title: Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.
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.
authors.affiliation: Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg. maria.pacheco@uni.lu.
authors.forename: Maria Pires
authors.initials: MP
authors.lastname: Pacheco
authors.affiliation: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg. elisabeth.john@uni.lu.
authors.forename: Elisabeth
authors.initials: E
authors.lastname: John
authors.affiliation: Genomics Research Unit, Luxembourg Institute of Health, L-1526, Luxembourg, Luxembourg. tony.kaoma@crp-sante.lu.
authors.forename: Tony
authors.initials: T
authors.lastname: Kaoma
authors.affiliation: Institute of Biomedicine, School of Medicine, University of Eastern Finland, 70211, Kuopio, Finland. merja.heinaniemi@uef.fi.
authors.forename: Merja
authors.initials: M
authors.lastname: Heinäniemi
authors.affiliation: Genomics Research Unit, Luxembourg Institute of Health, L-1526, Luxembourg, Luxembourg. nathalie.nicot@crp-sante.lu.
authors.forename: Nathalie
authors.initials: N
authors.lastname: Nicot
authors.affiliation: Genomics Research Unit, Luxembourg Institute of Health, L-1526, Luxembourg, Luxembourg. laurent-vallar@crp-sante.lu.
authors.forename: Laurent
authors.initials: L
authors.lastname: Vallar
authors.affiliation: Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg. jean-luc.bueb@uni.lu.
authors.forename: Jean-Luc
authors.initials: JL
authors.lastname: Bueb
authors.affiliation: Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg. lasse.sinkkonen@uni.lu.|Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg. lasse.sinkkonen@uni.lu.
authors.forename: Lasse
authors.initials: L
authors.lastname: Sinkkonen
authors.affiliation: Life Sciences Research Unit, University of Luxembourg, 162a, Avenue de la Faïencerie, L-1511, Luxembourg, Luxembourg. thomas.sauter@uni.lu.
authors.forename: Thomas
authors.initials: T
authors.lastname: Sauter
mesh_terms: D019070:Cell Lineage; D044127:Epigenesis, Genetic / Q000235:genetics*; D006801:Humans; D008264:Macrophages / Q000378:metabolism*; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D008957:Models, Genetic; D012984:Software; D059467:Transcriptome / Q000235:genetics*