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Differential Expression of Long Noncoding RNAs between Sperm Samples from Diabetic and Non-Diabetic Mice
To investigate the potential core reproduction-related genes associated with the development of diabetes, the expression profiles of long noncoding RNA (lncRNA) and messenger RNA (mRNA) in the sperm of diabetic mice were studied. We used microarray analysis to detect the expression of lncRNAs and co...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4847876/ https://www.ncbi.nlm.nih.gov/pubmed/27119337 http://dx.doi.org/10.1371/journal.pone.0154028 |
Sumario: | To investigate the potential core reproduction-related genes associated with the development of diabetes, the expression profiles of long noncoding RNA (lncRNA) and messenger RNA (mRNA) in the sperm of diabetic mice were studied. We used microarray analysis to detect the expression of lncRNAs and coding transcripts in six diabetic and six normal sperm samples, and differentially expressed lncRNAs and mRNAs were identified through Volcano Plot filtering. The function of differentially expressed mRNA was determined by pathway and gene ontology (GO) analysis, and the function of lncRNAs was studied by subgroup analysis and their physical or functional relationships with corresponding mRNAs. A total of 7721 lncRNAs and 6097 mRNAs were found to be differentially expressed between the diabetic and normal sperm groups. The diabetic sperm exhibited aberrant expression profiles for lncRNAs and mRNAs, and GO and pathway analyses showed that the functions of differentially expressed mRNAs were closely related with many processes involved in the development of diabetes. Furthermore, potential core genes that might play important roles in the pathogenesis of diabetes-related low fertility were revealed by lncRNA- and mRNA-interaction studies, as well as coding-noncoding gene co-expression analysis based on the microarray expression profiles. |
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