Objective: To study the genome wide alterations in sperm DNA methylation in male partners of idiopathic recurrent pregnancy loss (iRPL) cases and note regions as potential diagnostic markers.
Design: Case-control study and methylome analysis of human sperm.
Setting: Obstetrics and Gynaecology clinics.
Patient(s): Control group consists of apparently healthy fertile men having fathered a child within the last 2 years (n = 39); and case group consists of male partners of iRPL cases having ≥2 consecutive 1st trimester pregnancy losses (n = 47).
Intervention(s): None.
Main Outcome Measure(s): Sperm DNA samples of controls and cases were selected for whole genome bisulfite sequencing analysis based on the previously set thresholds of global methylation levels and methylation levels of imprinted genes (KvDMR and ZAC). Whole genome bisulfite sequencing of selected sperm genomic DNA was performed to identify differentially methylated CpG sites of iRPL cases compared with fertile controls. Pathway analysis of all the differentially methylated genes was done by Database for Annotation, Visualization, and Integrated Discovery annotation tool and Kyoto Encyclopedia of Genes and Genomes tool. Differentially methylated CpGs within genes relevant to embryo and placenta development were selected to further validate their methylation levels in study population by pyrosequencing.
Result(s): A total of 9497 differentially methylated CpGs with highest enrichment in intronic regions were obtained. In addition, 5352 differentially methylated regions and 2087 differentially methylated genes were noted. Signaling pathways involved in development were enriched on pathway analysis. Select CpGs within genes PPARG, KCNQ1, SETD2, and MAP3K4 showed distinct hypomethylated subpopulations within iRPL study population.
Conclusion(s): Our study highlights the altered methylation landscape of iRPL sperm, and their possible implications in pathways of embryo and placental development. The CpG sites that are hypomethylated specifically in sperm of iRPL subpopulation can be further assessed as predictive biomarkers.
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