Promoter-Based Integration in Plant Defense Regulation    

Abstract

A key unanswered question in plant biology is how a plant regulates metabolism to maximize performance across an array of biotic and abiotic environmental stresses. In this study, we addressed the potential breadth of transcriptional regulation that can alter accumulation of the defensive glucosinolate metabolites in Arabidopsis (Arabidopsis thaliana). A systematic yeast one-hybrid study was used to identify hundreds of unique potential regulatory interactions with a nearly complete complement of 21 promoters for the aliphatic glucosinolate pathway. Conducting high-throughput phenotypic validation, we showed that .75% of tested transcription factor (TF) mutants significantly altered the accumulation of the defensive glucosinolates. These glucosinolate phenotypes were conditional upon the environment and tissue type, suggesting that these TFs may allow the plant to tune its defenses to the local environment. Furthermore, the pattern of TF/promoter interactions could partially explain mutant phenotypes. This work shows that defense chemistry within Arabidopsis has a highly intricate transcriptional regulatory system that may allow for the optimization of defense metabolite accumulation across a broad array of environments.An organism's growth and fitness within its environment are largely determined by its ability to efficiently obtain and utilize energy and elements to create biomass to survive. Central to this process is primary metabolism, which determines the use of energy and chemicals from the environment to produce all of the necessary building blocks for cells and the resulting biomass. To optimize fitness, metabolism must be precisely tuned and coordinated to make the most efficient use of available resources. This basic supposition is central to a wide range of biological fields from the study of organismal growth to the study of interactions with the environment, and has led to strong interest in understanding how metabolism is regulated (Karban and Baldwin, 1997;Smith and Stitt, 2007).The last decade has seen an upsurge in studies investigating the transcriptional control over metabolism. This has largely been driven by three key areas of focus: regulation of sugar metabolism, amino acid metabolism, and secondary metabolism (Desvergne et al., 2006). In most organisms, Glc levels cause transcriptional reprograming of sugar metabolism and organismal development often by a mitogen-activated protein kinase cascade (Moore et al., 2003;Rolland et al., 2006). Transcriptional control over amino acid metabolism frequently involves General Control Nonderepressible4, whereas other transcriptional activators play a role in coordinating amino acid metabolism (Hope and Struhl, 1986;Neuwald and Landsman, 1997;Li et al., 2013). Within plants, there are also amino acid pathway-specific transcription factors (TFs) known for a couple of plant amino acid biosynthetic pathways (Celenza et al., 2005;Maruyama-Nakashita et al., 2006). In plants, the transcriptional regulation of secondary metabolites has also received signifi...

Architecture and Dynamics of the Jasmonic Acid Gene Regulatory Network

“…Analysis of the overlap between RAP2.6L-OX DEGs and the MeJA-induced coexpression clusters from this study revealed a specific enrichment for RAP2.6L targets in cluster 14, which as described above was itself overrepresented for genes associated with aliphatic glucosinolate production. Interestingly, a recent study showed that RAP2.6L can interact with several aliphatic glucosinolate biosynthetic gene promoters and, moreover, that rap2.6l mutants are perturbed in glucosinolate production (Li et al, 2014).…”

Reassess the t Test: Interact with All Your Data via ANOVA

“…If, for example, the treatment response was moderate and the mutant phenotype was not fully penetrant, typical results could resemble those shown in Figure 2 (Supplemental Table 2 and Supplemental Methods) (Kliebenstein et al, 1999(Kliebenstein et al, , 2002Li et al, 2014;TaylorTeeples et al, 2015). To illustrate this example, we modeled how jasmonate application affects glucosinolate accumulation in plants mutated in a single transcription factor that quantitatively controls jasmonate perception (Sønderby et al, 2007;Sønderby et al, 2010;Li et al, 2014). In this case, using t tests may lead researchers to assume from the second and third trials that the mutant responded to jasmonate like the wild type, while ignoring the first trial as an outlier.…”

Transcriptional networks governing plant metabolism

“…Therefore, CCA1 links together circadian regulation, nutrient metabolism, multiple light responses, and potentially pathways not yet analyzed. This ability of CCA1 to integrate diverse signals and influence numerous downstream pathways begins to illustrate how the hierarchical linear model of single pathway regulation breaks down when viewing the transcriptional regulatory mechanisms and their regulation of metabolic systems [69].…”

“…While this singular focus has led to abundant knowledge about how individual metabolic pathways are regulated, genomic and systems biology tools are showing the importance of links across pathways and metabolites to coordinate responses [67][68][69][70][71][72][73][74].…”