Dataset Information

Integration of omics studies indicates that species-dependent molecular mechanisms govern male fertility.

ABSTRACT:

Background

Comparative and comprehensive omics studies have recently been conducted to provide a comprehensive understanding of the biological mechanisms underlying infertility. However, because these huge omics datasets often contain irrelevant information, editing strategies for summarizing and filtering the data are necessary prerequisite steps for identifying biomarkers of male fertility. Here, we attempted to integrate omics data from spermatozoa with normal and below-normal fertility from boars and bulls, including transcriptomic, proteomic, and metabolomic data. Pathway enrichment analysis was conducted and visualized using g:Profiler, Cytoscape, EnrichmentMap, and AutoAnnotation to determine fertility-related biological functions according to species.

Results

In particular, gamete production and protein biogenesis-associated pathways were enriched in bull spermatozoa with below-normal fertility, whereas mitochondrial-associated metabolic pathways were enriched in boar spermatozoa with normal fertility. These results indicate that below-normal fertility may be determined by aberrant regulation of protein synthesis during spermatogenesis, and the modulation of reactive oxygen species generation to maintain capacitation and the acrosome reaction governs boar sperm fertility.

Conclusion

Overall, this approach demonstrated that distinct molecular pathways drive sperm fertility in mammals in a species-dependent manner. Moreover, we anticipate that searching for species-specific signaling pathways may aid in the discovery of fertility-related biomarkers within large omics datasets.

SUBMITTER: Park YJ

PROVIDER: S-EPMC9979430 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Integration of omics studies indicates that species-dependent molecular mechanisms govern male fertility.

Park Yoo-Jin YJ Pang Won-Ki WK Pang Myung-Geol MG

Journal of animal science and biotechnology 20230302 1

<h4>Background</h4>Comparative and comprehensive omics studies have recently been conducted to provide a comprehensive understanding of the biological mechanisms underlying infertility. However, because these huge omics datasets often contain irrelevant information, editing strategies for summarizing and filtering the data are necessary prerequisite steps for identifying biomarkers of male fertility. Here, we attempted to integrate omics data from spermatozoa with normal and below-normal fertili ...[more]

PMID: 36859388

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Project description:Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify infertile bulls, no tools are currently available to enable precise predictions and prevent the commercialization of subfertile semen. Because male fertility is a multifactorial phenotype that is dependent on genetic, epigenetic, physiological and environmental factors, we hypothesized that an integrative analysis might help to refine our knowledge and understanding of bull fertility. We combined -omics data (genotypes, sperm DNA methylation at CpGs and sperm small non-coding RNAs) and semen parameters measured on a large cohort of 98 Montbéliarde bulls with contrasting fertility levels. Multiple Factor Analysis was conducted to study the links between the datasets and fertility. Four methodologies were then considered to identify the features linked to bull fertility variation: Logistic Lasso, Random Forest, Gradient Boosting and Neural Networks. Finally, the features selected by these methods were annotated in terms of genes, to conduct functional enrichment analyses. The less relevant features in -omics data were filtered out, and MFA was run on the remaining 12,006 features, including the 11 semen parameters and a balanced proportion of each type of-omics data. The results showed that unlike the semen parameters studied the-omics datasets were related to fertility. Biomarkers related to bull fertility were selected using the four methodologies mentioned above. The most contributory CpGs, SNPs and miRNAs targeted genes were all found to be involved in development. Interestingly, fragments derived from ribosomal RNAs were overrepresented among the selected features, suggesting roles in male fertility. These markers could be used in the future to identify subfertile bulls in order to increase the global efficiency of the breeding sector.

Dataset Information

Integration of omics studies indicates that species-dependent molecular mechanisms govern male fertility.

Background

Results

Conclusion

Publications

Integration of omics studies indicates that species-dependent molecular mechanisms govern male fertility.

Similar Datasets

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