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dc.contributor.authorHeinäniemi, Merja Hannele
dc.contributor.authorMaria Pires Pacheco
dc.contributor.authorElisabeth John
dc.contributor.authorTony Kaoma
dc.contributor.authorNathalie Nicot
dc.contributor.authorLaurent Vallar
dc.contributor.authorJean-Luc Bueb
dc.contributor.authorLasse Sinkkonen
dc.contributor.authorThomas Sauter
dc.date.accessioned2016-03-24T07:40:09Z
dc.date.available2016-03-24T07:40:09Z
dc.date.issued2015-10-19
dc.identifier10.1186/s12864-015-1984-4
dc.identifier.citationBMC Genomics 2015 16:809fi_FI
dc.identifier.issn1471-2164
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/31
dc.descriptionArticle
dc.description.abstractBackground 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.fi_FI
dc.language.isoenfi_FI
dc.publisherBioMed Centralfi_FI
dc.relation.urihttp://dx.doi.org/10.1186/s12864-015-1984-4
dc.rightsCC BY https://creativecommons.org/licenses/by/4.0/
dc.subjectMetabolic modellingfi_FI
dc.subjectMacrophage differentiationfi_FI
dc.subjectHigh regulatory loadfi_FI
dc.subjectActive enhancerfi_FI
dc.subjectRegulation of metabolismfi_FI
dc.titleIntegrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic networkfi_FI
dc.typehttp://purl.org/eprint/type/JournalArticle
dc.description.versionPublisher’s pdf
dc.contributor.departmentTerveystieteiden tiedekunta
uef.solecris.id36164340
dc.type.publicationinfo:eu-repo/semantics/article
dc.rights.accessrights© Authors
dc.relation.doi10.1186/s12864-015-1984-4
dc.relation.issn1471-2164


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