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

iScience

Simon Andrews¹  Abraham Mains²  Sharlene Murdoch²  Felix Krueger²  Marta Sales-Pardo²  Shahzabe Mukhtar²  Manusnan Suriyalaksh²  Anne Segonds-Pichon²  Celia Raimondi²  Olivia Casanueva³  Rebeca Aldunate⁴  Roger Guimerà⁵ 

[1] Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain;Babraham Institute, Babraham, Cambridge CB22 3AT, UK;Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain;Escuela de Biotecnología, Facultad de Ciencias, Universidad Santo Tomas, Santiago, Chile;ICREA, Barcelona 08010, Catalonia, Spain;

关键词: Genetics;    Genomics;    Bioinformatics;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: 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.

【 授权许可】

Unknown