Sperm DNA Methylation Epimutation Biomarkers for Male Infertility and FSH Therapeutic Responsiveness

Abstract

Male factor infertility is increasing and recognized as playing a key role in reproductive health and disease. The current primary diagnostic approach is to assess sperm quality associated with reduced sperm number and motility, which has been historically of limited success in separating fertile from infertile males. The current study was designed to develop a molecular analysis to identify male idiopathic infertility using genome wide alterations in sperm DNA methylation. A signature of differential DNA methylation regions (DMRs) was identified to be associated with male idiopathic infertility patients. A promising therapeutic treatment of male infertility is the use of follicle stimulating hormone (FSH) analogs which improved sperm numbers and motility in a sub-population of infertility patients. The current study also identified genome-wide DMRs that were associated with the patients that were responsive to FSH therapy versus those that were non-responsive. This novel use of epigenetic biomarkers to identify responsive versus non-responsive patient populations is anticipated to dramatically improve clinical trials and facilitate therapeutic treatment of male infertility patients. The use of epigenetic biomarkers for disease and therapeutic responsiveness is anticipated to be applicable for other medical conditions.

Figure 1

Infertility patients’ semen and sperm parameters upon recruitment (Pre-Conc 0) prior to FSH therapeutic treatment (Pre-Conc 1) and after 3 months of treatment (Post-Conc 2) for individual patients listed (color designated). Sample analyses for all patients are presented in (a) Semen concentration, (b) Percent motility sperm, and (c) Total motility count (TMC) (semen volume x concentration x motility). Infertility patients responding with >2-fold change following treatment are presented, (d) Semen concentration, (e) Percent motility sperm, and (f) TMC. The y-axis is magnitude of change between collections.

Figure 2

DMR identifications. (a) Fertility vs Infertility Sperm DMR Analysis. The number of DMRs found using different p-value cutoff thresholds. The all window column shows all DMRs. The multiple window column shows the number of DMRs containing at least two adjacent significant windows and the number of DMRs with each specific number of significant windows at a p-value threshold of 1e-05. (b) Infertility patient responder vs non-responder sperm DMRs. The number of DMRs found using different p-value cutoff thresholds. The all window column shows all DMRs. The multiple window column shows the number of DMRs containing at least two significant windows. The number of DMRs with each specific number of significant windows at a p-value threshold of 1e-05. (c) Venn diagram DMR signature for fertile vs infertile p < 1e-05 and DMR signature responder vs. non-responder at p < 1e-05 and p < 0.001. (d) DMR associated gene categories.

Figure 3

DMR genomic characteristics. (a) Chromosomal Locations of Fertility vs Infertility DMR Analysis. The DMR locations on the individual chromosomes. All DMRs at a p-value threshold of p < 1e-05 are shown with the red arrowhead and clusters of DMRs with the black boxes. (b) Responder DMR Signature Chromosomal Locations. The DMR locations (red arrowhead) and clusters of DMRs (black box) on the individual chromosomes. All DMRs at a p-value threshold of p < 1e-05 are shown. (c) DMR CpG density in the Fertility vs Infertility DMRs. The number of DMRs at different CpG densities. All DMRs at a p-value threshold of p < 1e-05 are shown. (d) The Responder signature DMR CpG density (number per 100 bp). The number of DMRs at different CpG densities are presented. All DMRs at a p-value threshold of 1e-05 are shown. (e) Fertility vs Infertility DMR lengths in kilobases. All DMRs at a p-value threshold of 1e-05 are shown. (f) The Responder signature DMRs size in kilobases. All DMRs at a p-value threshold of 1e-05 are shown.

Figure 4

Principal component analysis. (a) Fertility vs Infertility DMR Principal Component Analysis for Individuals. The samples are plotted by the first three principal components. The underlying data is the RPKM read depth for the DMRs. The color code is listed for the fertile and infertile patients. (b) Fertility vs Infertility DMR Principal Component Analysis for Individuals. The samples are plotted by the first three principal components. The underlying data is the RPKM read depth for the DMRs. The color code is listed for the fertile and infertile patients. Selection failure correlations for fertile and infertile patients not used to generate the epigenetic signature. PCA Infertile vs Fertile p < 10⁻⁵. (c) Responder and non-responder PCA analysis for DMRs at p < 1e-05. The first three principal components used and samples color code index indicated. The underlying data is the RPKM read depth for all DMRs. (d) The number of DMR for fertility versus infertility comparison for all permutation analyses. The vertical red line shows the number of DMR found in the original analysis. All DMRs are defined using an edgeR p-value threshold of p < 1e-05.