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BMC Genomics. 2015 Oct 19;16:809. doi: 10.1186/s12864-015-1984-4.

Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.

Author information

1
Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg. maria.pacheco@uni.lu.
2
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg. elisabeth.john@uni.lu.
3
Genomics Research Unit, Luxembourg Institute of Health, L-1526, Luxembourg, Luxembourg. tony.kaoma@crp-sante.lu.
4
Institute of Biomedicine, School of Medicine, University of Eastern Finland, 70211, Kuopio, Finland. merja.heinaniemi@uef.fi.
5
Genomics Research Unit, Luxembourg Institute of Health, L-1526, Luxembourg, Luxembourg. nathalie.nicot@crp-sante.lu.
6
Genomics Research Unit, Luxembourg Institute of Health, L-1526, Luxembourg, Luxembourg. laurent-vallar@crp-sante.lu.
7
Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg. jean-luc.bueb@uni.lu.
8
Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg. lasse.sinkkonen@uni.lu.
9
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg. lasse.sinkkonen@uni.lu.
10
Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg. thomas.sauter@uni.lu.

Abstract

BACKGROUND:

The 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.

METHODS:

Here 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.

RESULTS:

To 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.

CONCLUSIONS:

By 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.

PMID:
26480823
PMCID:
PMC4617894
DOI:
10.1186/s12864-015-1984-4
[Indexed for MEDLINE]
Free PMC Article

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