Abstract
BackgroundGene expression is controlled over a wide range at the transcript level through complex interplay between DNA and regulatory proteins, resulting in profiles of gene expression that can be represented as normal, graded, and bimodal (switch-like) distributions. We have previously performed genome-scale identification and annotation of genes with switch-like expression at the transcript level in mouse, using large microarray datasets for healthy tissue, in order to study the cellular pathways and regulatory mechanisms involving this class of genes. We showed that a large population of bimodal mouse genes encoding for cell membrane and extracellular matrix proteins is involved in communication pathways. This study expands on previous results by annotating human bimodal genes, investigating their correspondence to bimodality in mouse orthologs and exploring possible regulatory mechanisms that contribute to bimodality in gene expression in human and mouse.ResultsFourteen percent of the human genes on the HGU133A array (1847 out of 13076) were identified as bimodal or switch-like. More than 40% were found to have bimodal mouse orthologs. KEGG pathways enriched for bimodal genes included ECM-receptor interaction, focal adhesion, and tight junction, showing strong similarity to the results obtained in mouse. Tissue-specific modes of expression of bimodal genes among brain, heart, and skeletal muscle were common between human and mouse. Promoter analysis revealed a higher than average number of transcription start sites per gene within the set of bimodal genes. Moreover, the bimodal gene set had differentially methylated histones compared to the set of the remaining genes in the genome.ConclusionThe fact that bimodal genes were enriched within the cell membrane and extracellular environment make these genes as candidates for biomarkers for tissue specificity. The commonality of the important roles bimodal genes play in tissue differentiation in both the human and mouse indicates the potential value of mouse data in providing context for human tissue studies. The regulation motifs enriched in the bimodal gene set (TATA boxes, alternative promoters, methlyation) have known associations with complex diseases, such as cancer, providing further potential for the use of bimodal genes in studying the molecular basis of disease.
Highlights
IntroductionGene expression is controlled over a wide range at the transcript level through complex interplay between DNA and regulatory proteins, resulting in profiles of gene expression that can be represented as normal, graded, and bimodal (switch-like) distributions
Gene expression is controlled over a wide range at the transcript level through complex interplay between DNA and regulatory proteins, resulting in profiles of gene expression that can be represented as normal, graded, and bimodal distributions
Our results indicate that a sizable number of genes with bimodal expression in mouse are bimodal in human, with recurring roles of cell-cell communication and communication with the extracellular environment
Summary
Gene expression is controlled over a wide range at the transcript level through complex interplay between DNA and regulatory proteins, resulting in profiles of gene expression that can be represented as normal, graded, and bimodal (switch-like) distributions. We have previously performed genome-scale identification and annotation of genes with switch-like expression at the transcript level in mouse, using large microarray datasets for healthy tissue, in order to study the cellular pathways and regulatory mechanisms involving this class of genes. We showed that a large population of bimodal mouse genes encoding for cell membrane and extracellular matrix proteins is involved in communication pathways. Our recent work applied an automated high-throughput method to classify genes with bimodal expression profiles within the mouse genome based on microarray experiments performed on healthy tissues using the Affymetrix MGU74Av2 microarray platform [1]. As a natural extension of this work, we have applied the same automated high-throughput method to classify genes with bimodal expression in the human genome and compared the list with human orthologs of mouse bimodal genes. We looked into the transcript-level regulation of bimodal genes using a variety of bioinformatics databases
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