Gene regulatory network structure, function and evolution

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DESCRIPTION (provided by applicant): The transcriptional regulation of gene expression is pivotal to all biological processes. Each of our ~20,000 protein-coding genes must be expressed at the right place, time and level, as well as under the right developmental or physiological circumstances. Consequently, inappropriate gene expression is implicated in a myriad of human diseases, including congenital disorders, cancer and obesity. Transcription has been studied intensively for decades, resulting in a detailed picture of the basic biochemical mechanisms of mRNA production. However, we know little about gene regulation at a 'systems level', i.e. how TFs function together in complex gene regulatory networks (GRNs) to faithfully orchestrate the expression of large sets of genes. Our long-term goal is to comprehensively characterize the structure, function and evolution of complex metazoan GRNs to gain insights into global mechanisms of gene regulation. It is becoming increasingly clear that textbook explanations of gene regulation in which a TF binds DNA in the genome and upon doing so regulates the most proximal gene are too simplistic because many physical TF binding events lack an apparent regulatory consequence. There are several explanations for this, ranging from technical (e.g. detection limits, attribution of a bindng event to the wrong gene) to biological (e.g. redundancy between TFs, condition-dependent effects). Conversely, regulatory interactions are not necessarily due to a direct effect. For instance, TFs can function in cascades to propagate functional regulation. A major challenge is to combine physical and regulatory interactions to increase our understanding of the mechanisms of gene regulation in the context of complex multicellular organisms. Many GRN studies focus either solely on physical TF interactions, whereas others focus primarily on regulatory interactions. However, integrated GRNs that combine both are scarce and, when available are relatively small in scale. If we had high-quality, large- scale physical and regulatoy interaction data, as well as spatiotemporal and conditional gene expression data, we could build increasingly precise GRNs. Here, we will continue our studies on the nematode C. elegans to map and integrate physical and regulatory GRNs, which will help us to go beyond mapping to understanding the regulatory mechanisms of gene expression at a systems level in a complex multicellular organism.