Cross-Species Comparative DNA Methylation Reveals Novel Insights into Complex Trait Genetics among Cattle, Sheep, and Goats

Abstract

The cross-species characterization of evolutionary changes in the functional genome can facilitate the translation of genetic findings across species and the interpretation of the evolutionary basis underlying complex phenotypes. Yet, this has not been fully explored between cattle, sheep, goats, and other mammals. Here, we systematically characterized the evolutionary dynamics of DNA methylation and gene expression in 3 somatic tissues (i.e. brain, liver, and skeletal muscle) and sperm across 7 mammalian species, including 3 ruminant livestock species (cattle, sheep, and goats), humans, pigs, mice, and dogs, by generating and integrating 160 DNA methylation and transcriptomic data sets. We demonstrate dynamic changes of DNA hypomethylated regions and hypermethylated regions in tissue-type manner across cattle, sheep, and goats. Specifically, based on the phylo-epigenetic model of DNA methylome, we identified a total of 25,074 hypomethylated region extension events specific to cattle, which participated in rewiring tissue-specific regulatory network. Furthermore, by integrating genome-wide association studies of 50 cattle traits, we provided novel insights into the genetic and evolutionary basis of complex phenotypes in cattle. Overall, our study provides a valuable resource for exploring the evolutionary dynamics of the functional genome and highlights the importance of cross-species characterization of multiomics data sets for the evolutionary interpretation of complex phenotypes in cattle livestock.

Introduction

Comparative epigenomics studies have proposed that changes in gene regulation could lead to phenotypic diversity and species-specific traits (Mendizabal et al. 2014; Hernando-Herraez et al. 2015; Guevara et al. 2021). DNA methylation is a stable epigenomic mark that plays an important role in gene regulation (e.g. genomic imprinting and silencing), embryonic development, and evolution (Jones 2012; Li et al. 2018; Lu et al. 2021; Hu et al. 2023). Cross-species comparisons of DNA methylome have revealed the evolutionary features of methylation and contributed to the understanding of the genetic basis of complex traits and diseases in humans (Fukuda et al. 2017; Blake et al. 2020). For instance, cross-species comparisons of DNA methylation in the brain (Jeong et al. 2021), placenta (Schroeder et al. 2015), bone (Housman et al. 2020), and heart (Pai et al. 2011) have revealed a considerable set of species-specific DNA hypomethylated regions (HypoMRs). These DNA HypoMRs are involved in embryonic development and species-specific phenotypes, such as cognitive behavior in humans (Mendizabal et al. 2016). Furthermore, a previous study in 13 mammalian species reported that genes with hypomethylated promoters were mainly involved in developmental processes, whereas genes with hypermethylated promoters were predominantly enriched in immune system processes (Hu et al. 2023). Although comparative methylome analysis has been widely performed in primates, especially in humans, it has not been extensively studied in ruminant livestock species. By comparing sperm DNA methylation in cattle, humans, and mice, Fang et al. (2019) revealed that genes with cattle-specific hypomethylated promoters were significantly enriched for genome-wide association study (GWAS) signals of milk production traits, while genes with conserved hypermethylated promoters were highlighted by immune-related traits (Fang et al. 2019). However, it remains unclear how changes in DNA methylation across multiple tissues during ruminant livestock evolution affect their gene expression and complex traits.

Cattle, sheep, and goats are economically important ruminant livestock species that provide essential proteins and fur products for humans. A better understanding of the evolutionary divergence in DNA methylation between ruminant livestock species and other mammals will enable us to unveil the regulatory mechanisms underlying ruminant-specific molecular and complex phenotypes. Although the Functional Annotation of Animal Genomes (FAANG) and Farm animal Genotype-Tissue Expression (FarmGTEx) projects have made broader efforts to generate comprehensive annotations in livestock, including ruminants (Foissac et al. 2019; Halstead et al. 2020; Goszczynski et al. 2021; Liu et al. 2022), the systematic investigation of how evolution shapes the ruminant-specific landscape of DNA methylation and its impact on phenotypic variation has not been conducted yet.

Here, to investigate the evolutionary features of DNA methylation and their impacts on gene expression in ruminant livestock, we integrated 160 whole-genome bisulfite sequencing (WGBS, 24 newly generated) and RNA sequencing (RNA-seq, 18 newly generated) data sets from 3 somatic tissues (i.e. brain, liver, and skeletal muscle) and sperm, representing all 3 germ layers and germline cells across 7 mammals, including 3 ruminant livestock species (i.e. cattle, sheep, and goats) and 4 nonruminants (i.e. pigs, dogs, humans, and mice) (Fig. 1A and supplementary fig. S1, Supplementary Material online). By further integrating 177 publicly available multiomics data sets (e.g. histone marks and chromatin accessibility), we explored phylogeny-specific and tissue-specific variations of epigenomic marks and their potential functional implications among cattle, sheep, and goats. Finally, we explored the impact of HypoMR and hypermethylated region (HyperMR) evolution on complex traits by examining large GWAS summary statistics in cattle and sheep. Collectively, our study provides a systematic census of epigenomic evolution and lays the foundation for understanding the evolutionary mechanisms influencing complex traits in cattle, sheep, and goats.

Fig. 1.

General characterization of methylome and transcriptome data sets of 3 somatic tissues and sperm in 7 mammals. a) The number of WGBS and RNA-seq samples from 3 somatic tissues (brain, liver, and skeletal muscle) and sperm in 7 mammals (cattle, sheep, goats, humans, mice, pigs, and dogs), representing all 3 germ layers and germline cells. b) UMAP analysis of samples (n = 73) based on DNA methylation levels of 13,207 orthologous CpGs (coverage ≥ 5×). c) Sample (n = 87) clustering as (b) but based on gene expression levels of 11,140 orthologous genes (≥ 5 read counts in 80% of samples). Proportion of orthologous CpG methylation (d) and gene expression (e) variance explained by tissues and species. Genes and CpGs were ranked from the highest to the lowest according to their gene expression and methylation variance explained by tissue types and defined the top 5% as tissue-specific genes or CpGs while the bottom 5% or those with no explained DNA methylation variance by tissue types as tissue-shared genes or CpGs.

Results

Evolutionary Dynamics of DNA Methylation and Transcriptome across Tissues and Species

Overall, we generated 41.96 billion clean reads with an average mapping rate of 75.91% and an average coverage of 25.76× for WGBS data, as well as 8.9 billion reads with an average mapping rate of 87.05% for RNA-seq data (supplementary table S1, Supplementary Material online). An average of 14,964 protein-coding genes (PCGs) expressed (transcripts per million [TPM] ≥ 0.1) across tissues and species, which accounted for 69.62% of all the annotated PCGs of the corresponding annotated genome for each species (Ensembl v104) (supplementary fig. S2A, Supplementary Material online). Additionally, we integrated 177 assays for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) data sets with 16.16 billion mapped reads to unveil the comprehensive characteristics of epigenomic evolution across mammalian species (supplementary table S1C and D, Supplementary Material online).

To investigate DNA methylome evolution in cattle, sheep, and goats, we aligned noncattle methylomes to the cattle reference genome at single-CpG site resolution using MULTIZ alignment (Blanchette et al. 2004). We detected an average of 3.8 million orthologous CpG sites (coverage ≥ 10×) with an average methylation level of 0.62 (ranging from 0.54 to 0.79) across all samples (supplementary fig. S2B and C and table S2, Supplementary Material online). Sperm showed a higher DNA methylation level than the other 3 somatic tissues, which was consistent with previous reports in cattle and primates (Qu et al. 2018; Zhou et al. 2020) (supplementary figs. S2C and S3A to D, Supplementary Material online).

Both uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE) analysis of samples based on variation in DNA methylation and gene expression revealed clear separation of tissues, followed by species (Fig. 1B and C and supplementary fig. S4, Supplementary Material online). Methylomes of somatic tissues showed a lower correlation with those of sperm (supplementary fig. S4, Supplementary Material online). Consistent with UMAP and t-SNE visualization, tissue-type explained a greater of variation in both DNA methylation and gene expression, followed by species, using a random-effect model (Fig. 1D and E). We ranked genes and CpGs from the highest to the lowest according to their gene expression and methylation variance explained by tissue types and defined the top 5% as tissue-specific genes or CpGs while the bottom 5% or those with no explained DNA methylation variance by tissue types as tissue-shared genes or CpGs. Based on Gene Ontology (GO) enrichment analysis, tissue-shared genes or their promoters with tissue-shared CpGs were highly enriched in basic biological processes, such as DNA repair and cell division. In contrast, tissue-specific genes or their promoters with tissue-specific CpGs were significantly engaged in tissue-related GO biological processes, such as cholesterol homeostasis for the liver, muscle contraction for skeletal muscle, and neurological system process for the brain (supplementary table S3, Supplementary Material online).

Distinct Evolutionary Rates of Multiomics Phenotypes across Tissues

To investigate the evolutionary rates of DNA methylome and transcriptome across species, we constructed polygenetic trees using DNA methylation and gene expression in 3 somatic tissues and sperm separately (Fig. 2A and supplementary fig. S5, Supplementary Material online). In general, the polygenetic trees of both DNA methylation and gene expression across 3 somatic tissues and sperm were consistent with the known phylogeny of these species based on the genomic information (Soares et al. 2013). Notably, the total lengths of trees for both DNA methylome and transcriptome varied widely among tissues (Fig. 2B and C), suggesting that the evolutionary constraints of the functional genome are different across tissues during species evolution. Sperm evolved significantly faster than the other 3 somatic tissues at both DNA methylation and gene expression (Fig. 2B and C; Wilcoxon test P < 2e⁻¹⁶), potentially attributable to sperm competition during spermatogenesis and strongly positive selection of the reproductive system (Brawand et al. 2011; Ramm et al. 2014; Wang et al. 2020). Moreover, the order of the evolutionary rates of DNA methylation across tissues in promoters, CpG islands, CpG shores, introns, and 3′ untranslated regions (UTRs) was consistent with that of gene expression (Fig. 2C). For example, the shorter tree branches of the brain, compared with those in the other 2 somatic tissues and sperm, suggested the slower evolution of both DNA methylation and gene expression, which agrees with previous studies on gene expression and translation in mammals (Wang et al. 2020).

Fig. 2.

Divergence evolutionary rates of methylomes and transcriptomes across tissues in 7 mammals. a) Polygenetic trees of DNA methylation (liver) and gene expression (liver) were constructed based on pairwise distance matrices (1-Spearman's correlation coefficient) using the NJ method. The pairwise distance matrices of expression and DNA methylation were calculated using expression levels of 11,140 expressed 1:1 orthologous genes and methylation levels of 13,207 orthologous CpGs (coverage ≥ 5×), respectively. The genomic polygenetic tree from the multiple alignment of 7 species was constructed using the unrestricted single-nucleotide model. b) Total length of tree branch of gene expression for 3 somatic tissues and sperm for 4 species (cattle, pigs, humans, and mice) due to data availability. c) Total length of tree branch of DNA methylation in different genome regions across 3 somatic tissues and sperm across 4 species (cattle, sheep, goats, and humans). The pairwise distance matrices of DNA methylation were calculated using methylation levels of 188,373 orthologous CpGs. d) Comparison of tree length of epigenomic marks and gene expression for the liver across 4 species (cattle, pigs, humans, and mice) due to data availability. Polygenetic trees of epigenomic marks were constructed based on signal intensities or DNA methylation levels in promoters (±2 kb around TSSs) of 11,122 annotated 1:1 orthologous genes. All polygenetic trees were verified using the 1,000 bootstrapping analysis. “****” indicated P < 0.0001.

Furthermore, we investigated the relationship among DNA methylation, gene expression, and chromatin states. As expected, the polygenetic trees of epigenomic marks and gene expression were highly consistent, while evolutionary rates varied substantially across gene expression and epigenomic marks (Fig. 2D and supplementary figs. S6 and S7, Supplementary Material online). The total branch length of gene expression tree was significantly shorter (Wilcoxon test P < 0.0001) than that of the other epigenomic marks, which reflects the higher evolutionary constraint of gene expression than epigenetic regulation, partially due to the known buffering effect of gene expression (Berthelot et al. 2018; Danko et al. 2018).

Species-Specific HypoMRs Exhibit a High Tissue Specificity

HypoMRs play an important role in embryonic development and species evolution (Wagner et al. 2014; Qu et al. 2018). The contiguous domains of low methylation were termed HypoMRs, which were identified using hidden Markov model (HMM) (Molaro et al. 2011; Song et al. 2013; Qu et al. 2018). Therefore, we identify HypoMRs in each tissue across 7 mammals separately and align noncattle HypoMRs to the cattle reference genome. In total, we detected 122,126 (115.63 Mb), 116,579 (106.76 Mb), 114,235 (103.43 Mb), 81,649 (72.31 Mb), 21,502 (22.41 Mb), 113,547 (115.73 Mb), and 92,368 (85.93 Mb) HypoMRs located in orthologous genome in cattle, sheep, goats, pigs, dogs, humans, and mice, respectively (supplementary table S2, Supplementary Material online). In general, sperm showed the longest length of HypoMRs, followed by brain, liver, and finally skeletal muscle (supplementary table S2, Supplementary Material online). This pattern was consistent among cattle, goats, and humans. We have further identified ruminant-specific HypoMRs, which are shared among cattle, sheep, and goats but not found in other mammals. Among them are 2,950 (0.89 Mb), 1,911 (0.50 Mb), 2,457 (0.68 Mb), and 1,393 (0.55 Mb) ruminant-specific HypoMRs in the brain, liver, skeletal muscle, and sperm, respectively. In addition, we detected an average of 3,439 (1.25 Mb) species-specific HypoMRs and an average of 14,402 (13.17 Mb) conserved HypoMRs in all species across 3 somatic tissues and sperm (Fig. 3A and supplementary fig. S8A to C, Supplementary Material online).

Fig. 3.

Dynamic changes in HypoMRs at a tissue-specific manner. a) Total length of lineage/species-specific HypoMRs in sperm. b) Fraction of sperm orthologous HypoMRs across ancestral and extant species. c) Overlap of CS-HypoMRs across 3 somatic tissues (brain, liver, and skeletal muscle) and sperm. The number of CS-HypoMRs is listed on the top of the plot. d) Heatmap (right) shows DNA methylation levels of CS-HypoMRs across 3 somatic tissues and sperm. The enriched GO biological processes detected by GREAT and TFs are plotted next to CS-HypoMRs of the corresponding tissues.

Furthermore, we estimated the proportion of HypoMRs in the orthologous genome across ancestral and extant species. The proportion of HypoMRs in the orthologous genome increased along the branch of genome phylogeny (Fig. 3B and supplementary fig. S8D and F, Supplementary Material online). For example, in sperm, conserved HypoMRs accounted for 4.9% of the orthologous genome, 5.5% in the ruminant common ancestor, and 6.0% to 6.49% in cattle, sheep, and goats (Fig. 3B). In the brain, conserved HypoMRs accounted for 8.6% of the orthologous genome, 9.27% in the ruminant common ancestor, and 9.46% to 10.37% in the 3 extant ruminant livestock species (cattle, sheep, and goats) (supplementary fig. S8D, Supplementary Material online). In the liver, conserved HypoMRs accounted for 6.24% of the orthologous genome, 7% in the ruminant common ancestor, and 7.62% to 7.78% in cattle, sheep, and goats (supplementary fig. S8E, Supplementary Material online). In skeletal muscle, conserved HypoMRs accounted for 8.59% of the orthologous genome, 8.83% in the ruminant common ancestor, and 8.98% to 9.86% in cattle, sheep, and goats (supplementary fig. S8F, Supplementary Material online). Three somatic tissues and sperm conserved HypoMRs were significantly enriched in diverse biological processes, such as basic biological processes (peptidyl-asparagine hydroxylation) and developmental processes (optic chiasma development and dorsal spinal cord development) (supplementary table S4, Supplementary Material online).

Species-specific HypoMRs exhibited a high tissue specificity (Fig. 3C and supplementary fig. S9, Supplementary Material online). Take cattle as the example, only 14 cattle-specific HypoMRs (CS-HypoMRs) were shared across 3 somatic tissues and sperm. Genes targeted by tissue-shared CS-HypoMRs were significantly enriched in fundamental molecular functions, such as tRNA dihydrouridine synthesis (supplementary table S5, Supplementary Material online). A tissue-shared CS-HypoMR was located in ABTB1, which is highly expressed across multiple tissues in cattle and is involved in protein–protein interactions and normal development (Dai et al. 2000; Fang et al. 2020) (supplementary fig. S10A and B, Supplementary Material online). GO enrichment analysis demonstrated that CS-HypoMRs tend to regulate tissue-specific functional genes, such as those involved in the regulation of glial cell differentiation (brain), coenzyme A (CoA) biosynthetic process (liver), skeletal muscle satellite cell commitment (skeletal muscle), and establishment of planar polarity (sperm) (Fig. 3D and supplementary table S6, Supplementary Material online). For instance, PANK1, which plays roles in CoA biosynthesis and insulin homeostasis (Garcia et al. 2012; Leonardi et al. 2014), was highly expressed in cattle liver, with CS-HypoMRs located in the gene body (supplementary fig. S10A and C, Supplementary Material online). The motif enrichment analysis revealed that tissue-specific transcription factors (TFs) were significantly (P < 0.01) enriched in CS-HypoMRs of the corresponding tissue (Fig. 3D, supplementary fig. S10D and table S7, Supplementary Material online). For example, CS-HypoMRs contained significantly more HNF4A motifs in the liver than in the other somatic tissues and sperm (supplementary fig. S10E, Supplementary Material online). HNF4A regulates hepatocyte differentiation and liver fat storage (Alder et al. 2014; Ang et al. 2018; Lee et al. 2021). In summary, tissue-specific TFs may interact with species-specific HypoMRs, which further regulate gene expression in the corresponding tissues.

HypoMR Gain Events Mainly Contribute to Methylation Loss during the Evolution of Cattle, Sheep, and Goats

To obtain a more detailed view of HypoMR dynamics during the evolution of cattle, sheep, and goats, we classified HypoMRs into 4 types, including 2 HypoMR gain events (HypoMR birth and extension) and 2 HypoMR loss events (HypoMR death and contraction) (Qu et al. 2018). In general, there were more HypoMR gain events than HypoMR loss events, consistent in 3 somatic tissues and sperm across species (Fig. 4A and supplementary fig. S11, Supplementary Material online). In particular, HypoMR extensions comprised 35.05% to 74.04% (an average of 54.84%) of HypoMR gain events across 7 mammals in the 3 somatic tissues and sperm, while HypoMR contractions comprised 65.03% to 89.19% (an average of 79.57%) of HypoMR loss events. Moreover, we found that the genomic distribution of HypoMR extension and contraction events was different from that of HypoMR birth and death events in all species (supplementary fig. S12, Supplementary Material online). On average, 60.08% of HypoMR births and 55.73% of HypoMR deaths were distal (distance > 10 kb) to the transcription start sites (TSSs), in contrast to 45.21% of HypoMR extensions and 37.51% of HypoMR contractions. We further performed an enrichment analysis of HypoMR events in 14 chromatin states previously defined by ChromHMM in cattle across 3 somatic tissues (Kern et al. 2021) and found that HypoMR births and deaths showed significant depletion in promoter-relevant chromatin states (e.g. “Active_Promoter” and “CTCF/Promoter”), whereas there was significant enrichment in enhancer regions (e.g. “Active_Enhancer” and “Primed_Enhancer”) (Fig. 4B and supplementary fig. S13, Supplementary Material online). This was consistent with the observation that enhancers evolve faster than promoters in the mammalian liver (Villar et al. 2015). HypoMR extension events were more frequent (Wilcoxon test P < 2.2e⁻¹⁶) than HypoMR birth events in cattle across 3 somatic tissues and sperm, and HypoMR death events were more frequent (Wilcoxon test P < 2.2e⁻¹⁶) than HypoMR contraction events (supplementary figs. S14 to S17, Supplementary Material online). Our results are consistent with observations in sperm across mammals, suggesting that existing HypoMRs may have contributed to the emergence of new HypoMRs during species evolution (Qu et al. 2018). We further investigated the dynamics of HypoMR gain events across different tissues in cattle and found that HypoMR gain events exhibited tissue-specific hypomethylation (supplementary fig. S18, Supplementary Material online). For example, sperm HypoMR birth and extension events in cattle had lower methylation levels in sperm than in other tissues. An average of 77.65% (the total number was 25,074) of cattle HypoMR extensions were tissue-specific. Only 61 HypoMR extensions were shared among the 3 somatic tissues and sperm in cattle (Fig. 4C). GO enrichment analysis demonstrated that genes with tissue-specific HypoMR extensions in cattle reflected the known biological functions of corresponding tissues, such as nephron tubule development (brain), response to carbohydrates (liver), skeletal system morphogenesis (skeletal muscle), and embryonic organ morphogenesis (sperm) (supplementary table S8, Supplementary Material online). In summary, HypoMR gain events may occur at tissue-dependent regulatory patterns.

Fig. 4.

Evolutionary features of HypoMRs in cattle. a) Total genomic size of 4 HypoMR evolutionary events identified in the brain across species and lineages. b) Enrichment analysis of cattle brain HypoMR events in 14 chromatin states which were previously predicted in the cattle cortex (Kern et al. 2021). The fold enrichment of each HypoMR for each chromatin state was calculated by observed value/expected value. c) Overlap of HypoMR extension events among 3 somatic tissues and sperm in cattle. The number of HypoMR extension events is listed on the top of the plot. d) t-SNE plot shows the clustering of 20,918 all cattle HypoMR extensions based on DNA methylation levels, chromatin accessibility, and histone modifications (H3K4me1, H3K4me3, and H3K27ac) of 1 kb around HypoMR extensions across 3 somatic tissues and sperm. e) Heatmap presents the signal intensity of epigenomic modifications (DNA methylation, H3K4me1, H3K4me3, and H3K27ac) and chromatin accessibility across all the 11 clusters of HypoMR extension events in cattle.

Identification of Gene Regulatory Networks Affected by CS-HypoMR Extensions

Compared with sheep and goats, cattle have a substantial amount of functional annotation data sets. Therefore, we aim to investigate the epigenomic changes across cattle tissues in order to further enhance functional annotation data. We performed the unsupervised K-means cluster analysis to classify clusters of cattle HypoMR extension regions showing similar epigenomic patterns (Ma et al. 2020). In total, we detected 11 clusters of cattle HypoMR extension events by integrating 4 epigenomic marks (H3K27ac, H3K4me1, H3K4me3, and DNA methylation) and chromatin accessibility (Fig. 4D). The epigenomic patterns of 4 clusters exhibited a strong tissue specificity in cattle, including Clusters 2, 5, 6, and 9 (Fig. 4E and supplementary fig. S19, Supplementary Material online). For example, regulatory regions of Cluster 6 (n = 1,140) exhibited strong enhancer signatures in cattle brain, characterized by concurrent gain of H3K27ac and H3K4me1, with evident loss of H3K4me3 and DNA methylation (supplementary fig. S19, Supplementary Material online). Genes regulated by HypoMR extension regions in Cluster 6 were significantly (false discovery rate [FDR] < 0.01) enriched in positive regulation of female receptivity and D-serine biosynthetic process (supplementary table S9, Supplementary Material online). Regulatory regions of Cluster 9 regulatory regions (n = 536) presented promoter function with gain in H3K4me3 and loss of DNA methylation in cattle sperm, genes regulated by which were significantly enriched in Sertoli cell fate commitment. Moreover, regulatory regions of Cluster 5 (n = 1,740) showed a gain in chromatin accessibility and loss of DNA methylation in skeletal muscle. Genes regulated by Cluster 5 demonstrated significant enrichment in muscle development processes, such as muscle structure development and striated muscle cell development. Altogether, these results suggest that HypoMR extensions during species evolution contribute to the rewriting of epigenomic regulatory networks, which further regulate the expression of tissue-specific functional genes, in line with the hypothesis of tissue-driven molecular evolution (Gu and Su 2007).

Evolutionary HyperMRs in Cattle, Sheep, and Goats

Previous studies have reported that HyperMRs regulate gene transcription by silencing regulatory elements (Easwaran et al. 2012; Ehrlich 2019). HyperMRs are contiguous regions of hypermethylated CpGs or contiguous domains of hypermethylated CpGs separated by only a few unmethylated CpGs, which were identified using a 3-state HMM (Song et al. 2013; Qu et al. 2018). We thus identified HyperMRs in each tissue across 7 mammals separately and investigated whether HyperMRs likewise evolved in a tissue-type manner. In total, we identified 646,684 (675.36 Mb), 659,501 (690.01 Mb), 645,343 (670.73 Mb), 481,628 (480.19 Mb), 166,225 (186.65 Mb), 547,386 (595.80 Mb), and 492,531 (509.77 Mb) HyperMRs located in orthologous genome in cattle, sheep, goats, pigs, dogs, humans, and mice, respectively. An average of 2,462 (0.53 Mb) and 74,132 (60.10 Mb) species-specific and conserved HyperMRs were detected, respectively, across 3 somatic tissues and sperm (Fig. 5A and supplementary fig. S20A to C, Supplementary Material online). Consistent with species-specific HypoMRs, species-specific HyperMRs showed low tissue conservation across 3 somatic tissues and sperm (Fig. 5B and supplementary fig. S20D to F, Supplementary Material online). For instance, a total of 118 cattle-specific HyperMRs (CS-HyperMRs) were shared across all 3 somatic tissues and sperm (Fig. 5B), and these regions exhibited a lower enrichment or even depletion for active regulatory elements, compared with CS-HypoMRs (Fig. 5C and supplementary fig. S21A and B, Supplementary Material online). To further investigate the difference of species-specific HyperMRs and HypoMRs in gene regulation, we examined 4 epigenetic marks (i.e. H3K27ac, H3K4me1, H3K4me3 and ATAC-seq) (Kern et al. 2021). Relative to CS-HypoMRs, CS-HyperMR showed low active histone mark signals and chromatin accessibility (Fig. 5D and supplementary fig. S21C and D, Supplementary Material online). The following functional annotation suggested that CS-HyperMRs were significantly enriched in tissue-related biological processes, e.g. triglyceride homeostasis and regulation of cytokine secretion (Fig. 5E and supplementary table S10, Supplementary Material online). These observations suggested that both species-specific HyperMRs and HypoMRs display the strong tissue specificity and play a role in regulating tissue-specific gene expression.

Fig. 5.

Evolutionary changes in DNA HyperMRs. a) Total length of lineage/species-specific HyperMRs in the brain. b) Overlap of CS-HyperMRs across 3 somatic tissues (brain, liver, and skeletal muscle) and sperm. The number of CS-HyperMRs is listed on the top of the plot. c) Enrichment analysis of cattle-specific brain HyperMR and HypoMRs in 14 chromatin states which were previously predicted in the cattle cortex (Kern et al. 2021). The fold enrichment of each HyperMR for each chromatin state was calculated by observed value/expected value. d) Aggregation plots of ChIP-seq (i.e. H3K27ac, H3K4me1, and H3K4me3) and ATAC signals in the brain across cattle-specific brain HyperMRs and HypoMRs. e) The enriched GO biological processes detected for CS-HyperMRs of the corresponding tissues by GREAT.

Evolutionary HypoMRs and HyperMRs Enhance the Genetic Elucidation of Complex Traits in Cattle and Sheep

Species-conserved/specific HypoMRs and HyperMRs may be polymorphic in their epigenetic status within the population. Single-nucleotide polymorphisms (SNPs) located within the evolutionary or species-specific HypoMRs and HypoMRs might affect the methylation level and regulatory function of HypoMRs and HypoMRs. Therefore, we investigated the enrichment of quantitative trait loci (QTLs) and GWAS signals of complex traits along different types of HypoMRs and HypoMRs. We only performed QTL enrichment analysis for cattle and sheep QTLs as there are comparatively fewer QTLs available in goat QTLdb. QTL enrichment analysis revealed that conserved HypoMRs and HyperMRs had a significantly higher enrichment (P < 0.001) for QTLs of 489 cattle traits (obtained from cattle QTLdb, Release 45, 2021 August 23) and 110 sheep traits (obtained from sheep QTLdb, Release 50, 2023 April 25) than divergent HypoMRs and HyperMRs (supplementary fig. S22, Supplementary Material online). For instance, conserved HypoMRs had a significantly higher enrichment (P < 0.001) for QTLs of cattle traits than CS-HypoMRs (supplementary fig. S22A, Supplementary Material online). Moreover, conserved HypoMRs and HyperMRs of a tissue were highly enriched for QTLs of complex traits matched with the known biology of the corresponding tissue (Fig. 6A and supplementary fig. S23, Supplementary Material online), such as conserved HypoMRs for sperm acrosome integrity rate QTL and conserved HyperMRs for sperm tail abnormalities QTL.

Fig. 6.

Evolutionary conserved HypoMRs and HyperMRs are significantly enriched for genetic associations of complex traits in cattle. a) QTL enrichments of 489 cattle traits, representing 6 trait categories, in conserved sperm HypoMRs and HyperMRs. Cattle QTL were downloaded from Cattle QTL database (Release 45, 2021 August 23). The fold enrichment of each methylated regions (MRs) for each cattle trait was calculated by observed value/expected value. b) GWAS signal enrichment analysis of conserved and cattle-specific MRs for 50 complex traits in cattle. c) CCDC157 as an example, with conserved HypoMRs. The dots plot (up) are GWAS results of daughter calving ease within 500 kb regions around the conserved HypoMR of CCDC157 in cattle. The down is the genome track for DNA methylation profiles of CCDC157 across 7 species of sperm.

Furthermore, given that cattle has a large number of phenotypic records that are measured with high accuracy and GWAS summary data sets, we investigate if conserved methylation may help to annotate noncoding variants in cattle. We performed a comparison of disparities in GWAS enrichment analysis between conserved methylation regions (e.g. HypoMRs and HyperMRs) and cattle-specific methylation regions, which are characterized by low or high methylation levels exclusive to cattle despite sequence conservation. Enrichment analysis of GWAS signals of 50 complex traits in cattle confirmed that both HypoMRs and HyperMRs with higher conservation showed a higher enrichment (Fig. 6B and supplementary table S11, Supplementary Material online). Take HypoMRs as the example, on average, GWAS signals of an average of 32 complex traits in cattle were significantly (P < 0.01) enriched in conserved HypoMRs of all the 3 somatic tissues and sperm, while only 4 complex traits were significantly (P < 0.01) enriched in CS-HypoMRs. For example, CCDC157 is involved in spermatogenesis and associated with idiopathic nonobstructive azoospermia (Reinke Aaron et al. 2013; Wang et al. 2018; Yuan et al. 2019), which harbored conserved HypoMRs in sperm and was highly expressed in the male reproductive system of cattle (supplementary fig. S24, Supplementary Material online). Several significant genetic variants (P < 1.0e⁻⁵) associated with daughter calving ease (Dtr_Calv_Ease) in cattle were located within ±10 kb of conserved HypoMRs of CCDC157 in sperm (Fig. 6C). Overall, our results suggest that conserved DNA methylation may contribute to annotating noncoding variants associated with complex traits in cattle, in contrast to cattle-specific DNA methylation.

Discussion

DNA methylation plays a regulatory role in the evolution of species-specific traits, e.g. neuropsychiatric traits in humans (Vermunt et al. 2016; Rizzardi et al. 2021), but its dynamic changes and the epigenomic mechanisms of complex traits in cattle, sheep, and goats have not been fully characterized. Here, we report a comprehensive cross-species and cross-tissue analysis of DNA methylome and transcriptome in 3 somatic tissues (brain, liver, and skeletal muscle) and sperm from cattle, sheep, goats, and 4 nonruminants (i.e. humans, pigs, mice, and dogs), thus elucidating the evolutionary mechanism of DNA methylation across mammals. Using GWAS summary data sets from 50 cattle complex traits, we further demonstrated the relationships between the evolutionary conservation of DNA methylation and genetic risk for complex traits in ruminant livestock.

Comparative functional genomic analyses have been widely used to investigate the interspecies regulatory evolutionary changes underlying phenotypic divergence, especially in primates. Hernando-Herraez et al. (2013) demonstrated the importance of species-specific methylation patterns in the evolution of species-specific phenotypes (e.g. upright locomotion for humans) by comparing DNA methylation in peripheral blood among humans, chimpanzees, gorillas, bonobos, and orangutans (Hernando-Herraez et al. 2013). Blake et al. (2020) reported that human-specific methylation patterns of tissue-specific genes contribute to human-specific adaptations in humans, chimpanzees, and rhesus macaques, such as paraxial mesoderm morphogenesis in lungs (Blake et al. 2020). In this study, we used comparative functional genomic analysis to identify ruminant-specific and tissue-specific methylation patterns, which are likely to disproportionally harbor traits/disease-causing variations in cattle, sheep, and goats.

Our findings showed that epigenomic marks (i.e. DNA methylation, histone modifications, and chromatin accessibility) and gene expression played a synergistic role in the evolution of cattle, sheep, and goats, and the conservation of gene expression was higher than that of the other 6 epigenomic marks. In a previous study on primate CD4+ T cells (Danko et al. 2018), the evolutionary rates of enhancer buffer at the gene expression level suggested that the crosstalk of multiple epigenomic marks may restrict the evolution rates of gene expression. Notably, sperm evolved more rapidly than the other 3 somatic tissues, and the brain was the slowest in all epigenomic marks and expression levels, which could be explained by the strong positive selection of sperm competition (Birkhead and Pizzari 2002; Wang et al. 2020). Moreover, we also found that the sperm exhibited a higher level of DNA methylation and the longest average length of HypoMRs compared with the other 3 somatic tissues. Sperm plays a crucial role in achieving successful artificial insemination in livestock, and the DNA methylome marks of sperm have a significant impact on the early development and health of offspring (Wang et al. 2023). The identification of lineage-specific sperm DNA methylation could provide an explanation for the observed lineage-specific variations in phenotypic traits and fertility, which contribute to the rapid genetic improvement of cattle, sheep, and goats.

We found that HypoMRs in somatic tissues mainly appeared in the active regulatory regions, such as “Active Promoter” and “Active Enhancer.” However, the HyperMRs were less enriched in the active region near TSS. In particular, the majority of species-specific MRs (i.e. HypoMRs and HyperMRs) and HypoMR gain events was tissue-specific and regulated genes associated with tissue-related functions. Moreover, tissue-specific TFs were significantly engaged in species-specific HypoMRs of the corresponding tissue, such as SOX17 (brain) and MYOG (skeletal muscle). These observations suggest that the evolution of DNA methylation may be tissue-specific in mammals, and this warrants further investigation. Moreover, our study's limitation is that it only utilized 7 mammalian species, which constrains a comprehensive assessment of the evolutionary trend of DNA methylation in mammalian evolution to a certain extent. In the future, it is necessary to add more species to explore the evolutionary trend of DNA methylation by ancestral state reconstruction.

Finally, we observed that conserved HypoMRs and HyperMRs were significantly enriched for the major traits in cattle, which may contribute to the discovery of functionally causative genetic variants of complex traits. In humans, ancient regulatory regions have been significantly enriched for genetic risks associated with complex traits and diseases (Diehl and Boyle 2018; Alizada et al. 2021; Jeong et al. 2021). For example, Jeong et al. (2021) reported that conserved neuron-hypo CpG DMRs in primates were strongly enriched for GWAS signals of brain-related traits (Jeong et al. 2021). Hujoel et al. (2019) also found that the enrichment of ancient regulatory elements (i.e. promoters and enhancers) for GWAS signals was significantly (P < 4e⁻¹⁴) higher than that of all regulatory elements (Hujoel et al. 2019). Our results further suggest that although the phenotypes of complex traits differ greatly between humans and livestock, the genetic mechanisms underlying traits and diseases may be partly conserved in ancient mammals. Therefore, these results suggest that classifying DNA methylation based on evolutionary conservation could narrow the regions of candidate risk related to complex traits. Moreover, the limitation of our study is lack of an analysis of the diverse breeds within each species, which is necessary to expand the sample size for the exploration of QTLs.

In conclusion, we profiled dynamic changes in multitissue DNA methylation and transcriptome across species and tissues and revealed the evolutionary features of DNA hypomethylation and hypermethylation among cattle, sheep, and goats. The reported epigenetic evolution could facilitate the elucidation of the association between conserved/derived regulatory mechanisms and the genetic mechanisms underlying complex traits in cattle, sheep, and goats.

Materials and Methods

Sample Description

We collected 3 somatic tissues (brain, liver, and skeletal muscle) and sperm from 3 ruminant livestock species—cattle (3 to 4 yr old; Holstein, healthy), goats (2 to 3 yr old; Yunshang black goat, healthy), and sheep (2 to 3 yr old; Texel, healthy) with 3 biological replicates. Samples were stored in liquid nitrogen until use. All animal experiments were conducted according to the guidelines of Animal Welfare Committee of China Agricultural University, Beijing, China. WGBS and RNA-seq assays were performed on the same samples. The description of WGBS and RNA-seq assays for the liver has been reported in our previous study (Chen et al. 2022). For nonruminants (pigs, dogs, mice, and humans), only samples from healthy and adult individuals were used.

WGBS Data Sets

We prepared 24 WGBS libraries from extracted DNA, as previously described (Liu et al. 2019). Briefly, genomic DNA was extracted and sonicated (300 bp), followed by terminal repair and addition of the A base to the 3′ terminus. DNA fragments were then ligated to cytosine-methylated barcodes and treated with bisulfite twice using the EZ DNA Methylation-Gold Kit (Zymo Research, Orange, CA, USA). The DNA was amplified by polymerase chain reaction (PCR) and selected for size. Qualified libraries were sequenced on the Illumina HiSeq X Ten platform to obtain 150 bp paired-end reads (ANOROAD, Beijing, China).

Public WGBS data sets were downloaded from the Gene Expression Omnibus (GEO) database, and the corresponding Sequence Read Archive (SRA) numbers are provided in supplementary table S1A, Supplementary Material online.

WGBS Analysis

The raw reads were trimmed using Trim Galore v0.5.0 and mapped to the respective reference genome of each species using Bismark v0.23.0 (Krueger and Andrews 2011) with default parameters: ARS-UCD1.2, Oar_rambouillet_v1.0, ARS1, Sscrofa11.1, CanFam3.1, hg38, and mm39. The reads were then deduplicated and used to calculate bisulfite conversion rates and methylation levels and identify HypoMRs/HyperMRs using the MethPipe software (Song et al. 2013). The mapping statistics are provided in supplementary table S1A, Supplementary Material online. CpG sites covered by at least 10 reads were used for further analysis, and HypoMRs/HyperMRs with < 5 CpG sites were filtered out.

Methylome Alignment

Pairwise alignments with the cattle genome assembly ARS-UCD1.2 were generated for each species from the repeat-masked genomic sequence using lastal (Kiełbasa et al. 2011) and RepeatMasker (Tarailo-Graovac and Chen 2009) software. We then performed multiple genome alignments to the cattle genome using MULTIZ software, following previously described methods (Blanchette et al. 2004). The locations of the CpG sites in each species and their corresponding coordinates in the cattle reference genome were obtained from alignment blocks containing sequences from all 7 species. We eliminated any regions that were more than 1 kb in size and contained no CpGs or had zero read coverage in at least 1 species (Qu et al. 2018).

RNA-Seq Data Sets and Analysis

For RNA-seq analysis, the same samples were obtained from cattle, sheep, and goats. Total RNA was extracted from the frozen tissues using the standard Trizol method. RNA with 3′ polyadenylated (poly (A)) tails was selected using oligo dT magnetic beads, followed by fragmentation, reverse transcription, and terminal repair. Qualified libraries were sequenced on the Illumina HiSeq X Ten system using the 150 bp paired-end sequencing protocol (ANOROAD, Beijing, China).

Publicly available RNA-seq data sets for the corresponding tissues of 4 nonruminants were downloaded from SRA database, and the corresponding accession numbers are listed in supplementary table S1B, Supplementary Material online.

Raw reads were filtered as low-quality reads using Trimmomatic v0.39 (Bolger et al. 2014) and aligned using STAR v2.7.6 (Dobin et al. 2013) with the default parameters to the respective reference genome of each species. The mapping statistics are provided in supplementary table S1B, Supplementary Material online. Next, read counts were obtained using the featureCounts program (Liao et al. 2014) to quantify the gene expression levels in each sample. We filtered genes with low expression levels (< 5 counts in 80% of samples). The count data were normalized using the trimmed mean of M values method in edgeR (Robinson et al. 2010) and transformed into log-transformed counts per million (CPM). We obtained TPM using StringTie v2.1.4.

ChIP-Seq Analysis

ChIP-seq data sets were downloaded from the SRA database, and the corresponding SRA numbers are provided in supplementary table S1C, Supplementary Material online. The ChIP-seq data sets were trimmed using Trim Galore v0.5.0 and mapped to the reference genome using BWA v0.7.17 (Li and Durbin 2009). Next, PCR duplicates were removed using Picard v 2.25. Histone mark signals were calculated using bamCoverage in deepTools v3.1.1 (Ramírez et al. 2014) with the following parameter: “—normalizeUsing RPKM.”

ATAC-Seq Data Sets and Analysis

ATAC-seq data sets were obtained from the SRA database, and the accession numbers are listed in supplementary table S1D, Supplementary Material online. The ATAC-seq data sets were filtered using Trim Galore v0.5.0 and mapped using bowtie2 v2.4.2 with the following parameters: “-X 2000.” Duplicated reads were filtered using Picard v2.25. The signal intensity was calculated using bamCoverage in deepTools v3.1.1 with the parameter: “—normalizeUsing RPKM.”

Variance Decomposition

We decomposed the variance explained by each factor (e.g. species or tissue) for each 1:1 orthologous genes and CpGs using the lme4 library (Bates et al. 2015) in R. We fit the gene expression levels (log₂CPM) and the methylation levels of orthologous CpGs using the following random-effect model:

$$\mathrm{Expression}/\mathrm{methylation}\approx (1|\mathrm{species})+(1|\mathrm{tissue})$$

Phylogenic Tree Construction

We constructed phylogenic trees using the neighbor-joining (NJ) method in the ape R package. The pairwise distance between species was calculated based on Spearman's correlation using orthologous gene expression levels genes and orthologous CpG methylation levels across species (Brawand et al. 2011). A total of 11,140 expressed (≥ 5 counts in 20% samples) 1:1 orthologous genes and 13,207 to 188,373 orthologous CpGs were used in this analysis. We estimated the evolutionary rate of gene expression and DNA methylation using 1,000 bootstrap analyses.

To present the comprehensive dynamic changes in the transcriptome and epigenomic landscapes, we calculated the normalized signal intensities of histone marks and chromatin accessibility for promoters (2.0 kb of TSSs) in 1:1 orthologous genes using bamCoverage in deepTools v3.1.1 with the setting “--normalizeUsing RPKM.” Histone marks included H3K4me1, H3K4me3, H3K27ac, and H3K27me3. Furthermore, we determined the methylation level of promoters and expression levels for orthologous genes. Finally, we constructed epigenomic and expression trees based on pairwise distance matrices using the NJ method. The distance was calculated as 1-Spearman's correlation coefficient.

Detection of Species-Specific/Conserved HypoMRs/HyperMRs and Evolution Events

We identified species-specific/conserved HypoMRs/HyperMRs using collapsebed program in the adssrc software as previously described (Qu et al. 2018). Conserved HypoMRs or HyperMRs were defined as regions that were hypomethylated or hypermethylated in all species. To identify HypoMR evolutionary events, we first determined HypoMRs in ancestral species using an interdependent-site phylo-epigenetic model. HypoMR events were inferred based on the corresponding parent and child species in the branch of the phylogenetic tree. The detailed methods can be found in the study of Qu et al. (2018).

Identification of Regulatory Network

Regulatory networks for cattle HypoMR extension regions across somatic tissues and sperm were constructed using a multilayered multiomics approach with Seurat v4.1.3 packages (Satija et al. 2015; Ma et al. 2020; Yun et al. 2021). Firstly, cattle HypoMR extension regions were merged using mergeBed from BEDTools v2.30.0 (Quinlan and Hall 2010). Within 2 kb bins at HypoMR extension regions (±1 kb from HypoMR extensions, n = 20,918), normalized read counts for chromatin accessibility and histone marks (e.g. H3K27ac, H3K4me1, and H3K4me3) and mean methylation levels of each somatic tissue (brain, liver, and skeletal muscle) and sperm were calculated as the input data matrix. Similar to single-cell RNA-seq analysis with the Seurat package, all 20,918 HypoMR extension regions were treated as separate points across 17 conditions (with 5 epigenomic marks × 3 somatic tissues [brain, liver, and skeletal muscle] and 2 epigenomic marks in sperm). All separated clusters (n = 11) were visualized using t-SNE.

Functional Enrichment Analysis

The investigated HypoMRs and HyperMRs were converted from cattle genomic locations (bosTau9) to human genomic locations (hg38) using the LiftOver tool (Kuhn et al. 2013) with the setting “minMatch = 0.5.” Next, we conducted an enrichment analysis of GO biological processes for these converted HypoMRs and HyperMRs using GREAT v 4.0.4 (McLean et al. 2010) with default parameters. Furthermore, we conducted enrichment analysis of GO terms for tissue-conserved/specific genes or their promoters containing tissue-conserved/specific CpGs using DAVID v6.8 (Sherman et al. 2022). We also conducted an enrichment analysis of TF motifs for species-specific HypoMRs using findMotifsGenome.pl (Homer v4.11) (Heinz et al. 2010) with the parameters “-nomotif.” To further calculate the distribution density of TFs in species-specific HypoMRs, we scanned HypoMRs for TF motifs using RSAT matrix-scan v 1.214 (Turatsinze et al. 2008).

GWAS Enrichment Analysis

To investigate the correlation between lineage-specific HypoMRs and HyperMRs (e.g. CS-HypoMRs) and genetic variants of complex traits in livestock, we collected summary statistics for GWAS of 45 traits in cattle (Jiang et al. 2019; Costilla et al. 2020; Freebern et al. 2020). Moreover, we performed GWAS for 5 sperm traits in Holstein including sperm concentration (SC), ejaculate volume (VE), motility of sperms (MS), number of sperms (NSP), and the number of motile sperms (NMSP) using GCTA with the command “--mlma-loco.” The GWAS population included 4,000 individuals with phenotypic data and 1,508 individuals with genotypic data (Yin et al. 2019). Phenotypes used in GWAS were deregressed proofs computed using the method described by VanRaden et al. (2009) (VanRaden et al. 2009). The genotypic data were imputed from 54K to 777 K and SNPs with DR² < 0.5 were excluded (Teng et al. 2020). Next, we selected alleles with MAF > 0.01 and no significant deviation from Hardy–Weinberg equilibrium (P > 1.0E⁻⁶) for GWAS. Lastly, we performed GWAS enrichment analyses of 50 cattle traits for the given HypoMRs and HyperMRs (e.g. CS-HypoMRs) using the hypergeometric test with the QGG library in R (Fang et al. 2020). The GWAS data with P < 0.01 were used to calculate the fold enrichment for HypoMRs and HyperMRs using (C/A)/(B/D), where A, B, C, and D are the number of bases in a given HypoMR or HyperMR (e.g. CS-HypoMRs), the number of GWAS SNPs of a trait, overlapping between the given HypoMR or HyperMR and GWAS SNPs of a trait, and the total size of reference genome, respectively.

Other Downstream Bioinformatics Analyses

We used the regioneR (Gel et al. 2016) package in R (Permutation test: 1000) to calculate the enrichment fold of conserved or lineage-specific HypoMRs and HyperMRs in cattle QTLs (obtained from cattle QTLdb, Release 45, 2021 August 23) and chromatic states in cattle. We used Genomic Association Tester (GAT) (Heger et al. 2013) to calculate the enrichment fold of conserved or lineage-specific HypoMRs and HyperMRs in sheep QTLs (obtained from sheep QTLdb, Release 50, 2023 April 25). QTLs with intervals length >20 kb were removed. For each complex trait, we only considered the nonoverlapped QTLs reported from multiple studies.

Supplementary Material

Supplementary material is available at Molecular Biology and Evolution online.

Author Contributions

Y.Y., L.F., and S.Z. designed and supervised the study; S.C., S.L., S.S., H.Y., and K.Q. performed sample collection and data generation; S.L., S.S., J.Z., Y.T., and W.L. contributed to the computational analysis; S.C. performed the data analysis and wrote the manuscript. Y.Y., L.F., J.L., Y.W., X.D., and G.L. revised the manuscript. All authors read and approved the final manuscript.

Funding

This study was financially supported by the National Key R&D Program of China (2021YFD1200903, 2021YFD1200900, 2023YFF1000902), the National Natural Science Foundation of China (32302706, 32072718), the NSFC-PSF Joint Project (31961143009), the earmarked fund for CARS-36, the Program for Changjiang Scholar and Innovation Research Team in University (IRT-15R62), and the Seed Fund (CAU). This study was supported by High-performance Computing Platform of China Agricultural University.

Conflict of interest statement. None declared.

Data Availability

WGBS and RNA-seq data generated in this study have been submitted to the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) under accession numbers GSE211353 and GSE213317.