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BMC Syst Biol. 2017 Jun 12;11(1):60. doi: 10.1186/s12918-017-0438-9.

Remodeling adipose tissue through in silico modulation of fat storage for the prevention of type 2 diabetes.

Author information

1
Department of Biochemistry, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Canada.
2
Institute of Nutrition and Functional Foods, Université Laval, Quebec City, Canada.
3
School of Nutrition, Université Laval, Quebec City, Canada.
4
Division of Endocrinology, Department of Medicine, Centre de recherche du CHUS, Université de Sherbrooke, Sherbrooke, Canada.
5
Centre de Recherche de l'Institut universitaire de cardiologie et de pneumologie de Québec, Quebec City, QC, Canada.
6
Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada. rafael.najmanovich@umontreal.ca.

Abstract

BACKGROUND:

Type 2 diabetes is one of the leading non-infectious diseases worldwide and closely relates to excess adipose tissue accumulation as seen in obesity. Specifically, hypertrophic expansion of adipose tissues is related to increased cardiometabolic risk leading to type 2 diabetes. Studying mechanisms underlying adipocyte hypertrophy could lead to the identification of potential targets for the treatment of these conditions.

RESULTS:

We present iTC1390adip, a highly curated metabolic network of the human adipocyte presenting various improvements over the previously published iAdipocytes1809. iTC1390adip contains 1390 genes, 4519 reactions and 3664 metabolites. We validated the network obtaining 92.6% accuracy by comparing experimental gene essentiality in various cell lines to our predictions of biomass production. Using flux balance analysis under various test conditions, we predict the effect of gene deletion on both lipid droplet and biomass production, resulting in the identification of 27 genes that could reduce adipocyte hypertrophy. We also used expression data from visceral and subcutaneous adipose tissues to compare the effect of single gene deletions between adipocytes from each compartment.

CONCLUSIONS:

We generated a highly curated metabolic network of the human adipose tissue and used it to identify potential targets for adipose tissue metabolic dysfunction leading to the development of type 2 diabetes.

KEYWORDS:

Adipocytes; Biomass production; Diabetes; Flux balance analysis; Lipid droplet production; Lipid metabolism; Metabolic network; in-silico single gene deletion

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