Abstract
BackgroundThe 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.MethodsHere 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.ResultsTo 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.ConclusionsBy 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1984-4) contains supplementary material, which is available to authorized users.
Highlights
The reconstruction of context-specific metabolic models from and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine
At the epigenetic level the availability of various transcription factor (TF) binding sites through chromatin decondensation at context-specific enhancers is regulated by the interplay of TFs and post-translational histone modifications deposited by the recruited co-activators [5]
These enhancer clusters usually reside in insulated chromatin loops or domains and often overlap with so called TF hotspots, suggesting that their target genes are under high regulatory load from multiple TFs and enhancers, integrating numerous different signals to promote proper cellular phenotype, including the appropriate metabolic network [12, 13]
Summary
The reconstruction of context-specific metabolic models from and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity Their role in regulation of metabolism and their placement within the metabolic networks has not been studied. Recent work on genome-wide analysis of active enhancers has revealed that important genes determining cellular identity, such as TFs, are often controlled by large and strong clusters of multiple enhancers called super-enhancers or stretch-enhancers that are active in a cell type-specific manner [9,10,11] These enhancer clusters usually reside in insulated chromatin loops or domains and often overlap with so called TF hotspots, suggesting that their target genes are under high regulatory load from multiple TFs and enhancers, integrating numerous different signals to promote proper cellular phenotype, including the appropriate metabolic network [12, 13]. The role of high regulatory load genes in the metabolic networks has not been studied previously
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