Sperm histone H3 lysine 4 tri-methylation serves as a metabolic sensor of paternal obesity and is associated with the inheritance of metabolic dysfunction

Abstract

Objective: Parental environmental exposures can strongly influence descendant risks for adult disease. How paternal obesity changes the sperm chromatin leading to the acquisition of metabolic disease in offspring remains controversial and ill-defined. The objective of this study was to assess (1) whether obesity induced by a high-fat diet alters sperm histone methylation; (2) whether paternal obesity can induce metabolic disturbances across generations; (3) whether there could be cumulative damage to the sperm epigenome leading to enhanced metabolic dysfunction in descendants; and (4) whether obesity-sensitive regions associate with embryonic epigenetic and transcriptomic profiles. Using a genetic mouse model of epigenetic inheritance, we investigated the role of histone H3 lysine 4 methylation (H3K4me3) in the paternal transmission of metabolic dysfunction. This transgenic mouse overexpresses the histone demethylase enzyme KDM1A in the developing germline and has an altered sperm epigenome at the level of histone H3K4 methylation. We hypothesized that challenging transgenic sires with a high-fat diet would further erode the sperm epigenome and lead to enhanced metabolic disturbances in the next generations.

Methods: To assess whether paternal obesity can have inter- or transgenerational impacts, and if so to identify potential mechanisms of this non-genetic inheritance, we used wild-type C57BL/6NCrl and transgenic males with a pre-existing altered sperm epigenome. To induce obesity, sires were fed either a control or high-fat diet (10% or 60% kcal fat, respectively) for 10-12 weeks, then bred to wild-type C57BL/6NCrl females fed a regular diet. F₁ and F₂ descendants were characterized for metabolic phenotypes by examining the effects of paternal obesity by sex, on body weight, fat mass distribution, the liver transcriptome, intraperitoneal glucose, and insulin tolerance tests. To determine whether obesity altered the F₀ sperm chromatin, native chromatin immunoprecipitation-sequencing targeting H3K4me3 was performed. To gain insight into mechanisms of paternal transmission, we compared our sperm H3K4me3 profiles with embryonic and placental chromatin states, histone modification, and gene expression profiles.

Results: Obesity-induced alterations in H3K4me3 occurred in genes implicated in metabolic, inflammatory, and developmental processes. These processes were associated with offspring metabolic dysfunction and corresponded to genes enriched for H3K4me3 in embryos and overlapped embryonic and placenta gene expression profiles. Transgenerational susceptibility to metabolic disease was only observed when obese F₀ had a pre-existing modified sperm epigenome. This coincided with increased H3K4me3 alterations in sperm and more severe phenotypes affecting their offspring.

Conclusions: Our data suggest sperm H3K4me3 might serve as a metabolic sensor that connects paternal diet with offspring phenotypes via the placenta. This non-DNA-based knowledge of inheritance has the potential to improve our understanding of how environment shapes heritability and may lead to novel routes for the prevention of disease. This study highlights the need to further study the connection between the sperm epigenome, placental development, and children's health.

Summary sentence: Paternal obesity impacts sperm H3K4me3 and is associated with placenta, embryonic and metabolic outcomes in descendants.

Keywords: Chromatin; Epigenetic inheritance; Metabolism; Obesity; Sperm.

Graphical abstract

Figure 1

Paternal obesity induces transgenerational metabolic phenotypes in a sex-specific manner that are enhanced in KDM1A descendants. A) Experimental mouse model depicting breeding scheme and generations studied. Male C57BL/6NCrl (WT) and KDM1A^+/- transgenics (TG, C57BL/6NCrl) were fed either a control diet (CON) or high-fat diet (HFD) from weaning for 10-12 weeks, then mated to 8-week-old C57BL/6NCrl females fed a regular chow diet (CD). Animals studied per experimental group: F₀ (n=15-25 males), F₁ (n=28-49 per sex) and F₂ (n=8-21 per sex). Created with BioRender.com. B) Experimental timeline for metabolic testing and downstream experiments performed for each generation (F_0-2). Metabolic profiles were measured after the diet intervention at 15 weeks of age and included: baseline blood glucose, and intraperitoneal glucose and insulin tolerance tests (ipGTT and ipITT, respectively). Visceral adipose depots were weighed (mWAT: mesenteric white adipose tissue and gWAT: gonadal white adipose tissue) and the left lateral lobe of the liver used for RNA-sequencing (RNA-seq). Sperm from cauda epididymides were used for chromatin immunoprecipitation followed by sequencing (ChIP-seq), targeting histone H3 lysine 4 tri-methylation (H3K4me3). Created with BioRender.com. C) Age-matched male mice fed either a control (left) or a high-fat diet (right) for 12 weeks. D) Glucose tolerance test. Blood glucose levels before and after (shaded in grey) an intraperitoneal glucose injection, after overnight fasting (15 ±1 hour) at 4 months of age in F₀ males (i), F₁ males (ii), F₂ males (iii), F₁ females (iv) and F₂ females (v). E) Insulin tolerance test. Blood glucose levels before and after (shaded in grey) an intraperitoneal insulin injection, after a 6-hour (±1 hour) fasting at 4 months of age in F₀ males (i), F₁ males (ii), F₂ males (iii), F₁ females (iv) and F₂ females (v). Results are shown as mean ± SEM. Significance for main effects of diet, genotype, time, and for diet-genotype interactions are shown above each graph. NS, not significant (P > 0.05). Significance for pairwise comparisons are shown as the following: ∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, ∗∗∗∗P<0.0001 (in blue; WT CON vs WT HFD, in green; TG CON vs TG HFD) and ^#P<0.05, ##P<0.01 (WT HFD vs TG HFD).

Figure 2

Paternal obesity is associated with altered gene expression in the livers of the F₀-F_2. A-J) Heatmaps of normalized expression values scaled by row (z-score) for transcripts that code for differentially expressed hepatic genes (Lancaster p-value<0.05) for each comparison assessed across sex and generation. Individual transcripts (rows) are ordered by k-means clustering and samples (columns) are arranged by hierarchical clustering, using complete-linkage clustering based on Euclidean distance. F₀ WT CON vs WT HFD males (A), F₀ TG CON vs TG HFD males (B), F₀ WT HFD vs TG HFD males (C), F₁ WT CON vs WT HFD males (D), F₁ TG CON vs TG HFD males (E), F₁ WT HFD vs TG HFD males (F), F₁ WT CON vs WT HFD females (G), F₁ TG CON vs TG HFD females (H), F₁ WT HFD vs TG HFD females (I), and F₂ WT HFD vs TG HFD males (J). i-x) Alluvial plots depicting frequency distributions of significant (colored boxes) and non-significant (grey boxes) genes for each comparison and their overlap across genotype (i-iii), across F₀ and F₁ males (iv-vi), across F₁ males and females (vii-ix) and across F₁ and F₂ males (x). Significance of overlap between differentially expressed genes lists was calculated by Fisher’s exact test. P-values are included for each comparison above the respective alluvial plot.

Figure 3

Obesity-induced hepatic transcriptome disturbances show functional similarities across genotype, sex and generation. A-C) Heatmaps of significant gene ontology (GO) terms clustered by functional similarity, comparing enriched biological functions for each comparison of interest across genotype (A), sex (B) and generation (C). Columns represent enriched GO terms which are ordered by hierarchical clustering based on Wang's semantic similarity distance and ward.D2 aggregation criterion. Each row represents a comparison of interest for which enriched GO terms were annotated based on the list of significant genes. The color gradient depicts the GO term enrichment significance (-log10 p-value). Interactive versions of these figures can be found in Supplemental files 1–3 and the complete lists of significantly enriched GO terms can be found in Tables S5–7.

Figure 4

Genomic location, directionality change and functions of regions with altered H3K4me3 enrichment by obesity. A) Histogram showing frequency distributions of read abundances in 150 bp windows throughout the genome. Windows with an abundance below log2(4) fold over background bins of 2,000 bp were filtered out as indicated by the vertical red line. Enriched regions less than 100 bp apart were merged for a maximum width of 5,000 bp, conferring a total of 30,745 merged enriched regions. Reads were counted in merged enriched regions and normalized counts were used for downstream analyses. (see Material and Methods) B–C) Principal component analysis on normalized counts at merged enriched regions comparing WT CON vs WT HFD (B) and TG CON vs TG HFD (C). The top 5% regions contributing to separation of samples along Principal Component 1 (in B; PC1; x axis) or PC2 (in C; y axis) were selected. The PERMANOVA p-values indicating significance associated with dietary treatment are included under each PCA plot. D) Heatmaps of log2 normalized counts of deH3K4me3 regions in sperm with increased enrichment in WT HFD (i; n = 1,323), decreased enrichment in WT HFD (ii; n = 215), increased enrichment in TG HFD (iii; n = 1,067) and decreased enrichment in TG HFD (iv; n = 471) in each group. Samples (columns) and regions (rows) are arranged by hierarchical clustering using complete-linkage clustering based on Euclidean distance. Colored boxes indicate sample groups (light blue = WT CON, dark blue = WT HFD, light green = TG CON, dark green = TG HFD). E-G). Venn diagrams showing the overlap of deH3K4me3 in sperm of WT HFD (blue) and in TG HFD (green), for all detected regions (E), those gaining H3K4me3 (F) and those losing H3K4me3 (G). H) Barplots showing the distribution of altered regions based on the distance from the TSS of the nearest gene, for regions with increased enrichment in WT HFD (i; n = 1,323), decreased enrichment in WT HFD (ii; n = 215), increased enrichment in TG HFD (iii; n = 1,067), and decreased enrichment in TG HFD (iv; n = 471). The color gradient represents the distance of the regions to TSS in kilobase. I) Gene ontology analysis of diet-induced deH3K4me3 regions at promoters with increased enrichment in WT HFD (i; n = 381), decreased enrichment in WT HFD (ii; n = 34), increased enrichment in TG HFD (iii; n = 230) and decreased enrichment in TG HFD (iv; n = 150). Barplots show 8 selected significant GO terms with their respective -log2(p-value). Tables S9–12 include the complete lists of significantly enriched GO terms.

Figure 5

Additive effects of KDM1A overexpression and diet-induced obesity in the sperm epigenome at the level of H3K4me3. A) Principal component analysis on normalized counts at merged enriched regions comparing WT CON vs TG HFD. The top 5% regions contributing to separation of samples along Principal Component 2 (PC2; y axis) were selected. The PERMANOVA p-value under the plot indicates significance. B–C) Profile plots of RPKM H3K4me3 counts +/− 1 kilobase around the center of regions with increased H3K4me3 (B) and +/− 2.5 kilobase around the center of regions with decreased H3K4me3 enrichment in TG HFD (C). D-E) Line plots showing the median of normalized sperm H3K4me3 counts for each experimental group at regions showing a significant trend (n = 264, adjusted p-value<0.2) with a linear increase in H3K4me3 enrichment (D; n = 9) or a linear decrease in H3K4me3 enrichment (E; n = 255) from WT CON, WT HFD, TG CON to TG HFD groups. F) Gene ontology analysis on the regions associated with a significant linear trend at promoters (n = 104). Barplots show 8 selected significant GO terms with their respective -log2(p-value). Table S13 includes the complete list of significantly enriched GO terms.

Figure 6

Sperm H3K4me3 regions sensitive to obesity occur at genes with an open chromatin state and expressed in the pre-implantation embryo. A) Heatmaps of RPKM counts signal +/− 10 kilobase around the center of regions enriched with H3K4me3 in sperm (i; n = 30,745) and regions with obesity-induced deH3K4me3 in sperm (ii; n = 2,836) for H3K4me3 enrichment levels in sperm (this study), 2-cell embryo (Liu et al., 2016), 2-cell embryo on the paternal allele and MII oocyte (Zhang et al., 2016), and for chromatin accessibility signal in sperm (Jung et al., 2017), 4-cell embryo and morula embryo (Liu et al., 2019). B) Scatterplots showing H3K4me3 enrichment in sperm (x axis; log2 counts + 10), chromatin accessibility signal (y axis; log2 counts + 10; (Jung et al., 2017)) and gene expression levels (color gradient; log2 FPKM + 10; (Liu et al., 2019)) in 4-cell (i,ii,v,vi) or in morula (iii,iv,vii,viii) embryos, at either all genes with promoters enriched with H3K4me3 in sperm (i-iv) or at diet-sensitive genes (v-viii). The top row of scatterplots includes lowly-expressed genes (bottom 50%) in 4-cell (i and v) or morula (iii or vii) embryos. The bottom row of scatterplots includes highly-expressed genes (top 50%) in 4-cell (ii and iv) or morula (vi and viii) embryos. Pearson's correlation coefficients and their associated p-values are indicated above each scatterplot, comparing H3K4me3 enrichment in sperm versus H3K4me3 enrichment in 4-cell or morula embryos. C) Gene ontology analysis of genes expressed in the 4-cell (i) or the morula (ii) embryos, overlapping with diet-sensitive promoters in sperm. Barplots show 8 selected significant GO terms with their respective -log2(p-value). Tables S14–15 include the complete lists of significantly enriched GO terms.

Figure 7

Obesity-induced deH3K4me3 regions overlap with genes marked by H3K4me3 and expressed in the trophectoderm and placenta. A) Heatmaps of RPKM counts signal +/− 5 kilobase around the center of regions enriched with H3K4me3 in sperm (i; n = 30,745) and at regions with diet-induced deH3K4me3 in sperm (n = 2,836) for H3K4me3 enrichment levels in sperm (this study), trophectoderm (TE) (Liu et al., 2016) and placenta (Shen et al., 2012). B) Gene ontology analysis of regions enriched with H3K4me3 in sperm, TE and placenta (top 75% from A i) (i), regions enriched with H3K4me3 in sperm only (bottom 25% from A i) (ii), diet-sensitive regions enriched with H3K4me3 in sperm, TE and placenta (top 75% from A ii) (iii), and diet-sensitive regions enriched with H3K4me3 in sperm only (bottom 25% from A ii) (iv). Barplots show 8 selected significant GO terms with their respective -log2 (p-value). Tables S16-19 include the complete lists of significantly enriched GO terms. C) Scatterplots showing H3K4me3 enrichment at promoters in sperm (x axis; log2 counts + 10), H3K4me3 enrichment (y axis; log2 counts + 10) and gene expression levels (color gradient; log2 FPKM + 10) in the trophectoderm (i,ii,v,vi; (Liu et al., 2016)) or in the placenta (iii,iv,vii,viii; (Shen et al., 2012; Chu et al., 2019)), at either all genes with promoters enriched with H3K4me3 in sperm (i-iv) or at diet-sensitive genes (v-viii). The top row of scatterplots includes lowly-expressed genes (bottom 50%) in trophectoderm (i and v) or placenta (iii or vii). The bottom row includes highly-expressed genes (top 50%) in trophectoderm (ii and iv) or placenta (vi and viii). Pearson's correlation coefficients and associated p-values are indicated above each scatterplot, comparing H3K4me3 enrichment in sperm versus H3K4me3 enrichment in the trophectoderm or placenta. D) Gene ontology analysis of genes expressed in the trophectoderm (i) or the placenta (ii), overlapping with diet-sensitive promoters in sperm. Barplots show 8 selected significant GO terms with their respective -log2(p-value)^.Tables S20–21 include the complete lists of significantly enriched GO terms.

Fig S1

Paternal obesity increases body weight and fat accruement across generations. A) Cumulative energy intake during the dietary treatment. The amount of food consumed weekly per cage was measured and the cumulative caloric intake per mouse was calculated based on the calorie content specific to each diet. B) Growth trajectories of F₀ sires before and during the 12-week diet intervention. C) Total body weight at 4 months of age in F₀ males (i), F₁ males (ii), F₂ males (iii), F₁ females (iv) and F₂ females (v). D) Mesenteric white adipose tissue (mWAT) weight at necropsy in F₀ males (i), F₁ males (ii), F₂ males (iii), F₁ females (iv) and F₂ females (v). E) Gonadal white adipose tissue (gWAT) weight at necropsy in F₀ males (i), F₁ males (ii), F₂ males (iii), F₁ females (iv) and F₂ females (v). Results are shown as mean ± SEM. Significance for main effects of diet, genotype, time, and for diet-genotype interactions are shown above each graph. NS, not significant (P > 0.05). Significance for pairwise comparisons are shown as the following: ∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, ∗∗∗∗P<0.0001 (in blue; WT CON vs WT HFD, in green; TG CON vs TG HFD) and #P<0.05, ##P<0.01, ###P<0.001 (WT HFD vs TG HFD).

Fig S2

Paternal obesity alters metabolic profiles across generations in a sex-specific manner. A) Baseline blood glucose levels after overnight fasting (15 ± 1 hour) at 4 months of age in F₀ males (i), F₁ males (ii), F₂ males (iii), F₁ females (iv) and F₂ males (v). B) Glucose tolerance test area under the curve (AUC) for F₀ males (i), F₁ males (ii), F₂ males (iii), F₁ females (iv) and F₂ females (v). C) Insulin tolerance test AUC for F₀ males (i), F₁ males (ii), F₂ males (iii), F₁ females (iv) and F₂ females (v). The AUC was calculated using the trapezoidal rule from individual animal glucose tolerance test curves (in Figure 1D) and insulin tolerance test curves (in Figure 1E). Results are shown as mean ± SEM. Significance for main effects of diet, genotype, and for diet-genotype interactions are shown above each graph. NS, not significant (P > 0.05). Significance for pairwise comparisons are shown as the following: ∗P<0.05, ∗∗P<0.01, ∗∗∗P<0.001, ∗∗∗∗P<0.0001 (in blue; WT CON vs WT HFD, in green; TG CON vs TG HFD) and #P<0.05, ##P<0.01 (WT HFD vs TG HFD).

Fig S3

LiverRNA-sequencing data quality assessment and normalization. A) Pearson correlation heatmaps on transcripts with variance stabilizing transformation (VST), before (i) and after (ii) correcting for RIN values in F₀ and F₁ samples run on an illumina HiSeq platform, and in F₂ samples run on an illumine NovaSeq platform (iii). Color gradients indicate the Pearson correlation coefficients for each pairwise comparison of samples. B) Principal component analysis on transcripts with variance stabilizing transformation (VST), with samples labeled by RIN value before (i) and after (ii) correcting for RINs in F₀ and F₁ samples (illumina HiSeq) and in F₂ samples (illumina NovaSeq) (iii). C) Principal component analysis on transcripts with variance stabilizing transformation (VST), with samples labeled by sex before (i) and after (ii) correcting for RINs in F₀ and F₁ samples (illumina HiSeq).

Fig S4

SpermChIP-sequencing data quality assessment and normalization. A-B) Spearman correlation heatmaps for genomic regions enriched with H3K4me3, before (A) and after (B) TMM normalization and batch correction. Colored boxes indicate sample groups (light blue=WT CON, dark blue=WT HFD, light green=TG CON, dark green=TG HFD) and numbers (from 1 to 5) indicate the sample batch. Color gradients indicate the Spearman correlation coefficients for each pairwise comparison of samples. C-D) MA-plots of pairwise comparisons between WT CON (rep 1) and all other samples, before (C) and after (D) TMM normalization and batch correction.

Fig S5

Obesity-sensitive H3K4me3 regions are predominantly located in CpG islands, promoters, exons, and intergenic regions. A-D) Upset plots show genome annotation identifying the functional regions with obesity-induced differential enrichment of H3K4me3 in sperm according to directionality change, with increased enrichment in WT HFD (A), decreased enrichment in TG HFD (B), increased enrichment in TG HFD (C) and decreased enrichment in TG HFD (D). Horizontal bars on the left represent the number of regions belonging to each genomic annotation (set size). Vertical bars represent the number of regions belonging to intersecting annotations (intersection size). Intersection sets are represented by connecting nodes. Horizontal bars on the right represent the enrichment (z-score) for each respective annotation compared to what would be expected by chance if regions of similar sizes were randomly located across the genome (p<0.05, 1,000 permutations). Dark grey bars represent significant enrichment whereas light grey bars are not significant. Genome browser snapshots show genes with deH3K4me3 in sperm (WT CON light blue, WT HFD dark blue, TG CON light green and TG HFD dark green).

Fig S6

Obesity alters sperm H3K4me3 at genes expressed in the4-celland morula embryos, trophectoderm and placenta. A) Venn diagrams showing the overlap between genes expressed in the 4-cell embryo and genes expressed in the morula embryo, with genes with H3K4me3-enriched promoters in sperm (i) or with genes with diet-induced deH3K4me3 at promoters in sperm (ii). B) Venn diagrams showing the overlap between genes expressed in the trophectoderm and genes expressed in the placenta, with genes with H3K4me3-enriched promoters in sperm (i) or with genes with diet-induced deH3K4me3 at promoters in sperm (ii).

Fig S7

Obesity-induced changes in H3K4me3 enrichment in sperm show minor overlap withgenes altered in adult offspring liver. A) Scatterplot showing liver RNA expression values (y axis; log2 counts +1) and sperm H3K4me3 enrichment values (x axis; log2 counts + 1) for genes with paternal-diet induced differential expression in livers of F₁ males overlapping with deH3K4me3 at promoters in sperm. B) Venn diagram showing the overlap of genes enriched with diet-induced deH3K4me3 at promoters in sperm and genes with paternal-diet induced differential expression in livers of F₁ males. C) Heatmap of significant GO terms, comparing enriched biological functions in diet-induced sperm differentially enriched regions at promoters and liver differentially expressed genes in F₁ males WT and TG HFD. Rows represent enriched GO terms which are ordered by hierarchical clustering based on Wang’s semantic similarity distance and ward.D2 aggregation criterion. Each column represents a comparison of interest for which enriched GO terms were annotated based on the list of significant genes. The color gradient depicts the GO term enrichment significance (-log10 p-value). An interactive version of this heatmap can be found in Supplemental file 5 and the complete list of significantly enriched GO terms can be found in Table S22.