摘要：SummaryWe design a “wisdom-of-the-crowds” GRN inference pipeline and couple it to complex network analysis to understand the organizational principles governing gene regulation in long-livedglp-1/NotchCaenorhabditis elegans. The GRN has three layers (input, core, and output) and is topologically equivalent to bow-tie/hourglass structures prevalent among metabolic networks. To assess the functional importance of structural layers, we screened 80% of regulators and discovered 50 new aging genes, 86% with human orthologues. Genes essential for longevity—including ones involved in insulin-like signaling (ILS)—are at the core, indicating that GRN's structure is predictive of functionality. We usedin vivoreporters and a novel functional network covering 5,497 genetic interactions to make mechanistic predictions. We used genetic epistasis to test some of these predictions, uncovering a novel transcriptional regulator,sup-37, that works alongside DAF-16/FOXO. We present a framework with predictive power that can accelerate discovery inC. elegansand potentially humans.Graphical abstractDisplay OmittedHighlights•Gene-regulatory inference provides global network of long-lived animals•The large-scale topology of the network has an hourglass structure•Membership to the core of the hourglass is a good predictor of functionality•Discovered 50 novel aging genes, includingsup-37, a DAF-16 dependent geneGenetics; Genomics; Bioinformatics
