ArticleNucleic acids research2014
Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 45 citations in OpenAlex.
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- High expression of CD52 in adipocytes: a potential therapeutic target for obesity with type 2 diabetes.Aging · 2021Article
- Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes.Biology · 2021Article
- A Novel Glycosyltransferase-Related Gene Signature for Overall Survival Prediction in Patients with Ovarian Cancer.International journal of general medicine · 2021Article
- ER-Negative Breast Cancer Is Highly Responsive to Cholesterol Metabolite Signalling.Nutrients · 2019Article
- Discovering metabolic disease gene interactions by correlated effects on cellular morphology.Molecular metabolism · 2019Article
- Temporal enhancer profiling of parallel lineages identifies AHR and GLIS1 as regulators of mesenchymal multipotency.Nucleic acids research · 2019Article
- Cell Models and Their Application for Studying Adipogenic Differentiation in Relation to Obesity: A Review.International journal of molecular sciences · 2016Review
- Personalized Cardiovascular Disease Prediction and Treatment-A Review of Existing Strategies and Novel Systems Medicine Tools.Frontiers in physiology · 2016Review
- Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network.BMC genomics · 2015Article
- Cell type-selective disease-association of genes under high regulatory load.Nucleic acids research · 2015Article
- Extensive weight loss reveals distinct gene expression changes in human subcutaneous and visceral adipose tissue.Scientific reports · 2015Article
- Mechanistic Role of MicroRNAs in Coupling Lipid Metabolism and Atherosclerosis.Advances in experimental medicine and biology · 2015Article
- Systematic Analysis of Integrated Gene Functional Network of Four Chronic Stress-related Lifestyle Disorders.Genome integrity · 2015Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic diseases and comorbidities represent an ever-growing epidemic where multiple cell types impact tissue homeostasis. Here, the link between the metabolic and gene regulatory networks was studied through experimental and computational analysis. Integrating gene regulation data with a human metabolic network prompted the establishment of an open-sourced web portal, IDARE (Integrated Data Nodes of Regulation), for visualizing various gene-related data in context of metabolic pathways. Motivated by increasing availability of deep sequencing studies, we obtained ChIP-seq data from widely studied human umbilical vein endothelial cells. Interestingly, we found that association of metabolic genes with multiple transcription factors (TFs) enriched disease-associated genes. To demonstrate further extensions enabled by examining these networks together, constraint-based modeling was applied to data from human preadipocyte differentiation. In parallel, data on gene expression, genome-wide ChIP-seq profiles for peroxisome proliferator-activated receptor (PPAR) γ, CCAAT/enhancer binding protein (CEBP) α, liver X receptor (LXR) and H3K4me3 and microRNA target identification for miR-27a, miR-29a and miR-222 were collected. Disease-relevant key nodes, including mitochondrial glycerol-3-phosphate acyltransferase (GPAM), were exposed from metabolic pathways predicted to change activity by focusing on association with multiple regulators. In both cell types, our analysis reveals the convergence of microRNAs and TFs within the branched chain amino acid (BCAA) metabolic pathway, possibly providing an explanation for its downregulation in obese and diabetic conditions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.