The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network

PLoS One. 2016 May 26;11(5):e0156024. doi: 10.1371/journal.pone.0156024. eCollection 2016.

Abstract

Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet contributes to improving the understanding of normal glomerular function and will be useful for detecting target cytoskeleton molecules of interest that may be involved in glomerular diseases in future studies.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Biomarkers / metabolism*
  • Cytoskeleton / metabolism*
  • Diabetic Nephropathies / chemically induced
  • Diabetic Nephropathies / metabolism*
  • Diabetic Nephropathies / pathology
  • Disease Models, Animal
  • Gene Regulatory Networks*
  • Humans
  • Kidney Diseases / metabolism*
  • Kidney Diseases / pathology
  • Kidney Glomerulus / metabolism*
  • Kidney Glomerulus / pathology
  • Male
  • Protein Interaction Maps
  • Puromycin Aminonucleoside / toxicity
  • Rats
  • Rats, Sprague-Dawley

Substances

  • Biomarkers
  • Puromycin Aminonucleoside

Grants and funding

This work was supported by the following organizations: the National Basic Research Program of China (973 Program, No. 2012CB517700 www.nsfc.gov.cn), the National Nature Science Foundation of China (Nos. 30830105, 81170657, 81225025 and 91229201 www.nsfc.gov.cn), and the Nature Science Foundation of Beijing (No. 7072080 www.bjnsf.org) and the Beijing Key Laboratory of Molecular Diagnosis and Study of Pediatric Genetic Diseases (www.bjkw.gov.cn). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.