Gene network based analysis identifies a coexpression module involved in regulating plasma lipids with high-fat diet response

Plasma lipids are modulated by gene variants and many environmental factors, including diet-associated weight gain. However, understanding how these factors jointly interact to influence molecular networks that regulate plasma lipid levels is limited. Here, we took advantage of the BXD recombinant i...

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Published in:The Journal of nutritional biochemistry 2023-09, Vol.119, p.109398-109398, Article 109398
Main Authors: Xu, Fuyi, Ziebarth, Jesse D, Goeminne, Ludger JE, Gao, Jun, Williams, Evan G, Quarles, Leigh D, Makowski, Liza, Cui, Yan, Williams, Robert W, Auwerx, Johan, Lu, Lu
Format: Article
Language:English
Subjects:
Animals
Bayesian network
BXD recombinant inbred mice
Diet, High-Fat - adverse effects
Gene Regulatory Networks
Humans
Lipids
Mendelian randomization
Mice
Modular specificity
Obesity - genetics
Obesity - metabolism
Plasma lipid
PPAR gamma - genetics
Weight Gain
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Summary:Plasma lipids are modulated by gene variants and many environmental factors, including diet-associated weight gain. However, understanding how these factors jointly interact to influence molecular networks that regulate plasma lipid levels is limited. Here, we took advantage of the BXD recombinant inbred family of mice to query weight gain as an environmental stressor on plasma lipids. Coexpression networks were examined in both nonobese and obese livers, and a network was identified that specifically responded to the obesogenic diet. This obesity-associated module was significantly associated with plasma lipid levels and enriched with genes known to have functions related to inflammation and lipid homeostasis. We identified key drivers of the module, including Cidec, Cidea, Pparg, Cd36, and Apoa4. The Pparg emerged as a potential master regulator of the module as it can directly target 19 of the top 30 hub genes. Importantly, activation of this module is causally linked to lipid metabolism in humans, as illustrated by correlation analysis and inverse-variance weighed Mendelian randomization. Our findings provide novel insights into gene-by-environment interactions for plasma lipid metabolism that may ultimately contribute to new biomarkers, better diagnostics, and improved approaches to prevent or treat dyslipidemia in patients. [Display omitted]
ISSN:0955-2863
1873-4847
DOI:10.1016/j.jnutbio.2023.109398