Reference : Integrated metabolic modelling reveals cell-type specific epigenetic control points o...
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
http://hdl.handle.net/10993/22507
Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network
English
Pacheco, Maria Irene* mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
John, Elisabeth* mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Kaoma, Tony mailto [Luxembourg Institute of Health > Genomics Research Unit]
Heinäniemi, Merja mailto [University of Eastern Finland > Institute of Biomedicine]
Nicot, Nathalie mailto [Luxembourg Institute of Health > Genomics Research Unit]
Vallar, Laurent mailto [Luxembourg Institute of Health > Genomics Research Unit]
Bueb, Jean-Luc mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Sinkkonen, Lasse mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Sauter, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
* These authors have contributed equally to this work.
19-Oct-2015
BMC Genomics
BioMed Central
16
809
Yes (verified by ORBilu)
International
1471-2164
[en] Metabolic modelling ; Macrophage differentiation ; High regulatory load ; Active enhancer ; Regulation of metabolism
[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.
Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group)
Fonds National de la Recherche - FnR
Researchers
http://hdl.handle.net/10993/22507
10.1186/s12864-015-1984-4
http://www.biomedcentral.com/1471-2164/16/809

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