Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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

ABSTRACT: The reconstruction of cell type- and patient-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important at the age of personalized 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. 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. 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. 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. ChIP-Seq was performed with chromatin from macrophages differentiated in vitro for 11 days from primary human CD14+ monocytes isolated from the blood of three different anonymous male donors. For each donor one ChIP sample using an antibody against H3K27ac and one input sample were sequenced. Please see the individual samples for further details.

ORGANISM(S): Homo sapiens

SUBMITTER: Lasse Sinkkonen

PROVIDER: E-GEOD-68798 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:The reconstruction of cell type- and patient-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important at the age of personalized 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. 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. 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. 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.

Project description:Chronic immune activation is a hallmark of human immunodeficiency virus (HIV) infection and the best prognostic indicator of disease progression. Suppressing HIV viremia by antiretroviral therapy (ART) restores normal immune response and effectively prolongs life. In HIV-infected individuals who are coinfected with hepatitis C virus (HCV) the immune system is activated despite effective HIV antiretroviral therapy controlling viral load. Here we examined CD14+ monocyte gene expression by high-density microarray analysis and T cell subsets, CD4 and CD8, by flow cytometry to characterize immune activation in monoinfected HCV, monoinfected HIV and HIV/HCV coinfected subjects with undetected HIV viral load. To determine the impact of coinfection on cognition, subjects were evaluated in 7 domains for neuropsychological (NP) performance, which was summarized as global deficit scores (GDS). Gene expression analysis of CD14+ monocytes from coinfected subjects revealed an elevated type 1 interferon (IFN) response profile unique to coinfection. For both CD4 and CD8 T cells, coinfection triggered significantly increased expression of activation markers CD38 and HLA-DR. In the coinfected group, mild cognitive impairment was associated with a type 1 IFN monocyte response but not plasma lipopolysaccharide. These observations raise the possibility that cognitive impairment evident in the HIV/HCV population is associated with the IFN response detected in coinfected individuals. Monocytes isolated from healthy controls (n=17), HCV monoinfected (n=19) and HIV/HCV coinfected (n=17) were analyzed for gene expression using high-density microarrays. Whole blood was collected in Vacutainer CPT tubes (BD Biosciences) and PBMCs were enriched by centrifugation. Typically, three million CD14+ monocytes were isolated from 30 ml of whole blood using an anti-CD14 monoclonal antibody immunomagnetic - ferrous bead conjugate according to the manufacturer’s instructions (Miltenyi Biotech). Monocyte purity exceeded 97% with <1% T or B cell contamination as determined by flow cytometry. Monocyte RNA was isolated using a Qiagen RNeasy Micro Kit with an RNA integrity value exceeding 9. Complementary DNA was synthesized and labeled with biotin (iExpress iAmplify kit, Applied Microarrays) and hybridized to Codelink Whole Human Genome Bioarrays (55K probes, Applied Microarrays). Slides were scanned (Axon GenePix 4000B, Molecular Devices), analyzed (CodeLink Expression Software Kit v4.1) and microarray data were normalized with loess normalization using R and Bioconductor package. Determination of differential gene expression and multiple testing correction / false discovery rate adjustments were performed using GeneSpring GX 7.3 software package (Agilent). Microarray data was analyzed using a variety of custom data analytic techniques for gene expression profile identification as described previously. Correlations were determined by Spearman rank correlation coefficient.

Project description:Background: Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide, most of which are caused by atherosclerosis. Discerning processes that participate in macrophage-to-foam cell formation are critical for understanding the basic mechanisms underlying atherosclerosis. To explore the molecular mechanisms of foam cell formation, the differentially expressed proteins were identified. Methods: In this paper, human monocytes, macrophage colony-stimulating factor induced macrophages, and oxidized low-density lipoprotein induced foam cells were cultured, and tandem mass tag (TMT) labeling combined with mass spectrometry (MS) were performed to find associations between foam cell transformation and proteome profiles. Results: Totally, 5146 quantifiable proteins were identified, among which 1515 and 182 differentially expressed proteins (DEPs) were found in macrophage/monocyte and foam cell/macrophage, respectively, using a cutoff of 1.5-fold change. Subcellular localization analysis revealed that downregulated DEPs of macrophages/monocytes were mostly located in the nucleus and upregulated DEPs of foam cells/macrophages mostly located in the plasma membrane and extracellular. Functional analysis of DEPs demonstrated that cholesterol metabolism related proteins were upregulated in foam cells, whereas the immune response-related proteins were downregulated in foam cells. The protein-interaction network showed that the DEPs with the highest interaction intensity between macrophages and foam cells were mainly concentrated in lysosomes and the endoplasmic reticulum. Conclusions: This study for the first time to perform quantitative proteomic investigation by TMT labeling and LC-MS/MS to identify differentially expressed proteins in human monocyte, macrophage, and foam cell. The results confirmed cholesterol metabolism was upregulated in foam cells, while immune response was suppressed, which suggested that foam cells were not the population that promote inflammation. In addition, KEGG enrichment analysis and protein-protein interaction indicated that the differentially expressed proteins locating in the endoplasmic reticulum and lysosomes may be key targets to regulate foam cell formation. These data provide a basis for identifying the potential proteins associated with the molecular mechanism involved in the transformation of macrophages to foam cells.

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

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