Integrated in vivo multiomics analysis identifies p21-activated kinase signaling as a driver of colitis
Source: NCBI BioProject (ID PRJNA378174)

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Project name: Mus musculus
Description: Background and aims: To gain insight into the pathogenesis of chronic colonic inflammation (colitis), we performed a multi-omic analysis that integrates RNA microarray, total protein mass spectrometry, and phospho-protein measurements from a mouse model. We used this multi-dimensional dataset to track information flow from RNA to protein to phospho-protein and to ascertain which facets of inflammation are described by each data stream. Using trans-omic co-expression network analysis, we find that there are distinct modes of regulation present in the conserved and divergent correlation structures of the three data streams. As a result, each data stream provides a unique viewpoint on the molecular pathogenesis of colitis. Nevertheless, multiple independent computational analyses identified increased signaling through p21-activated kinase (Pak) during colitis, and chemical inhibition of Pak1/2 suppressed inflammation in mice. These studies provide a comprehensive view of the state of signaling in the context of colitis and identify Pak as a therapeutic target.We used microarrays to examine changes in global gene expression patterns associated with chronic inflammation induced by the T cell transfer model of Inflammatory Bowel Disease.Overall design: Rag1 knockout mice were injected with inflammation-inducing Naïve T cells or, as a negative control, regulatory T cells. Animals were sacrificed when they showed signs of severe inflammation (weight loss, diarrhea). Colons were opened longitudinally and a thin strip of tissue was excised and snap frozen from the cecum to the rectum. This experiment includes data from 5 control animals and 3 severely inflamed animals.
Data type: Transcriptome or Gene expression
Sample scope: Multiisolate
Relevance: ModelOrganism
Organization: Douglas Lauffenburger, Bioengineering, Massachusetts Institute of Technology
Literatures
  1. PMID: 29487189
Last updated: 2017-03-06