Format

Send to

Choose Destination
Nucleic Acids Res. 2014 Feb;42(3):1474-96. doi: 10.1093/nar/gkt989. Epub 2013 Nov 5.

Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network.

Author information

1
Life Sciences Research Unit, University of Luxembourg, 162a Avenue de la Faïencerie, L-1511 Luxembourg, Luxembourg, Biozentrum, Universität Basel and Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, 4056 Basel, Switzerland, Institute for Systems Biology, 401 Terry Avenue North, 98109-5234, Seattle, Washington, USA, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, House of Biomedicine, 7 Avenue des Hauts-Fourneaux, L-4362 Esch/Alzette, Luxembourg and Department of Biotechnology and Molecular Medicine, A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, FI-70211 Kuopio, Finland.

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.

PMID:
24198249
PMCID:
PMC3919568
DOI:
10.1093/nar/gkt989
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center