Gene regulatory network inference in long-lived C. elegans reveals modular properties that are predictive of novel aging genes

长寿秀丽隐杆线虫的基因调控网络推断揭示了可预测新衰老基因的模块化特性

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作者:Manusnan Suriyalaksh, Celia Raimondi, Abraham Mains, Anne Segonds-Pichon, Shahzabe Mukhtar, Sharlene Murdoch, Rebeca Aldunate, Felix Krueger, Roger Guimerà, Simon Andrews, Marta Sales-Pardo, Olivia Casanueva

Abstract

We 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-lived glp-1/Notch Caenorhabditis 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 used in vivo reporters 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 in C. elegans and potentially humans.

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