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

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.

Integrated In Vitro and In Silico Modeling Delineates the Molecular Effects of a Synbiotic Regimen on Colorectal-Cancer-Derived Cells

“…To complement the in vitro modeling, we coupled a CBM (Orth et al, 2010) of COAD cells with a model of LGG (Magnú sdó ttir et al, 2017) A context-specific model of COAD was reconstructed, starting with the genome-scale human metabolic reconstruction Recon2 (Thiele et al, 2013) and data from The Cancer Genome Atlas (TCGA) dataset (Rahman et al, 2015), as well as an extension to the FASTCORMICS workflow (Pacheco et al, 2015) that uses RNA-seq data (M.P.P., T.B., D. Ternes, D. Kulms, S.G., E.L., T.S., unpublished data) ( Figure 1C). The models were further contextualized with in vitro data by using growth rates and secretion product ratios generated in this study.…”

“…Then, FASTCC (Vlassis et al, 2014) was run to remove any inconsistent reactions. The mediumconstrained Recon 2 model and the expression data from COAD patients from the TCGA dataset were used as input for a modified version of FASTCORMICS workflow (Pacheco et al, 2015) that allows processing of RNA-Seq datasets.…”

“…For the drug target identification, a pipeline was established that automatically retrieved gene and drug interactions as well as their relation to cancer (Adapted from (Pacheco et al, 2015)). The pipeline retrieves data from selected databases and was implemented in MATLAB 2017b.…”

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

“…In addition to switch-and valve-based integration methods, there are also pruning methods such as MBA [116], FASTCORE [117], mCADRE [118] and OnePrune [72], which only retain a core set of reactions in the metabolic model. FASTCORMICS is a faster adaptation of FASTCORE which facilitates data integration by pre-processing and produces multiple metabolic models [119]. Similarly, the cost optimisation reaction dependency assessment (CORDA) algorithm performs a four-step dependency assessment before calculating flux whilst minimising cost production i.e.…”

DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

“…Several methods were proposed in the literature to automatically reconstruct context-specific metabolic networks from gene or protein expression [7][8][9][13][14][15][16][17]. This process is done by solving an optimization problem to find the sub-network from the GSMN that maximizes the agreement with the experimental data.…”