Galhardo2014 Data: Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network [ChIP-seq]

By (Secondary Ownership. The experiment uses only third-party data.)

Abstract from Pubmed:

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) gamma, CCAAT/enhancer binding protein (CEBP) alpha, 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.
histone modifications CEBPA networks ChIP-seq LXR differentiation PPARG transcription factors
Show history? PUBLIC
Illumina Genome Analyzer II

Some things you might want to do with these data..

Input Data

Sample Groups and Experimental Factors

Genome Snapshots

'Genome snapshots' are assorted genomic regions that the creators of this experiment considered of particular interest.

TOM1L2 locus

GPAM locus

ACSL1 locus

Main Experimental Results

This is a selection of main results from the data analysis in this experiment. All intermediate results and more data are available from the workflow designer.

Other data generated in this experiment:

Typical analysis workflows my generate dozens or even hundreds of outputs. To condense the amount of information into more easily digestible portions, GeneProf will, by default, only display the experiments input data (here: input data) and a selection of the most important outputs (here: main results). You can drill down into the details of the analysis via the workflow designer or you can display other intermediate outputs here.

Analysis Workflow

This is a schematic representation of the analysis workflow used in this experiment. For more details (parameters, etc.) use the fully-featured workflow designer.
Colours represent types of data (sequences, genomic regions, features, references and files.
Get a simple, static workflow diagram as PNG | JPEG | PDF | SVG | EPS, or get the complete, detailed version as PNG | JPEG | PDF | SVG | EPS.