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

Pacheco, Maria; John, Elisabeth; Kaoma, Tony; Heinäniemi, Merja; Nicot, Nathalie; Vallar, Laurent; Bueb, Jean-Luc; Sinkkonen, Lasse; Sauter, Thomas

doi:10.1186/s12864-015-1984-4

Reference : Integrated metabolic modelling reveals cell-type specific epigenetic control points o...


	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Biochemistry, biophysics & molecular biology
	To cite this reference:	http://hdl.handle.net/10993/22507

Title :	Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network
Language :	English
Author, co-author :	Pacheco, Maria [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	John, Elisabeth* [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Kaoma, Tony [Luxembourg Institute of Health > Genomics Research Unit]
	Heinäniemi, Merja [University of Eastern Finland > Institute of Biomedicine]
	Nicot, Nathalie [Luxembourg Institute of Health > Genomics Research Unit]
	Vallar, Laurent [Luxembourg Institute of Health > Genomics Research Unit]
	Bueb, Jean-Luc [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	Sinkkonen, Lasse [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	Sauter, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	* These authors have contributed equally to this work.
Publication date :	19-Oct-2015
Journal title :	BMC Genomics
Publisher :	BioMed Central
Volume :	16
Issue/season :	809
Peer reviewed :	Yes (verified by ORBi^lu)
Audience :	International
e-ISSN :	1471-2164
Keywords :	[en] Metabolic modelling ; Macrophage differentiation ; High regulatory load ; Active enhancer ; Regulation of metabolism
Abstract :	[en] 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.
Research centres :	Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group)
Funders :	Fonds National de la Recherche - FnR
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/22507
DOI :	10.1186/s12864-015-1984-4
Other URL :	http://www.biomedcentral.com/1471-2164/16/809

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