title: Switch-like genes populate cell communication pathways and are enriched for extracellular proteins.
abstract: BACKGROUND
  Recent studies have placed gene expression in the context of distribution profiles including housekeeping, graded, and bimodal (switch-like). Single-gene studies have shown bimodal expression results from healthy cell signaling and complex diseases such as cancer, however developing a comprehensive list of human bimodal genes has remained a major challenge due to inherent noise in human microarray data. This study presents a two-component mixture analysis of mouse gene expression data for genes on the Affymetrix MG-U74Av2 array for the detection and annotation of switch-like genes. Two-component normal mixtures were fit to the data to identify bimodal genes and their potential roles in cell signaling and disease progression.
  
  
  RESULTS
  Seventeen percent of the genes on the MG-U74Av2 array (1519 out of 9091) were identified as bimodal or switch-like. KEGG pathways significantly enriched for bimodal genes included ECM-receptor interaction, cell communication, and focal adhesion. Similarly, the GO biological process "cell adhesion" and cellular component "extracellular matrix" were significantly enriched. Switch-like genes were found to be associated with such diseases as congestive heart failure, Alzheimer's disease, arteriosclerosis, breast neoplasms, hypertension, myocardial infarction, obesity, rheumatoid arthritis, and type I and type II diabetes. In diabetes alone, over two hundred bimodal genes were in a different mode of expression compared to normal tissue.
  
  
  CONCLUSIONS
  This research identified and annotated bimodal or switch-like genes in the mouse genome using a large collection of microarray data. Genes with bimodal expression were enriched within the cell membrane and extracellular environment. Hundreds of bimodal genes demonstrated alternate modes of expression in diabetic muscle, pancreas, liver, heart, and adipose tissue. Bimodal genes comprise a candidate set of biomarkers for a large number of disease states because their expressions are tightly regulated at the transcription level.
authors.affiliation: Center for Integrated Bioinformatics, School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA. ame28@drexel.edu
authors.forename: Adam
authors.initials: A
authors.lastname: Ertel
authors.forename: Aydin
authors.initials: A
authors.lastname: Tozeren
mesh_terms: D000818:Animals; D002450:Cell Communication / Q000235:genetics*; D005109:Extracellular Matrix / Q000378:metabolism; D016326:Extracellular Matrix Proteins / Q000235:genetics / Q000378:metabolism*; D005812:Genes, Switch; D016678:Genome; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D015398:Signal Transduction / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D016326:Extracellular Matrix Proteins; D014157:Transcription Factors