The branched-chain amino acids (BCAAs) Leu, Ile, and Val 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, transcript coexpression analyses revealed positive correlations among BCAA catabolism genes in stress, development, diurnal/circadian, and light data sets. A core subset of BCAA catabolism genes, including those encoding putative branched-chain ketoacid dehydrogenase subunits, is highly expressed during the night in plants on a diel cycle and in prolonged darkness. Mutants defective in these subunits 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 overaccumulate leaf BCAAs. 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 darktreated 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.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. 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;Araújo et al., 2010;Ding et al., 2012;Kochevenko et al., 2012).The BCAA biosynthetic pathway and its regulation have been investigated in Arabidopsis (Arabidopsis 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). Strong correlations between the levels of free BCAAs were found in wildtype Arabidopsis seeds and tomato (Solanum lycopersicum) fruits Lu et al., 2008), which suggests coregulation 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. However, despite long-term interest in the desirability of optim...

Plant transcription factors (TFs) that interact with specific sequences via DNA-binding domains are crucial for regulating transcriptional initiation and are fundamental to plant development and environmental response. In addition, expansion of TF families has allowed functional divergence of duplicate copies, which has contributed to novel, and in some cases adaptive, traits in plants. Thus, TFs are central to the generation of the diverse plant species that we see today. Major plant agronomic traits, including those relevant to domestication, have also frequently arisen through changes in TF coding sequence or expression patterns. Here our goal is to provide an overview of plant TF evolution by first comparing the diversity of DNA-binding domains and the sizes of these domain families in plants and other eukaryotes. Because TFs are among the most highly expanded gene families in plants, the birth and death process of TFs as well as the mechanisms contributing to their retention are discussed. We also provide recent examples of how TFs have contributed to novel traits that are important in plant evolution and in agriculture.This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

ORCID IDs: 0000-0003-0863-0384 (S.U.); 0000-0001-6470-235X (S.-H.S.). Plants are exposed to a variety of environmental conditions, and their ability to respond to environmental variation depends on the proper regulation of gene expression in an organ-, tissue-, and cell type-specific manner. Although our knowledge of how stress responses are regulated is accumulating, a genome-wide model of how plant transcription factors (TFs) and cis-regulatory elements control spatially specific stress response has yet to emerge. Using Arabidopsis (Arabidopsis thaliana) as a model, we identified a set of 1,894 putative cis-regulatory elements (pCREs) that are associated with high-salinity (salt) up-regulated genes in the root or the shoot. We used these pCREs to develop computational models that can better predict salt up-regulated genes in the root and shoot compared with models based on known TF binding motifs. In addition, we incorporated TF binding sites identified via large-scale in vitro assays, chromatin accessibility, evolutionary conservation, and pCRE combinatorial relationships in machine learning models and found that only consideration of pCRE combinations led to better performance in salt up-regulation prediction in the root and shoot. Our results suggest that the plant organ transcriptional response to high salinity is regulated by a core set of pCREs and provide a genome-wide view of the cis-regulatory code of plant spatial transcriptional responses to environmental stress.

Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.