PLoS ONEDevelopmental BiologyPeer-reviewed journal article
Control of human testis-specific gene expression
Abstract
BackgroundAs a result of decades of effort by many investigators we now have an advanced level of understanding about several molecular systems involved in the control of gene expression. Examples include CpG islands, promoters, mRNA splicing and epigenetic signals. It is less clear, however, how such systems work together to integrate the functions of a living organism. Here I describe the results of a study to test the idea that a contribution might be made by focusing on genes specifically expressed in a particular tissue, the human testis.\n\nExperimental DesignA database of 239 testis-specific genes was accumulated and each was examined for the presence of features relevant to control of gene expression. These include: (1) the presence of a promoter, (2) the presence of a CpG island (CGI) within the promoter, (3) the presence in the promoter of a transcription factor binding site near the transcription start site, (4) the level of gene expression, and (5) the above features in genes of cell types such as spermatocyte and spermatid that differ in their extent of differentiation.\n\nResultsOf the 107 database genes with an annotated promoter, 56 were found to have one or more transcription factor binding sites near the transcription start site. Three of the binding sites observed, Pax-5, AP-2A and GR, stand out in abundance suggesting they may be involved in testis-specific gene expression. Compared to less differentiated testis-specific cells, genes of more differentiated cells were found to be (1) more likely to lack a CGI, (2) more likely to lack introns and (3) higher in expression level. The results suggest genes of more differentiated cells have a reduced need for CGI-based regulatory repression, reduced usage of gene splicing and a smaller set of expressed proteins
- organism: human
- assay: DNA methylation
- assay: CUT&Tag
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell type annotation
- approach: statistical model
- Citations
- 7
- FWCI
- 0.33
- Field percentile
- top 47%
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