Novel brown adipose tissue candidate genes predicted by the human gene connectome

Author:

Salazar-Tortosa Diego F.,Enard David,Itan Yuval,Ruiz Jonatan R.

Abstract

AbstractBrown adipose tissue (BAT) is a promising therapeutic target against obesity. Therefore, research on the genetic architecture of BAT could be key for the development of successful therapies against this complex phenotype. Hypothesis-driven candidate gene association studies are useful for studying genetic determinants of complex traits, but they are dependent upon the previous knowledge to select candidate genes. Here, we predicted 107 novel-BAT candidate genes in silico using the uncoupling protein one (UCP1) as the hallmark of BAT activity. We first identified the top 1% of human genes predicted by the human gene connectome to be biologically closest to the UCP1, estimating 167 additional pathway genes (BAT connectome). We validated this prediction by showing that 60 genes already associated with BAT were included in the connectome and they were biologically closer to each other than expected by chance (p < 2.2 × 10−16). The rest of genes (107) are potential candidates for BAT, being also closer to known BAT genes and more expressed in BAT biopsies than expected by chance (p < 2.2 × 10−16; p = 4.39 × 10–02). The resulting new list of predicted human BAT genes should be useful for the discovery of novel BAT genes and metabolic pathways.

Funder

Marie S. Curie Global Fellowship within the European Union research and innovation framework programme

Spanish Ministry of Economy and Competitiveness, Fondo de Investigación Sanitaria del Instituto de Salud Carlos III

Fondos Estructurales de la Unión Europea

Fundación Iberoamericana de Nutrición

Redes temáticas de investigación cooperativa RETIC

AstraZeneca HealthCare Foundation

Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía

University of Granada Plan Propio de Investigación

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep Learning Integration with Phenotypic Similarities and Heterogeneous Networks for Drug-Target Interaction Prediction;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

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