The translation of genotype into phenotype largely depends on genes being expressed in the appropriate cell types at the correct time. These expression patterns are largely determined by transcription factors (TFs) controlling specific gene sets which together result in gene regulatory networks (GRN). GRNs may be elucidated using TF-centered approaches, such as DNA-affinity purification and chromatin immunoprecipitation sequencing (DAP- & ChIP-seq, respectively). Alternatively, the generation of thousands of gene expression samples has allowed the implementation of robust methods for TF-target inference. As part of my research, I developed strategies that integrate several high-throughput data types to identify transcription factor regulators of a broad spectrum of metabolic pathways in several plant systems. Specifically, I established frameworks for the analysis of Camelina sativa, maize (Zea mays), and Arabidopsis thaliana with species-specific tailored pipelines. Data resources availability by species-guided pipeline differed between species. In Camelina, I combined expression and DAP-seq assays to identify transcriptional regulators of lipid metabolism. In maize, I integrated expression variation, expression quantitative loci (eQTLs), and DAP- & ChIP-seq to build a multiple-layer network predicting regulators of phenylpropanoid, lipids, and carbon metabolism. Lastly, for Arabidopsis, utilizing a vast collection of RNA-seq samples, protein-DNA interactions (PDI), and protein-protein interactions (PPI), I tested co-regulation models that incorporate the influence of TF physical interactors on TF-target co-expression profiles. This comprehensive analysis also enabled the prediction of high-level TF complexes, providing valuable insights for refining models of TF regulation based on co-expression. Together, my studies contributed new knowledge to the regulatory hypotheses of specific metabolic pathways in plants, establishing a framework for elucidating GRN in other systems.
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