The circadian clock relies on posttranslational modifications to set the timing for degradation of core regulatory components, which drives clock progression. Ubiquitin-modifying enzymes that target clock components for degradation mainly recognize phosphorylated substrates. Degradation of the circadian clock component PERIOD 2 (PER2) is mediated by its phospho-specific recognition by β-transducin repeat–containing proteins (β-TrCPs), which are F-box–containing proteins that function as substrate recognition subunits of the SCFβ-TRCPubiquitin ligase complex. However, this mode of regulating PER2 stability falls short of explaining the persistent oscillatory phenotypes reported in biological systems lacking functional elements of the phospho-dependent PER2 degradation machinery. We identified PER2 as a previously uncharacterized substrate for the ubiquitin ligase mouse double minute 2 homolog (MDM2) and found that MDM2 targeted PER2 for degradation in a manner independent of PER2 phosphorylation. Deregulation of MDM2 plays a major role in oncogenesis by contributing to the accumulation of genomic and epigenomic alterations that favor tumor development. MDM2-mediated PER2 turnover was important for defining the circadian period length in mammalian cells, a finding that emphasizes the connection between the circadian clock and cancer. Our results not only broaden the range of specific substrates of MDM2 beyond the cell cycle to include circadian components but also identify a previously unknown regulator of the clock as a druggable node that is often found to be deregulated during tumorigenesis.
Many signaling and other genes known as “hidden” drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID.
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