Abstract
BackgroundRecent 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.ResultsSeventeen 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.ConclusionThis 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.
Highlights
Recent studies have placed gene expression in the context of distribution profiles including housekeeping, graded, and bimodal
Genes with bimodal expression were enriched within the cell membrane and extracellular environment
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
Summary
Recent studies have placed gene expression in the context of distribution profiles including housekeeping, graded, and bimodal (switch-like). Existing biological annotation is a useful supplement to machine learning techniques used for determining regulatory connections [10,11] These techniques are sensitive to differential expression as well as small concerted changes in levels of gene expression, yet they may not adequately address changes with respect to the global behavior of gene expression – where transcript levels may either be tightly regulated within a narrow range, or fluctuate widely as a function of environmental cues or tissue specialization. Other approaches have used a numeric value representing the degree of tissue specificity within one tissue or tissue subset versus all others [17,18] These studies are typically performed on a small number of samples within each tissue type; they effectively describe genes with large variation between distinct tissues
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