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Genetic and epigenetic control of metabolic health.

ABSTRACT: Obesity is characterized as an excess accumulation of body fat resulting from a positive energy balance. It is the major risk factor for type 2 diabetes (T2D). The evidence for familial aggregation of obesity and its associated metabolic diseases is substantial. To date, about 150 genetic loci identified in genome-wide association studies (GWAS) are linked with obesity and T2D, each accounting for only a small proportion of the predicted heritability. However, the percentage of overall trait variance explained by these associated loci is modest (~5-10% for T2D, ~2% for BMI). The lack of powerful genetic associations suggests that heritability is not entirely attributable to gene variations. Some of the familial aggregation as well as many of the effects of environmental exposures, may reflect epigenetic processes. This review summarizes our current knowledge on the genetic basis to individual risk of obesity and T2D, and explores the potential role of epigenetic contribution.

SUBMITTER: Schwenk RW

PROVIDER: S-EPMC3854991 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Genetic and epigenetic control of metabolic health.

Schwenk Robert Wolfgang RW Vogel Heike H Schürmann Annette A

Molecular metabolism 20130925 4

Obesity is characterized as an excess accumulation of body fat resulting from a positive energy balance. It is the major risk factor for type 2 diabetes (T2D). The evidence for familial aggregation of obesity and its associated metabolic diseases is substantial. To date, about 150 genetic loci identified in genome-wide association studies (GWAS) are linked with obesity and T2D, each accounting for only a small proportion of the predicted heritability. However, the percentage of overall trait var ...[more]

PMID: 24327950

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Project description:BackgroundThe reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied.MethodsHere we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks.ResultsTo understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism.ConclusionsBy integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic genes at pathway entry points such as transporters within the macrophage metabolic network. Analysis of these control points through further integration of metabolic and gene regulatory networks in various contexts could be beneficial in multiple fields from identification of disease intervention strategies to cellular reprogramming.

Dataset Information

Genetic and epigenetic control of metabolic health.

Publications

Genetic and epigenetic control of metabolic health.

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