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Asthenozoospermia, in which sperm motility is affected, is one of the primary causes of male infertility. However, the exact mechanism responsible for the defective motility remains unknown. It is important to identify the precise proteins or pathways involved in sperm motility. The present study analyzed five asthenozoospermic sperm samples and five healthy controls using TMT-based quantitative method and identified 152 differentially expressed proteins, with 84 upregulated and 68 downregulated in asthenozoospermia. Four proteins (GPI, MDH1, PGAM1 and PGAM2) were found in several over-represented energy metabolism pathways using bioinformatics analysis. Glucose-6-phosphate isomerase (GPI), a rate-limiting enzyme converting glucose-6-phosphate to fructose-6-phosphate, was found to be significantly decreased in asthenozoospermia by Western blotting and ELISA on an extended sample size. Furthermore, substitution of glucose with fructose-6-phosphate significantly promoted asthenozoospermic sperm motility in vitro. Taken together, our results suggest that the poor motility of sperm in asthenozoospermia may partly result from defects in GPI-associated energy metabolism. SIGNIFICANCE: To identify the key proteins or pathways involved in sperm motility, the accurate TMT-based quantitative method was applied to characterize protein profiles of asthenozoospermic sperm. GPI, an enzyme involved in energy metabolism, was found to be differentially abundant, and validated by extended sample analysis. The supplement of the product of GPI, fructose-6-phosphate, could significantly improve sperm motility. Our study could provide new insights into the molecular basis of sperm motility and the improvement of motility in asthenozoospermia.
Pubmed ID: 31394311
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Gene expression analysis software using Java.
View all literature mentionsToppGene Suite is a one-stop portal for gene list enrichment analysis and candidate gene prioritization based on functional annotations and protein interactions network. ToppGene Suite is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis.
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