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Metabolic Decisions in Development and Disease | EK27


S2, F2-Uncovering transcriptional regulation of metabolism at a systems level in Caenorhabditis elegans


Mar 27, 2021 12:00am ‐ Mar 27, 2021 12:00am

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S2, F2-Uncovering transcriptional regulation of metabolism at a systems level in Caenorhabditis elegans Shivani Nanda1, Wen Wang2, Xuhang Li1, Chad L. Myers2, Lutfu Safak Yilmaz1, Albertha JM Walhout1 1 Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA 2 Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA The spatiotemporal control of gene expression helps maintain organismal homeostasis under fluctuating dietary and environmental conditions. For instance, transcriptional regulation of metabolic genes can lead to metabolic rewiring- a process where alternative pathways are utilized to meet changing metabolic demands. The majority of previous studies for transcriptional regulation of metabolism have focused on individual metabolic genes and pathways. However, the extent of transcriptional regulation of metabolism in animals and on a global scale remains largely unknown. Large-scale gene expression datasets from a variety of dietary and environmental conditions offer an opportunity to investigate transcriptional regulation of metabolism globally. We study the transcriptional regulation of metabolism in the multicellular Caenorhabditis elegans at a systems level. We developed a computational pipeline to infer transcriptome-metabolome relationships. We have quantified the extent of transcriptional regulation of metabolism during development, across tissues and changing temperature stimulus. By applying a supervised approach, we have uncovered metabolic pathways that are transcriptionally coregulated. We define functions for uncharacterized metabolic genes and define pathway boundaries within the existing metabolic network by combining metabolic flux relationships and coexpression network of metabolic genes to extract potential tightly coregulated clusters of metabolic pathways. Overall, this study broadens our understanding of transcriptional regulation of metabolism and determine which parts of metabolism are transcriptionally (co)regulated.

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