexpected deleterious traits. For example, Zhu and Galili’s attempts to increase seed free lysine content led to an undesirable low germination rate in Arabidopsis thaliana (Zhu and Galili, 2003; Zhu and Galili, 2004). This emphasizes the idea that regulation of metabolism and the connections between metabolic pathways are far more complex than previously thought. Thus, a deeper understanding in the architecture of the metabolic networks should lead us to a better-optimized engineering in plant metabolism. Besides serving as building blocks for proteins, BCAAs, together with BCAA-derived metabolites, have additional physiological functions. In plants, it was demonstrated that BCAAs and a wide variety of BCAA-derived products such as glucosinolates, fatty acids, acyl sugars, and volatiles contribute to normal growth, development and defense (Walters and Steffens, 1990; Pérez et al., 2002; Mikkelsen and Halkier, 2003; Taylor et al., 2004; Ishizaki et al., 2005; Matich and Rowan, 2007; Slocombe et al., 2008; Araujo et al., 2010; Gonda et al., 2010; Ding et al., 2 2012; Kochevenko et al., 2012; Zhang et al., 2014). The biological functions of BCAAs have been extensively studied in animals and humans. BCAAs, mainly Leu, were shown to serve as a regulator in a number of cell signaling pathways monitoring food intake, stimulating translation initiation, promoting fatty acid oxidation, and modulating hormone (mainly insulin) levels (Shimomura et al., 2004; Blomstrand et al., 2006; Layman and Walker, 2006; Norton and Layman, 2006). Recent studies also established the link between BCAAs and diseases such as certain types of diabetes and cancer - because of their roles as signaling pathway regulators (Adeva et al., 2012; McCormack et al., 2013; O'Connell, 2013; Wubetu et al., 2014), and the potential of using BCAAs as biomarkers for the progression of such diseases is being evaluated (Tom and Nair, 2006). 1.1.2 Free BCAA levels are strictly regulated BCAA homeostasis is well balanced in living organisms, and disruption in BCAA catabolism leads to a variety of consequences. In humans and animals, excessive BCAAs are efficiently degraded, perhaps consistent with a role of Leu as regulator in signaling pathways. The importance of a well-balanced BCAA catabolism was demonstrated by multiple severe metabolic disorders associated with enzymes involved in this pathway (Chuang et al., 2006). For an example, impaired branched-chain ketoacid dehydrogenase (BCKDH) activity leads to the maple syrup urine disease, resulting in symptoms including the buildup of BCAAs and their toxic by-products (ketoacids) in the blood and urine, and in worst case scenario, brain damage and death of patients (Harris et al., 1990). In plants, maintaining BCAA homeostasis seems much more complex, because plants both make and degrade these amino acids. Allosteric regulation is important for BCAA biosynthesis (Figure 1.2) (Halgand et al., 2002; Garcia and Mourad, 2004; 3 de Kraker et al., 2007; Curien et al., 2008), while no evidence was demonstrated for allosteric regulation on BCAA catabolism (Binder, 2010). The most consistent phenotype of plant mutants with impaired BCAA catabolic enzymes is the over-accumulation of free BCAAs and CoA intermediates in their dry seeds or mature fruits (Gu et al., 2010; Maloney et al., 2010; Lu et al., 2011; Angelovici et al., 2013). In addition, some BCAA catabolic mutants show altered levels of primary and specialized metabolites including amino acids that are biosynthetically unrelated, tricarboxylic acid (TCA) cycle intermediates and glucosinolates (Gu et al., 2010; Lu et al., 2011; Kochevenko et al., 2012). Certain BCAA catabolic mutants also exhibit aberrant reproductive growth such as abnormal flower and silique development (Ding et al., 2012), and early senescence in prolonged darkness (Araujo et al., 2010). These pleiotropic phenotypes suggest that disruption of BCAA catabolism impacts the function or regulation of other primary and specialized metabolic pathways during normal growth and development, and under energylimited conditions in plants. 1.1.3 Plant BCAA biosynthesis and herbicide development targeting this pathway Considerable effort has been devoted to studying plant BCAA biosynthesis (Figure 1.2), mostly due to the popular demand for developing low-use-rate herbicides of low animal toxicity two to three decades ago. The three BCAAs are synthesized in plant plastids - including chloroplasts - in parallel pathways sharing a set of four enzymes (as highlighted in grey rectangles in Figure 1.2) by using different substrates. The first common enzyme is acetolactate synthase (ALS, also known as acetohydroxy acid synthase, or AHAS), which catalyzes the conversion of two molecules of pyruvate into acetolactate in the formation of Val and Leu, or one molecule of pyruvate and one molecule of 2-oxobutanoate into 2-aceto-2-hydroxybutyrate in 4 Ile biosynthesis (Mourad and King, 1995; Singh, 1999; Binder, 2010). The discovery of a variety of structurally diverse herbicides inhibiting ALS activity was a key milestone in the history of weed control (see summary in Table 1.1) (Whitcomb, 1999; Tan et al., 2006; Duggleby et al., 2008). These herbicides are specific to plants, microbes, algae and fungi, but not animals. They also exhibit high selectivity, can work at extremely low concentrations, and lead to stunted growth, malformation and reduced seed production of sensitive plants (Duggleby et al., 2008). A series of non-transgenic crops that are resistant to herbicides were developed and commercialized by chemical mutagenesis or selection of tolerant varieties (Tranel and Wright, 2009). However, herbicide resistance in weeds rapidly developed after the commercial release of the first ALS-inhibiting herbicide - chlorosulfuron (commercial name Glean, DuPont Co.) - and to date at least 151 weed species were reported to have evolved resistance to a variety of ALSinhibiting herbicides (Heap, 2015). To overcome this issue, mutations causing resistance were intensively identified and characterized genetically and biochemically (Haughn et al., 1988; Haughn and Somerville, 1990; Sathasivan et al., 1990; Hattori et al., 1992; Ott et al., 1996; Duggleby et al., 2008), and the crystal structures of the Arabidopsis ALS enzyme with or without herbicide binding were obtained (McCourt et al., 2006). This greatly facilitated our understanding on the molecular basis for weed resistance development, and enabled new visions for design of novel inhibitors from a more structure-based rationale (McCourt et al., 2006). 1.1.4 Plant BCAA catabolism Compared to the well-studied BCAA biosynthetic pathways, our understanding of plant BCAA catabolic pathways is far from complete. The genes proposed for A. thaliana BCAA catabolism were assigned based on sequence similarity with their animal counterparts (Figure 1.3) 5 (Binder, 2010). Enzymes in earlier steps of the catabolic pathways, particularly the Leu degradative pathway, have been partially identified and characterized in recent years (Fujiki et al., 2000; Daschner et al., 2001; Fujiki et al., 2001; Lutziger and Oliver, 2001; Zolman et al., 2001; Che et al., 2002; Diebold et al., 2002; Lange et al., 2004; Taylor et al., 2004; Schuster and Binder, 2005; Binder, 2010; Gu et al., 2010; Maloney et al., 2010; Lu et al., 2011; Ding et al., 2012; Angelovici et al., 2013). However, putative enzymes in later steps of BCAA degradation, especially the breakdown of Ile and Val, still require further evaluation. The catabolism of animal BCAAs, which has been extensively analyzed because of their roles in genetic disease, occurs in mitochondria (Harper et al., 1984; Harris et al., 1990; Chuang et al., 2006). However, there are ongoing controversies about whether the localization of plant BCAA catabolism is in the mitochondrion, peroxisome or both (Zolman et al., 2001; Lange et al., 2004; Taylor et al., 2004; Lucas et al., 2007; Binder, 2010), and the majority of the genes discovered to date appear to have mitochondrial targeting signals (Binder, 2010). 1.1.4.1 Shared early steps in BCAA catabolism The degradation of all three BCAAs shares one enzyme (branched-chain aminotransferase, or BCAT) and one enzyme complex (BCKDH) in the first two steps (Figure 1.3) (Binder, 2010). There are seven BCAT genes in the A. thaliana Columbia-0 genome (Table 1.2): six are transcribed and one with no transcript detected in a variety of tissues tested (Diebold et al., 2002). Based on a subcellular localization in chloroplasts (where plant BCAA biosynthesis occurs), BCAT3 and BCAT5 are annotated as being involved in BCAA biosynthesis (Diebold et al., 2002; Schuster and Binder, 2005; Knill et al., 2008), while BCAT1 (Schuster and Binder, 2005) and BCAT2 (Angelovici et al., 2013) are proposed to play roles in BCAA catabolism 6 based upon their mitochondrial localization. BCAT1 seems to be transcribed at low level in almost all tissues except roots, however, BCAT2 responds within hours to carbohydrate deficit (Schuster and Binder, 2005; Angelovici et al., 2013) and exhibits mRNA expression patterns strikingly similar to validated BCAA catabolism genes under various stresses and hormone treatments (Matsui et al., 2008; Mentzen et al., 2008; Urano et al., 2009; Angelovici et al., 2013). Angelovici and coworkers characterized T-DNA mutants defective in BCAT1 and BCAT2, and revealed that bcat2 mutants exhibit moderate but statistically significant increases in seed free BCAAs, while bcat1 mutants show no increases (Angelovici et al., 2013). These results suggest the hypothesis that A. thaliana BCAT1 serves a housekeeping role in BCAA catabolism while BCAT2 responds to environmental stimuli. In cultivated tomato (Solanum lycopersicum), two BCATs (SlBCAT1 and SlBCAT2) are targeted to the mitochondrion, and antisense-mediated reduction of SlBCAT1 led to BCAA increases in mature fruits, consistent with a role in BCAA catabolism (Maloney et al., 2010). The shared second step is catalyzed by the multi-subunit BCKDH complex (Figure 1.3) (Binder, 2010), which is well characterized in animals. This high molecular weight complex of up to 9 MDa converts branched-chain ketoacids (which are intermediates in both BCAA biosynthesis and degradation) into acyl-CoA esters (Figure 1.4). Despite the biochemical evidence demonstrating the existence of the enzyme activity in isolated A. thaliana mitochondria (Taylor et al., 2004), the functions of the genes encoding subunits of this complex still need to be evaluated. It is similar to mitochondrial and chloroplastic pyruvate dehydrogenases and mitochondrial α-ketoglutarate dehydrogenase, and shares enzyme subunits with the Glycine (Gly) decarboxylase complex (Oliver, 1994; Mooney et al., 2002). The biochemically characterized mammalian BCKDH is comprised of multiple copies of three proteins (Figure 1.4): the α- 7 ketoacid dehydrogenase/carboxylase E1 (E1α and E1β), dihydrolipoyl acyltransferase E2, and dihydrolipoyl dehydrogenase E3 (also known as the mitochondrial lipoamide dehydrogenase, or mtLPD) (Mooney et al., 2002; Lynch et al., 2003). The large size of this complex has hindered detailed in vitro characterization in plants, and identification of the plant BCKDH complex subunits is based upon sequence annotation rather than functional analysis. Pairs of paralogous genes are annotated as encoding A. thaliana E1α, E1β and E3 subunits, and one gene is annotated for E2 (Table 1.3). These assignments are not based on enzymology, but rather are from: 1) sequence similarity with proteins identified from other organisms, especially mammals, and 2) mitochondrial localization identified by tandem mass spectrometry (Fujiki et al., 2000; Mooney et al., 2000; Taylor et al., 2004). 1.1.4.2 Later steps in BCAA catabolism A. thaliana isovaleryl-CoA dehydrogenase (IVD) - the sole documented acyl-CoA dehydrogenase family member in mitochondria - is hypothesized to catalyze the third step in the degradation of all three BCAAs (Figure 1.3) (Daschner et al., 2001; Binder, 2010). Daschner and colleagues first isolated IVD1 cDNA, and demonstrated the strong substrate specificity toward isovaleryl-CoA (a Leu degradation intermediate) and isobutyryl-CoA (a Val degradation intermediate) in A. thaliana, indicating that it functions in the degradation of both Leu and Val (Daschner et al., 2001). In contrast to A. thaliana, two enzymes were found in potato: one with the same substrate specificity as A. thaliana IVD, while the other enzyme exhibits high catalytic efficiency and low Km with 2-methylbutyryl-CoA - an Ile degradation intermediate (Faivre‐Nitschke et al., 2001; Goetzman et al., 2005). However, no homolog of the latter gene has been found in other plant species, calling into question whether IVD has a role in Ile 8 catabolism across plant species (Goetzman et al., 2005). Two different A. thaliana mutants with IVD defects exhibit pleiotropic phenotypes with increases in 12 free seed amino acids, including the three BCAAs, (Gu et al., 2010). A recent study also indicated a link between IVD and the mitochondrial electron transport chain: during prolonged darkness, ivd1-2 mutant shows early senescence at a level intermediate between electron-transfer flavoprotein (ETF) and electrontransfer flavoprotein: ubiquinone oxidoreductase (ETFQO) complex mutants and wild-type plants (Araujo et al., 2010). BCAA catabolism separates into parallel branches after IVD (Figure 1.3). Methylcrotonyl-CoA carboxylase (MCCase) catalyzes the next step in Leu degradation, and functions as a biotin containing heterodimer, formed by α (MCCA) and β (MCCB) subunits (Alban et al., 1993; Anderson et al., 1998). A. thaliana mutants deficient in either MCCA (mcca1-1) or MCCB (mccb1-1) showed higher accumulation of all BCAAs, and amino acids that are biosynthetically unrelated - such as Arginine (Arg) and Histidine (His) - in seeds compared to wild type (Lu et al., 2011). Hydroxymethylglutaryl-CoA lyase (HML) is hypothesized to catalyze the last step in Leu degradation (Figure 1.3). The ability of A. thaliana HML to hydrolyze hydroxymethylglutarylCoA to acetyl-CoA was tested and confirmed in vitro (Lu et al., 2011). The A. thaliana hml1 mutants also exhibited over-accumulation of all BCAAs, and several amino acids that are biosynthetically diverse, including Arg, His and Tryptophan (Trp) (Lu et al., 2011). Although most genes in Leu degradation have been discovered, we still lack information of the genes participating in Val and Ile degradation. The group of Bonnie Bartel at Rice University characterized a gene called CHY1 (stands for β-HYDROXYISOBUTYRYL-CoA HYDROLASE 1) and demonstrated β-hydroxyisobutyryl-CoA hydrolase activity for the encoded 9 protein, suggesting that CHY1 participate at a later step downstream of IVD in Val degradation (Zolman et al., 2001). However, CHY1 protein appears to have a peroxisomal targeting signal (Zolman et al., 2001), which is different from the mitochondrial location of other BCAA catabolism genes characterized so far. Further study is needed to identify new pathway genes and clarify the subcellular localization of Val and Ile degradation in plants. In summary, most BCAA catabolic mutants identified and characterized to date exhibit over accumulation of free seed BCAAs (Gu et al., 2010; Lu et al., 2011; Angelovici et al., 2013), making it a signature phenotype and a reference for future studies. However, several unexpected phenotypes were also found in some mutants, including early senescence in prolonged darkness and accumulation of amino acids that are biosynthetically diverse (Araujo et al., 2010; Gu et al., 2010; Lu et al., 2011). However, whether these unexpected phenotypes apply to all BCAA catabolic mutants still needs to be examined. Regardless of the specificity of the phenotypes, existing data to date show that defects in amino acid catabolism lead to increases in biosynthetically unrelated amino acids, suggesting that plant amino acid metabolic networks are more interconnected than that was previously thought. These results reinforce the idea that there are gaps in our knowledge of the regulation of plant amino acid metabolism. 1.1.5 Regulation of BCAA biosynthesis and catabolism The accumulation of free BCAAs is co-regulated in wild-type A. thaliana seeds and tomato fruits (Schauer et al., 2006; Lu et al., 2008). This presumably is due in part to the fact that they share four common biosynthetic enzymes and three catabolic steps (Figure 1.2, Figure 1.3). Recent studies also revealed that BCAA levels oscillate in diurnal and circadian conditions (Espinoza et al., 2010) and increase during prolonged darkness in rosette leaves of wild-type 10 Arabidopsis plants (Ishizaki et al., 2005), suggesting a coordinate regulation of the parallel pathways of BCAA biosynthesis and/or catabolism. The well-characterized BCAA biosynthetic pathways have three enzymes subject to allosteric regulation: threonine deaminase (TD), ALS, and isopropylmalate synthase (IPMS) (Figure 1.2) (Binder, 2010). TD catalyzes the first step in Ile biosynthesis, and Ile allosterically inhibits its activity by affecting the quaternary structure (Halgand et al., 2002). It is reported that Val impedes the inhibition by competing with Ile for binding to TD (Halgand et al., 2002). ALS leads to the biosynthesis of all three BCAAs and its activity is inhibited by all three BCAAs, with synergistic positive effects documented between Leu and Ile, as well as Leu and Val (Curien et al., 2008). IPMS initiates Leu biosynthesis from 2-oxoisovalerate - the last intermediate in Val biosynthesis, hence it is feedback inhibited by Leu (de Kraker et al., 2007). Aside from allosteric regulation, additional regulatory mechanisms have not been found to date for BCAA biosynthesis. Transcripts of BCAA biosynthesis genes have been analyzed in plants under a variety of conditions (Less and Galili, 2008; Espinoza et al., 2010). However, strong common patterns regarding spatiotemporal regulation or responses to internal and external stimuli were not found. Although we know less about the genes participating in BCAA catabolism, results of recent studies suggest that there is co-regulation of steady state levels of the mRNAs of the BCAA catabolism genes identified so far, due to their hypothesized role in the dark. It was reported that (Ishizaki et al., 2005; Ishizaki et al., 2006; Araujo et al., 2010), in addition to catalyzing the third step in the degradation of BCAAs, IVD helps plants survive under energylimited conditions by serving as a source of electrons for the mitochondrial electron transport chain (miETC) via the electron-transfer flavoprotein α and β subunits (ETFα and ETFβ) and the 11 electron-transfer flavoprotein ubiquinone oxidoreductase (ETFQO) (Figure 1.3). Evidence for this role is that, in prolonged darkness: 1) the ivd1-2 mutant becomes senescent faster than wild type, and 2) mutants defective in ETFβ and ETFQO accumulate more BCAAs and IVD substrate isovaleryl-CoA. In addition, the expression of several other genes proposed or validated to be involved in BCAA catabolism - including BCAT2, BCKDH E2, IVD, MCCA and MCCB - is rapidly induced following transition from light to dark, and is inhibited by sucrose (Fujiki et al., 2000; Daschner et al., 2001; Schuster and Binder, 2005). These observations suggest that IVD1 and other BCAA catabolism gene transcripts might be co-regulated in the dark, and the encoded enzymes possibly contribute to plant fitness under energy-limited conditions. In another study, Mentzen and coworkers at Iowa State University performed a global coexpression analysis using microarray data from 70 experiments and pointed out a coexpression supermodule capable of maintaining cellular energy balance via catabolism, and it included not only BCAA degradation, but also catabolism of other amino acids, carbohydrate catabolism, lipid breakdown and cell wall degradation (Mentzen et al., 2008). In this study, they also demonstrated that a basic leucine zipper (bZIP) transcription factor (TF) candidate bZIP1 (At5g49450) was included in the supermodule, and was hypothesized to participate in the regulation of these catabolic processes, but no experimental validation was performed (Mentzen et al., 2008). The Gad Galili group at the Weizmann Institute of Science utilized publicly available microarray data to seek for co-regulated transcripts and study interactions within and between the Aspartate (Asp) -family and aromatic amino acid networks (Less et al., 2010). Two distinct gene modules regulated by two oppositely expressed subsets of genes in Asp-derived amino acid metabolism were revealed. BCAT2 belongs to one of the two highly coexpressed modules, 12 together with genes encoding the bifunctional lysine-ketoglutarate reductase/saccharopine dehydrogenase (LKR/SDH), methionine γ lyase (MGL) and threonine aldolase (THA) at committed steps towards the degradation of Lysine, Methionine and Threonine, respectively. Other genes known and proposed for BCAA catabolism were not included in this study, and thus expression correlation cannot be evaluated from this work. Further examination of whether other BCAA catabolism genes exhibit coexpression with the highly co-regulated amino acid catabolic genes LKR/SDH, MGL and THA1 should aid in our understanding of the complex and delicate regulation of plant amino acid metabolic networks. In summary, published transcript analyses suggest a common expression pattern for BCAA catabolism genes and a potential regulation of BCAA catabolism at the transcript level. However, no comprehensive evaluation using all known and proposed BCAA catabolism genes is published. Such an in-depth analysis would aid in functional characterization of new pathway genes and to reveal additional physiological role(s) of plant BCAA catabolism. 13 1.2 Transcript coexpression analysis in functional studies in plant metabolism 1.2.1 Overview of two major transcript profiling techniques Tremendous effort has been devoted to the development of novel technologies for reliable and high-throughput quantification of the transcripts in biological samples. Hybridization- and sequencing-based approaches are the two main types for such transcript profiling techniques, and both approaches were invented two decades ago (Schena et al., 1995; Velculescu et al., 1995; Wang et al., 2009). Because of lower workload and less cost, hybridization-based methods became favorable at first and were rapidly developed. However, as faster and cheaper sequencing technologies became available in the past few years, more and more researchers often prefer to use sequencing-based transcript profiling approaches in their studies now. Hybridization-based approaches include custom-made microarrays and commercial high-density oligo photo-lithographically produced microarrays such as the Affymetrix GeneChip arrays (Woo et al., 2004). They require prior knowledge of the DNA sequence for probe design, and involve incubation of fluorescently labeled cDNA with microarray chips. In contrast, no prior knowledge is required for sequencing-based approaches, because the cDNA sequence is directly determined. Being the most popular sequencing-based approach, 'RNA sequencing' (RNA-Seq) takes advantage of the recently developed next generation sequencing technology to accurately quantify the transcriptome without prior knowledge of transcriptome space (Brautigam and Gowik, 2010). Several platforms have been developed to date, with Illumina HiSeq sequencing system being one of the widely used platforms due to its reliability and efficiency (Liu et al., 2012). Currently, GeneChip and RNA-Seq are the two major approaches for high-throughput transcript profiling (Mantione et al., 2014). 14 The commercialization of the custom-designed microarray GeneChips made DNA microarray technologies relative inexpensive and capable of measuring transcripts in a highthroughput manner (Table 1.4). The Affymetrix ATH1 GeneChip was designed for A. thaliana (Hennig et al., 2003; Redman et al., 2004). Tremendous amount of data were generated and shared among scientists, and platforms and applications were created for data storage and comparison (Craigon et al., 2004; Zimmermann et al., 2004). However, several disadvantages are associated with this technique: 1) the probe design on chips depends on prior knowledge of DNA sequence, thus its application is restricted to organisms with well-explored genomes; 2) low transcriptome coverage per chip, due to the limited number of probe spots; 3) limited detection range, because of its high background during hybridization at the low end of the range and signal saturation during detection for high steady-state level transcripts; and 4) low reproducibility, therefore sophisticated normalization is required for comparison across different experiments (Wang et al., 2009; Mantione et al., 2014). The recently developed RNA-Seq technique has advantages compared to hybridizationbased methods (Table 1.4). It is compatible with organisms without a sequenced genome or extensive transcriptome information, and can detect more features including exon/intron boundaries, splice variants, novel transcripts, fusion genes, and so on (Marioni et al., 2008; Maher et al., 2009; Arnvig et al., 2011; Howard et al., 2013). RNA-Seq also provides accurate detection of transcripts within a large dynamic range. This approach is highly reproducible, and therefore no sophisticated normalization is required for comparison across experiments (Marioni et al., 2008; Gonzalez-Ballester et al., 2010; Zhao et al., 2014). However, several considerations still need to be addressed: 1) certain biases towards longer transcripts and the end or body of transcripts could be introduced by the fragmentation methods used during library preparation 15 (Wang et al., 2009); 2) the short reads acquired by standard Illumina RNA-Seq methods complicate analysis procedures; and 3) the large size of data files (~5Gb per sample) impedes public data sharing and storage (Wang et al., 2009; Zhao et al., 2014). As the cost of sequencing continues to fall, RNA-Seq clearly is more advantageous and is expected to become increasingly commonly used for quantitative and qualitative assessment of the transcriptome among researchers (Gonzalez-Ballester et al., 2010; Nookaew et al., 2012; Howard et al., 2013). However, GeneChips are still quite cost-efficient, and the large amount of public data that has been generated so far makes it extremely valuable until the amount of data generated by RNA-Seq catches up. In the future, GeneChips might be restricted to specialized applications, such as clinical diagnosis (Mantione et al., 2014). 1.2.2 Utilizing transcript coexpression in hypothesis generation and testing Technological advances have greatly facilitated the capture of huge amounts of transcriptome data. To utilize these data and further our understanding of plant biology, correlative approaches have been adopted mainly by searching for transcript coordination (Tohge and Fernie, 2012), with the underlying assumption that correlated genes are likely to be functionally related and involved in similar or identical processes (Saito et al., 2008). Several web-based coexpression applications have been developed for A. thaliana, for example, ATTEDII (Obayashi et al., 2009; Obayashi et al., 2011), AraNet (Hwang et al., 2011; Lee et al., 2015) and Expression Angler of the Bio-Array Resource (Toufighi et al., 2005). In addition, custombased coexpression analysis protocols have also rapidly evolved, allowing for a more in depth analysis of specific pathways (Reich et al., 2006; Teknomo, 2006; Aoki et al., 2007; Langfelder and Horvath, 2008). Although correlation does not prove that the co-regulated genes work in the 16 same biological process, it is a good way to generate hypotheses for experimental testing (Fukushima et al., 2009; Stitt, 2013). The construction of coexpression networks using validated genes as baits has facilitated the identification of novel genes in plant metabolic pathways where transcriptional regulation are important, such as cell wall biosynthesis (Brown et al., 2005; Persson et al., 2005; Mutwil et al., 2009) and specialized metabolism (Rischer et al., 2006; Hirai et al., 2007; Saito et al., 2008; Yonekura-Sakakibara et al., 2008; Fukushima et al., 2009; De Luca et al., 2012; Kliebenstein, 2012; Higashi and Saito, 2013). In addition, genes that participate in signaling pathways or respond to specific environmental perturbations have also been identified via this approach: for example, genes responding to cold (Hannah et al., 2005), or involved in jasmonate signaling (McGrath et al., 2005). Moreover, pathway regulators can also be identified through this approach. For example, two TFs - MYB28 and MYB29 - regulating aliphatic glucosinolate biosynthesis were identified and experimentally evaluated in A. thaliana (Hirai et al., 2007). Although correlation-based gene functional identification has been successful in advancing our understanding of specialized metabolism, fewer examples were demonstrated for primary metabolism, perhaps due to its complex pathway topology and overlapping functionality (Stitt, 2013). Successful examples include the demonstration of transcriptional regulation of the starch degradation pathway (Smith et al., 2004), and the identification of novel transporters in C4 photosynthesis (Furumoto et al., 2011; Pick et al., 2011) and in photorespiration (Eisenhut et al., 2013; Pick et al., 2013). In addition to characterizing individual pathways, coexpression analysis also expedites elucidation of the extensive coordination and communication between metabolic pathways (Fukushima et al., 2009). For example, studies on diverse biological processes such as seed 17 germination (Bassel et al., 2011) and dark-induced senescence (Araujo et al., 2011) revealed that pathways sharing similar physiological functions can be highly correlated. 1.2.3 Considerations Several considerations need to be taken into account when performing transcript coexpression analysis in functional studies. First, transcript levels are subject to spatial and temporal regulation (Brady et al., 2007; Matas et al., 2011; Petricka et al., 2012; Rogers et al., 2012; Moussaieff et al., 2013), making conditional transcript co-regulations hard to be revealed without using relevant datasets (Usadel et al., 2009). Second, coexpression analysis alone does not guarantee the finding of all genes in the same pathway, in part due to the complex regulation and redundancy in plant metabolism (Stitt, 2013). Third, transcript changes might not lead to alterations in protein and metabolite levels. It is known that the levels of transcripts, proteins and metabolites are poorly correlated, partially because of the diverse turnover rates that range from minutes to days (Gibon et al., 2006; Caldana et al., 2011; Baerenfaller et al., 2012; Buescher et al., 2012) and the complicated regulatory mechanisms that act at multiple steps on both synthesis and degradation of transcripts, proteins and metabolites (Bailey-Serres, 1999; Piques et al., 2009; Li et al., 2012; Liu et al., 2012). 18 1.3 Aims of this research The goal of this dissertation research is to achieve a better understanding of the genes and enzymes participating in BCAA catabolism and their regulation at the transcript level in A. thaliana. The first aim was to study the transcriptional regulation of BCAA catabolism genes. Transcript coexpression analyses were performed to look for coexpression among proposed and experimentally validated BCAA catabolism genes, and to identify conditions where coexpression might occur. Expression profiling comparisons were done to evaluate proposed genes and potential pathway regulators, and to examine transcriptional divergence among enzyme paralogs. Promoter mutagenesis was performed with the goal to locate the cis-element(s) responsible for the diurnal oscillation of BCAA catabolism gene transcripts. These results are described in Chapter 2. Another aim was to evaluate and explore the function(s) of putative and validated BCAA catabolic enzymes within and beyond BCAA catabolism. Mutants were characterized under normal growth conditions and prolonged darkness, and the results are described in Chapter 3. Taken together, these results provide insights into the regulation and physiological roles of BCAA catabolism, and emphasize the complex inter-connections within the plant metabolic networks. 19 APPENDIX 20 Figure 1.1 Structures of the three branched-chain amino acids. 21 Figure 1.2 Known enzymes and allosteric regulation of BCAA biosynthesis. Enzyme names are abbreviated in rectangles with shared ones highlighted in grey. Allosteric inhibition is indicated by red lines. The restoring effect of Val on TD inhibition by Ile is shown as a dotted blue line. Four shared enzymes are: ALS, acetolactate synthase; KARI, ketoacid reductoisomerase; DHADH, dihydroxyacid dehydratase; and BCAT, branched-chain aminotransferase. TD, threonine deaminase; IPMS, isopropylmalate synthase; IPMI, isopropylmalate isomerase; IPMDH, isopropylmalate dehydrogenase; α-KG, α-ketoglutarate. Modified from Binder, 2010. 22 Figure 1.3 Proposed A. thaliana BCAA catabolic pathway. Enzyme names are abbreviated in rectangles with BCAA catabolic enzymes highlighted in grey. Validated BCAA catabolic enzyme activities are surrounded by solid lines, and putative BCAA catabolic enzyme activities by dashed lines. Metabolic processes directly or indirectly connected to BCAA catabolism in the mitochondrion are represented in ellipses. Reactions with one step are shown with solid arrows, and those with multiple steps with dashed arrows. Enzyme activities specific to Leu, Ile and Val degradation are highlighted in orange, blue and pink, respectively. Hypothesized IVD enzyme 23 Figure 1.3 (cont’d) activity towards non-BCAA catabolic intermediates is indicated with a green arrow. BCAT, branched-chain aminotransferase; α-KG, α-ketoglutarate; BCKDH, branched-chain ketoacid dehydrogenase; mtLPD, mitochondrial lipoamide dehydrogenase; IVD, isovaleryl-CoA dehydrogenase; ETF, electron transfer flavoprotein; ETFQO, electron-transfer flavoprotein: ubiquinone oxidoreductase; ETC, electron transport chain; MCC, 3-methylcrotonyl-CoA carboxylase; E-CoAH, enoyl-CoA hydratase; HMG-CoA, 3-hydroxy-3-methyl-glutaryl-CoA; HML, 3-hydroxyl-3-methylglutaryl-CoA lyase; CHY, β-hydroxyisobutyryl-CoA hydrolase; TCA, tricarboxylic acid. 24 Figure 1.4 Schematic representation of the reaction mechanism of the BCKDH complex. The BCKDH complex is composed of three components (E1, E2 and E3) and α-ketoisovalerate was arbitrarily chosen as the substrate in this figure. TPP, thiamine pyrophosphate; Lip, lipoyl domains. This model is based on the mammalian BCKDH complex. Modified from Lynch et al., 2003. 25 Table 1.1 Summary of acetolactate synthase inhibiting herbicides. Chemical family Imidazolinone Sulfonylurea Triazolopyrimidine Pyrimidinylthio (or oxy)-benzoate Herbicide common name Trade name Imazamox Raptor Imazapic Cadre, Plateau Imazapyr Arsenal, Chopper, Stalker Imazaquin Image, Scepter Imazethapyr Pursuit Chlorimuron-ethyl Classic Chlorsulfuron Glean, Corsair Halosulfuron-methyl Permit, Manage Metsulfuron-methyl Ally, Escort, Manor Nicosulfuron Accent Cloransulam Firstrate Flumetsulam Python Diclosulam Strongarm Florasulam Orion, GoldSky, FirstStep, and Spitfire Penoxsulam Granite Bispyribac-sodium Nominee Pyrithiobac-sodium Staple Sulfonylaminocarbonyltriazolinone Flucarbazone-sodium Everest, Sierra Propoxycarbazone-sodium Attribut, Olympus 26 Crop use Soybeans, other legumes, forests Weed Spectrum Mostly broadleaf species, but also some grass species Corn, soybeans, small grains, turfgrass, noncropland Corn, soybeans Very broad spectrum control of both grasses and broadleaves Rice, turfgrass, cotton Broad spectrum control of grasses, broadleaf weeds and sedges Certain grasses and broadleaf species Wheat, rye, triticale Active on broadleaf species; little activity on grasses Table 1.2 Summary of A. thaliana branched-chain aminotransferases (BCATs). Name AGI Designated metabolic pathway Subcellular localization Detection approach* BCAT1 AT1G10060 BCAA catabolism mitochondria GFP BCAT2 AT1G10070 BCAA catabolism mitochondria GFP BCAT3 AT3G49680 plastid GFP & MS BCAT4 AT3G19710 Met-derived glucosinolate chain elongation cytosol GFP BCAT5 AT5G65780 BCAA biosynthesis plastid, mitochondria** GFP & MS BCAT6 AT1G50110 / cytosol GFP BCAT7*** AT1G50090 / / / * BCAA biosynthesis & Met-derived glucosinolate chain elongation GFP, green fluorescent protein; MS, mass spectrometry. ** The mitochondrial subcellular localization of BCAT5 was only shown in a proteomic study, which is questionable and likely due to contamination as was mentioned by the authors (Taylor et al., 2004). *** BCAT7 transcripts were not detected in a variety of tissues (Diebold et al., 2002). 27 Table 1.3 List of putative BCKDH enzyme subunits in A. thaliana. Enzyme AGI Annotation E1α1 AT1G21400 α subunit of branched-chain ketoacid dehydrogenase E1, putative E1α2 AT5G09300 α subunit of branched-chain ketoacid dehydrogenase E1, putative E1β1 AT1G55510 β subunit of branched-chain ketoacid dehydrogenase E1, putative E1β2 AT3G13450 β subunit of branched-chain ketoacid dehydrogenase E1, putative E2 AT3G06850 branched-chain ketoacid dehydrogenase E2, putative mtLPD1 AT1G48030 branched-chain ketoacid dehydrogenase E3, putative mtLPD2 AT3G17240 branched-chain ketoacid dehydrogenase E3, putative 28 Table 1.4 Comparison between GeneChip and RNA-Seq. 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Sahra collected data from four microarray datasets, calculated pairwise PCC values, determined thresholds for calling significant coexpression in each dataset, and provided R codes for drawing heat maps. I interpreted the transcript coexpression results, designed, performed and analyzed all other experiments, and wrote the first draft. 43 2.1 Abstract Rapid development of transcript profiling technologies facilitates the utilization of correlative analysis in hypothesis generation, while experimental validation becomes the costly and time-consuming step. Although successful examples have been demonstrated using this approach to study plant specialized metabolism, limited cases were shown for primary metabolism. Branched-chain amino acids (BCAAs) are essential amino acids for humans and other animals because they cannot be synthesized de novo. Plants synthesize BCAAs and are the main source of these essential nutrients for humans and agriculturally important animals in their diets. However, the BCAA contents in plant foods are insufficient to meet dietary needs. Therefore, an optimized BCAA metabolism is desired. However, the full BCAA catabolic network is still not completely understood in plants, and limited information is available regarding its regulation. In this chapter, transcript analyses were performed to provide evidence supporting the participation of putative branched-chain ketoacid dehydrogenase subunits. The results revealed positive correlations between transcripts of proposed and validated BCAA catabolism genes in plants subjected to various stress conditions and light quality and quantities, and revealed regulation under diurnal/circadian conditions. These gene transcripts show coordinated oscillation in diurnal and circadian treatments, and exhibit altered expression patterns in mutants defective in components of the circadian clock and photoreceptors, suggesting that the clock and light regulate BCAA catabolism. In addition, BCAA catabolic enzyme gene transcripts are elevated and remain high during prolonged darkness, supporting the hypothesis that BCAA catabolic enzymes serve physiological roles in the dark. Moreover, possible functional divergence is suggested by transcript profile differences between four pairs of 44 BCAA catabolism gene paralogs, and is supported by no transcript compensation in single mutants of these paralogs. Together, these results provide evidence that putative catabolic enzymes participate in BCAA catabolism, and suggest transcriptional regulation of BCAA catabolism. 45 2.2 Introduction With the rapid development of transcript profiling technologies, scientists are able to obtain a better view of the quantity and quality of the entire transcriptome to date. Correlative approaches have been developed to utilize large-scale transcript data in advancing our understanding in plant biology, with the underlying hypothesis being genes correlated tend to participate in the same process or are functionally related (Saito et al., 2008). One of such correlative approaches – transcript coexpression analysis with validated genes of a particular biological process as baits - has been successfully used for identification of novel pathway genes and regulators in cell wall biosynthesis and secondary metabolism (Brown et al., 2005; Persson et al., 2005; Hirai et al., 2007; De Luca et al., 2012). However, fewer examples were demonstrated in primary metabolism - possibly due to the complicated and highly interconnected pathway topology and overlapping functionality (Stitt, 2013). The branched-chain amino acids (BCAAs) Leu, Ile and Val are among nine amino acids essential for humans and other animals because they cannot be synthesized de novo (Harper et al., 1984). Plants synthesize BCAAs and are the main source of these essential nutrients in the diets of humans and agriculturally important animals. Strong correlations between the levels of free BCAAs were found in wild-type Arabidopsis thaliana seeds and tomato (Solanum lycopersicum) fruits (Schauer et al., 2006; Lu et al., 2008), which suggests co-regulation of biosynthesis and/or degradation. This presumably is due - at least in part - to the fact that they share four common biosynthetic enzymes and three catabolic steps (Figure 1.2, Figure 1.3). In addition, recent studies revealed that free BCAA levels oscillate in diurnal and circadian conditions (Espinoza et al., 2010) and increase during prolonged darkness in rosette leaves of A. 46 thaliana wild-type plants (Ishizaki et al., 2005). These results further suggest a coordinated regulation of the parallel pathways between BCAA biosynthesis and/or catabolism. The BCAA biosynthetic pathway and its regulation have been investigated in A. thaliana and other plants for the past two decades, in large part because of the commercial importance of herbicides that inhibit acetohydroxy acid synthase, which is the committing enzyme of BCAA biosynthesis (Singh and Shaner, 1995; Aubert et al., 1997; Singh, 1999; McCourt et al., 2006; Tan et al., 2006; Binder, 2010; Chen et al., 2010; Yu et al., 2010). Three enzymes - threonine deaminase, acetolactate synthase and isopropylmalate synthase - locating at committed steps towards the biosynthesis of individual BCAAs are subject to allosteric regulation and feedback inhibited by the synthesized amino acids (Figure 1.2) (Binder, 2010). Aside from allosteric regulation, additional regulatory mechanisms - such as transcriptional regulation - have not been found to date for BCAA biosynthesis (Less and Galili, 2008; Espinoza et al., 2010). Despite long-term interest in the desirability of optimizing the content of these essential amino acids in plants, the genes and proteins that constitute the full BCAA catabolic network are not completely characterized in A. thaliana or any other plant, and there is much to learn about the genetic and biochemical regulation of this process (Figure 1.3). Evidence suggesting a common expression pattern for validated and proposed BCAA catabolism genes was demonstrated in several studies. Dramatic increases in the transcript levels of A. thaliana BRANCHED-CHAIN AMINOTRANSFERASE 2 (BCAT2), BRANCHED-CHAIN KETOACID DEHYDROGENASE E2 (E2), ISOVALERYL-COA DEHYDROGENASE 1 (IVD1), METHYLCROTONYL-COA CARBOXYLASE A1 (MCCA1) and METHYLCROTONYL-COA CARBOXYLASE B1 (MCCB1) were observed following transition from light to dark, and the induction could be inhibited by sucrose (Fujiki et al., 2000; Daschner et al., 2001; Schuster and 47 Binder, 2005). Increased IVD1 transcripts were also demonstrated during prolonged darkness (Araujo et al., 2010). Moreover, the Eve Wurtele group at Iowa State University reported that several BCAA catabolism genes were in a coexpression supermodule capable of maintaining cellular energy balance via catabolism of a variety of compounds, including amino acids, carbohydrates, lipids and cell wall components (Mentzen et al., 2008). The Gad Galili group at the Weizmann Institute of Science demonstrated BCAT2 coexpressed with genes encoding the committed enzymes in Lys, Met and Thr degradation (Less and Galili, 2008). These results suggest a potential co-regulation of BCAA catabolism gene transcripts. However, no comprehensive evaluation using all known and proposed BCAA catabolism genes is published. Such an in-depth analysis would aid in functional characterization of new pathway genes and to reveal additional physiological role(s) of plant BCAA catabolism. Although genetic and biochemical evidence exist for the participation of A. thaliana enzymes BCAT2, IVD, MCCA, MCCB and HML in BCAA catabolism (Gu et al., 2010; Lu et al., 2011; Ding et al., 2012; Angelovici et al., 2013), much less is known about the genes and encoded proteins for the branched-chain ketoacid dehydrogenase (BCKDH) complex. Published biochemical evidence demonstrated the BCKDH complex enzyme activity in isolated A. thaliana mitochondria (Taylor et al., 2004). The better characterized mammalian BCKDH is comprised of multiple copies of three proteins: the α-ketoacid dehydrogenase/carboxylase E1 (E1α and E1β), dihydrolipoyl acyltransferase E2, and dihydrolipoyl dehydrogenase E3 (also known as the mitochondrial lipoamide dehydrogenase, mtLPD) (Mooney et al., 2002). This multi-megaDalton molecular mass complex catalyzes the second step of BCAA degradation, converting branched-chain ketoacids (intermediates in BCAA biosynthesis and catabolism) into acyl-CoA esters (Figure 1.2, Figure 1.3). The complex is homologous to mitochondrial and chloroplastic 48 pyruvate dehydrogenase complexes, and mitochondrial α-ketoglutarate dehydrogenase complex, and shares enzyme subunits with the Gly decarboxylase complex (Oliver, 1994; Mooney et al., 2002). The large size of the BCKDH complex has hindered a detailed in vitro characterization in plants, and identification of the plant BCKDH complex subunits is based upon sequence annotation rather than functional analysis. Pairs of paralogous genes are annotated as encoding A. thaliana E1α, E1β and E3 subunits, and one gene for E2. These assignments are based on: 1) sequence similarity with proteins identified from other organisms, especially mammals, and 2) mitochondrial localization evidence using tandem mass spectrometry, rather than enzyme activities (Fujiki et al., 2000; Mooney et al., 2000; Taylor et al., 2004). This chapter describes results from transcript profiling analyses used to evaluate genes encoding putative subunits of the A. thaliana BCKDH complex, and to study the regulation of BCAA catabolism at the transcript level. The steady-state mRNA levels for eight experimentally validated or proposed BCAA catabolism genes are co-regulated in a variety of conditions. The expression of these eight genes is altered in mutants defective in components of the circadian clock and photoreceptors. In addition, gene paralogs in this pathway exhibit different expression patterns and do not compensate for the loss of one paralog by elevating the transcript level of the other, suggesting possible functional divergence. These results provide evidence supporting the participation of the putative BCKDH complex subunits E1α1, E1β1, E1β2 and E2 in BCAA catabolism. Moreover, these data suggest a role for BCAA catabolism in maintaining amino acid homeostasis under day/night cycles and prolonged darkness. 49 2.3 Results 2.3.1 Known and hypothesized A. thaliana BCAA catabolism genes form a coexpression module Transcripts of the functionally validated A. thaliana BCAA catabolism genes BCAT2, IVD1, MCCA1 and MCCB1 were reported to increase rapidly following the transition from light to dark (Daschner et al., 2001; Fujiki et al., 2001; Schuster and Binder, 2005; Araujo et al., 2010; Angelovici et al., 2013). These results led us to hypothesize that candidate genes encoding additional enzymes in BCAA catabolism could be identified by their coexpression with these known genes. Coexpression analysis was performed with 13 genes encoding proteins proposed (based on sequence similarity) or experimentally validated to participate in BCAA catabolism (the gene list is shown in Table 2.1). First, to determine the extent of coexpression among these genes, pairwise Pearson’s Correlation Coefficients (PCCs) were calculated, and average-linkage hierarchical clustering was performed with four datasets: development, abiotic and biotic stress, and light datasets from AtGenExpress (Schmid et al., 2005; Kilian et al., 2007), and the diurnal/circadian dataset from the DIURNAL database (Mockler et al., 2007) (Figure 2.1, Table 2.2). The degrees of coexpression among the five validated BCAA catabolism ‘bait’ genes (BCAT2, IVD1, MCCA1, MCCB1 and HML1) were evaluated to establish the foundation for the coexpression analyses. In the stress dataset, all five bait genes form a coexpression module with significant expression correlations (Figure 2.1, Figure 2.2). In the other three datasets (development, diurnal/circadian and light), four out of the five bait genes form coexpression modules. These modules (one from each dataset) contain three common bait members - BCAT2, 50 IVD1 and MCCA1 - with MCCB1 in only the diurnal/circadian and the light modules, and HML1 only in the development (Figure 2.1, Figure 2.2). Because of the significant expression correlations among bait genes, these results indicate the feasibility of this approach for identifying candidate genes in the BCAA catabolic pathway. Next, coexpression of the eight proposed BCAA catabolism genes with the five bait genes was evaluated (dotted rectangles in Figure 2.1, Figure 2.2). The stress coexpression module contains the largest number of members: five proposed genes in addition to the five bait genes. Furthermore, four genes proposed to encode proteins of BCAA catabolism and four validated bait genes are in the light and the diurnal/circadian coexpression modules. In contrast, one of the proposed genes is in the development module that contains four bait genes. In summary, highly interconnected modules containing validated and proposed BCAA catabolic enzyme genes emerged from three datasets: stress, diurnal/circadian, and light. These modules have eight members in common: the validated bait genes BCAT2, IVD1, MCCA1 and MCCB1, and proposed BCKDH genes E1A1, E1B1, E1B2 and E2 (Figure 2.1, Figure 2.2, Table 2.2). 2.3.2 BCAA catabolic gene transcripts oscillate in diurnal/circadian conditions The observation that four out of five bait genes - and four out of eight proposed BCAA catabolic enzyme genes - are strongly coexpressed in the diurnal/circadian dataset (Figure 2.1, Figure 2.2), suggests that these genes are coordinately regulated by light and the circadian clock. Further examination of transcript profiles confirmed common oscillation patterns among these eight validated or proposed BCAA catabolism genes under most diurnal/circadian experiments (Figure 2.3). It is notable that these gene transcript levels are reduced during the day, and increased during the night in the rosette leaves of 4-week-old, short-day-grown (8h light/16h 51 dark) plants (Figure 2.4A). This common oscillation pattern is consistent with the previous observation that free BCAA levels fluctuate in day/night cycles and peak towards the end of the day (Gibon et al., 2006; Espinoza et al., 2010), suggesting that up-regulation of the catabolism genes contributes to the decreased free BCAAs at night. In addition, the eight transcripts also oscillate in a similar fashion in constant light after entrainment in light/dark or hot/cold cycles (Figure 2.3, Figure 2.4B), consistent with the hypothesis that BCAA catabolism is subject to regulation by the circadian clock. These results further demonstrated the coexpression of BCAA catabolic enzyme gene transcripts in diurnal/circadian conditions, especially on a day/night cycle. 2.3.3 Altered BCAA catabolic gene transcript levels in mutants defective in components of the circadian clock and photoreceptors The expression profiles of BCAT2, E1A1, E1B1, E1B2, E2, IVD1, MCCA1 and MCCB1 identified as highly coexpressed in the diurnal/circadian dataset (Figure 2.1, Figure 2.2) - were examined in mutants defective in various components of the circadian clock and photoreceptors for further evaluation of the participation of the clock and light in regulating BCAA catabolism. These gene transcripts exhibited highly correlated oscillation during short day in LHYOX (overexpressor of LATE ELONGATED HYPOCOTY, LHY) and phyB-9 (mutant defective in PHYTOCHROME B, PHYB) (Figure 2.5 A and B). In addition, expression profile comparison between the two clock mutants and their corresponding wild type lines revealed elevated transcript levels of these eight genes later at night during short day, with the maximum increased to two- to three-fold relative to that of the wild type (BCAT2 is shown in Figure 2.5C as an example). Together, these results are consistent with the hypothesis that the circadian clock and 52 light regulate BCAA catabolism directly or indirectly via transcription factor LHY and red/farred photoreceptor PhyB. 2.3.4 Regulatory regions for IVD1 transcript oscillation during short day revealed by promoter mutagenesis experiments Because of the highly co-regulated transcript levels of BCAT2, E1A1, E1B1, E1B2, E2, IVD1, MCCA1 and MCCB1, a common cis-element was hypothesized in the promoter of these genes to regulate their oscillation on day/night cycles. IVD1 was chosen as a representative for promoter mutagenesis assays to locate the regulatory cis-element(s). IVD1 promoter fragments with or without 5’ untranslated regions (UTRs) were inserted into a construct that has a luciferase (LUC) reporter next to the inserted region (Figure 2.6A). These constructs were transformed into A. thaliana Col-0 plants, and bioluminescence was monitored for primary transformants (see Methods and Materials). A total of 11 different combinations of various IVD1 promoter fragments with or without 5’ UTR were analyzed (Figure 2.6B) and compared to the IVD1 expression pattern in Col-0 during short day growth conditions (Figure 2.4A). Representative results are shown in Figure 2.6C. The IVD1 promoter fragment containing 100bp upstream from the transcription start site (TSS) and the 5’ UTR was necessary to drive LUC expression in a similar oscillation pattern with IVD1 in wild type under short day. Constructs containing either the 5’UTR alone or any promoter fragment without the 5’UTR failed to mimic the oscillation of IVD1 transcripts. Together, these results suggest that IVD1 transcript oscillation on a day/night cycle is regulated by a combined effect of the IVD1 -100bp promoter region and its 5’UTR. 53 2.3.5 BCAA catabolism gene transcripts increase during prolonged darkness Published studies revealed that transcripts of the known BCAA catabolism genes BCAT2, IVD1, MCCA1 and MCCB1 increase following transition from light to dark in plants grown under light-dark conditions and during prolonged darkness (Daschner et al., 2001; Fujiki et al., 2001; Schuster and Binder, 2005; Araujo et al., 2010). I used RNA-Seq analysis to ask whether the transcripts for the proposed BCKDH subunits are also upregulated in plants subjected to prolonged darkness. These experiments were performed using rosette leaves from 5-week-old, short-day-grown (8h light/16h dark) Col-0 wild-type plants moved to constant darkness for 6h, 24h, 48h and 72h (Figure 2.7). The transcripts of the eight genes found in the coexpression modules derived from stress, light and diurnal/circadian datasets (Figure 2.1, Figure 2.2) were increased nine- to 400-fold within the first 6h of prolonged darkness, and remained high until the last time point (gene names highlighted in blue in Figure 2.7). These results are consistent with the hypothesis that BCAA catabolic enzymes - including BCKDH subunits E1A1, E1B1, E1B2 and E2 - have one or more physiological roles in the dark. 2.3.6 Whole genome transcript coexpression analyses in prolonged darkness Because eight BCAA catabolism genes - BCAT2, E1A1, E1B1, E1B2, E2, IVD1, MCCA1 and MCCB1 - showed elevated transcripts in prolonged darkness (Figure 2.7), the pairwise PCCs were calculated to determine the extent of the coexpression among these genes in prolonged darkness. All gene pairs were significantly coexpressed (Table 2.3), with PCC values ranging from 0.918 (between E1B2 and E2) to 0.995 (between E1B2 and IVD1). To further identify genes that coexpress with the eight BCAA catabolism genes, pairwise PCCs were calculated for all genes detected in Col-0 wild-type plants in replicates of at least one out of the six time points 54 in my RNA-Seq dataset. A total of 3244 genes were found to be significantly coexpressed with the eight genes (with PCC r>0.854, which is the 95th percentile of the null distribution of 500,000 random gene pairs in my RNA-Seq dataset, see details in material and methods). To further reduce the number of coexpressed genes for gene ontology (GO) enrichment analyses, a more stringent threshold (r>0.900) was used, and 2338 genes were found to have PCCs above 0.900 with the eight BCAA catabolism genes. Out of the 2338 genes, 2084 genes coexpressed with at least two out of the eight genes in prolonged darkness, and the 2084 genes were used for GO enrichment analyses. Two GO enrichment analysis tools were used: the Biological Network Gene Ontology tool (BiNGO, http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html) (Maere et al., 2005), and the Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/home.jsp) (Huang da et al., 2009). A plant-specific, simplified ontology file, GOSlim-Plants, was selected for BiNGO to present a broad overview of enriched GO categories with focus on biological process (GO_BP), and 17 GO_BP slim terms were found to be enriched (Table 2.4). A more detailed GO enrichment analysis was performed using all GO_BP terms with the DAVID functional classification tool. 285 GO_BP terms were significantly enriched (corrected p-value<0.05), 94 clusters of functionally related GO terms were generated, and the top five clusters contain GO_BP terms related to RNA splicing, processing and metabolic processes, protein and macromolecule catabolic processes, cellular metabolism and regulation, fatty acid and lipid catabolism, and protein and macromolecule transport (Table 2.5). 55 2.3.7 Distinct expression patterns among BCAA catabolic enzyme paralogs There are four pairs of paralogous genes encoding known or proposed enzymes in the first two steps of BCAA catabolism (Table 2.1). However, it is not known whether all paralogs participate in BCAA catabolic process because BCAT activities are involved both in BCAA biosynthesis and degradation (Diebold et al., 2002), and some BCKDH subunits are similar to those of pyruvate dehydrogenases in mitochondria and chloroplasts, and mitochondrial αketoglutarate dehydrogenase and Gly decarboxylase (Oliver, 1994; Mooney et al., 2002). It was hypothesized that genes encoding these BCAA degradation enzyme paralogs would show coexpression with the documented catabolic enzymes because of their significant expression correlations revealed from our previous coexpression analyses (Figure 2.1, Figure 2.2). Indeed, four out of eight paralogs - BCAT2 and BCKDH subunits E1A1, E1B1 and E1B2 - are members of the coexpression modules identified from the light and the diurnal/circadian datasets (Figure 2.1, Figure 2.2). This observation provided evidence that these proteins have roles in BCAA catabolism. Expression divergence is often observed among paralogous gene pairs that can be due to modification of regulatory elements during evolution (Shariati and De Strooper, 2013), and likely results in function diversification (Stern and Orgogozo, 2008). To evaluate the degree of expression divergence, I compared the expression profiles of the four paralogous pairs. The flowering and seed development datasets were included in this analysis because the known BCAA catabolism genes BCAT2, IVD1 and MCCA1 and HML1 have increased transcripts during flowering and seed development (http://bar.utoronto.ca/efp/cgi-bin/efpWeb.cgi). In addition, data for the short-day growth (8h light/16h dark) experiments were also selected because of the highly coordinated transcript oscillations described above (Figure 2.4A). Moreover, RNA-Seq data 56 during prolonged darkness were also chosen because of the transcript increases of eight out of 13 proposed or validated BCAA catabolism genes shown above (Figure 2.7). All four pairs of paralogs were found to have divergent expression profiles in these datasets that followed two general patterns (Figure 2.8). First, expression of paralogs BCAT1/2, E1A1/2 and E1B1/2 was anti-correlated during the late stages of seed development (Figure 2.8, left panel, PCC value r=-0.98, -0.55, -0.87, respectively, for the last four time points), suggesting more important roles in seed amino acid regulation for the isoforms encoded by the more highlyexpressed transcripts. Varying degrees of anti-correlation were also found for BCAT1/2, E1A1/2 and mtLPD1/2 under short day (Figure 2.8, middle panel, r=-0.41, -0.82, -0.32, respectively), and between E1A1 and E1A2 during prolonged darkness (Figure 2.8, right panel, r=-0.94). The second pattern of expression divergence is illustrated by mtLPD1/2 during development (Figure 2.8, left panel), E1A1/2, E1B1/2 and mtLPD1/2 under short day growth conditions (Figure 2.8, middle panel), and BCAT1/BCAT2, E1B1/E1B2 and mtLPD1/mtLPD2 during prolonged darkness (Figure 2.8, right panel), where one copy is expressed at a higher level than the other (Ganko et al., 2007; Zou et al., 2009). Among these pairs, the expression profiles of E1B1 and E1B2 are most similar than the other pairs under short day and during prolonged darkness (r=0.66 and 0.99, respectively). In summary, examples of divergent expression profiles of paralogs can be distinguished in the development, short day diurnal cycling, or prolonged darkness datasets, indicating divergence in mRNA regulation. The differences in expression patterns suggest a possible functional divergence between the paralogs. In addition, this expression divergence under short day also explains why BCAT1, E1A2, mtLPD1 and mtLPD2 - genes proposed for BCAA 57 catabolism - are not found in the coexpression module in the diurnal/circadian dataset (Figure 2.1, Figure 2.2). 2.3.8 BCAA catabolism gene paralogs do not compensate at transcript level in prolonged darkness Deleting a duplicate gene often leads to less severe phenotype comparing to removing a singleton gene (Gu et al., 2003; Hanada et al., 2011), partially because paralogs sometimes compensate the loss of one copy by elevating the expression of the other (Kafri et al., 2005). To look for potential compensation at the transcript level, homozygous T-DNA insertion lines of these paralogs were obtained. Quantitative PCR analysis revealed that e1a1-1, e1a1-2, e1a2-1 and e1b2-1 are null alleles (Figure 2.9). Because the mtlpd2-2 mutant showed approximately 50% reduction of the mtLPD2 transcripts relative to wild type, it was not included in further evaluation. Unfortunately, no homozygous T-DNA lines could be identified for E1B1 and mtLPD1. The mRNA of BCAT1, BCAT2, E1A1, E1A2 and E1B1 were examined in 5-week-old bcat1-1, bcat2-1, e1a1-1, e1a2-1 and e1b2-1 single mutants treated in prolonged darkness for three days. For all transcripts tested, no statistical significance (p<0.05 determined by Student’s t-test) was detected between single mutants and corresponding wild types (Figure 2.10). These results revealed that BCAA catabolism gene paralogs BCAT1/BCAT2 and E1A1/E1A2 do not compensate for each other, and E1B1 does not compensate for the loss of E1B2 at transcript level in prolonged darkness, further suggesting functional divergence between these paralogs in prolonged darkness. 58 2.4 Discussion Advances in genomics technologies have created new opportunities to identify candidate genes and proteins for complex physiological and biochemical processes. This has created a situation where it is relatively inexpensive and straightforward to mine data and create hypotheses, while testing theories is often the costly and time consuming step. Analysis of large multi-subunit enzymes is especially complicated since it requires expression of multiple subunits, assembly of the component parts and establishment of an in vitro activity assay that faithfully represents the in vivo process. While A. thaliana genes were annotated for the component subunits, establishing in vitro functions of the proteins in a complex is problematic. The plant megadalton, multi-subunit BCKDH enzyme is an example of such a complex. In this chapter I describe results from transcript analyses that provide evidence of the participation of putative BCKDH complex subunits in BCAA catabolism, and suggest transcriptional regulation of BCAA catabolism. 2.4.1 Considerations in using transcript coexpression analysis in gene functional characterization There are important considerations in using gene expression analysis to develop or test hypotheses regarding gene function. First, it is essential to have a set of validated ‘bait’ genes for the analysis; in this study five bait genes of BCAA catabolism were employed (Table 2.1). Second, success of the approach requires that appropriate transcript profiling data be available. In this study I explored multiple microarray datasets (Figure 2.1, Table 2.2); these include a developmental dataset, with a variety of tissues (‘development’), plants grown under differing light and temperature regimes (‘diurnal/circadian’ and ‘light’), as well as abiotic and biotic stress 59 conditions (‘stress’). The broad choice of types of datasets was important in this study because each yielded coexpression modules of different sizes and structures (Figure 2.2). For example, analysis of the stress dataset produced a module with the largest number of nodes (10), while the diurnal/circadian dataset yielded a smaller (eight) but more highly connected module (Figure 2.2). In contrast, the development dataset was least useful, with a weakly connected module of four ‘bait’ genes that have no significant expression correlation with the proposed BCAA catabolic enzyme genes (Figure 2.2). Taken together, our results demonstrate the importance of using validated bait genes and different types of expression data to identify candidate genes. Coexpression analysis can be confounded in a number of ways. One scenario is when paralogous genes exist, with one copy that actively responds to environmental perturbations, while the other generally expresses at a much lower level (Keith et al., 1991; Duarte et al., 2006; Ganko et al., 2007; Szekely et al., 2008; Zou et al., 2009). For example, I found that the E1α subunit gene E1A1 is coexpressed with the other core BCAA catabolic genes, while the paralog E1A2 is not (Figure 2.1, Figure 2.2). This result suggests possible functional divergence, and functional characterization is needed for further evaluation, which is shown in Chapter 3. Using coexpression analysis to develop hypotheses regarding function also can be confounded if the protein product participates in multiple different processes. For example, mtLPD is proposed to function as a subunit of the mitochondrial pyruvate dehydrogenase, αketoglutarate dehydrogenase, Gly decarboxylase and BCKDH complexes (Lutziger and Oliver, 2001). Needs for this subunit in physiologically diverse processes might be responsible for the observed lack of co-regulation with other known and proposed genes of BCAA degradation (Figure 2.1, Figure 2.2). In addition, the single-copy and experimentally validated hydroxymethylglutaryl-CoA lyase gene HML1 displayed coexpression with other BCAA 60 catabolic genes in the stress and the development datasets (Figure 2.1, Figure 2.2). I hypothesize that lack of membership of HML1 mRNA in the light and the diurnal/circadian coexpression modules reflects that this enzyme participates in other metabolic processes (Ashmarina et al., 1994; Ashmarina et al., 1999). Alternatively, perhaps this and other non-co-regulated BCAA catabolic enzymes are subject to post-transcriptional regulation. 2.4.2 Regulation of BCAA catabolism by the circadian clock and light Due to their sessile nature, plants have evolved with complex regulatory mechanisms helping them with adaptation to the constantly changing environment. Light and temperature are two major environmental factors for plant growth and development (Went, 1953; Kami et al., 2010). The plant circadian clock can perceive such changes in light and temperature, and regulate relevant metabolic processes accordingly (Harmer et al., 2000). Moreover, once the clock is entrained by light and temperature, plants can predict cycling environmental changes and actively re-adjust themselves. As important primary metabolites, free BCAA levels were found to oscillate during diurnal and circadian conditions (Espinoza et al., 2010). However, no information was available regarding to the regulation of BCAA metabolism gene transcripts or enzymes in diurnal and circadian conditions that contributes to the metabolite fluctuation. In this analysis, transcripts of eight BCAA catabolism genes (BCAT2, E1A1, E1B1, E1B2, E2, IVD1, MCCA1 and MCCB1) were found to coordinately oscillate in diurnal and circadian conditions (Figure 2.1, Figure 2.3, Figure 2.4), and these genes are directly or indirectly regulated by components of the circadian clock and photoreceptors (Figure 2.5). These data provides first hand evidence that is consistent with the hypothesis that BCAA catabolism is regulated by the clock and light at transcript level. In addition, results shown in this chapter revealed that both the 61 -100bp promoter region and 5’ UTR of IVD1 are required for rhythmic oscillation of the IVD1 transcript during short day (Figure 2.6). This suggests that multiple regulators might be involved in maintaining rhythmic BCAA catabolism gene expression during day/night cycles. 2.4.3 Induction of BCAA catabolism gene expression by dark Prolonged darkness is often used by scientists to study plant response upon carbon starvation. Several metabolic processes were found to be upregulated and essential for plant survival in such conditions, including autophagy (Hanaoka et al., 2002; Thompson and Vierstra, 2005) and the mitochondrial electron transport (Ishizaki et al., 2005; Ishizaki et al., 2006; Schertl and Braun, 2014). In this study, elevated transcript levels of BCAA catabolism genes BCAT2, E1A1, E1B1, E1B2, E2, IVD1, MCCA1 and MCCB1 were demonstrated in the first 6h in prolonged darkness, and the transcripts remained high until the last time point (3d) (Figure 2.7). These results suggest that BCAA catabolism serves physiological role(s) in the dark. Testing of this hypothesis and related results are shown in Chapter 3. In an attempt to locate cis-element(s) regulating the dark inducibility of IVD1, I used the same 2-week-old transgenic lines containing LUC reporter driven by various regions of IVD1 promoter with or without its 5’ UTR – the ones created for investigation of the cis-element(s) regulating the diel oscillation of IVD1 (Figure LUC 2.6A) – to look for induction in bioluminescence during a 2-day dark treatment. However, no transgenic lines tested showed consistent luminescence induction by prolonged darkness. To eliminate the possibility that ATP depletion was the cause, 2-week-old seedlings were transferred to soil, grown for an additional two weeks, and tested for luminescence induction by prolonged darkness again. However, no consistent induction was observed, suggesting that IVD1 promoter with 5’ UTR is not sufficient 62 for its elevated transcript level in prolonged darkness. Besides promoter and 5’ UTR, introns were shown to stimulate gene expression in both monocots and dicots (Callis et al., 1987; Luehrsen and Walbot, 1991; Norris et al., 1993; Xu et al., 1994). For an example, either of the first two introns of the A. thaliana phosphoribosylanthranilate transferase gene PAT1 – which encodes an enzyme in tryptophan biosynthesis - enhances mRNA accumulation without affecting the rate of transcription(Rose and Last, 1997; Rose, 2002, 2004, 2008). Further investigation should examine the effect of IVD1 introns on gene expression to test this hypothesis. 63 2.5 Material and methods 2.5.1 Plant materials and growth conditions A. thaliana ecotype Columbia CS60000, T-DNA lines bcat1-1 (SALK_138630), bcat2-1 (SALK_037854), e1a1-1 (SALK_071680), e1a1-2 (WiscDsLox470G12), e1a2-1 (SAIL_113_D07), e1b2-1 (SALK_098054) and mtlpd2-2 (SALK_027039) were obtained from the Arabidopsis Biological Resource Center (ABRC). Homozygous mutant lines that were obtained or validated were deposited at ABRC: bcat1-1 stock CS68922, bcat2-1 CS68923, e1a11 CS68924, e1a1-2 CS68925, e1a2-1 CS68926, e1b2-1 CS68927, mtlpd2-2 CS68928, double mutants e1a1-1; e1a2-1 CS68929 and e1a1-2; e1a2-1 CS68930. The ivd1-2, mcca1-1, mccb1-1, and hml1-2 mutants were described previously (Gu et al., 2010; Lu et al., 2011). All plants, including double mutants, were genotyped and confirmed to be homozygous with primers in Table 2.3 prior to further analyses. Plants were grown in soil in chambers at 21C with fluorescent lamps (100 μmol m-2 s-1) under different photoperiods (16h for LD, 8h for SD, and continuous darkness). Seeds for amino acid assay were harvested from mature plants grown under LD. Leaves for transcript analysis and amino acid assay were harvested from plants grown under SD for 5 weeks and subjected to various lengths of prolonged darkness after the end of the night. 2.5.2 Transcript coexpression analysis Coexpression analysis of the previously documented and hypothesized BCAA catabolic enzyme genes was performed with the following microarray datasets: stress (AtGenExpress, abiotic and biotic stress treatments in roots and shoots with time points from 0.5 to 24h) (Kilian 64 et al., 2007), development (AtGenExpress, atlas of developmental stages consisting of a variety of tissues) (Schmid et al., 2005), light (AtGenExpress, light treatments of different wavelengths, fluence and durations) (Kilian et al., 2007) and diurnal/circadian (Diurnal, combinations of light and temperature conditions) (Mockler et al., 2007). The development, light and diurnal/circadian data were downloaded in a Robust Multi-array Average normalized form from: http://www.weigelworld.org/resources/microarray/AtGenExpress/ and ftp://www.mocklerlab.org/diurnal/. For the stress dataset, the degrees of differential expression (in the form of fold change) was obtained from an earlier study (Zou et al., 2009). For all datasets, pairwise Pearson’s Correlation Coefficient (PCC) values were calculated among the genes of interest using SciPy library in Python (http://www.scipy.org/) (Jones et al., 2001). To identify gene pairs with significantly higher than randomly expected PCC values, 500,000 gene pairs were selected randomly from each dataset to calculate PCCs and establish a null PCC distribution. The 95th percentile of each null PCC distribution was used as the threshold for calling two genes as significantly positively correlated with a 5% false positive rate (arrows in Figure 2.1; Figure 2.3; Table 2.2). Coexpression modules were defined by clustered genes with significant correlations in each dataset. Heat maps were generated with levelplot within the R lattice package (Sarkar and Sarkar, 2007). 2.5.3 IVD1 promoter mutagenesis experiments IVD1 promoter fragments (-1500bp, -750bp, -500bp, -350bp, -200bp and -100bp, with or without 5’UTR, where +1 denotes the IVD1 transcriptional start site) were isolated from Col-0 genomic DNA (primers see Table 2.3) and cloned into pZPXomegaLUC+ (Schultz et al., 2001). All constructs were sequenced prior to further analyses to check for mutations and/or unwanted 65 DNA fragments introduced during cloning. Constructs were transformed into A. thaliana wild type (Col-0) by floral dip (Clough and Bent, 1998). Two-week-old primary transformants (T1s) were selected by gentamycin resistance and tested for bioluminescence under short day using a Berthold LB960XS3 luminometer (Michael and McClung, 2002; Liu et al., 2013). Average luciferase intensity at every hour for each construct during the 2-day short day growth was calculated based on ~24 T1s tested at the same time. 2.5.4 RNA-Seq Analysis Leaf tissues from Col-0, ivd1-2 and hml1-2 treated for 0h, 6h, 24h, 48h and 72h in prolonged darkness and 72h under SD were harvested for RNA-seq. For each genotype under the same treatment, two replicates were grown and harvested a month apart from each other. Therefore, in total 36 samples were sequenced. Total RNA from 9th to 12th rosette leaves was extracted using RNeasy plant mini kit (Qiagen, Germantown, MD) and on-column digestion performed with RNase-free DNase Set (Qiagen, Germantown, MD). RNA quality was assessed using the Agilent 2100 Bioanalyzer with the RNA 6000 Pico Chip (Agilent Technologies, Santa Clara, CA). Library construction and sequencing were conducted by the Michigan State University Research Technology Support Facility using Illumina Tru-Seq Stranded kit and following the manufacturer’s protocols. Six samples were multiplexed and sequenced in one lane using the Illumina HiSeq 2500 sequencer, and 50 nucleotide single end reads were generated. 16 to 60 million reads were obtained per sample. For read processing and assembly, the sequencing adapters were removed using the following parameters in Trimmomatic version 0.30 (Bolger et al., 2014): ILLUMINACLIP: 66 TruSeq3-SE, SLIDINGWINDOW: 4:15, and MINLEN:35. Processed reads were filtered by fastq_quality_filter in the FASTX-toolkit version 0.0.13.2 (http://hannonlab.cshl.edu/fastx_toolkit/index.html), satisfying the criterion that > 85% bases must have Q-score ≥ 20. The processed and filtered reads were mapped to the A. thaliana reference genome (TAIR10) using TopHat version 1.4.1 (Trapnell et al., 2009), sorted by SAMtools version 0.1.19 (Li et al., 2009), and analyzed with Cuffdiff version 2.1.1 (Trapnell et al., 2013). The transcript levels represented as Fragments Per Kilobase of exon model per Million mapped reads (FPKM) were visualized with CummeRbund version 2.6.1 (Goff et al., 2012) in R version 3.0.3 (Statistical Package, 2012). The RNA-Seq dataset was deposited in the National Center for Biotechnology Information Gene Expression Omnibus under GEO accession number GSE67956. Raw read counts from each sample were normalized to Counts Per Million reads (CPM), and an expression matrix was generated for wild-type plants at all six time points each with two replicates. Pairwise Pearson’s Correlation Coefficient (PCC) values were calculated using the SciPy library in Python (http://www.scipy.org/) (Jones et al., 2001). The 95th percentile of the null PCC distribution from 500,000 random gene pairs was calculated and used as the threshold for calling two genes as significantly positively correlated with a 5% false positive rate (indicated in Table 2.3). Tables containing all genes that have PCCs with the eight highly coexpressed BCAA catabolism genes (BCAT2, E1A1, E1B1, E1B2, E2, IVD1, MCCA1 and MCCB1) above the thresholds (either 95th percentile of the random PCC distribution or r=0.9000) were generated, and only genes showing significant coexpression with at least two out of the eight BCAA catabolism genes were included for gene ontology enrichment analyses. The BiNGO plugin for Cytoscape version 3.0.3 was used for analyzing GO enrichment 67 (http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html) (Maere et al., 2005), and DAVID version 6.7 was used for functional annotation clustering (http://david.abcc.ncifcrf.gov/home.jsp) (Huang da et al., 2009). Parameters used were indicated in table legends of Table 2.4 and Table 2.5. 2.5.5 RNA extraction and quantitative reverse transcription-PCR analysis Total RNA from 9th to 12th rosette leaves was extracted using RNeasy plant mini kit (Qiagen, Germantown, MD) and digested with RNase-free DNase Set (Qiagen, Germantown, MD) on-column. First-strand cDNA was synthesized from 2 µg of total RNA with M-MLV reverse transcriptase (Invitrogen, Carlsbad, CA) and oligo(dT)12-18 primer (Invitrogen, Carlsbad, CA) (Kotewicz et al., 1985). Gene-specific primers were designed to span two or more exons as listed in Table 2.3. Quantitative PCR analyses were performed on a 7500 Fast Real-Time PCR System with Power or Fast SYBR green PCR master mix (Applied Biosystems, Waltham, MA), with ACT2 (At3G18780) transcript level used as an internal control (Czechowski et al., 2005). 68 2.6 Acknowledgments I would like to thank Sahra Uygun for help with coexpression analysis, Kathleen Imre and David Hall for isolating homozygous T-DNA mutants, and Linsey Newton for providing vectors and guidance for luciferase assays. 69 APPENDICES 70 APPENDIX A Yeast one hybrid screen for TFs interacting with IVD1 promoter and 5’UTR My previous analyses demonstrated that genes proposed or experimentally validated to participate in BCAA catabolism - including BCAT2, E1A1, E1B1, E1B2, E2, IVD1, MCCA1 and MCCB2 - coexpress in the stress, diurnal/circadian and light datasets, have common oscillation patterns during diurnal and circadian conditions, and show elevated transcript levels in prolonged darkness. These results suggest a common regulatory mechanism for these eight genes. I hypothesized that one or a group of TFs bound to the promoters of these eight genes and regulated their expression. To find the promoter-binding TFs, IVD1 was selected as a representative, and 880bp upstream of the IVD1 transcriptional start site (TSS, +1) and its 5’UTR were included for yeast one hybrid (Y1H) screen against an A. thaliana TF library containing ~2000 TFs. Three overlapping fragments were cloned into pLacZi-GW vectors (Gendron et al., 2012): fragment #1 corresponds to -180bp to +200bp relative to the TSS of IVD1, fragment #2 -530bp to -150bp, and fragment #3 -880bp to -500bp. The three constructs were sent to the Pruneda-Paz lab at University of California, San Diego for the Y1H screen (Pruneda-Paz et al., 2014). TFs presented in Table 2.7 are ones with high confidence as concluded by the PrunedaPaz lab. A total of 72 TFs were found to interacting with fragment #1, 45 with #2 and 35 with #3. Combined with the results from the luciferase assay that the -100bp IVD1 promoter region with 5’UTR was able to drive its diurnal oscillation (Figure 2.6), TFs found to bind to fragment #1 should be considered for future evaluation. 71 APPENDIX B Figures and tables Figure 2.1 Transcript coexpression analysis of known or proposed BCAA catabolism gene transcripts in wild type (Col-0). Four datasets were tested: development (top left), stress (top right), diurnal/circadian (bottom left), and light (bottom right). Values represent the Pearson’s Correlation Coefficient (PCC) for each gene pair. Arrows indicate the PCC value representing the 95th percentile of each null PCC distribution for individual datasets. Transcript names for validated genes are shown in bold and italic text, and those for proposed genes in regular italic 72 Figure 2.1 (cont’d) text. Dotted rectangles represent coexpression modules in each dataset, defined by clustered genes with significant expression correlations. Heat maps were generated using microarray data from AtGenExpress for stress, development and light datasets (Schmid et al., 2005; Kilian et al., 2007), and the Diurnal database for diurnal/circadian dataset (Mockler et al., 2007). 73 Figure 2.2 Graphical representation of transcript correlation modules among the four datasets. Graphs were constructed using development (top left), stress (top right), diurnal/circadian (bottom left), and light (bottom right) datasets. Nodes represent transcripts and edges indicate pairwise PCC values exceeding the threshold in each database. Transcript names of validated genes are in bold, and proposed genes in regular font. The size of nodes corresponds 74 Figure 2.2 (cont’d) to the connectivity of the transcript (smaller node indicates fewer significant correlations with other transcripts, and vice versa). The thickness of edges correlates with their PCC values. See Table 2.2 for PCC values and the corresponding threshold in each dataset. The network graphs were created using Cytoscape. 75 Figure 2.3 Heat map of known and proposed BCAA catabolism gene expression profiles under diurnal/circadian conditions. Microarray data were obtained from the Diurnal database (Mockler et al., 2007), and normalized to the maximum expression levels of each gene in every treatment. Pearson’s Correlation Coefficient and average linkage were used for gene clustering. Gene names in orange text represent members of the highly coexpressed module identified from the diurnal/circadian dataset. Refer to the Diurnal database website (http://diurnal.mocklerlab.org/) for detailed information on each condition. COL, Col-0; LDHH, 12h light/12h dark and 24h hot; SD, 8h light/16h dark; DD(DDHC), entrained on 24h dark and 12h hot/12h cold, and subjected to 24h dark and 24h hot; LDHC, 12h light/12h dark and 12h hot/12h cold; LDHH-Smith, 12h light/12h dark and 24h hot (Smith et al., 2004); LDHH-Stitt, 12h light/12h dark and 24h hot (Bläsing et al., 2005); LL(LDHC), entrained on LDHC and subjected to 24h light and 24h hot; LL(LLHC), 76 Figure 2.3 (cont’d) entrained on 24h light and 12h hot/12h cold, and subjected to 24h light and 24h hot; LL12(LDHH), entrained on LDHH and subjected to 24h light and 24h hot; LL23(LDHH), entrained on LDHH and subjected to 24h light and 24h hot; LLHC, 24h light and 12h hot/12h cold. 77 Figure 2.3 (cont’d) 78 Figure 2.4 Expression profiles of highly coexpressed BCAA catabolism genes under short day (A) and constant light (LL(LDHC), B). Rosette leaves from 4-week-old Col-0 plants were used in microarray experiments from the Diurnal database (http://diurnal.mocklerlab.org) (Mockler et al., 2007). Each gene was normalized to its maximal expression. White bars on the x axis 79 Figure 2.4 (cont’d) represent the time in the light (A) or subjective day (B), black bars represent the time in the dark, and grey bars represent the time in the subjective night. See Figure 2.3 for details on conditions. 80 Figure 2.5 Analyses on BCAA catabolism gene transcripts in A. thaliana circadian clock and photoreceptor mutants. BCAA catabolism gene transcripts coordinately oscillate in LHYOX (A) and phyB-9 (B) under short day. Rosette leaves from 4-week-old Col-0 plants were used in microarray experiments from the Diurnal database (http://diurnal.mocklerlab.org) (Mockler et al., 2007). Each gene was normalized to its maximal expression. White bars on the x 81 Figure 2.5 (cont’d) axis represent the time in the light, and black bars represent the time in the dark. (C) Increased BCAA catabolism gene transcripts in LHYOX (left) and phyB-9 (right) compared to corresponding wild type at night during short day. BCAT2 is shown as an example, and the same changes were also found for E1A1, E1B1, E1B2, E2, IVD1, MCCA1 and MCCB1. White bars on the x axis represent the time in the light, and black bars represent the time in the dark. Microarray data obtained from the Diurnal database (http://diurnal.mocklerlab.org) (Mockler et al., 2007). 82 Figure 2.6 IVD1 promoter mutagenesis experiments. (A) Simplified working flow. Constructs containing luciferase reporter gene (LUC) driven by different IVD1 promoter fragments with or without 5’UTR were transformed into A. thaliana wildtype Col-0 plants by agrobacteriummediated transformation. Primary transformants (T1s) were selected against antibiotics and LUC activity were monitored by luminometer. (B) Fragmentation scheme for IVD1 promoter with or without 5’UTR. All fragments were monitored for bioluminescence. Fragment name texts 83 Figure 2.6 (cont’d) highlighted in colors were shown in C as representatives. (C) LUC intensity for selected IVD1 promoter/5’UTR fragments. Bioluminescence was recorded every hour for two days under short day (8h light/16h dark). Average intensity of >20 T1s were normalized to the maximum intensity of each fragment. Among the four fragments shown, p100 + 5’UTR and p750 + 5’UTR best mimic the IVD1 transcript oscillation in Col-0 (shown in Figure 2.4A). TSS, transcription start site; ATG, translation start site. 84 Figure 2.7 Heat map of log10 (FPKM+1) transcript levels of known and proposed BCAA catabolism genes in prolonged darkness in Col-0. Values are the mean of two independent biological replicates, which were grown and sequenced separately. FPKM, fragments per kilobase of transcript per million mapped reads. Genes with names in blue text showed statistically significantly increased transcript levels during prolonged darkness compared with time zero (p<0.05, determined by exact test implemented in edgeR with Benjamini-Hochberg correction at FDR<0.05) (Chen et al., 2011). 85 Figure 2.8 Expression analysis of paralogous genes known or proposed to be involved in BCAA catabolism. Microarray data were obtained from (left column) the AtGenExpress development dataset (Schmid et al., 2005) and (middle column) the Diurnal 86 Figure 2.8 (cont’d) database (Mockler et al., 2007), respectively; data for prolonged darkness were obtained from RNA-seq experiments (right column). White bars on the x axis in short day represent the time in the light, and black bars represent the time in the dark. Gene expression values were converted to log2 and shown on the y axis. Gene pairs indicated in each row are: A, BCAT1 vs. BCAT2; B, E1A1 vs. E1A2; C, E1B1 vs. E1B2; and D, mtLPD1 vs. mtLPD2. 87 Figure 2.9 Characterization of BCKDH complex subunit mutants. (A) Schematic representations of the T-DNA insertion sites of newly characterized BCAA catabolic mutants. Exons are shown as white rectangles, UTRs as grey rectangles, and introns as solid lines. The sites of T-DNA insertion confirmed in this study are indicated by black triangles. (B) Decreased BCAA catabolic enzyme transcript accumulation in e1a1-1, e1a1-2, e1a2-1, e1b2-1, and mtlpd22 mutants. Values represent mean ± SE from four biological replicates. An asterisk indicates a 88 Figure 2.9 (cont’d) significant difference determined by the Student’s t-test (p<0.05). ND, not detectable. The normalized E1B2 transcript level in e1b2-1 is less than 0.5% relative to Col-0. 89 Figure 2.10 Single mutant transcript analysis for compensation between gene paralogs. Values represent mean ± SE from four biological replicates. Transcripts tested are indicated on 90 Figure 2.10 (cont’d) top of each figure, and mutants (and corresponding wild type) at the bottom. Transcript levels of tested genes were normalized to ACT2 transcript levels, and shown as fold changes relative to the WT. Transcripts were measured from rosette leaves of 5-week-old plants treated in prolonged darkness for 3 days. 91 Table 2.1 List of genes encoding experimentally validated and computationally annotated BCAA catabolic enzymes. Gene AGI Annotation Paralogous gene Duplication type* BCAT1 AT1G10060 Branched-chain aminotransferase 1, putative BCAT2 Tandem BCAT2 AT1G10070 Branched-chain aminotransferase 2 BCAT1 Tandem E1A1 AT1G21400 α subunit of branched-chain ketoacid dehydrogenase E1, putative E1A2 Before α-WGD E1A2 AT5G09300 α subunit of branched-chain ketoacid dehydrogenase E1, putative E1A1 Before α-WGD E1B1 AT1G55510 β subunit of branched-chain ketoacid dehydrogenase E1, putative E1B2 α-WGD E1B2 AT3G13450 β subunit of branched-chain ketoacid dehydrogenase E1, putative E1B1 α-WGD E2 AT3G06850 Branched-chain ketoacid dehydrogenase E2, putative None / mtLPD1 AT1G48030 Branched-chain ketoacid dehydrogenase E3, putative mtLPD2 α-WGD mtLPD2 AT3G17240 Branched-chain ketoacid dehydrogenase E3, putative mtLPD1 α-WGD IVD1 AT3G45300 Isovaleryl-CoA dehydrogenase None / MCCA1 AT1G03090 α subunit of 3-methylcrotonyl-CoA carboxylase None / MCCB1 AT4G34030 β subunit of 3-methylcrotonyl-CoA carboxylase None / HML1 AT2G26800 Hydroxymethylglutaryl-CoA lyase None / * Duplication type was derived from the physical location of the gene paralogs on chromosomes and published data (Bowers et al., 2003). α-WGD: most recent whole genome duplication event. 92 Table 2.2 Pairwise Pearson's Correlation Coefficients (PCCs) for transcripts of BCAA catabolism genes*, **. Development 95th percentile of random PCC distribution = 0.60 BCAT1 BCAT2 E1A1 E1A2 E1B1 E1B2 E2 mtLPD1 mtLPD2 IVD1 MCCA1 MCCB1 HML1 BCAT1 1.000 0.031 0.586 0.576 0.454 0.379 0.433 -0.049 -0.426 -0.124 -0.123 -0.034 -0.006 BCAT2 0.031 1.000 0.550 -0.173 0.356 0.177 0.138 -0.280 0.220 0.704 0.580 0.044 0.666 E1A1 0.586 0.550 1.000 0.196 0.436 0.315 0.359 -0.216 -0.216 0.387 0.241 0.113 0.251 E1A2 0.576 -0.173 0.196 1.000 0.370 -0.049 0.408 -0.516 -0.335 -0.241 -0.325 -0.133 -0.289 E1B1 0.454 0.356 0.436 0.370 1.000 0.410 0.673 -0.544 0.008 0.443 0.411 0.140 0.163 E1B2 0.379 0.177 0.315 -0.049 0.410 1.000 0.600 0.191 -0.261 0.440 0.511 0.510 0.307 E2 0.433 0.138 0.359 0.408 0.673 0.600 1.000 -0.420 -0.058 0.407 0.458 0.330 -0.029 mtLPD1 -0.049 -0.280 -0.216 -0.516 -0.544 0.191 -0.420 1.000 -0.105 -0.282 -0.075 0.206 0.018 mtLPD2 -0.426 0.220 -0.216 -0.335 0.008 -0.261 -0.058 -0.105 1.000 0.301 0.201 -0.257 0.084 IVD1 -0.124 0.704 0.387 -0.241 0.443 0.440 0.407 -0.282 0.301 1.000 0.706 0.251 0.599 MCCA1 -0.123 0.580 0.241 -0.325 0.411 0.511 0.458 -0.075 0.201 0.706 1.000 0.548 0.322 MCCB1 -0.034 0.044 0.113 -0.133 0.140 0.510 0.330 0.206 -0.257 0.251 0.548 1.000 -0.041 HML1 0.666 0.251 -0.289 0.163 0.307 -0.029 0.018 0.084 0.599 0.322 -0.041 1.000 * -0.006 PCCs above the 95th percentile threshold for each dataset are highlighted in red, and the values are indicated below. ** Genes that have PCCs above threshold with one or more other genes are highlighted in yellow. 93 Table 2.2 (cont’d) Stress 95th percentile of random PCC distribution = 0.47 BCAT1 BCAT2 E1A1 E1A2 E1B1 E1B2 E2 mtLPD1 mtLPD2 IVD1 MCCA1 MCCB1 HML1 BCAT1 1.000 -0.133 0.005 -0.027 0.120 0.134 -0.417 0.102 -0.284 0.011 0.094 0.081 -0.385 BCAT2 -0.133 1.000 0.777 -0.064 0.425 0.487 0.631 -0.538 0.607 0.767 0.608 0.125 0.275 E1A1 0.005 0.777 1.000 -0.149 0.545 0.497 0.543 -0.462 0.477 0.839 0.622 0.282 0.464 E1A2 -0.027 -0.064 -0.149 1.000 0.173 0.110 0.200 0.293 -0.032 -0.142 -0.113 0.236 -0.270 E1B1 0.120 0.425 0.545 0.173 1.000 0.825 0.535 -0.298 0.009 0.668 0.701 0.653 0.288 E1B2 0.134 0.487 0.497 0.110 0.825 1.000 0.685 -0.287 0.022 0.669 0.787 0.656 0.239 E2 -0.417 0.631 0.543 0.200 0.535 0.685 1.000 -0.250 0.357 0.605 0.634 0.504 0.480 mtLPD1 0.102 -0.538 -0.462 0.293 -0.298 -0.287 -0.250 1.000 -0.478 -0.432 -0.340 0.300 -0.249 mtLPD2 -0.284 0.607 0.477 -0.032 0.009 0.022 0.357 -0.478 1.000 0.393 0.113 -0.330 0.202 IVD1 0.011 0.767 0.839 -0.142 0.668 0.669 0.605 -0.432 0.393 1.000 0.742 0.449 0.380 MCCA1 0.094 0.608 0.622 -0.113 0.701 0.787 0.634 -0.340 0.113 0.742 1.000 0.524 0.328 MCCB1 0.081 0.125 0.282 0.236 0.653 0.656 0.504 0.300 -0.330 0.449 0.524 1.000 0.125 HML1 0.275 0.464 -0.270 0.288 0.239 0.480 -0.249 0.202 0.380 0.328 0.125 1.000 -0.385 94 Table 2.2 (cont’d) Diurnal/circadian 95th percentile of random PCC distribution = 0.45 BCAT1 BCAT2 E1A1 E1A2 E1B1 E1B2 E2 mtLPD1 mtLPD2 IVD1 MCCA1 MCCB1 HML1 BCAT1 1.000 0.195 -0.017 -0.109 0.287 0.155 0.186 0.059 -0.392 -0.002 0.227 0.298 -0.278 BCAT2 0.195 1.000 0.497 -0.222 0.515 0.670 0.633 -0.152 -0.334 0.506 0.816 0.565 -0.092 E1A1 -0.017 0.497 1.000 -0.363 0.629 0.740 0.533 -0.286 -0.409 0.750 0.644 0.511 -0.068 E1A2 -0.109 -0.222 -0.363 1.000 -0.240 -0.284 0.031 -0.070 0.379 -0.115 -0.190 -0.090 -0.155 E1B1 0.287 0.515 0.629 -0.240 1.000 0.756 0.745 -0.173 -0.487 0.635 0.746 0.811 -0.005 E1B2 0.155 0.670 0.740 -0.284 0.756 1.000 0.797 -0.354 -0.399 0.794 0.874 0.717 -0.039 E2 0.186 0.633 0.533 0.031 0.745 0.797 1.000 -0.435 -0.320 0.816 0.862 0.810 -0.046 mtLPD1 0.059 -0.152 -0.286 -0.070 -0.173 -0.354 -0.435 1.000 -0.107 -0.627 -0.320 -0.110 0.204 mtLPD2 -0.392 -0.334 -0.409 0.379 -0.487 -0.399 -0.320 -0.107 1.000 -0.239 -0.385 -0.482 -0.050 IVD1 0.506 0.750 -0.115 0.635 0.794 0.816 -0.627 -0.239 1.000 0.748 0.591 -0.161 MCCA1 0.227 0.816 0.644 -0.190 0.746 0.874 0.862 -0.320 -0.385 0.748 1.000 0.820 -0.006 MCCB1 0.298 0.565 0.511 -0.090 0.811 0.717 0.810 -0.110 -0.482 0.591 0.820 1.000 0.071 HML1 -0.092 -0.068 -0.155 -0.005 -0.039 -0.046 0.204 -0.050 -0.161 -0.006 0.071 1.000 -0.002 -0.278 95 Table 2.2 (cont’d) Light 95th percentile of random PCC distribution = 0.90 BCAT1 BCAT2 E1A1 E1A2 E1B1 E1B2 E2 mtLPD1 mtLPD2 IVD1 MCCA1 MCCB1 HML1 BCAT1 1.000 0.622 0.589 -0.146 0.612 0.618 0.510 -0.388 -0.398 0.520 0.589 0.555 -0.210 BCAT2 0.622 1.000 0.972 0.101 0.894 0.983 0.843 -0.762 -0.328 0.785 0.931 0.958 -0.752 E1A1 0.589 0.972 1.000 0.134 0.927 0.952 0.855 -0.667 -0.349 0.816 0.908 0.958 -0.722 E1A2 -0.146 0.101 0.134 1.000 -0.021 0.072 -0.172 0.292 0.621 -0.314 -0.144 0.017 -0.462 E1B1 0.612 0.894 0.927 -0.021 1.000 0.908 0.935 -0.630 -0.544 0.912 0.906 0.923 -0.567 E1B2 0.618 0.983 0.952 0.072 0.908 1.000 0.885 -0.780 -0.397 0.818 0.958 0.969 -0.738 E2 0.510 0.843 0.855 -0.172 0.935 0.885 1.000 -0.710 -0.680 0.964 0.941 0.927 -0.587 mtLPD1 -0.388 -0.762 -0.667 0.292 -0.630 -0.780 -0.710 1.000 0.328 -0.664 -0.796 -0.738 0.472 mtLPD2 -0.398 -0.328 -0.349 0.621 -0.544 -0.397 -0.680 0.328 1.000 -0.776 -0.586 -0.517 0.068 IVD1 0.520 0.785 0.816 -0.314 0.912 0.818 0.964 -0.664 -0.776 1.000 0.907 0.886 -0.452 MCCA1 0.589 0.931 0.908 -0.144 0.906 0.958 0.941 -0.796 -0.586 0.907 1.000 0.973 -0.645 MCCB1 0.555 0.958 0.958 0.017 0.923 0.969 0.927 -0.738 -0.517 0.886 0.973 1.000 -0.722 HML1 -0.752 -0.722 -0.462 -0.567 -0.738 -0.587 0.472 0.068 -0.452 -0.645 -0.722 1.000 -0.210 96 Table 2.3 Pairwise Pearson's Correlation Coefficients (PCCs) among eight BCAA catabolism genes with elevated transcript levels in prolonged darkness. 95th percentile of random PCC distribution = 0.854 BCAT2 E1A1 E1B1 E1B2 E2 IVD1 MCCA1 MCCB1 BCAT2 1.000 0.983 0.980 0.967 0.942 0.963 0.959 0.974 E1A1 0.983 1.000 0.985 0.976 0.922 0.975 0.957 0.985 E1B1 0.980 0.985 1.000 0.993 0.933 0.986 0.972 0.988 E1B2 0.967 0.976 0.993 1.000 0.918 0.995 0.964 0.991 E2 0.942 0.922 0.933 0.918 1.000 0.936 0.986 0.919 IVD1 0.963 0.975 0.986 0.995 0.936 1.000 0.971 0.989 MCCA1 0.959 0.957 0.972 0.964 0.986 0.971 1.000 0.960 MCCB1 0.974 0.985 0.988 0.991 0.919 0.989 0.960 1.000 97 Table 2.4 Gene ontology enrichment analysis by BiNGO*. GO-ID Description p-value corr p-value count 9056 catabolic process 1.46E-12 3.40E-11 102 9628 response to abiotic stimulus 9.74E-07 8.24E-06 132 7275 multicellular organismal development 1.38E-04 7.14E-04 163 9605 response to external stimulus 2.05E-04 9.53E-04 33 9791 post-embryonic development 7.29E-04 3.08E-03 92 6464 protein modification process 1.41E-03 5.45E-03 121 6139 nucleobase, nucleoside, nucleotide and nucleic acid 1.58E-03 5.87E-03 109 metabolic process 3 reproduction 1.65E-03 5.92E-03 94 9987 cellular process 5.57E-03 1.73E-02 601 9719 response to endogenous stimulus 6.42E-03 1.93E-02 82 9653 anatomical structure morphogenesis 7.14E-03 2.08E-02 54 16043 cellular component organization 7.55E-03 2.13E-02 90 9606 tropism 8.25E-03 2.26E-02 9 9790 embryonic development 1.30E-02 3.46E-02 45 6950 response to stress 1.43E-02 3.68E-02 163 6810 transport 1.65E-02 4.15E-02 134 6629 lipid metabolic process 1.88E-02 4.61E-02 57 * 2084 genes that coexpressed with at least two out of the eight BCAA catabolism genes BCAT2, E1A1, E1B1, E1B2, E2, IVD1, MCCA1, and MCCB1 - were used in this analysis with the following parameters: Selected ontology file: GOSlim-Plants, with focus on biological process Selected statistical test: hypergeometric test Selected correction: Benjamini & Hochberg False Discovery Rate correction Selected significance level: 0.05 Testing option: use whole annotation as reference set 98 Table 2.5 Functional annotation clustering by DAVID*. Annotation Cluster 1 Enrichment Score: 9.09 GO-ID Description Count p-value 16071 mRNA metabolic process 49 2.30E-17 3.73E-14 3.85E-14 6397 mRNA processing 42 1.25E-14 1.02E-11 2.10E-11 8380 RNA splicing 36 3.16E-13 1.71E-10 5.30E-10 16070 RNA metabolic process 95 4.73E-09 3.84E-07 7.93E-06 6396 RNA processing 68 6.65E-08 4.32E-06 1.12E-04 398 nuclear mRNA splicing, via spliceosome 15 6.69E-06 2.79E-04 1.12E-02 375 RNA splicing, via transesterification reactions 15 3.16E-05 1.05E-03 5.30E-02 15 3.16E-05 1.05E-03 5.30E-02 377 RNA splicing, via transesterification reactions with bulged adenosine as nucleophile corr p-value FDR Annotation Cluster 2 Enrichment Score: 8.31 GO-ID Description Count p-value 44248 cellular catabolic process 135 1.89E-12 7.68E-10 3.17E-09 44265 cellular macromolecule catabolic process 94 3.83E-11 1.25E-08 6.43E-08 87 5.64E-10 9.16E-08 9.45E-07 51603 proteolysis involved in cellular protein catabolic process corr p-value FDR 44257 cellular protein catabolic process 87 9.71E-10 1.43E-07 1.63E-06 30163 protein catabolic process 88 1.62E-09 2.20E-07 2.73E-06 19941 modification-dependent protein catabolic process 85 1.84E-09 2.30E-07 3.09E-06 85 1.84E-09 2.30E-07 3.09E-06 43632 modification-dependent macromolecule catabolic process 9057 macromolecule catabolic process 97 9.50E-09 7.35E-07 1.59E-05 9056 catabolic process 157 2.74E-08 2.02E-06 4.60E-05 6508 proteolysis 123 2.44E-06 1.16E-04 4.09E-03 6511 ubiquitin-dependent protein catabolic process 40 2.87E-04 7.48E-03 4.80E-01 99 Table 2.5 (cont’d) Annotation Cluster 3 Enrichment Score: 5.76 GO-ID Description Count p-value 50789 regulation of biological process 386 6.86E-11 1.86E-08 1.15E-07 19222 regulation of metabolic process 268 8.37E-11 1.94E-08 1.40E-07 65007 biological regulation 420 9.08E-11 1.84E-08 1.52E-07 80090 regulation of primary metabolic process 249 1.58E-10 2.85E-08 2.64E-07 60255 regulation of macromolecule metabolic process 252 3.38E-09 3.66E-07 5.66E-06 31323 regulation of cellular metabolic process 243 3.61E-09 3.67E-07 6.06E-06 50794 regulation of cellular process 352 4.54E-09 4.10E-07 7.62E-06 225 4.98E-08 3.52E-06 8.35E-05 51171 regulation of nitrogen compound metabolic process 226 5.02E-08 3.40E-06 8.42E-05 45449 regulation of transcription 221 9.74E-08 6.09E-06 1.63E-04 9889 regulation of biosynthetic process 228 9.79E-08 5.89E-06 1.64E-04 31326 regulation of cellular biosynthetic process 228 9.79E-08 5.89E-06 1.64E-04 10556 regulation of macromolecule biosynthetic process 225 1.11E-07 6.42E-06 1.85E-04 10468 regulation of gene expression 236 1.26E-07 7.04E-06 2.11E-04 260 2.95E-07 1.60E-05 4.94E-04 19219 6139 regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process nucleobase, nucleoside, nucleotide and nucleic acid metabolic process corr p-value FDR 6807 nitrogen compound metabolic process 305 7.34E-07 3.85E-05 1.23E-03 34641 cellular nitrogen compound metabolic process 295 1.50E-06 7.41E-05 2.52E-03 51252 regulation of RNA metabolic process 126 8.68E-06 3.52E-04 1.46E-02 6355 regulation of transcription, DNA-dependent 123 2.60E-05 8.80E-04 4.36E-02 6350 transcription 136 4.64E-04 1.12E-02 7.76E-01 44260 cellular macromolecule metabolic process 493 5.27E-04 1.25E-02 8.81E-01 10467 gene expression 226 7.22E-01 9.91E-01 1.00E+02 34645 cellular macromolecule biosynthetic process 174 1.00E+00 1.00E+00 1.00E+02 9059 macromolecule biosynthetic process 174 1.00E+00 1.00E+00 1.00E+02 9058 biosynthetic process 267 1.00E+00 1.00E+00 1.00E+02 100 Table 2.5 (cont’d) 44249 cellular biosynthetic process 247 1.00E+00 1.00E+00 1.00E+02 Annotation Cluster 4 Enrichment Score: 4.85 GO-ID Description Count p-value corr p-value FDR 46395 carboxylic acid catabolic process 26 2.24E-09 2.60E-07 3.76E-06 16054 organic acid catabolic process 26 2.24E-09 2.60E-07 3.76E-06 9062 fatty acid catabolic process 13 3.79E-06 1.71E-04 6.36E-03 6635 fatty acid beta-oxidation 12 4.01E-06 1.76E-04 6.72E-03 19395 fatty acid oxidation 12 1.27E-05 4.79E-04 2.13E-02 34440 lipid oxidation 12 1.27E-05 4.79E-04 2.13E-02 30258 lipid modification 14 1.54E-05 5.70E-04 2.59E-02 44242 cellular lipid catabolic process 15 9.50E-05 2.71E-03 1.59E-01 6631 fatty acid metabolic process 24 7.52E-02 4.37E-01 7.31E+01 16042 lipid catabolic process 20 2.54E-01 7.89E-01 9.93E+01 Annotation Cluster 5 Enrichment Score: 4.79 GO-ID Description Count p-value 8104 protein localization 79 4.29E-09 4.10E-07 7.20E-06 15031 protein transport 77 4.70E-09 4.02E-07 7.88E-06 45184 establishment of protein localization 77 4.70E-09 4.02E-07 7.88E-06 16192 vesicle-mediated transport 48 1.35E-06 6.88E-05 2.27E-03 33036 macromolecule localization 89 6.15E-06 2.63E-04 1.03E-02 46907 intracellular transport 57 1.16E-05 4.47E-04 1.94E-02 51641 cellular localization 67 1.59E-05 5.72E-04 2.66E-02 51649 establishment of localization in cell 62 2.50E-05 8.63E-04 4.19E-02 6886 intracellular protein transport 42 4.38E-05 1.39E-03 7.34E-02 70727 cellular macromolecule localization 45 4.87E-05 1.46E-03 8.17E-02 34613 cellular protein localization 43 5.10E-05 1.51E-03 8.55E-02 6810 transport 182 5.64E-02 3.76E-01 6.22E+01 101 corr p-value FDR Table 2.5 (cont’d) 51234 establishment of localization 182 6.28E-02 3.97E-01 6.63E+01 51179 localization 186 7.18E-02 4.31E-01 7.13E+01 * 2084 genes that coexpressed with at least two out of the eight BCAA catabolism genes BCAT2, E1A1, E1B1, E1B2, E2, IVD1, MCCA1, and MCCB1 - were used in GO enrichment analysis focusing on biological processes related GO terms. Selected ontology file: GOTERM_BP_ALL Selected correction: Benjamini-Hochberg method 285 GO terms were enriched, 94 clusters of similar GO terms were generated by DAVID functional annotation clustering, and the top five clusters were shown in this table. 102 Table 2.6 Primers for genotyping, qPCR and IVD1 promoter mutagenesis experiments. Primer Sequence Note LP_SALK_138630 TGAACCTGTATGTGGAGGAGG Genotyping primer for bcat1-1 RP_SALK_138630 TTCAAAAGCTTTTGATGGGTG Genotyping primer for bcat1-1 LP SALK_037854 CAAATTCAACGATTTGCCAAG Genotyping primer for bcat2-1 RP SALK_037854 TTTTACCCAACGTTTGTTTGC Genotyping primer for bcat2-1 LP_SALK_071680C ACCTTTACCATGACTTGTGCG Genotyping primer for e1a1-1 RP_SALK_071680C AGTTGGAGATGGATACGGATG Genotyping primer for e1a1-1 LP SALK_098054 GATGTTGGATTTGGTGGTGTC Genotyping primer for e1b2-1 RP SALK_098054 TGGAACCTATATACCTCTGCCTC Genotyping primer for e1b2-1 LP SALK_027039 TTGTTCTCGTTGCATATGCTG Genotyping primer for mtlpd2-2 RP SALK_027039 CATCTTCTTCGGCTTTGTGAG Genotyping primer for mtlpd2-2 LP SALK_137966 AATATCTTGCTCATGGCCATG Genotyping primer for mcca1-1 RP SALK_137966 TGCAGCCTTTCTTAATGCTTC Genotyping primer for mcca1-1 LP SALK_117349 CATATTTTTAGCAGGACCGCC Genotyping primer for mccb1-1 RP SALK_117349 AGCACAGGATACTGCCATCAC Genotyping primer for mccb1-1 LP SALK_145226 TTCCTTTGCACCTGCAGATAC Genotyping primer for hml1-2 RP SALK_145226 GAAGTTGGTCCAAGAGATGGC Genotyping primer for hml1-2 LBa1 TGGTTCACGTAGTGGGCCATCG Universal left genotyping primer for SALK lines P1 At1g21400 GGTTTGCTAGATCCAAAACCC Genotyping primer for e1a1-2 P2 At1g21400 AGAACCCGGTAACATGGAATC Genotyping primer for e1a1-2 p745 AACGTCCGCAATGTGTTATTAAGTTGTC Universal left genotyping primer for WiscDsLox lines LP_SAIL_113_D07 TTTTTACAGACGAAGGCCTTG Genotyping primer for e1a2-1 RP_SAIL_113_D07 CTCTTCACCGATTGCAGTAGC Genotyping primer for e1a2-1 LB3 TAGCATCTGAATTTCATAACCAATCTCGATACAC Universal left genotyping primer for SAIL lines 103 Table 2.6 (cont’d) GABI_756G02-LP LPAATCTGCAAAGCAACCACAAC Genotyping primer for ivd1-2 GABI_756G02-RP RPACCTGCAGAGGAATATGGAGG Genotyping primer for ivd1-2 LB-GABI-KAT-o8409 ATATTGACCATCATACTCATTGC Insertion specific genotyping left primer for ivd1-2 AT1G21400L3 CGTATTTGAGTCCCTTCGGTA qPCR primer for E1A1 AT1G21400R3 TTTCATCTCCGATGTGTAACC qPCR primer for E1A1 AT5G09300L2 CACGAATACGCCAACAATCA qPCR primer for E1A2 AT5G09300R2 TCATCAAGAACACGGTAGCA qPCR primer for E1A2 At1g55510L ATCCTCGGTCTTATGTCTTT qPCR primer for E1B1 At1g55510R CCAATGCCAAATCCAACAAT qPCR primer for E1B1 At3g13450L AGGTTCCGACATAACTCTTG qPCR primer for E1B2 At3g13450R CACTGAGGTCTCAACGATTT qPCR primer for E1B2 LPD2_At3g17240L CTCGGTGGTACTTGTCTTAA qPCR primer for mtLPD2 LPD2_At3g17240R CAACCGAAGAGACCTTAACA qPCR primer for mtLPD2 IVD_1500P_F CACCTGAGGATGATAATGAGAAG forward primer for amplifying IVD1-1500 promoter with CACC at 5' IVD_750P_F CACCTGACACATTGTATCGCAT forward primer for amplifying IVD1-750 promoter with CACC at 5' IVD_500P_F CACCTCAATGAGTCAATGTTAAAC forward primer for amplifying IVD1-500 promoter with CACC at 5' IVD_350P_F CACCTTAACGTCGTTGTACATGAA forward primer for amplifying IVD1-350 promoter with CACC at 5' IVD_200P_F CACCTCCTAAACACTAATGTGTTC forward primer for amplifying IVD1-200 promoter with CACC at 5' IVD_100P_F CACCATGGTATAATAGAGCAGTGT forward primer for amplifying IVD1-100 promoter with CACC at 5' IVD_0P_F CACCGACTCATTGCTCATATCTTC Forward primer for amplifying IVD1 5'UTR with CACC at 5' IVD_P_R ATTGGTCCATCTAATCTAGTTCCG reverse primer for amplifying IVD1 promoter IVD_5UTR_R ATCTTCGTTATTACCGGTAAG PCR primer for IVD1 5'UTR region. Located at 3' end of 5'UTR of IVD1 104 Table 2.7 List of TFs interacting with IVD1 promoter and 5'UTR by Y1H assay. Fragment AGI Transcription factor family #1 AT1G04370 AP2-EREBP #1 AT1G14580 C2H2 #1 AT1G21000 PLATZ #1 AT1G22190 AP2-EREBP #1 AT1G25330 bHLH #1 AT1G46480 HB #1 AT1G50640 AP2-EREBP #1 AT1G51140 bHLH #1 AT1G64620 C2C2-DOF #1 AT1G66140 C2H2 #1 AT1G66600 WRKY #1 AT1G68550 AP2-EREBP #1 AT1G72570 AP2-EREBP #1 AT1G73730 EIL #1 AT1G76420 NAC #1 AT1G76900 TUB #1 AT1G78080 AP2-EREBP #1 AT2G02820 MYB #1 AT2G20880 AP2-EREBP #1 AT2G29580 C3H #1 AT2G31230 AP2-EREBP #1 AT2G31370 bZIP #1 AT2G35530 bZIP #1 AT2G37060 CCAAT/CCAAT-HAP3 #1 AT2G38340 AP2-EREBP #1 AT2G45410 LOB/AS2 105 Table 2.7 (cont’d) #1 AT2G45420 LOB/AS2 #1 AT2G46870 ABI3-VP1 #1 AT3G02990 HSF #1 AT3G04280 Orphans #1 AT3G07670 SET/PcG #1 AT3G10030 TRIHELIX #1 AT3G10470 C2H2 #1 AT3G10500 NAC #1 AT3G15540 AUX-IAA #1 AT3G16770 AP2-EREBP #1 AT3G17609 bZIP #1 AT3G20310 AP2-EREBP #1 AT3G21880 C2C2-CO-like #1 AT3G23030 AUX-IAA #1 AT3G23220 AP2-EREBP #1 AT3G30210 MYB #1 AT3G53200 MYB #1 AT3G54320 AP2-EREBP #1 AT3G59060 bHLH #1 AT3G61250 MYB #1 AT3G61630 AP2-EREBP #1 AT3G62100 AUX-IAA #1 AT4G00270 GeBP #1 AT4G01720 WRKY #1 AT4G14770 CPP #1 AT4G18020 ARR-B/G2-like #1 AT4G21040 C2C2-DOF #1 AT4G31550 WRKY 106 Table 2.7 (cont’d) #1 AT4G35040 bZIP #1 AT4G35280 C2H2 #1 AT4G37730 bZIP #1 AT4G37750 AP2-EREBP #1 AT4G39100 PHD #1 AT4G39780 AP2-EREBP #1 AT5G05410 AP2-EREBP #1 AT5G10140 MADS #1 AT5G15840 C2C2-CO-like #1 AT5G18450 AP2-EREBP #1 AT5G40220 MADS #1 AT5G53950 NAC #1 AT5G56860 C2C2-GATA #1 AT5G57390 AP2-EREBP #1 AT5G61590 AP2-EREBP #1 AT5G65130 AP2-EREBP #1 AT5G65310 HB #1 AT5G66730 C2H2 #2 AT1G04370 AP2-EREBP #2 AT1G08540 SIGMA70-like #2 AT1G22190 AP2-EREBP #2 AT1G28160 AP2-EREBP #2 AT1G47270 TUB #2 AT1G51140 bHLH #2 AT1G72010 TCP #2 AT1G72570 AP2-EREBP #2 AT1G73730 EIL #2 AT1G78080 AP2-EREBP 107 Table 2.7 (cont’d) #2 AT2G20180 bHLH #2 AT2G20880 AP2-EREBP #2 AT2G22540 MADS #2 AT2G24430 NAC #2 AT2G27230 bHLH #2 AT2G31460 REM(B3) #2 AT2G37430 C2H2 #2 AT2G39030 GNAT #2 AT3G04280 Orphans #2 AT3G15500 NAC #2 AT3G16770 AP2-EREBP #2 AT3G23220 AP2-EREBP #2 AT3G23240 AP2-EREBP #2 AT3G52540 OFP #2 AT3G57600 AP2-EREBP #2 AT3G61630 AP2-EREBP #2 AT3G61740 PHD #2 AT4G00238 GeBP #2 AT4G00270 GeBP #2 AT4G00390 GeBP #2 AT4G33280 ABI3-VP1 #2 AT4G36900 AP2-EREBP #2 AT4G37750 AP2-EREBP #2 AT4G38960 Orphans #2 AT4G39160 MYB-related #2 AT5G05410 AP2-EREBP #2 AT5G06160 C2H2 #2 AT5G18450 AP2-EREBP 108 Table 2.7 (cont’d) #2 AT5G20240 MADS #2 AT5G25790 CPP #2 AT5G27130 MADS #2 AT5G47390 MYB-related #2 AT5G57390 AP2-EREBP #2 AT5G61590 AP2-EREBP #2 AT5G65130 AP2-EREBP #3 AT1G04370 AP2-EREBP #3 AT1G07640 C2C2-DOF #3 AT1G12260 NAC #3 AT1G14580 C2H2 #3 AT1G15580 AUX-IAA #3 AT1G25580 NAC #3 AT1G26610 C2H2 #3 AT1G49480 ABI3-VP1 #3 AT1G53160 SBP #3 AT1G64620 C2C2-DOF #3 AT1G68480 C2H2 #3 AT1G72570 AP2-EREBP #3 AT1G75490 AP2-EREBP #3 AT1G76420 NAC #3 AT2G23660 LOB/AS2 #3 AT2G47190 MYB #3 AT3G02990 HSF #3 AT3G06490 MYB #3 AT3G10000 TRIHELIX #3 AT3G10030 TRIHELIX #3 AT3G10470 C2H2 109 Table 2.7 (cont’d) #3 AT3G16770 AP2-EREBP #3 AT3G23210 bHLH #3 AT3G27810 MYB #3 AT3G57920 SBP #3 AT3G59060 bHLH #3 AT3G62100 AUX-IAA #3 AT4G35040 bZIP #3 AT4G35280 C2H2 #3 AT5G04390 C2H2 #3 AT5G15480 C2H2 #3 AT5G18240 G2-like #3 AT5G59340 HB #3 AT5G63790 NAC #3 AT5G67450 C2H2 110 LITERATURE CITED 111 LITERATURE CITED Angelovici R, Fait A, Fernie AR, Galili G (2011) A seed high-lysine trait is negatively associated with the TCA cycle and slows down Arabidopsis seed germination. 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PLoS Genet 5: e1000581 119 Chapter 3 Characterization of mutants defective in BCAA catabolism 120 3.1 Abstract The branched-chain amino acids (BCAAs) leucine, isoleucine and valine are among nine essential amino acids that must be obtained from the diet of humans and other animals, and can be nutritionally limiting in plant foods. Despite genetic evidence of its importance in regulating seed amino acid levels, the full BCAA catabolic network is not completely understood in plants, and limited information is available regarding its regulation. In this study, mutants defective in putative branched-chain ketoacid dehydrogenase complex subunits were demonstrated to accumulate higher levels of BCAAs in mature seeds, providing genetic evidence for their function in BCAA catabolism. In addition, prolonged dark treatment caused the mutants to undergo senescence early and over-accumulate leaf BCAAs compared with the isogenic wildtype plants. These results extend the previous evidence that BCAAs can be catabolized and serve as respiratory substrates at multiple steps. Moreover, comparison of amino acid profiles between mature seeds and dark-treated leaves revealed differences in amino acid accumulation when BCAA catabolism is perturbed. Together, these results demonstrate the consequences of blocking BCAA catabolism during both normal growth conditions and under energy-limited conditions. 121 3.2 Introduction The branched-chain amino acids (BCAAs) Leu, Ile and Val are among nine amino acids essential for human and other animals because they cannot be synthesized de novo (Harper et al., 1984). Plants synthesize BCAAs and are the main source of these essential nutrients in the diets of humans and agriculturally important animals. In addition to their nutritional value, BCAAs and BCAA-derived metabolites such as glucosinolates, fatty acids and acyl sugars, contribute to plant growth, development, defense and flavor (Mikkelsen and Halkier, 2003; Taylor et al., 2004; Ishizaki et al., 2005; Slocombe et al., 2008; Araujo et al., 2010; Ding et al., 2012; Kochevenko et al., 2012). Despite long-term interest in optimizing the content of these essential amino acids in plants, the genes and proteins that constitute the full BCAA catabolic network are not completely characterized in A. thaliana or any other plant, and there is much to learn about the genetic and biochemical regulation of this process. Recent observations using mutants blocked in BCAA catabolism (Figure 1.3) indicate that the regulation of seed amino acid metabolism has unexpected, and potentially important, features. Previous work revealed that mutants defective in the A. thaliana mitochondrial enzymes branched-chain aminotransferase 2 (BCAT2), isovaleryl-CoA dehydrogenase (IVD), α (MCCA) and β (MCCB) subunits of 3-methylcrotonyl-CoA carboxylase, and hydroxymethylglutaryl-CoA lyase (HML) exhibit increases in all three free BCAAs in mature seeds (Gu et al., 2010; Lu et al., 2011; Angelovici et al., 2013). While it is logical that defects in the enzymes early in the catabolic pathway (the bcat2 and ivd1 mutants) would cause accumulation of Leu, Ile and Val, it was not expected that mutants blocked in three enzymes specific to Leu degradation (mcca1, mccb1 and hml1) would also accumulate Ile and Val. Even more surprising is that 122 biosynthetically unrelated amino acids - including His and Arg - accumulate to higher levels in seeds of ivd1, mcca1-1, mccb1-1 and hml1 mutants compared to the wild type (Gu et al., 2010; Lu et al., 2011). This suggests that the A. thaliana amino acid networks are more interconnected than previously thought, and reveals that there are important gaps in our knowledge of the regulation of amino acid metabolism. Recent studies in A. thaliana revealed that BCAA catabolism plays physiological roles beyond maintaining free amino acid homeostasis (Ishizaki et al., 2005; Araujo et al., 2010). In addition to catalyzing the third step in the degradation of BCAAs, IVD helps plant survive under energy-limited conditions by serving as a source of electrons for the mitochondrial electron transport chain via the electron-transfer flavoprotein α and β subunits (ETFα and ETFβ) and the electron-transfer flavoprotein ubiquinone oxidoreductase (ETFQO) (Figure 1.3). Two lines of evidence for this role are that the ivd1-2 mutant becomes senescent faster than wild type in prolonged darkness, and mutants defective in ETFβ and ETFQO accumulate more free BCAAs and the IVD substrate isovaleryl-CoA (Ishizaki et al., 2005; Ishizaki et al., 2006; Araujo et al., 2010). In addition, the transcripts of the functionally validated BCAA catabolism genes BCAT2, IVD1, MCCA1 and MCCB1 rapidly increase following transition from light to dark, and this increase is inhibited by sucrose (Fujiki et al., 2000; Che et al., 2002; Binder, 2010; Angelovici et al., 2013). These observations suggest that IVD and other BCAA catabolic enzymes contribute to plant fitness under energy-limited conditions. Although genetic and biochemical evidence exists for the participation of A. thaliana enzymes BCAT2, IVD, MCCA, MCCB and HML in BCAA catabolism (Gu et al., 2010; Lu et al., 2011; Ding et al., 2012; Angelovici et al., 2013), much less is known about the genes and encoded proteins for the branched-chain ketoacid dehydrogenase (BCKDH) complex. The A. 123 thaliana BCKDH complex is a megadalton enzyme complex comprised of multiple subunits (See Chapter 1 and the Introduction of Chapter 2 for descriptions of the BCKDH complex and its subunits). BCKDH enzyme activity was detected in isolated A. thaliana mitochondria (Taylor et al., 2004). However, the size of this complex has hindered its detailed in vitro characterization in plants. The current identification of its subunits is based upon sequence annotation rather than functional analysis (Fujiki et al., 2000; Mooney et al., 2000; Taylor et al., 2004). In my dissertation research, a functional genomics analysis of genes annotated as encoding subunits of the BCKDH complex was performed. A variety of transcript analyses were utilized to provide evidence for the participation of the putative BCKDH complex subunits in BCAA catabolism, and the results are described in Chapter 2. This chapter reports results of characterization of BCAA catabolic mutants, including different tissues and following various treatments. Mutants in highly coexpressed BCAA catabolism genes described in Chapter 2 accumulate higher levels of BCAAs in seeds and have enhanced senescence and increased amino acid accumulation in leaves of plants subjected to prolonged darkness. These data provide experimental evidence for the participation of the putative BCKDH subunit genes E1A1, E1A2, E1B1, E1B2 and E2 in BCAA catabolism, reinforcing the importance of BCAA catabolism in regulating amino acid homeostasis under day-night cycles and prolonged darkness, and during seed development. These results are consistent with the hypothesis that the A. thaliana BCAA catabolic network interacts with energy metabolism at multiple steps. 124 3.3 Results 3.3.1 Double mutants of BCKDH E1α subunits accumulate increased seed free BCAAs The coexpression analyses described in Chapter 2 (Figure 2.1, Figure 2.2) led to the hypothesis that the putative BCKDH E11, E1β1, E1β2 and E2 subunits, which are co-regulated with the known BCAA catabolic enzymes, function in BCAA degradation. Because ivd1-2, mcca1-1, mccb1-1, hml1 (Gu et al., 2010; Lu et al., 2011) and bcat2 mutants (Angelovici et al., 2013) have increased free BCAAs in mature seeds, I hypothesized that mutants defective in bona fide BCKDH subunits would share this phenotype. To test this idea, the free BCAA content of mature dry seed was examined in homozygous mutants of proposed BCAA catabolic enzyme genes described in Chapter 2 (Figure 2.9). The positive control mutants bcat1-1, bcat2-1, ivd1-2, mcca1-1, mccb1-1, and hml1-2 (Figure 3.1, Table 3.1) displayed BCAA changes similar to those previously reported (Gu et al., 2010; Lu et al., 2011; Angelovici et al., 2013). Some of the single mutants defective in the putative BCKDH subunits yielded small but significant (p<0.05) changes in free BCAAs. For example, both of the e1a1 mutants and e1b2-1 had modest increases (1.5 to 5.8 fold relative to the wild type) in Ile and Val, or all three BCAAs (Figure 3.1, Table 3.1). I tested the hypothesis that the existence of paralogs was responsible for the modest increases in the single mutants by examining free seed BCAA levels in e1a1-1; e1a2-1 and e1a1-2; e1a2-1 double mutants, defective in both genes annotated as encoding E1α subunits. Consistent with the hypothesis, both double mutants had large and statistically significant (p<0.05) increases in seed free BCAAs: 45, 22- and nine-fold increases for Leu, Ile and Val, respectively (Figure 3.1, Table 3.1). These data 125 support the hypothesis that E1α1 and E1α2 contribute to the degradation of BCAAs during seed development. Because no null e1b1 mutants were available, constructs encoding artificial microRNAs (amiRNAs) targeting E1B1 were transformed into the e1b2-1 mutant background in an attempt to reduce expression of both paralogous E1β subunit genes (Figure 3.2). True breeding homozygous T3 transgenic lines from two independent primary transformants had E1B1 transcript reduction to ~30% of the wild type level (Figure 3.2A). The free amino acid content from the seeds of homozygous T3 lines exhibited moderate but significant (p<0.05) BCAA increases compared with wild type (Figure 3.2B). Thus, modest reduction of the E1B1 transcript in the e1b2-1 mutant background results in more seed free BCAAs. This supports the hypothesis that both E1β enzyme paralogs participate in BCAA catabolism during seed development. Genetic analysis of the role of E2 and mtLPDs was not possible since no null alleles were available for E2, mtLPD1 or mtLPD2. 3.3.2 BCAA catabolic mutants exhibit early senescence under prolonged darkness The increased seed free BCAAs in the mutant and amiRNA lines deficient in E1A1, E1A2, E1B1 and E1B2 support the hypothesis that the tested genes encode proteins of BCAA catabolism. Moreover, elevated transcript levels during prolonged darkness were observed for the eight highly coexpressed known or proposed BCAA catabolism genes identified in Chapter 2 (Figure 2.7), supporting the hypothesis that BCAA catabolic enzymes - including BCKDH subunits E1A1, E1B1, E1B2 and E2 - have one or more physiological roles in the dark. To explore this idea, BCAA catabolic mutant leaf morphology was monitored during prolonged darkness for 15 days. Prior to dark treatment, all of the lines being assessed were 126 green and showed normal rosette leaf morphology (Figure 3.3A, Figure 3.4). As previously described for ivd1-2 (Araujo et al., 2010), I found that mutants defective in the other validated Leu catabolic enzyme genes - MCCA1, MCCB1 and HML1 - exhibit enhanced dark-induced senescence phenotypes (Figure 3.3A, Figure 3.4). These results demonstrate that early leaf senescence is observed in mutants blocked from the middle to the end of the Leu catabolism pathway. I asked whether the putative BCKDH subunit mutants had abnormal dark induced senescence. The two mutants defective in the putative E1A1 subunit gene that is upregulated in the dark (e1a1-1 and e1a1-2) showed early dark-induced senescence (Figure 3.3A, Figure 3.4). Double mutants that combine these mutations with the loss of function allele for the paralogous gene (e1a1-1; e1a2-1 and e1a1-2; e1a2-1 double mutants) exhibited senescence phenotypes similar to the e1a1 single mutants. These results are not surprising because - in contrast to the dark inducibility of E1A1 transcript – E1A2 mRNA is not induced in the dark (Figure 2.7). In fact the e1a2-1 single mutant did not exhibit enhanced dark-induced senescence compared to the wild type control, even after 15 days. Two other single mutants, bcat2-1 and e1b2-1, also did not exhibit enhanced senescence. To assess viability following the 15-day dark treatment, plants were transferred back to 8h light/16h dark photoperiod and examined for new growth after one week. All genotypes recovered from this extended dark treatment except for ivd1-2, which had dry and yellow leaves at the end of the prolonged dark treatment (Figure 3.3A, Figure 3.4). These results demonstrate that, in addition to IVD1, the disruption of E1A1, MCCA1, MCCB1 or HML1 also results in early senescence in prolonged darkness. To complement the analysis of gross morphological changes, the maximum photochemical efficiency of Photosystem II (Fv/Fm) was used to quantify leaf senescence caused by prolonged darkness (Oh et al., 1997). Mutant chlorophyll fluorescence was indistinguishable 127 from that of wild type prior to dark treatment (Figure 3.3B, day 0). In contrast, on day nine the mutants that showed early senescence also exhibited statistically significantly lower F v/Fm values relative to the corresponding wild type (Figure 3.3B). 3.3.3 Enhanced leaf free Leu, Ile and Val accumulation during prolonged darkness The ivd1-2 mutant was demonstrated to have increased leaf free BCAAs during prolonged darkness (Araujo et al., 2010). To test whether other mutants defective in BCAA catabolism also show enhanced BCAA accumulation, free rosette leaf amino acid content was analyzed in wild type and catabolic mutants subjected to prolonged darkness. All three BCAAs increased in dark-treated wild-type plants at all three time points, and further increases due to blocked BCAA catabolism were seen for some combinations of mutants and amino acids (Figure 3.5, Table 3.2). The effects of all mutations, except for e1a2 and e1b2, were strongest and most consistent for Leu (eight to 14 fold increases compared with wild type at day nine), with the least significant increases in Ile. Of the mutants, e1a2-1 and e1b2-1 displayed the least significant increases compared to wild type and this is especially obvious for Leu and Val. The small impact of these mutations is reminiscent of the results with free seed amino acid changes (Figure 3.1, Table 3.1). The E1B1-silenced e1b2-1 plants were also evaluated for leaf amino acid content after six days in prolonged darkness. The homozygous T3 lines from two independent primary transformants showed stronger BCAA increases compared with the e1b2-1 mutant and type (Figure 3.2C). In summary, the BCAA increases in the mutants support the hypothesized roles of BCKDH E11, E11 and E12 subunits in BCAA catabolism - especially in Leu degradation during prolonged darkness. 128 Taken together, analysis of the mutants and amiRNA lines demonstrated that disruption of BCAT2, E11, E11, E12, IVD, MCCA, MCCB, HML enzymes led to early senescence and/or increased leaf free BCAAs in prolonged darkness. These results further support the hypothesis that the full BCAA catabolic pathway plays a role in plant survival under carbonlimited conditions. These data also add to the evidence that E1A1, E1B1 and E1B2 encode subunits of the BCKDH complex. 3.3.4 Beyond Leu, Ile and Val: Blocking branched-chain ketoacid dehydrogenase causes broad changes in leaf and seed amino acids In addition to accumulating seed free BCAAs, ivd1-2, mcca1-1, mccb1-2 and hml1-2 mutants were previously shown to have increased levels of biosynthetically seemingly unrelated amino acids, with increased Arg and His common to all (Gu et al., 2010; Lu et al., 2011). I asked whether this inter-pathway phenomenon was observed in mutants annotated as defective in BCKDH subunit genes (Figure 3.6, Table 3.1, Table 3.2). The most obvious sign of cross pathway seed amino acid accumulation was seen in the e1a1-1; e1a2-1 and e1a1-2; e1a2-1 double mutants, which had two to seven fold and statistically significant (p<0.001) increases in Arg, His, Met and Ser (Figure 3.6A, Table 3.1). The e1b2-1 mutant also had more modest, but quite widespread increases in multiple seed amino acids, despite the presence of the intact paralogous E1B1 gene. As previously reported for mutants blocked later in the pathway (Gu et al., 2010; Lu et al., 2011), high seed His was a hallmark of all the putative BCKDH subunit mutants, with the exception of e1a2-1. The positive control ivd1-2 mutant showed the expected highly pleiotropic amino acid changes in both seeds and leaves by the end of the dark treatment: it exhibited significant increases for 16 out of 19 amino acids detected in seeds as was previously 129 reported (Gu et al., 2010; Lu et al., 2011) (Figure 3.6A, Table 3.1), and 17 out of 19 amino acids detected by the end of the nine-day dark treatment (Figure 3.6B, Table 3.2). The ivd1-2 mutant displayed severe dehydration and senescence when tissue was extracted after 9d in the dark. Thus, the data from 6d dark treated ivd1-2 mutant plants - which is similar to the 9d data in Figure 3.6B (Table 3.3) - is likely to be more reliable. With the possible exception of increased Asp and Met, dark-induced leaf amino acid changes were less consistent across the mutants in BCKDH and in other enzymes of BCAA catabolism. 130 3.4 Discussion In the current research, I took a systems approach to hypothesizing and testing functions of putative BCKDH subunits through a combination of sequence similarity, transcript coexpression analyses, and mutant characterization. I provided evidence showing the participation of putative BCKDH subunits in BCAA catabolism, and demonstrated examples of genetic redundancy and functional divergence between enzyme paralogs. Moreover, my data revealed that BCAA catabolic mutants defective at multiple steps in the pathway exhibit early senescence with high leaf free BCAAs in prolonged darkness, supporting a previous hypothesis that BCAA degradation generates alternative sources of energy in plants under energy-limited conditions (Ishizaki et al., 2005; Araujo et al., 2010). 3.4.1 Evidence that BCAA catabolism and energy metabolism interact at multiple steps In this study, I demonstrated that eight out of the 13 proposed or validated BCAA catabolism genes are coexpressed and share common transcript oscillation patterns in diurnal and circadian treatments (Figure 2.1, Figure 2.2). These findings are consistent with the observed fluctuation of free BCAA levels on diel cycles (Gibon et al., 2006; Espinoza et al., 2010), demonstrating the physiological importance of BCAA catabolism at night. BCAAs are proposed to provide their downstream catabolic products - acetoacetate, acetyl-CoA and propionyl-CoA to the tricarboxylic acid (TCA) cycle for energy generation (Figure 1.3) (Anderson et al., 1998). In addition, Mentzen and coworkers performed a global coexpression analysis with microarray data from 70 experiments, and pointed out a coexpression supermodule capable of maintaining cellular energy balance via catabolism (Mentzen et al., 2008). This supermodule contained genes 131 encoding enzymes in the catabolism of amino acids (including BCAAs), carbohydrates, lipids and cell wall components. Despite the proposed role of BCAA catabolism in energy generation, no vegetative or reproductive phenotype was observed for BCAA catabolic mutants grown in photoperiods with 16h, 12h or 8h light (Lu et al., 2011, and day 0 in Figure 3.3 and Figure 3.4 of this study), in contrast to the reported aberrant reproductive development of the mcca1 and mccb1 mutants (Ding et al., 2012). My results suggest a limited role of BCAA catabolism in providing TCA cycle substrates in day/night cycling conditions (Lu et al., 2011 and this study). While diurnal regulation is more physiologically relevant, the prolonged darkness assay is experimentally convenient for studying the intricate regulation of energy metabolism and interacting metabolic processes in plants. Success using such an approach was previously demonstrated in identifying and characterizing mutants defective in genes participating energy related processes including autophagy, mitochondrial electron transport chain and starch metabolism (Gibon et al., 2004; Ishizaki et al., 2005; Ishizaki et al., 2006; Liu and Bassham, 2012). During energy-limited conditions, autophagy was demonstrated to promote organelle degradation including chloroplasts, partially contributing to the increase of free amino acid pools including free BCAAs in vegetative tissues (Hanaoka et al., 2002; Wada et al., 2009; Izumi et al., 2010). Consistent with this idea, in the current study we also observed dramatic free amino acid increases including BCAAs, aromatic amino acids, Lys, His, Asn and Arg in the wild type within three days in prolonged darkness (Table 3.2). In addition, Lys metabolism was previously demonstrated to interact with plant energy metabolism and the high seed Lys KD genotype showed reduced levels of the TCA cycle intermediates (Angelovici et al., 2011). My results are consistent with the notion that multiple steps of the BCAA degradation pathway provide alternative sources of energy under long-term dark treatment conditions. Prior 132 to the current study, IVD was the only enzyme in BCAA catabolism that was shown to play a role in plant survival in energy-limited conditions, because ivd1-2 exhibited enhanced senescence relative to the wild type in prolonged darkness (Araujo et al., 2010). The authors hypothesized that the early senescence of ivd1-2 resulted from deficiency in supplying electrons to the mitochondrial electron transport chain and/or providing the BCAA catabolic products to the TCA cycle (Ishizaki et al., 2005; Araujo et al., 2010) (Figure 1.3). In my study, BCAA catabolic mutants defective in enzymes both upstream (e1a1 single mutants and e1a1; e1a2 double mutants) and downstream (mcca1-1, mccb1-1 and hml1-2 mutants) of IVD displayed enhanced senescence in prolonged darkness (Figure 3.3, Figure 3.4), supporting the hypothesized role of BCAA catabolism in providing TCA cycle substrates in energy-limited conditions. Interestingly, ivd1-2 exhibited dehydrated and yellowed rosette leaves more rapidly than the other mutants (Figure 3.4), and was the only mutant that did not recover after the 15d dark treatment (Figure 3.3). This is consistent with the hypothesis that IVD influences energy homeostasis in multiple ways, not only by providing BCAA catabolic CoA intermediates to the mitochondrial electron transport chain, but also by catabolizing additional substrates such as phytanoyl-CoA and aromatic amino acids (Araujo et al., 2010). The observation that e1a1 single mutants and e1a1; e1a2 double mutants, which are defective in subunits of the upstream BCKDH complex, showed enhanced senescence at a weaker level than ivd1-2 (Figure 3.3, Figure 3.4) supports the hypothesized broader substrate range for IVD. Taken together, my results provide genetic evidence supporting the hypothesized interaction between BCAA catabolism and energy metabolism at multiple steps, and are consistent for a broader substrate range for IVD in plants. 133 3.5 Material and methods 3.5.1 Free amino acid analysis by LC-MS/MS Free amino acids from dry seeds or the 10th and 11th rosette leaves were extracted and analyzed by modifying a previously described method (Gu et al., 2007; Lu et al., 2008; Gu et al., 2012). 1 μM of five heavy amino acids were added to the extraction buffer for a more accurate quantification: Leu-d10 for Leu and Ile, His-d3 for His, Trp-d5 for Trp, Val-d8 for Val and Phe-d8 for all other amino acids. Details on the detection of selected ion monitoring pairs were as described previously (Angelovici et al., 2013). The amino acid quantities were normalized to the fresh weight of the harvested samples. All heavy amino acids were purchased from Cambridge Isotope Laboratories (Tewksbury, MA). 3.5.2 Generation of E1B1-silenced e1b2-1 mutant To clone the artificial micro RNA (amiRNA) constructs, two amiRNAs targeting E1B1 were designed using the WMD3-Web MicroRNA Designer (http://wmd3.weigelworld.org/cgibin/webapp.cgi): amiE1B1-1 (5’-TATGCGATTACATTAGTCCTT-3’) and amiE1B1-2 (5’TAACTACAGATAGTACGCCTA-3’). The precursor amiRNAs were cloned by overlapping PCR following protocols provided by WMD3-Web MicroRNA Designer (http://wmd3.weigelworld.org/downloads/Cloning_of_artificial_microRNAs.pdf), amplified by Gateway-compatible primers (Table 3.4) and inserted into pEarley100 (Earley et al., 2006). The resultant constructs were transformed into the e1b2-1 single mutant. All progeny lines were tested for the presence of the amiRNA and T-DNA prior to further analyses. 134 3.5.3 Determination of the PSII photochemical efficiency All chlorophyll fluorescence experiments were performed at the Center for Advanced Algal and Plant Phenotyping (CAAPP) at Michigan State University (http://www.prl.msu.edu/caapp) with previously described setups for the growth chambers (Attaran et al., 2014). Plants were grown under SD for five weeks and then subjected to prolonged darkness for 14 days. Chlorophyll fluorescence parameters were measured each day at the end of the subjective night from day 0 to 14 in prolonged darkness. Experiments were repeated twice and representative results were shown. 135 3.6 Acknowledgments I would like to thank Dr. Ruthie Angelovici for providing heavy amino acid standards and MS method for amino acid detection, Lijun Chen for help with fine-tuning the MS method and assistance in using the LC-MS/MS, Dr. Ronghui Pan for providing vectors and protocols for amiRNA mutant generation, and Linda Savage and David Hall for help with PSII maximum photochemical efficiency measurements. 136 APPENDIX 137 Figure 3.1 Changes in free BCAA content in dry seeds of mutants relative to wild type. The bars show the fold change of individual amino acids in each mutant compared to the wild type (horizontal dashed line) grown at the same time. Four or more biological replicates were measured for each genotype. An asterisk indicates a significant difference from the wild type, determined by the Student’s t-test (p<0.05). Error bars represent means ± SE. The experiments were done at least three times with similar results obtained, and representative results are shown. 138 Figure 3.2 Transcript and mutant analyses of E1B1-silenced e1b2-1 lines. (A) Relative E1B1 transcript abundance in two homozygous E1B1-silenced e1b2-1 T3 lines (progeny seed pools of independent primary transformants #1 and #3) by qPCR. The y axis values represent the normalized E1B1 transcript levels relative to Col-0 (shown as the horizontal dashed line, n=5, mean ± SE). 139 Figure 3.2 (cont’d) The E1B1 transcript levels were normalized to ACT2 transcript levels. An asterisk indicates a significant difference from the wild type, determined by the Student’s t-test (p<0.05). Leaf tissues were harvested by the end of night on a 16h light/8h dark photoperiod. (B) Relative levels of seed free BCAAs in homozygous E1B1-silenced e1b2-1 T4 seeds (progeny seed pools of #1 and #3). The y axis values represent the amino acid levels relative to Col-0 (shown as the dashed line, n=4, mean ± SE). An asterisk indicates a significant difference from the wild type, determined by the Student’s t-test (p<0.05). (C) Relative levels of leaf free BCAAs in homozygous E1B1-silenced e1b2-1 T3 lines (progeny seed pools of #1 and #3) after 6 days in prolonged darkness. The y axis values represent the amino acid levels relative to Col-0 (shown as the dashed line, n=5, mean ± SE). An asterisk indicates a significant difference from the wild type, determined by the Student’s t-test (p<0.05). 140 Figure 3.3 Phenotypes of BCAA mutants subjected to prolonged darkness. (A) Photographs of 5-week-old, short-day-grown A. thaliana plants taken prior to (0d) and after 15 days of prolonged darkness. The leaves of e1a1-1, e1a1-2, both e1a1; e1a2 double mutants, ivd1-2, mcca1-1, mccb1-1, and hml1-2 were visibly yellowed and dehydrated following 15 days of prolonged darkness 141 Figure 3.3 (cont’d) compared to the wild type. The experiments were done at least three times with similar results, and representative results are shown. (B) Analysis of the maximum photochemical efficiency of PSII (Fv/Fm) to quantify the kinetics of leaf senescence. Plants were grown under the same conditions as in (A). Values are means ± SE of three to five biological replicates. An asterisk indicates a significant difference from the wild type, determined by the Student’s t-test (p<0.05). The two bar graphs represent data from plants grown in separate flats. Each experiment was done twice with similar results, and representative results are shown. 142 Figure 3.4 Phenotypes of BCAA mutants subjected to prolonged darkness - early time points. Photographs of 5-week-old, short-day-grown Arabidopsis plants taken prior to (0d) and after 3, 6, 9 and 13 days of prolonged darkness. The leaves of e1a1-1, e1a1-2, both e1a1; e1a2 double mutants, ivd1-2, mcca1-1, mccb1-1, and hml1-2 were visibly yellowed and dehydrated 143 Figure 3.4 (cont’d) following 13 days of prolonged darkness compared to the wild type. The ivd1-2 mutant started showing visible senescence symptoms at day 6. The experiments were done at least three times with similar results, and representative results are shown. Plants in the upper and lower panels were grown and assayed in individual experiments. Two replicates were shown for each mutant at every time point. 144 Figure 3.5 Relative levels of leaf free BCAAs in mutants during prolonged darkness. The y axis values represent the log2 transformed amino acid levels normalized to the wild type (Col-0) at day 0. Values are means ± SE of five biological replicates. * indicates significant difference from the wild type at the same time point, determined by the Student’s t-test p<0.05, ** p<0.01, *** p<0.001. 145 Figure 3.6 Heat map showing the effect of disrupting known or proposed BCAA catabolism genes on amino acid homeostasis. Comparisons of amino acid contents between (A) dry seeds and (B) leaves of 5-week-old plants kept in the dark for 9d. Values represent mean fold change (log2) in mutants compared to the wild type. Four biological replicates were used for analyzing seed amino acid content, and five for leaf. Maximum color intensities correspond to -1.3 and +1.3, which are equivalent to fold change of 0.4 and 2.5, respectively. 146 Table 3.1 Mutant seed free amino acid profiles relative to the wild type (Col-0). Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val * bcat1-1 Mean* SE 0.80 0.05 1.19 0.08 0.97 0.07 1.78 0.32 1.15 0.04 1.17 0.07 1.56 0.12 1.08 0.02 1.09 0.05 1.09 0.03 0.99 0.03 1.03 0.04 1.07 0.05 1.06 0.21 1.01 0.06 0.61 0.08 1.23 0.07 1.04 0.05 1.04 0.06 t-test** 0.23 0.37 0.91 0.35 0.47 0.50 0.13 0.48 0.57 0.35 0.95 0.87 0.63 0.95 0.95 0.30 0.20 0.78 0.82 bcat2-1 Mean SE 0.98 0.17 1.01 0.16 0.72 0.09 0.94 0.09 0.89 0.14 0.87 0.11 0.75 0.20 1.56 0.18 2.79 0.41 3.83 0.63 1.53 0.34 1.58 0.51 1.46 0.27 1.80 0.57 1.34 0.24 1.24 0.24 1.27 0.17 1.31 0.37 2.39 0.31 t-test 0.92 0.95 0.29 0.76 0.78 0.51 0.50 0.05 0.02 0.01 0.24 0.37 0.19 0.28 0.25 0.58 0.39 0.55 0.02 Mean 0.73 1.36 0.86 0.90 0.89 1.04 0.80 2.28 5.41 1.49 1.06 1.09 1.04 1.10 0.97 1.13 0.95 1.10 1.32 e1a1-1 SE 0.11 0.18 0.05 0.11 0.20 0.30 0.07 0.44 0.31 0.03 0.08 0.09 0.03 0.07 0.16 0.11 0.06 0.06 0.07 t-test 0.30 0.18 0.49 0.45 0.64 0.91 0.27 0.10 0.00 0.05 0.64 0.56 0.57 0.40 0.89 0.34 0.53 0.45 0.03 Mean 0.82 1.16 0.81 0.80 0.71 0.69 0.51 2.44 5.83 1.25 0.87 0.98 0.98 1.04 0.77 0.85 1.16 1.08 1.31 e1a1-2 SE 0.13 0.07 0.05 0.09 0.05 0.09 0.07 0.19 0.22 0.06 0.07 0.10 0.03 0.07 0.04 0.04 0.11 0.12 0.08 t-test 0.48 0.13 0.37 0.10 0.02 0.03 0.03 0.00 0.00 0.21 0.36 0.92 0.75 0.71 0.07 0.02 0.23 0.63 0.02 Mean 0.79 0.92 0.97 1.14 1.03 1.34 0.92 0.97 0.97 0.84 0.87 0.98 1.00 1.00 1.08 1.08 0.87 0.90 1.00 e1a2-1 SE 0.23 0.06 0.09 0.16 0.09 0.21 0.12 0.10 0.04 0.04 0.09 0.06 0.06 0.10 0.07 0.12 0.04 0.07 0.05 t-test 0.53 0.40 0.87 0.46 0.83 0.21 0.70 0.81 0.79 0.38 0.38 0.90 0.99 0.98 0.52 0.56 0.11 0.47 0.96 The mean represents the fold change between the averages of five biological replicates of mutants and Col-0 grown at the same time. ** 't-test' indicates the value at which the mean measurement deviates from expectation (for example '0.01' means significant at <0.01). 147 Table 3.1 (cont’d) e1a1-1;e1a2-1 Mean SE t-test 0.60 0.08 0.15 2.18 0.20 0.00 0.62 0.04 0.12 0.59 0.04 0.00 0.44 0.05 0.00 0.43 0.05 0.00 0.63 0.06 0.07 6.78 0.65 0.00 25.39 1.06 0.00 56.96 3.32 0.00 1.40 0.14 0.02 2.18 0.14 0.00 1.71 0.08 0.00 1.21 0.06 0.07 2.17 0.17 0.00 0.64 0.09 0.00 1.08 0.09 0.41 1.26 0.08 0.08 8.35 0.40 0.00 e1a1-2;e1a2-1 Mean SE t-test 0.62 0.06 0.16 2.39 0.16 0.00 0.58 0.03 0.09 0.64 0.03 0.00 0.49 0.04 0.00 0.48 0.06 0.00 0.84 0.10 0.38 6.93 0.50 0.00 27.92 1.42 0.00 59.78 2.45 0.00 1.60 0.16 0.01 2.27 0.12 0.00 1.80 0.07 0.00 1.35 0.10 0.02 2.34 0.18 0.00 0.76 0.07 0.01 1.14 0.07 0.16 1.45 0.09 0.01 8.93 0.41 0.00 e1b2-1 Mean SE 1.28 0.07 1.84 0.23 1.42 0.12 1.21 0.08 1.24 0.11 1.30 0.11 0.96 0.08 1.64 0.12 1.67 0.10 1.59 0.11 1.42 0.13 1.70 0.15 1.42 0.14 1.23 0.11 1.44 0.10 1.04 0.19 1.54 0.08 1.52 0.13 1.60 0.11 148 t-test 0.02 0.03 0.04 0.14 0.16 0.10 0.89 0.02 0.00 0.01 0.04 0.01 0.05 0.18 0.02 0.89 0.00 0.02 0.01 Mean 3.18 10.67 1.39 0.68 2.60 0.63 4.00 13.35 15.96 19.11 4.87 5.22 3.12 2.51 5.05 1.65 7.16 3.95 11.12 ivd1-2 SE 0.14 0.33 0.05 0.05 0.10 0.03 0.12 0.69 0.32 0.37 0.14 0.21 0.11 0.22 0.22 0.08 0.27 0.14 0.33 t-test 0.00 0.00 0.01 0.06 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.04 0.00 0.00 0.00 Mean 0.78 1.09 0.54 0.82 0.68 0.42 0.58 2.23 4.33 5.81 0.85 1.08 0.97 0.85 1.51 0.79 0.94 0.86 2.32 mcca1-1 SE t-test 0.05 0.07 0.07 0.32 0.03 0.02 0.08 0.27 0.03 0.11 0.02 0.01 0.13 0.43 0.32 0.03 0.66 0.01 0.78 0.01 0.05 0.06 0.11 0.55 0.05 0.78 0.22 0.60 0.14 0.03 0.11 0.23 0.12 0.65 0.08 0.22 0.23 0.01 Table 3.1 (cont’d) Mean 0.92 1.21 0.61 0.72 0.75 0.49 0.89 2.06 4.52 6.40 0.91 1.13 1.14 0.89 1.42 0.83 1.05 0.94 2.73 mccb1-1 SE 0.10 0.18 0.09 0.07 0.09 0.07 0.04 0.15 0.45 0.80 0.16 0.15 0.13 0.12 0.14 0.05 0.08 0.05 0.22 t-test 0.53 0.32 0.03 0.12 0.16 0.00 0.82 0.00 0.00 0.01 0.61 0.46 0.43 0.58 0.05 0.26 0.58 0.46 0.00 hml1-2 Mean SE 1.43 0.18 3.00 0.38 1.24 0.12 1.06 0.14 1.35 0.04 1.47 0.10 1.67 0.30 3.30 0.20 5.82 0.43 6.59 0.41 1.26 0.06 1.76 0.23 1.27 0.11 2.04 0.47 2.26 0.22 1.21 0.22 2.38 0.24 1.42 0.17 3.64 0.31 t-test 0.09 0.01 0.16 0.76 0.09 0.01 0.27 0.00 0.00 0.00 0.02 0.04 0.12 0.11 0.01 0.45 0.01 0.09 0.00 149 Table 3.2 Mutant leaf free amino acid levels in prolonged darkness (µmol/mg FW). Col-0 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val * 0d in dark 3d in dark 6d in dark 9d in dark * Mean SE Mean SE Mean SE Mean SE 1.37 0.09 2.48 0.13 2.54 0.24 3.17 0.24 0.03 0.01 2.36 0.14 3.70 0.34 4.14 0.42 0.12 0.01 1.37 0.06 2.68 0.20 4.18 0.34 1.55 0.19 0.59 0.07 0.97 0.08 0.83 0.04 0.88 0.05 0.78 0.04 1.06 0.11 1.53 0.10 6.20 0.31 6.65 0.43 7.95 0.63 9.05 0.40 0.27 0.03 0.30 0.04 0.35 0.05 0.36 0.05 0.08 0.00 1.42 0.07 2.14 0.13 2.38 0.10 0.04 0.00 1.44 0.16 1.57 0.16 1.06 0.07 0.07 0.01 1.35 0.06 1.26 0.13 0.84 0.07 0.11 0.01 1.35 0.08 2.02 0.19 1.94 0.15 0.02 0.00 0.07 0.01 0.12 0.01 0.15 0.01 0.12 0.00 2.69 0.12 4.11 0.24 4.29 0.22 0.17 0.01 0.16 0.02 0.09 0.01 0.09 0.01 0.41 0.02 0.91 0.07 1.75 0.21 1.98 0.14 0.37 0.04 0.82 0.10 0.77 0.14 0.57 0.04 0.02 0.00 0.84 0.03 1.33 0.09 1.54 0.07 0.03 0.00 1.00 0.06 1.21 0.13 1.46 0.15 0.16 0.01 3.17 0.17 3.86 0.29 3.48 0.14 The mean represents the average of five biological replicates (µmol/mg fresh weight). ** Significance by Student's t-test, compared to wild types at the same time point: p<0.05, p<0.01, p<0.001. 150 Table 3.2 (cont’d) bcat2-1 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark ** Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.56 0.20 0.412 2.22 0.20 0.283 1.46 0.13 0.002 1.66 0.19 0.000 0.05 0.01 0.123 2.51 0.15 0.472 3.89 0.17 0.624 4.04 0.38 0.854 0.12 0.02 0.641 1.45 0.11 0.496 2.64 0.19 0.872 4.22 0.44 0.943 1.41 0.16 0.587 1.00 0.10 0.004 1.36 0.08 0.004 1.26 0.11 0.003 0.97 0.12 0.496 0.78 0.07 0.987 0.88 0.08 0.194 1.46 0.17 0.746 5.77 0.36 0.373 5.95 0.46 0.282 6.97 0.45 0.222 9.37 0.81 0.725 0.24 0.05 0.608 0.27 0.04 0.622 0.17 0.03 0.006 0.47 0.09 0.291 0.12 0.01 0.010 1.46 0.11 0.793 2.08 0.13 0.727 2.60 0.20 0.330 0.06 0.01 0.108 2.06 0.27 0.069 2.70 0.28 0.003 2.55 0.33 0.001 0.14 0.03 0.036 4.59 0.32 0.000 6.24 0.30 0.000 6.66 0.42 0.000 0.12 0.01 0.153 1.41 0.16 0.714 1.60 0.11 0.069 1.61 0.15 0.134 0.02 0.00 0.991 0.09 0.01 0.167 0.14 0.01 0.153 0.18 0.02 0.210 0.14 0.01 0.061 2.77 0.22 0.728 3.95 0.21 0.613 4.37 0.26 0.823 0.18 0.01 0.643 0.23 0.02 0.022 0.10 0.01 0.651 0.11 0.01 0.137 0.54 0.06 0.086 1.16 0.17 0.222 1.48 0.15 0.311 1.82 0.24 0.586 0.42 0.06 0.431 0.99 0.14 0.361 0.82 0.11 0.818 0.66 0.08 0.363 0.03 0.00 0.008 0.81 0.02 0.580 1.16 0.07 0.150 1.62 0.10 0.543 0.05 0.01 0.056 0.98 0.07 0.858 1.14 0.10 0.679 1.34 0.23 0.676 0.23 0.03 0.042 3.66 0.29 0.162 4.73 0.26 0.037 5.16 0.29 0.000 151 Table 3.2 (cont’d) e1a1-1 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.31 0.07 0.607 1.74 0.08 0.000 1.02 0.11 0.000 0.86 0.08 0.000 0.04 0.01 0.369 2.36 0.22 0.983 3.86 0.40 0.756 4.01 0.20 0.784 0.11 0.01 0.475 1.43 0.10 0.593 2.92 0.33 0.551 4.00 0.27 0.683 1.28 0.12 0.260 0.99 0.07 0.001 1.58 0.16 0.005 1.38 0.07 0.000 0.90 0.07 0.773 0.72 0.06 0.427 0.90 0.10 0.291 1.17 0.06 0.009 5.37 0.35 0.092 5.43 0.13 0.021 6.19 0.55 0.050 7.71 0.56 0.069 0.23 0.04 0.478 0.23 0.02 0.150 0.22 0.04 0.055 0.42 0.05 0.399 0.09 0.00 0.013 1.32 0.07 0.328 2.04 0.21 0.668 2.35 0.09 0.827 0.03 0.00 0.060 2.26 0.32 0.038 3.29 0.35 0.001 3.21 0.48 0.001 0.07 0.01 0.779 5.38 0.21 0.000 8.00 0.77 0.000 9.41 0.18 0.000 0.11 0.01 0.873 1.31 0.09 0.753 1.39 0.19 0.029 1.13 0.11 0.000 0.02 0.00 0.767 0.09 0.00 0.047 0.16 0.02 0.055 0.22 0.02 0.001 0.12 0.00 0.765 2.66 0.10 0.896 4.03 0.34 0.845 4.43 0.12 0.588 0.17 0.01 0.935 0.17 0.02 0.749 0.09 0.01 0.635 0.11 0.01 0.163 0.33 0.03 0.044 1.02 0.08 0.330 1.46 0.26 0.397 1.22 0.06 0.000 0.27 0.02 0.043 0.83 0.04 0.938 0.75 0.12 0.892 0.54 0.05 0.662 0.02 0.00 0.526 0.84 0.04 0.976 1.29 0.08 0.700 1.64 0.09 0.416 0.03 0.00 0.392 0.97 0.06 0.772 1.08 0.18 0.549 1.06 0.16 0.084 0.17 0.01 0.277 3.82 0.16 0.013 5.48 0.46 0.009 6.08 0.15 0.000 152 Table 3.2 (cont’d) e1a1-2 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.39 0.10 0.868 2.01 0.15 0.030 1.34 0.17 0.001 0.76 0.10 0.000 0.03 0.01 0.790 2.29 0.23 0.799 4.30 0.23 0.164 3.61 0.48 0.420 0.12 0.01 0.909 1.68 0.12 0.038 3.50 0.36 0.067 5.43 0.80 0.173 1.47 0.15 0.753 1.08 0.10 0.001 1.61 0.23 0.021 1.53 0.16 0.002 0.99 0.08 0.244 0.86 0.06 0.301 1.12 0.17 0.752 1.55 0.16 0.888 5.96 0.36 0.617 6.13 0.20 0.291 6.68 0.97 0.290 8.14 0.87 0.362 0.23 0.02 0.286 0.25 0.04 0.464 0.20 0.04 0.027 0.46 0.09 0.331 0.09 0.01 0.126 1.37 0.07 0.602 2.22 0.19 0.729 2.89 0.29 0.128 0.04 0.00 0.271 2.06 0.33 0.116 1.68 0.16 0.654 3.58 0.81 0.015 0.07 0.00 0.785 5.18 0.14 0.000 8.22 0.66 0.000 11.45 1.25 0.000 0.11 0.01 0.707 1.40 0.07 0.632 1.57 0.18 0.101 1.01 0.10 0.000 0.03 0.00 0.137 0.09 0.01 0.079 0.18 0.02 0.004 0.38 0.08 0.014 0.12 0.00 0.699 2.66 0.12 0.892 4.36 0.34 0.557 5.45 0.60 0.100 0.17 0.01 0.856 0.19 0.02 0.325 0.10 0.01 0.500 0.11 0.02 0.270 0.37 0.03 0.305 1.04 0.07 0.230 1.34 0.25 0.227 1.91 0.38 0.870 0.35 0.03 0.804 0.86 0.05 0.716 0.61 0.10 0.373 1.08 0.32 0.140 0.02 0.00 0.393 0.80 0.03 0.510 1.44 0.07 0.338 2.41 0.34 0.037 0.03 0.00 0.124 0.97 0.08 0.788 1.27 0.14 0.750 1.44 0.30 0.973 0.17 0.01 0.280 3.84 0.17 0.013 6.24 0.43 0.000 7.26 0.49 0.000 153 Table 3.2 (cont’d) e1a2-1 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.30 0.11 0.610 3.63 0.40 0.022 3.07 0.32 0.253 3.31 0.28 0.712 0.04 0.01 0.618 2.55 0.30 0.580 3.90 0.22 0.533 4.29 0.26 0.774 0.12 0.01 0.966 1.84 0.18 0.029 3.55 0.31 0.032 4.49 0.39 0.558 1.46 0.16 0.706 1.03 0.12 0.006 1.25 0.14 0.059 0.88 0.07 0.538 0.89 0.08 0.901 1.15 0.14 0.030 1.30 0.15 0.145 1.77 0.25 0.384 5.80 0.33 0.390 7.92 0.48 0.066 8.70 0.87 0.415 9.36 0.69 0.700 0.20 0.04 0.182 0.33 0.03 0.569 0.26 0.03 0.106 0.47 0.05 0.101 0.09 0.00 0.012 1.57 0.08 0.203 2.32 0.14 0.271 2.61 0.16 0.241 0.05 0.00 0.319 1.53 0.21 0.751 1.67 0.26 0.594 0.98 0.08 0.491 0.09 0.01 0.125 1.78 0.18 0.043 1.48 0.16 0.239 1.07 0.08 0.046 0.11 0.01 0.567 1.77 0.18 0.053 2.26 0.20 0.325 2.37 0.14 0.052 0.02 0.00 0.363 0.09 0.01 0.159 0.15 0.01 0.037 0.17 0.02 0.374 0.13 0.00 0.103 3.02 0.20 0.176 4.41 0.29 0.299 4.46 0.22 0.584 0.17 0.01 0.872 0.23 0.03 0.105 0.12 0.01 0.156 0.10 0.01 0.803 0.50 0.06 0.162 1.81 0.24 0.005 2.35 0.28 0.097 2.58 0.29 0.082 0.38 0.03 0.826 1.53 0.23 0.017 1.07 0.15 0.143 0.82 0.12 0.083 0.03 0.00 0.083 0.91 0.04 0.153 1.42 0.07 0.616 1.87 0.18 0.111 0.03 0.00 0.820 1.17 0.13 0.234 1.41 0.08 0.312 2.14 0.23 0.023 0.18 0.01 0.188 3.64 0.21 0.102 4.47 0.24 0.106 3.97 0.27 0.128 154 Table 3.2 (cont’d) e1a1-1;e1a2-1 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.33 0.13 0.809 2.67 0.21 0.448 1.26 0.08 0.000 1.28 0.16 0.000 0.04 0.01 0.570 2.31 0.24 0.853 4.35 0.23 0.066 4.57 0.19 0.372 0.13 0.02 0.555 1.78 0.15 0.023 3.49 0.31 0.051 4.67 0.28 0.279 1.67 0.20 0.671 1.34 0.06 0.000 1.74 0.14 0.000 1.71 0.08 0.000 1.00 0.07 0.191 0.98 0.08 0.040 1.13 0.14 0.520 1.37 0.07 0.209 6.50 0.29 0.491 7.00 0.25 0.497 6.67 0.54 0.212 8.62 0.52 0.517 0.21 0.04 0.205 0.29 0.04 0.932 0.23 0.02 0.029 0.23 0.03 0.045 0.09 0.01 0.060 1.51 0.06 0.388 2.25 0.16 0.420 2.63 0.12 0.128 0.05 0.01 0.475 2.40 0.37 0.036 1.63 0.24 0.567 1.76 0.20 0.007 0.09 0.01 0.175 6.08 0.19 0.000 8.94 0.67 0.000 10.57 0.37 0.000 0.11 0.01 0.532 1.47 0.11 0.374 1.78 0.13 0.454 1.60 0.11 0.084 0.03 0.00 0.527 0.11 0.01 0.002 0.18 0.01 0.001 0.25 0.02 0.000 0.13 0.01 0.507 2.86 0.12 0.309 4.39 0.27 0.253 4.74 0.16 0.113 0.18 0.01 0.593 0.27 0.03 0.004 0.10 0.01 0.491 0.15 0.01 0.003 0.52 0.09 0.263 1.46 0.12 0.002 1.77 0.22 0.786 1.79 0.12 0.313 0.46 0.08 0.335 1.48 0.18 0.006 0.85 0.10 0.509 0.83 0.08 0.014 0.02 0.00 0.104 0.80 0.02 0.342 1.26 0.07 0.347 1.76 0.12 0.136 0.03 0.00 0.722 1.04 0.06 0.657 1.52 0.09 0.085 1.63 0.13 0.398 0.17 0.01 0.213 3.96 0.18 0.005 6.25 0.42 0.000 6.95 0.16 0.000 155 Table 3.2 (cont’d) e1a1-2;e1a2-1 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.57 0.07 0.099 2.82 0.30 0.318 1.41 0.13 0.001 1.24 0.14 0.000 0.06 0.01 0.021 2.66 0.33 0.423 4.68 0.33 0.015 4.22 0.15 0.869 0.12 0.01 0.679 1.96 0.20 0.021 3.79 0.34 0.011 4.40 0.38 0.671 1.67 0.19 0.682 1.55 0.12 0.000 1.99 0.19 0.000 1.44 0.10 0.000 1.00 0.05 0.098 1.18 0.17 0.053 1.28 0.15 0.168 1.26 0.10 0.069 6.39 0.27 0.645 7.25 0.36 0.304 7.41 0.63 0.730 7.47 0.56 0.034 0.20 0.02 0.087 0.30 0.05 0.977 0.30 0.02 0.348 0.32 0.06 0.688 0.11 0.01 0.003 1.52 0.08 0.377 2.40 0.18 0.102 2.43 0.06 0.626 0.04 0.01 0.810 2.17 0.50 0.205 1.41 0.09 0.518 1.60 0.14 0.006 0.12 0.01 0.016 6.48 0.26 0.000 9.99 0.69 0.000 10.43 0.27 0.000 0.13 0.01 0.148 1.75 0.10 0.007 1.92 0.14 0.953 1.24 0.08 0.001 0.03 0.00 0.202 0.11 0.01 0.001 0.18 0.02 0.008 0.23 0.02 0.001 0.13 0.01 0.426 2.97 0.10 0.096 4.53 0.28 0.135 4.64 0.12 0.183 0.19 0.02 0.249 0.27 0.03 0.006 0.11 0.01 0.193 0.11 0.01 0.160 0.58 0.06 0.018 1.59 0.15 0.002 2.05 0.26 0.298 1.38 0.16 0.010 0.50 0.07 0.137 1.47 0.20 0.015 1.06 0.14 0.140 0.69 0.07 0.189 0.03 0.00 0.030 0.83 0.03 0.919 1.36 0.07 0.867 1.80 0.11 0.068 0.03 0.00 0.207 1.19 0.11 0.139 1.84 0.14 0.001 1.37 0.16 0.706 0.21 0.02 0.016 4.31 0.18 0.000 6.55 0.34 0.000 6.54 0.20 0.000 156 Table 3.2 (cont’d) e1b2-1 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.25 0.09 0.352 2.60 0.15 0.554 2.36 0.15 0.580 2.94 0.25 0.503 0.05 0.01 0.078 2.30 0.16 0.514 3.76 0.09 0.907 3.73 0.31 0.441 0.10 0.01 0.116 1.36 0.13 0.468 2.45 0.19 0.248 3.76 0.33 0.392 1.34 0.14 0.376 0.73 0.05 0.066 1.01 0.04 0.559 0.85 0.07 0.833 0.83 0.08 0.629 0.77 0.07 0.543 0.75 0.04 0.018 1.43 0.16 0.619 5.77 0.37 0.382 6.78 0.34 0.460 6.65 0.33 0.100 9.77 0.72 0.394 0.24 0.04 0.600 0.26 0.04 0.486 0.18 0.03 0.005 0.43 0.07 0.357 0.09 0.00 0.097 1.33 0.06 0.680 1.88 0.07 0.112 2.27 0.16 0.571 0.04 0.00 0.484 1.29 0.14 0.930 1.64 0.21 0.833 1.46 0.15 0.032 0.08 0.01 0.461 1.89 0.12 0.001 1.78 0.08 0.003 1.06 0.08 0.037 0.11 0.01 0.786 1.23 0.10 0.719 1.65 0.09 0.088 1.71 0.18 0.340 0.02 0.00 0.883 0.07 0.00 0.561 0.10 0.01 0.192 0.13 0.01 0.512 0.12 0.01 0.722 2.55 0.12 0.638 3.77 0.15 0.292 4.08 0.26 0.545 0.18 0.01 0.436 0.18 0.02 0.677 0.09 0.01 0.698 0.09 0.01 0.910 0.35 0.04 0.189 0.93 0.09 0.356 1.25 0.11 0.051 1.79 0.23 0.485 0.37 0.04 0.952 0.79 0.11 0.710 0.57 0.07 0.265 0.58 0.07 0.878 0.02 0.00 0.096 0.77 0.03 0.843 1.20 0.06 0.187 1.56 0.08 0.873 0.03 0.00 0.111 0.93 0.08 0.907 1.29 0.06 0.573 1.08 0.11 0.051 0.16 0.01 0.658 3.42 0.13 0.136 4.05 0.21 0.609 3.48 0.23 0.999 157 Table 3.2 (cont’d) ivd1-2 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.58 0.14 0.210 0.88 0.07 0.000 3.49 0.58 0.012 10.33 2.26 0.011 0.05 0.01 0.182 2.51 0.21 0.563 5.72 0.52 0.003 8.92 0.94 0.001 0.13 0.01 0.594 2.18 0.18 0.001 5.00 0.59 0.006 12.03 1.22 0.000 1.66 0.15 0.674 1.75 0.18 0.000 2.21 0.26 0.001 3.91 0.55 0.000 1.05 0.07 0.071 0.73 0.05 0.364 1.02 0.18 0.911 1.91 0.15 0.051 6.87 0.26 0.115 5.15 0.27 0.010 5.67 1.10 0.181 11.30 1.46 0.165 0.24 0.03 0.485 0.16 0.02 0.012 0.43 0.10 0.379 1.68 0.54 0.038 0.11 0.01 0.003 1.48 0.12 0.700 5.40 1.04 0.011 14.19 1.46 0.000 0.06 0.01 0.074 1.77 0.18 0.195 1.55 0.30 0.959 2.59 0.48 0.013 0.12 0.02 0.033 2.72 0.11 0.000 3.92 0.81 0.014 9.30 1.33 0.000 0.13 0.01 0.021 1.63 0.14 0.117 1.96 0.30 0.822 3.75 0.64 0.021 0.03 0.00 0.130 0.07 0.00 0.963 0.70 0.19 0.013 1.93 0.28 0.000 0.14 0.01 0.043 2.73 0.19 0.852 10.10 1.93 0.012 26.23 2.54 0.000 0.21 0.01 0.013 0.19 0.01 0.261 0.63 0.16 0.010 2.13 0.35 0.000 0.54 0.05 0.046 1.46 0.21 0.030 3.67 0.71 0.016 11.20 1.48 0.000 0.50 0.07 0.126 0.86 0.08 0.781 1.13 0.21 0.118 3.22 0.56 0.001 0.03 0.00 0.018 0.82 0.03 0.748 3.22 0.62 0.016 10.02 1.15 0.000 0.04 0.01 0.103 1.13 0.08 0.199 2.93 0.51 0.004 7.68 1.27 0.001 0.22 0.02 0.022 3.93 0.20 0.011 9.90 1.86 0.010 24.92 3.02 0.000 158 Table 3.2 (cont’d) mcca1-1 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.31 0.07 0.584 3.07 0.29 0.082 1.36 0.13 0.001 0.80 0.11 0.000 0.05 0.01 0.172 2.58 0.24 0.440 4.00 0.16 0.399 4.75 0.22 0.225 0.12 0.01 0.843 1.74 0.15 0.041 3.13 0.14 0.127 5.14 0.48 0.121 1.58 0.16 0.904 1.48 0.17 0.000 1.73 0.13 0.000 1.58 0.11 0.000 0.93 0.04 0.453 0.76 0.06 0.761 0.68 0.04 0.008 1.10 0.08 0.004 5.73 0.20 0.224 5.56 0.31 0.056 5.02 0.34 0.002 5.92 0.38 0.000 0.19 0.03 0.067 0.25 0.03 0.390 0.30 0.03 0.255 0.36 0.07 0.955 0.09 0.01 0.088 1.52 0.14 0.552 2.16 0.07 0.868 2.88 0.10 0.002 0.04 0.00 0.323 2.48 0.28 0.007 2.99 0.42 0.007 3.13 0.59 0.006 0.09 0.02 0.382 5.71 0.39 0.000 8.20 0.33 0.000 10.61 0.32 0.000 0.11 0.01 0.800 1.26 0.13 0.561 1.24 0.07 0.003 0.87 0.07 0.000 0.03 0.00 0.330 0.10 0.01 0.015 0.18 0.01 0.003 0.30 0.02 0.000 0.12 0.00 0.861 2.83 0.18 0.512 4.35 0.18 0.336 5.33 0.25 0.006 0.18 0.02 0.615 0.19 0.02 0.341 0.07 0.01 0.156 0.11 0.01 0.115 0.38 0.03 0.346 1.33 0.17 0.042 1.64 0.16 0.801 1.54 0.13 0.035 0.36 0.05 0.967 0.83 0.08 0.947 0.50 0.08 0.129 0.55 0.06 0.763 0.02 0.00 0.783 0.81 0.03 0.533 1.38 0.08 0.804 1.91 0.10 0.009 0.03 0.00 0.959 1.01 0.07 0.890 1.21 0.10 0.902 1.17 0.15 0.179 0.18 0.02 0.258 4.26 0.31 0.008 5.92 0.24 0.000 7.50 0.27 0.000 159 Table 3.2 (cont’d) mccb1-1 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.40 0.10 0.851 3.05 0.27 0.073 1.62 0.10 0.005 1.04 0.07 0.000 0.04 0.00 0.154 2.73 0.15 0.090 4.68 0.34 0.032 5.02 0.17 0.076 0.15 0.01 0.095 1.70 0.08 0.006 3.35 0.23 0.027 5.04 0.30 0.075 1.66 0.18 0.702 1.39 0.13 0.000 1.82 0.10 0.000 1.75 0.09 0.000 1.16 0.08 0.011 0.76 0.05 0.689 0.86 0.07 0.165 1.22 0.07 0.024 6.68 0.29 0.275 5.49 0.21 0.031 5.72 0.46 0.014 7.50 0.46 0.021 0.22 0.04 0.345 0.23 0.03 0.209 0.27 0.04 0.126 0.30 0.05 0.366 0.11 0.01 0.000 1.50 0.09 0.497 2.35 0.13 0.213 2.82 0.13 0.013 0.03 0.01 0.299 2.02 0.30 0.112 2.67 0.40 0.048 2.39 0.48 0.021 0.08 0.01 0.320 5.83 0.32 0.000 9.06 0.54 0.000 10.06 0.26 0.000 0.12 0.01 0.315 1.33 0.09 0.916 1.59 0.09 0.093 1.25 0.05 0.001 0.03 0.00 0.017 0.10 0.01 0.000 0.19 0.02 0.001 0.28 0.01 0.000 0.13 0.01 0.450 2.89 0.13 0.270 4.59 0.26 0.117 5.13 0.13 0.005 0.23 0.02 0.007 0.20 0.02 0.230 0.08 0.00 0.302 0.11 0.01 0.124 0.42 0.03 0.847 1.26 0.09 0.007 1.91 0.21 0.542 2.02 0.16 0.862 0.37 0.04 0.973 0.82 0.05 0.992 0.72 0.13 0.904 0.67 0.09 0.339 0.02 0.00 0.012 0.87 0.04 0.499 1.43 0.09 0.635 1.95 0.10 0.005 0.03 0.00 0.495 1.14 0.07 0.137 1.57 0.16 0.086 2.02 0.31 0.124 0.18 0.01 0.091 4.07 0.25 0.008 6.66 0.35 0.000 7.89 0.35 0.000 160 Table 3.2 (cont’d) hml1-2 Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0d in dark 3d in dark 6d in dark 9d in dark Mean SE t-test Mean SE t-test Mean SE t-test Mean SE t-test 1.47 0.09 0.409 2.65 0.17 0.412 1.30 0.09 0.001 0.75 0.10 0.000 0.07 0.01 0.001 2.55 0.13 0.346 3.85 0.20 0.848 5.63 0.56 0.049 0.12 0.01 0.569 1.31 0.10 0.657 2.38 0.22 0.174 4.65 0.34 0.346 1.46 0.11 0.679 1.38 0.08 0.000 1.69 0.08 0.000 1.70 0.29 0.015 0.83 0.07 0.639 0.59 0.07 0.027 0.47 0.03 0.000 1.39 0.22 0.590 5.20 0.30 0.032 4.62 0.22 0.001 4.21 0.18 0.000 6.75 0.69 0.015 0.19 0.03 0.076 0.19 0.03 0.047 0.14 0.03 0.002 0.22 0.04 0.032 0.10 0.01 0.076 1.38 0.07 0.693 1.90 0.09 0.161 3.61 0.67 0.105 0.04 0.01 0.962 2.03 0.26 0.074 1.68 0.15 0.614 2.93 0.62 0.016 0.12 0.02 0.031 5.31 0.23 0.000 7.51 0.32 0.000 10.67 0.59 0.000 0.13 0.01 0.100 1.09 0.09 0.056 1.19 0.08 0.001 1.31 0.15 0.007 0.03 0.01 0.329 0.09 0.00 0.011 0.15 0.01 0.008 0.33 0.04 0.002 0.13 0.01 0.330 2.62 0.10 0.701 3.89 0.16 0.464 5.36 0.36 0.025 0.18 0.01 0.649 0.14 0.01 0.243 0.07 0.00 0.047 0.12 0.01 0.142 0.45 0.03 0.368 1.12 0.10 0.101 1.32 0.09 0.089 1.99 0.27 0.984 0.36 0.03 0.830 0.84 0.14 0.913 0.37 0.03 0.026 0.79 0.14 0.171 0.03 0.00 0.034 0.85 0.05 0.823 1.37 0.07 0.820 2.59 0.42 0.037 0.04 0.00 0.171 0.96 0.13 0.815 1.47 0.11 0.190 2.04 0.23 0.056 0.20 0.02 0.051 3.74 0.14 0.016 5.43 0.25 0.001 9.09 1.48 0.005 161 Table 3.3 Leaf amino acid profiles of ivd1-2 relative to wild type (Col-0) at 6d and 9d in prolonged darkness. Amino acid Ala Arg Asn Asp Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val * Mean* 1.37 1.55 1.86 2.27 0.96 0.71 1.20 2.52 0.99 3.11 0.97 5.92 2.45 6.80 2.09 1.46 2.41 2.42 2.57 6d in dark SE 0.23 0.14 0.22 0.27 0.17 0.14 0.29 0.48 0.19 0.64 0.15 1.58 0.47 1.77 0.40 0.27 0.46 0.42 0.48 t-test 0.012 0.003 0.006 0.001 0.911 0.181 0.379 0.011 0.959 0.014 0.822 0.013 0.012 0.010 0.016 0.118 0.016 0.004 0.010 Mean 3.26 2.15 2.88 4.70 1.25 1.25 4.71 5.97 2.46 11.10 1.93 13.17 6.11 23.07 5.66 5.66 6.49 5.28 7.16 9d in dark SE 0.71 0.23 0.29 0.66 0.10 0.16 1.52 0.62 0.46 1.59 0.33 1.92 0.59 3.79 0.75 0.99 0.75 0.87 0.87 t-test 0.011 0.001 0.000 0.000 0.051 0.165 0.038 0.000 0.013 0.000 0.021 0.000 0.000 0.000 0.000 0.001 0.000 0.001 0.000 The mean represents the fold change between the averages of five biological replicates of ivd1-2 and Col-0 at the same time point. 162 Table 3.4 Primers for amiRNA generation. Primer Sequence Note amiRNA_A_cacc CACCCTGCAAGGCGATTAAGTTGGGTAAC amiRNA_a primer with cacc at 5', for amiRNA inserts cloning into pENTR-d/topo amiRNA_B GCGGATAACAATTTCACACAGGAAACAG amiRNA_B, for amiRNA inserts cloning into pENTR-d/topo amiE1B1-1_I gaTATGCGATTACATTAGTCCTTtctctcttttgtattcc I miR-s primer designed for amiE1B1-1 amiE1B1-1_II gaAAGGACTAATGTAATCGCATAtcaaagagaatcaatga II miR-a primer designed for amiE1B1-1 amiE1B1-1_III gaAAAGACTAATGTATTCGCATTtcacaggtcgtgatatg III miR*s primer designed for amiE1B1-1 amiE1B1-1_IV gaAATGCGAATACATTAGTCTTTtctacatatatattcct IV miR*a primer designed for amiE1B1-1 amiE1B1-2_I gaTAACTACAGATAGTACGCCTAtctctcttttgtattcc I miR-s primer designed for amiE1B1-2 amiE1B1-2_II gaTAGGCGTACTATCTGTAGTTAtcaaagagaatcaatga II miR-a primer designed for amiE1B1-2 amiE1B1-2_III gaTAAGCGTACTATCAGTAGTTTtcacaggtcgtgatatg III miR*s primer designed for amiE1B1-2 amiE1B1-2_IV gaAAACTACTGATAGTACGCTTAtctacatatatattcct IV miR*a primer designed for amiE1B1-2 pEARLEY-100_For CATCGTGGAAAAAGAAGACGT Forward primer for amplifying/genotyping insert on pEarleyGate100 vector pEARLEY-100_Rev AGGATCTGAGCTACACATGCT Reverse primer for amplifying/genotyping insert on pEarleyGate100 vector 163 LITERATURE CITED 164 LITERATURE CITED Anderson MD, Che P, Song J, Nikolau BJ, Wurtele ES (1998) 3-Methylcrotonyl-coenzyme A carboxylase is a component of the mitochondrial leucine catabolic pathway in plants. 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Promoter mutagenesis experiments demonstrated that both the -100bp promoter region and 5’UTR are necessary for the diurnal oscillation of the IVD1 transcript. In addition, mutant characterization provides evidence that putative BCKDH enzyme subunits E1α1, E1α2, E1β1 and E1β2 participate in BCAA catabolism during normal growth conditions. Mutants deficient in BCAA catabolism undergo early senescence and accumulate leaf free BCAAs during prolonged darkness, supporting the hypothesized connection of BCAA catabolism with the plant energy metabolism. Taken together, my dissertation research provides insights into the regulation and physiological roles of BCAA catabolism, and explored the physiological role(s) of BCAA catabolism within and beyond amino acid metabolism during normal growth conditions and under energy-limited conditions. 169 4.2 Future perspectives 4.2.1 Exploration of the physiological role(s) of BCAA catabolism in stress conditions In my dissertation research, I performed transcript coexpression analyses, and found that 10 out of the 13 genes proposed or experimentally validated to be involved in BCAA catabolism are positively coexpressed in the stress dataset (Figure 2.1, Figure 2.2). Because little information is known regarding to changes in BCAA contents and the physiological function(s) of BCAA catabolism upon abiotic and biotic stress treatments, it would be interesting to further explore the expression profiles of the 10 significantly coexpressed genes to find out the treatments (stimuli) that contribute to the transcript coexpression, and to test if the gene expression responses reflect the physiological function(s). To further study the role of BCAA catabolism in the identified stress treatments, BCAA catabolic mutants could be treated with the identified stimuli, both morphological phenotypes and changes in BCAA contents could be monitored and compared to the wild type. These experiments would further our understanding in the physiological role(s) of BCAA catabolism upon particular stress treatments, and help reveal the complex inter-connections within the plant metabolic networks. 4.2.2 Follow-up of the TFs interacting with the IVD1 promoter and 5’UTR by Y1H In my dissertation research, I demonstrated that the IVD1 -100bp promoter region with 5’UTR is necessary for IVD1 transcript diurnal oscillation (Figure 2.6). Y1H screen presented in Chapter 2 Appendix 1 demonstrated that a total of 72 TFs were able to bind with high confidence to fragment #1, which contains 180bp IVD1 promoter region upstream of TSS and its full 5’UTR (Table 2.7). In an earlier coexpression analysis that was collaborated with Sahra Uygun, we used 170 three algorithms - k-mean clustering, pair-wise Pearson Correlation Coefficient and mutual ranking from the ATTED-II database (http://atted.jp/) (Obayashi et al., 2009; Obayashi et al., 2011), to compute a list of genes that coexpress with the experimentally validated BCAA catabolic enzyme genes BCAT2, IVD1, MCCA1, MCCB1 and HML1. A scoring system was developed to present the times that a gene showed coexpression with any one of the five bait genes using individual algorithm. A total of 378 genes were found to coexpress with the five bait genes for three or more than three times. Among these 72 TFs identified from Y1H, 10 TFs (Table 4.1) were included in the coexpression gene list. Electrophoretic mobility shift assay could be performed to obtain a second line of evidence demonstrating the direct binding between the TF candidates and the IVD1 promoter. To further evaluate the in vivo role of the 10 TFs in regulating BCAA catabolism gene transcripts, homozygous T-DNA insertion lines and overexpression lines of these TF candidates should be obtained. BCAA catabolism gene transcripts and leaf BCAA contents could be examined, to compare the changes between the TF overexpression or mutant lines and the wild type under day-night cycles. In addition, my results also showed that the clock component LHY and photoreceptor PhyB participate in the regulation of BCAA catabolism genes in short day. It would be interesting to see if the transcript levels of the identified TF candidates are altered in the LHYOX (LHY overexpressor) and phyB-9 lines, and correlate the transcript changes of these TFs to BCAA catabolism genes. Taken together, these experiments could lead to the identification and characterization of TFs regulating BCAA catabolism genes, and would provide insights on how the circadian clock and light regulate primary metabolism in plants. 171 4.2.3 Evaluation of the effect of IVD1 introns on IVD1 transcript accumulation in prolonged darkness My analyses suggest that the IVD1 promoter and its 5’UTR were not sufficient for the elevated IVD1 transcript in prolonged darkness. One possibility could be that one or more introns are required for transcript accumulation (Callis et al., 1987; Luehrsen and Walbot, 1991; Norris et al., 1993; Xu et al., 1994; Rose and Last, 1997; Rose, 2008). To test this hypothesis, translational fusions of the IVD1 promoter with 5’UTR and different combinations of its transit peptide and/or introns, together with the LUC reporter gene could be generated and transformed into A. thaliana Col-0 wild type. Bioluminescence from primary transformants could be examined in prolonged darkness to evaluate the effect of introns on IVD1 transcript accumulation. These experiments would provide insights into the molecular basis of the regulation of BCAA catabolism. 4.2.4 Exploration of the interaction between the amino acid metabolic network and energy metabolism I identified 53 out of 481 amino acid metabolism genes that showed elevated transcript levels in prolonged darkness from my RNA-Seq experiments, including genes in BCAA catabolism, Lys catabolism, Gly biosynthesis and so on (Table A.2). This suggests that more amino acid metabolic pathways serve function(s) in the dark. To further explore their physiological function in prolonged darkness, homozygous T-DNA mutants defective in the genes shown in Table A.2 could be obtained, and morphological phenotypes, amino acid contents and TCA intermediates should be monitored and measured during prolonged darkness. These experiments would provide insights into the physiological roles of amino acid metabolic 172 pathways beyond maintaining amino acid homeostasis, and further our understanding of the connection between amino acid metabolism and energy metabolism in plants. 173 4.3 Practical implications The presented dissertation research has a variety of implications for our knowledge of plant metabolism, and provides insights into plant metabolic engineering of productivity and nutritional quality. In this dissertation research, amino acid increases - including all three BCAAs and multiple essential amino acids that are biosynthetically unrelated - were observed in seeds of BCAA catabolic mutants (Figure 3.6A). These results suggest uncharacterized interconnections within the plant amino acid metabolic networks. Previously published studies are consistent with this idea, and showed inter-connections regulating levels of six of the nine free essential amino acids (Ile, Leu, Lys, Met, Thr and Val) in seeds of a variety of plants (Karchi et al., 1994; Zhu and Galili, 2003; Jander et al., 2004; Joshi et al., 2006; Lee et al., 2008; Gu et al., 2010; Lu et al., 2011; Angelovici et al., 2013). Together, these results and my dissertation research indicate that modifying catabolism has potential for improving the nutritional quality of crop seeds and vegetative tissues. However, a variety of pleiotropic effects beyond amino acid metabolism were demonstrated, such as defects in vegetative and reproductive development or seed viability (Zhu and Galili, 2004; Lee et al., 2008; Ding et al., 2012), and early senescence in prolonged darkness in this study (Figure 3.3, Figure 3.4). While prolonged darkness is a rather artificial condition, the extent of the interaction between BCAA catabolism and energy metabolism during day-night cycles should be investigated in the future. It is not surprising that primary metabolic processes - which are relatively ‘old’, compared to specialized metabolism - are broadly connected and difficult to perturb without resultant undesirable changes (Milo and Last, 2012). While these pleiotropic phenotypes create 174 difficulties for metabolic engineering of productivity and nutritional quality, knowing the basis for these syndromes should lead to a deeper understanding of the architecture and regulation of the plant metabolic networks. 175 APPENDIX 176 Table 4.1 List of TFs that both interact with IVD1 -180/+200bp region and coexpress with BCAA catabolism genes AGI TF family AT1G21000 PLATZ AT1G64620 C2C2-DOF AT1G76900 TUB AT1G78080 AP2-EREBP AT3G10030 TRIHELIX AT3G16770 AP2-EREBP AT3G53200 MYB AT4G18020 ARR-B/G2-like AT4G39100 PHD AT5G61590 AP2-EREBP 177 LITERATURE CITED 178 LITERATURE CITED Angelovici R, Lipka AE, Deason N, Gonzalez-Jorge S, Lin H, Cepela J, Buell R, Gore MA, Dellapenna D (2013) Genome-wide analysis of branched-chain amino acid levels in Arabidopsis seeds. Plant Cell 25: 4827-4843 Callis J, Fromm M, Walbot V (1987) Introns increase gene expression in cultured maize cells. 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