Unknown

Dataset Information

0

Exploring genetic susceptibility to obesity through genome functional pathway analysis.


ABSTRACT:

Objective

Obesity has been reaching epidemic levels in recent decades, with a growing body of research identifying predisposing genetic components. To explore the relationship of genetic factors contributing to obesity, an analytical computer-based gene-profiling approach utilizing an updated list of clinically relevant and known obesity-related genes was undertaken.

Methods

An updated list of 494 genes reportedly associated with obesity was compiled, and the GeneAnalytics profiling software was utilized to interrogate genomic databases from GeneCards® to cross-reference obesity gene sets against tissues and cells, diseases, genetic pathways, gene ontology (GO)-biological processes and GO-molecular functions, phenotypes, and compounds.

Results

Obesity-related fields identified by GeneAnalytics algorithms included 8 diseases, 46 pathways, 62 biological processes, 22 molecular functions, 148 phenotypes, and 286 compounds impacting adipogenesis, signal transduction by G-protein coupled receptors, and lipid metabolism involving insulin-related genes (IGF1, INS, IRS1). GO-biological processes identified feeding behavior, cholesterol metabolic process, and glucose and cholesterol homeostasis pathways, while GO-molecular processes pertained to receptor binding, affecting glucose homeostasis, body weight, and circulating insulin and triglyceride levels.

Conclusions

The gene-profiling model suggests that pathogenesis of obesity relates to the coordination of biological responses to glucose and intracellular lipids possibly through a disruption of biochemical cascades and cellular signaling arising from affected receptors.

SUBMITTER: Gabrielli AP 

PROVIDER: S-EPMC5444946 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

altmetric image

Publications

Exploring genetic susceptibility to obesity through genome functional pathway analysis.

Gabrielli Alexander P AP   Manzardo Ann M AM   Butler Merlin G MG  

Obesity (Silver Spring, Md.) 20170505 6


<h4>Objective</h4>Obesity has been reaching epidemic levels in recent decades, with a growing body of research identifying predisposing genetic components. To explore the relationship of genetic factors contributing to obesity, an analytical computer-based gene-profiling approach utilizing an updated list of clinically relevant and known obesity-related genes was undertaken.<h4>Methods</h4>An updated list of 494 genes reportedly associated with obesity was compiled, and the GeneAnalytics profili  ...[more]

Similar Datasets

| S-EPMC4543151 | biostudies-literature
| S-EPMC4908583 | biostudies-literature
| S-EPMC3763965 | biostudies-literature
| S-EPMC7539174 | biostudies-literature
| S-EPMC3888264 | biostudies-literature
| S-EPMC3107984 | biostudies-literature
| S-EPMC16660 | biostudies-other
| S-EPMC3481354 | biostudies-literature
| S-EPMC8294037 | biostudies-literature
| S-EPMC4576730 | biostudies-literature