Accelerating Onset of Puberty Through Modification of Early Life Nutrition Induces Modest but Persistent Changes in Bull Sperm DNA Methylation Profiles Post-puberty

Abstract

In humans and model species, alterations of sperm DNA methylation patterns have been reported in cases of spermatogenesis defects, male infertility and exposure to toxins or nutritional challenges, suggesting that a memory of environmental or physiological changes is recorded in the sperm methylome. The objective of this study was to ascertain if early life plane of nutrition could have a latent effect on DNA methylation patterns in sperm produced post-puberty. Holstein-Friesian calves were assigned to either a high (H) or moderate (M) plane of nutrition for the first 24 weeks of age, then reassigned to the M diet until puberty, resulting in HM and MM groups. Sperm DNA methylation patterns from contrasted subgroups of bulls in the HM (ejaculates recovered at 15 months of age; n = 9) and in the MM (15 and 16 months of age; n = 7 and 9, respectively) were obtained using Reduced Representation Bisulfite Sequencing. Both 15 and 16 months were selected in the MM treatment as these bulls reached puberty approximately 1 month after the HM bulls. Hierarchical clustering demonstrated that inter-individual variability unrelated to diet or age dominated DNA methylation profiles. While the comparison between 15 and 16 months of age revealed almost no change, 580 differentially methylated CpGs (DMCs) were identified between the HM and MM groups. Differentially methylated CpGs were mostly hypermethylated in the HM group, and enriched in endogenous retrotransposons, introns, intergenic regions, and shores and shelves of CpG islands. Furthermore, genes involved in spermatogenesis, Sertoli cell function, and the hypothalamic-pituitary-gonadal axis were targeted by differential methylation when HM and MM groups were compared at 15 months of age, reflecting the earlier timing of puberty onset in the HM bulls. In contrast, the genes still differentially methylated in MM bulls at 16 months of age were enriched for ATP-binding molecular function, suggesting that changes to the sperm methylome could persist even after the HM and MM bulls reached a similar level of sexual maturity. Together, results demonstrate that enhanced plane of nutrition in pre-pubertal calves associated with advanced puberty induced modest but persistent changes in sperm DNA methylation profiles after puberty.

Keywords: Bovine; DNA methylation; nutritional programming; puberty; spermatozoa.

FIGURE 1

Diet and age do not induce genome-wide modifications of the sperm methylome. Correlation clustering was run on the methylation percentages calculated at CpGs covered more than 10 reads in at least four samples per group. Samples belonging to the three groups are shown in different colors (HM15, red; MM15, yellow; and MM16, green). (A) Correlation clustering ran on the whole dataset. Unpaired samples, mainly from the HM group, cluster apart (right part). (B,C) Correlation clustering run on unpaired samples from HM15 and MM15 groups (B) and HM15 and MM16 groups (C). Taken together, the results demonstrate that the inter-bull variability vs. intra-bull similarity appear to have a greater effect on global bull sperm methylome than any latent effect of dietary regimen per se.

FIGURE 2

Diet-related differentially methylated CpGs (DMCs) identified in sperm from pairwise comparisons between diet groups. (A) Venn diagram showing the 491 and 164 DMCs identified between HM15 vs. MM15 and HM15 vs. MM16 groups, respectively. A total of 580 unique DMCs were obtained. Among those DMCs, 75 (12.9%) are in the middle intersection, representing those that are common between both comparisons. DMCs were associated with 227 unique genes, 44 being common between both comparisons. (B) Volcano plots showing the variations of raw p-values (y-axis, -log10 scale) according to the differences in DNA methylation (x-axis, HM – MM differences) for the 580 unique DMCs, in the HM15 vs. MM15 (left) and the HM15 vs. MM16 (right) comparisons. The DMCs that passed the significance thresholds (adjusted p-value < 0.1 and difference in methylation ≥ 10%) are indicated in red for each comparison. Most of the 580 unique DMCs, including those that did not reach significance in the HM15 vs. MM16 comparison, showed a similar behavior in both comparison, with a difference in DNA methylation above 10% (indicated by the vertical dashed lines) and a distribution shifted towards positive values indicating a higher methylation in the HM group.

FIGURE 3

Annotation of the diet-related differentially methylated CpGs (DMCs). (A) Annotation of DMCs relative to gene features, CpG density and repetitive elements of the genome. Upstream gene regions (resp. downstream gene regions) represent DMCs located 10 kb upstream of the transcription start site (resp. 10 kb downstream of the transcription termination site). Red and green arrows represent a more than 25% decrease or increase in the relative abundance of genomic features compared to the background, respectively. An equal sign represents that the abundance of the element is less than 25% different to the background. (B) Results of enrichment analysis using the DAVID functional annotation tool. The analysis has been performed using the genes identified in the intersection between HM15 vs. MM15 and HM15 vs. MM16 comparisons (Figure 2A, 44 genes). The list of all genes covered by RRBS was used as the background (18,020 genes). The cluster corresponding to ATP binding molecular function showed an EASE enrichment score of 1.36, considering that scores above 1.3 are significant (Huang et al., 2009). Terms of gene ontology or Uniprot keywords enriched among the DMCs are indicated, as are the genes present in each category. The green color on the heatmap represents a correspondence between a gene and a term.

FIGURE 4

Validation of four diet-related differentially methylated regions (DMRs) by bisulfite-pyrosequencing. (A) The average methylation rate measured by RRBS is calculated for the differentially methylated CpGs (DMCs) included in the DMR and plotted against the average methylation rate measured by pyrosequencing. Each dot represents one sample from the HM15 (n = 7, in red), MM15 (n = 7, in yellow), and MM16 (n = 9, in green) groups. The least squares lines of best fit and Spearman’s rank correlation coefficients rho are indicated. All correlations were highly significant (Spearman’s rank correlation test; p < 0.0001). (B) Methylation percentages of individual CpGs assayed by pyrosequencing in HM15 (n = 7, in red), HM16 (n = 7, in blue), MM15 (n = 7, in yellow), and MM16 (n = 9, in green) groups. For each box, the middle line indicates the median and the edges the 25th/75th percentiles. CpGs are numbered according to their 5′–3′ position along the genome. CpGs found as DMCs by the RRBS analysis are shown in red. Asterisks indicate CpGs at which the methylation percentage measured by pyrosequencing is significantly different between HM and MM groups (p < 0.05, permutation test corrected for the stratification according to the age).