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[Mining the specifically expressed genes in sperms based on the bioinformatics methods]. | LitMetric

[Mining the specifically expressed genes in sperms based on the bioinformatics methods].

Nan Fang Yi Ke Da Xue Xue Bao

Institute of Molecular Biology, Department of Stomatology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.

Published: February 2009

Objective: To analyze the specifically expressed genes in sperms for better understanding of the molecular characteristics of sperms.

Methods: The hybridization data the genes in the sperms, oocytes and 10 normal tissues were retrieved from the GEO database to identify the genes expressed specifically in sperms and the patterns of their regulation using such bioinformatic tools as GATHER, PANTHER and DAVID.

Results And Conclusions: Comparison of the spermatozoal gene expression profiles with those of the normal tissues identified 8998 differentially expressed probes, among which 25 genes were up-regulated by over 200 folds in the sperms. Comparison of the gene expression profiles between the oocytes and normal tissues resulted in the identification of 8981 differentially expressed probes. Of the 1709 up-regulated genes in the sperm with a ratio>5, 1218 genes showed similar expressions in the oocytes and the normal tissues, and 129 were up-regulated and 362 down-regulated in the oocytes. The 362 genes up-regulated in the sperms but down-regulated in the oocytes were involved mainly in protein modification and metabolism and nucleic acid metabolism, but very few participated in the intracellular signaling pathways. Numerous transcriptional factors containing the KRAB domain and receptor- independent serine/threonine kinase were specifically overexpressed in sperms, and the a very high proportion of the genes specifically overexpressed in the sperms coincided with the overexpressed genes in the neural stem cells and embryonic stem cells. The genes involved in the glycolysis were down-regulated in the sperms. These findings in the genes specifically expressed in the sperms by data mining using bioinformatic methods may provide better insight into the molecular characteristics of the sperms.

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