Background: Spermatozoa have a remarkable epigenome in line with their degree of specialization, their unique nature and different requirements for successful fertilization. Accordingly, perturbations in the establishment of DNA methylation patterns during male germ cell differentiation have been associated with infertility in several species. While bull semen is widely used in artificial insemination, the literature describing DNA methylation in bull spermatozoa is still scarce. The purpose of this study was therefore to characterize the bull sperm methylome relative to both bovine somatic cells and the sperm of other mammals through a multiscale analysis. Results: The quantification of DNA methylation at CCGG sites using luminometric methylation assay (LUMA) highlighted the undermethylation of bull sperm compared to the sperm of rams, stallions, mice, goats and men. Total blood cells displayed a similarly high level of methylation in bulls and rams, suggesting that undermethylation of the bovine genome was specific to sperm. Annotation of CCGG sites in different species revealed no striking bias in the distribution of genome features targeted by LUMA that could explain undermethylation of bull sperm. To map DNA methylation at a genome-wide scale, bull sperm was compared with bovine liver, fibroblasts and monocytes using reduced representation bisulfite sequencing (RRBS) and immunoprecipitation of methylated DNA followed by microarray hybridization (MeDIP-chip). These two methods exhibited differences in terms of genome coverage, and consistently, two independent sets of sequences differentially methylated in sperm and somatic cells were identified for RRBS and MeDIP-chip. Remarkably, in the two sets most of the differentially methylated sequences were hypomethylated in sperm. In agreement with previous studies in other species, the sequences that were specifically hypomethylated in bull sperm targeted processes relevant to the germline differentiation program (piRNA metabolism, meiosis, spermatogenesis) and sperm functions (cell adhesion, fertilization), as well as satellites and rDNA repeats. Conclusions: These results highlight the undermethylation of bull spermatozoa when compared with both bovine somatic cells and the sperm of other mammals, and raise questions regarding the dynamics of DNA methylation in bovine male germline. Whether sperm undermethylation has potential interactions with structural variation in the cattle genome may deserve further attention. Keywords: DNA methylation, Sperm, Cattle, Satellite repeats * Correspondence: firstname.lastname@example.org UMR BDR, INRA, ENVA, Université Paris Saclay, 78350 Jouy en Josas, France Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Perrier et al. BMC Genomics (2018) 19:404 Page 2 of 18 Background In order to contribute knowledge in this field, we Sperm unique morphology and functions result from a established a thorough description of the methylome of long differentiation process that requires dynamic epigen- bull spermatozoa at different scales, using luminometric etic reprogramming of the genome , which starts with methylation assay (LUMA), methylated DNA immuno- the global erasure and reestablishment of DNA methyla- precipitation (MeDIP), reduced representation bisulfite tion marks in fetal and post-natal germ cells  and con- sequencing (RRBS) and pyrosequencing. We report here tinues throughout adulthood. The maintenance of DNA on the global DNA methylation level of bull sperm rela- methylation, the accumulation of non-coding RNAs, the tive to both bovine somatic cells and the sperm of other implementation of post-translational histone modifica- mammals, and on a comparison of genome-wide methy- tions or sperm-specific variants and finally histone-to- lation patterns between bovine sperm and somatic cells. protamine replacement then occur progressively during the sequential mitosis, meiosis, differentiation and matur- ation steps of spermatogenesis [3, 4]. The reorganization Methods of epigenetic marks during spermatogenesis enables a dra- Animals and cell/tissue collection matic compaction of the sperm nucleus, thus improving All study methods were implemented in accordance with motility and DNA damage protection in the female genital EU guidelines and regulations (Directive 2010/63/UE). tract, and plays a fundamental role in subsequent develop- For animals maintained in INRA facilities, the experi- ment of the embryo . Alterations to the epigenetic re- mental protocols were approved by the INRA local Eth- programming of the male germline may potentially affect ics Committee (COMETHEA, authorization numbers sperm functions and fertilization efficiency , and nu- 12/160 and Méjusseaumes Animal Care committee merous studies have reported associations between an ab- 0162503). The bull samples originated from bulls se- normal sperm epigenome and a low sperm count or lected for artificial insemination and were provided by sperm dysmorphia, fertilization failures, poor embryogen- commercial companies: Montbéliarde breed by GEN’- esis, low pregnancy outcomes and metabolic disorders af- IATEST (France) and UMOTEST (France), Holstein and fecting the offspring [7–17]. Accordingly, studies in Normande breeds by EVOLUTION (France) and Belgian human cohorts [12, 18, 19] and genetic or pharmaco- White Blue breed by AWE (Belgium). Other bovine tis- logical alterations to DNA methylation in mice [20–22] sues were collected from Holstein cows maintained at have emphasized the prominent role of DNA methylation the INRA experimental farm (UCEA, INRA, France). in male germ cell differentiation and male fertility. The ram, goat and boar semen and blood samples were Comparatively, studies on DNA methylation in bo- supplied by commercial companies (OSON, Capgenes vine spermatozoa are still scarce, and have often fo- and LNCR, respectively, France). Mice semen samples cused on candidate loci [23–25]. Recent genome-wide were collected from the caudal epididymis of 7-week-old studies have identified sperm DNA methylation marks male C57Bl/6JOlaHsd mice supplied by Harlan Labora- associated with subfertility in buffalo and bulls [26, 27], tory (Netherlands) and euthanized by cervical disloca- as well as regions that are hypermethylated in sperm tion. Stallion semen was supplied by Dr. M. Magistrini relative to the embryo, studied using a platform dedi- (UMR INRA 0085 PRC, France). Human sperm samples cated to small samples . However, a comprehensive originated from patients included in a PHRC METAS- view of the sperm methylome in bovine species is still PERME study, coordinated by Dr. R. Levy (Laboratoire lacking, even though this knowledge could enable d’Histologie Embryologie Cytogénétique CECOS, Hôpi- promising advances in the cattle industry. Indeed, do- tal Jean Verdier, France); this study received the approval mestication, the creation of highly specialized breeds from French ethics board (Conseil d’évaluation éthique and decades of genetic improvement have shaped the pour les recherches en santé, CERES) and all the pa- bovine genome . This undoubtedly has also had a tients gave their informed written consent to participate. profound impact on the methylome, since DNA methy- In Fig. 1c-d, semen and blood from the same individ- lation is directly affected by the CpG content of the uals were collected using standard procedures on bulls genome and its alteration by DNA polymorphism . and rams maintained in semen production centers. Total Whether these changes are of functional significance blood was used for DNA extraction. For both bulls and and contribute to the establishment of phenotypes rams, collected semen was extended with Optidyl (Cryo- needs to be ascertained. In addition, in a context of Vet) and either underwent direct DNA extraction (fresh genomic selection, more information on the epigenetic semen) or was subjected to standard techniques for features transferred to the embryo alongside the pater- semen processing (straw conditioning, freezing and stor- nal genetic heritage is necessary in order to improve age in liquid nitrogen; frozen semen). Other bull semen semen quality control procedures as well as to guaran- samples were in the form of frozen straws stored in li- tee semen fertility and proper embryo development. quid nitrogen. Perrier et al. BMC Genomics (2018) 19:404 Page 3 of 18 a b a b c c Sperm PBMC Belgian Blue Holstein Montbéliarde Normande n=185 n=73 n=14 n=112 n=41 n=18 c d p<0.05 p=0.06 Bull Ram Bull Ram Bull Ram Bull Ram fresh sperm frozen sperm fresh sperm frozen sperm sperm sperm blood blood Cattle Sheep Horse Pig Mouse Human Goat Global CCGG methylation in 45.5 ± 2.6 (min.) 69.4 76.1 (max.) 72.6 59.2 57.1 67.2 sperm (mean % ± SD) n=185 n=5 n=2 n=3 n=2 n=7 n=2 Number of CCGG sites 1,987,520 1,855,703 2,602,517 (max.) 2,366,897 1,548,665 (min.) 2,196,730 NA Gene features (%) Promoter-TSS 4.9 3.9 3.0 (min.) 3.4 6.1 7.2 (max.) NA Intron 27.0 30.4 20.8 (min.) 25.2 30.4 39.1 (max.) NA Exon 5.2 5.4 3.0 (min.) 4.1 7.1 7.3 (max.) NA TTS 2.0 2.4 1.5 (min.) 1.9 2.1 2.7 (max.) NA 3’UTR 0.4 (min.) 0.4 (min.) 1.1 0.8 1.1 1.8 (max.) NA 5’UTR 2.7 1.8 (min.) 1.9 3.2 7.1 8.6 (max.) NA Intergenic 57.8 55.7 68.7 (max.) 61.4 46.1 33.3 (min.) NA CpG density (%) Island 14.8 (max.) 11.3 NA 12.8 8.2 (min.) 12.1 NA Shore 12.8 13.0 NA 13.2 (max.) 5.3 (min.) 9.1 NA Shelve 6.0 6.2 (max.) NA 6.2 (max.) 2.8 (min.) 5.0 NA Open sea 66.4 (min.) 69.5 NA 67.8 83.7 (max.) 73.8 NA Repeats (%) LINE 16.9 15.3 6.3 5.9 (min.) 20.3 (max.) 7.0 NA SINE 11.5 12.7 13.6 26.7 10.3 (min.) 33.6 (max.) NA LTR 4.4 2.9 (min.) 3.8 3.1 12.6 (max.) 7.0 NA Satellite 1.6 (max.) 0.1 (min.) 0.3 0.2 0.1 (min.) 0.6 NA Other 2.5 2.3 2.7 2.7 1.8 (min.) 4.8 (max.) NA No overlapping repeat 63.1 66.7 73.3 (max.) 61.4 54.9 47.0 (min.) NA Fig. 1 (See legend on next page.) Global CCGG methylation (%) Global CCGG methylation (%) 40 50 60 70 40 50 60 70 80 Global CCGG methylation (%) Global CCGG methylation (%) 40 50 60 70 80 40 45 50 Perrier et al. BMC Genomics (2018) 19:404 Page 4 of 18 (See figure on previous page.) Fig. 1 Global DNA methylation level measured by LUMA is low in bovine sperm. a Global DNA methylation level in bovine sperm and PBMCs. Each colored dot represents one individual. The black dots and horizontal bars indicate the means ± standard deviations. The difference between cell types is highly significant (p < 2.2e-16, Welch’st-test). b Global DNA methylation level of sperm in four bovine breeds. The effect of the breed on CCGG methylation is significant (p < 0.05, one-way analysis of variance for independent samples). Significant differences between breeds are indicated by different letters (p < 0.05, multiple comparisons of means using Tukey’stest). c, d Global DNA methylation level in bull and ram samples. Significant differences between independent samples are indicated by asterisks (p < 0.05, permutation test), while paired samples are connected by plain lines. c Global DNA methylation level in blood cells and semen for bulls (n = 6) and rams (n =5). The p-values are indicated in red (bulls) and green (rams) for comparisons involving samples collected from the same individuals (permutation test for paired samples). d Global DNA methylation level in bull (n =6) and ram (n = 4) sperm cells from fresh and frozen semen. The difference between fresh and frozen semen is not significant. e Global sperm DNA methylation level and CCGG distribution in several mammalian species. The CCGG sites were annotated relative to gene features, CpG density and overlapping repeats. For each genomic feature examined, species with extreme values are indicated (min. and max.). In the bovine genome, CCGG sites are particularly enriched in CpG islands and satellites, and are within the ranges of other species for other genomic features. SD: standard deviation For all semen samples except those from humans and dithiothreitol (DTT) and 0.5 μg glycogen) in the pres- stallions, the possible contamination of spermatozoa by ence of 0.2 mg/ml proteinase K. After incubation with somatic cells was checked systematically under the 25 μg/ml RNAse A for 1 h at 37 °C, genomic DNA was microscope and confirmed to be below detectable levels. extracted twice using phenol and phenol:chloroform (1:1), The human and stallion semen samples contained ob- then ethanol precipitated and washed. The dried pellet servable somatic cells and were therefore processed as was re-suspended in TE buffer (10 mM Tris HCl pH 7.5, previously described to ensure the absence of potential 2 mM EDTA) and the DNA concentration was measured contamination: the human semen samples were sub- using a Qubit 2.0 Fluorometer (Invitrogen). Fresh bull jected to a stringent somatic cell lysis protocol  and semen, total blood from bulls and rams, fresh or frozen the sperm from stallions were purified by single layer semen from rams, boars, mice, goats, processed semen centrifugation using Androcoll-E-Large (SLU, Uppsala, from stallions and men were treated in an identical man- Sweden) . ner as the frozen straws from bull semen. Primary cultures of fibroblasts were derived from ear DNA extraction from liver samples was performed as skin biopsies from three separate adult heifers and cul- described elsewhere . The same procedure was used tured until passage 11 in Dulbecco’s modified Eagle for fibroblasts and monocytes, except that the cells were medium supplemented with 10% fetal calf serum and 1% lysed by the direct addition of lysis buffer and proteinase penicillin-streptavidin (Life Technologies) at 38 °C with K to the cell pellet. 5% CO . Livers were obtained from adult cows slaugh- The genotyping of two Montbéliarde bulls was per- tered at the INRA experimental facilities. Peripheral formed by LABOGENA (France) on semen and blood blood mononuclear cells (PBMCs) were isolated from DNA from the same individuals using the commercially blood collected from the jugular vein and centrifuged available BovineSNP50 v2 BeadChip (Illumina). Geno- using a Ficoll gradient. To obtain the monocyte fraction, types were determined using the Genotyping Module of PBMCs were incubated in the presence of microbeads GenomeStudio software (Illumina). For each animal, conjugated to monoclonal anti-CD14 antibodies (mouse copy number variations (CNVs) were searched for and IgG2a; Miltenyi Biotec) in MACS BSA buffer, for compared between tissues. The Log R Ratio (LRR, nor- 15 min. at 4 °C under gentle agitation. Magnetic separ- malized measurement of total signal intensity) and B Al- ation was then performed using MS Columns following lele Frequency (BAF, measurement of the allelic intensity the manufacturer’s instructions. The tissues and cells ratio) were used to infer copy number changes in the were snap-frozen in liquid nitrogen and stored at − 80 °C genome. For example, in the presence of a deletion, LRR until DNA extraction. values increase and BAF values cluster around 0 or 1 The sample types and experiments performed are but are absent at around 0.5, due to a lack of heterozy- summarized in Table 1. gotes. LRR and BAF were then plotted along the genome and compared between tissues. Genomic DNA extraction and genotyping One straw of bull semen was used for DNA extraction In silico analyses (about 20 million spermatozoa). After thawing at 37 °C, A script developed in house was used to extract the co- the semen was washed with phosphate buffer saline ordinates of all the CCGG sites present in the genomes (PBS) to remove the extender, and incubated overnight of different species (cattle, sheep, horse, pig, mouse and at 55 °C in 200 μl lysis buffer (10 mM Tris-HCl pH 7.5, human; Fig. 1e). The CCGG sites were then annotated 25 mM EDTA, 1% SDS, 75 mM NaCl, 50 mM relative to different gene features, CpG density and Perrier et al. BMC Genomics (2018) 19:404 Page 5 of 18 Table 1 Samples and experiments Experiment Species Breed/strain Sample type Sample number Figures LUMA Cattle Holstein PBMCs 73 Fig. 1a Cattle Belgian White Blue Sperm, frozen 14 Fig. 1a-b, e Cattle Holstein Sperm, frozen 112 Fig. 1a-b, e Cattle Montbéliarde Sperm, frozen 41 Fig. 1a-b, e Cattle Normande Sperm, frozen 18 Fig. 1a-b, e Cattle Montbéliarde Sperm, frozen 6 Fig. 1c-d Cattle Montbéliarde Total blood 6 Fig. 1c Sheep Ile-de-France Sperm, frozen 5 Fig. 1c-d, e Sheep Ile-de-France Total blood 5 Fig. 1c Cattle Montbéliarde Sperm, fresh 6 Fig. 1d Sheep Ile-de-France Sperm, fresh 4 Fig. 1d Horse Welsh Sperm, fresh 2 Fig. 1e Pig mixed Sperm, fresh 3 Fig. 1e Mouse C57Bl/6JOlaHsd Sperm, fresh 2 Fig. 1e Human Sperm, frozen 7 Fig. 1e Goat Alpine Sperm, frozen 2 Fig. 1e Genotyping Cattle Montbéliarde Sperm, fresh 2 Additional file 1: Figure S2 Cattle Montbéliarde Total blood 2 Additional file 1: Figure S2 MeDIP Cattle Holstein Sperm, frozen 4 Figs. 2, 3, 4, 6 Cattle Holstein Liver 4 Figs. 2, 3, 4, 6 Cattle Holstein Fibroblasts 3 Figs. 2, 3, 4, 6 RRBS Cattle Holstein Sperm, frozen 2 Figs. 2, 3, 5, 6 Cattle Holstein Monocytes 2 Figs. 2, 3, 5, 6 Cattle Holstein Fibroblasts 2 Figs. 2, 3, 5, 6 All the samples were independent, except for (i) the sperm and blood samples in Fig. 1c-d that were collected on the same bulls and rams, and (ii) two fibroblast samples and two livers that were collected on the same animals. Two independent amplifications of the same fibroblast cultures were used for MeDIP and RRBS. The sperm and blood DNA samples used for genotyping were the same as used for LUMA, Fig. 1c-d. PBMCs: peripheral blood mononuclear cells repeats by means of a pipeline developed in house FAANG/faang-methylation/tree/master/RRBS-toolkit/ (https://github.com/FAANG/faang-methylation/tree/ RR_genome). The RR genome fragments were then an- master/RRBS-toolkit/Annotation) and using the genome notated as explained above. annotation features indicated in Additional file 1: Table S1. The following criteria were applied: promoter-TSS, Luminometric methylation assay (LUMA) − 1000 to + 100 bp relative to the transcription start site Global DNA methylation levels were quantified using (TSS); TTS: -100 to + 1000 bp relative to the transcrip- LUMA, as previously described [33, 34]. Briefly, 1 μgof tion termination site (TTS); shore, up to 2000 bp from a genomic DNA was cleaved using the isochizomeres CpG island (CGI); and shelve up to 2000 bp from a HpaII (methylation sensitive) and MspI (non-methyla- shore. A site/fragment was considered to belong to a tion-sensitive) in two separate reactions and in the pres- CGI (respective shore and shelve) if an overlap of at ence of EcoRI to standardize for DNA amounts. The least 75% was observed between the site/fragment and three enzymes were purchased from New England Bio- the CGI (respective shore and shelve). A site/fragment labs. The protruding ends were then used as templates was considered as being overlapped by a repetitive elem- for pyrosequencing with the Pyromark Q24 device and ent whatever the extent of this overlapping. Pyromark Gold Q96 reagents (Qiagen). The lumino- In Additional file 1: Tables S2 and S3, in silico reduced metric signals produced by either the sequential incorp- representation (RR) genomes digested by MspI restric- oration of C and G nucleotides (reflecting the number of tion enzyme were produced for different species and CCGG sites digested by HpaII or MspI) or the sequen- using different size selection criteria by means of an- tial incorporation of A and T nucleotides (reflecting the other pipeline developed in house (https://github.com/ number of AATT sites digested by EcoRI), were then Perrier et al. BMC Genomics (2018) 19:404 Page 6 of 18 quantified using Pyromark Q24 software. Each sample contrasting behaviors of liver and fibroblasts. Among was assayed in duplicate. The global methylation per- the DMRs, those undermethylated in sperm were se- centage per sample was then calculated as follows: lected based on a positive value for both Pr -Pr liver sperm and Pr -Pr and were annotated as ex- fibroblasts sperm Average signal obtained with HpaII after EcoRI normalization Methylation% ¼ 100− 100 plained regarding the in silico analyses, together with Average signal obtained with MspI after EcoRI normalization the 27,684 regions of interest. Genes containing DMRs The conditions were compared using non-parametric were subjected to DAVID analysis (Database for Anno- tests suited to small samples (permutation tests for two tation, Visualization and Integrated Discovery; ) independent samples or for two paired samples accord- using genes containing the 27,684 regions of interest as ing to the situation, with Monte-Carlo sampling of the background. 100,000 permutations) or using t-test and analysis of variance when appropriated (larger samples with a nor- Reduced representation bisulfite sequencing (RRBS) and mal distribution). data analysis RRBS libraries were prepared as described elsewhere Methylated DNA immunoprecipitation (MeDIP), [37, 38]. Briefly, 200 ng of genomic DNA were digested microarray hybridization and data analysis by MspI (Thermo Scientific), end-repaired and ligated MeDIP and quality controls by PCR were performed as to 55 bp Illumina adapters for paired-end sequencing. described elsewhere . The antibody used for immu- Size selection by gel excision was performed in order to noprecipitation was BI-MECY-1000 5-methylcytidine select fragments ranging from 150 to 400 bp (genomic antibody (Eurogentec). To prevent any technical bias, fragments of 40-290 bp + adapters). The DNA was then the products of five independent MeDIP experiments purified using the MinElute gel extraction kit (Qiagen) were pooled for each sample. After moderate genome and then bisulfite-converted twice consecutively with amplification, the pooled MeDIP reactions and corre- the EpiTect bisulfite kit (Qiagen), following the manu- sponding input DNA were labelled with Cy3 and Cy5 facturer’s instructions for DNA extracted from FFPE and hybridized on a Roche-NimbleGen 3x720K micro- tissues. Converted DNA was amplified with Pfu Turbo array, with technical dye-swaps for every sample. The Cx hotstart DNA polymerase (Agilent) using 14 PCR microarray targeted the promoter region (− 2000 to + cycles for sperm and fibroblasts and 12 cycles for 1360 bp relative to the gene start) of 21,296 bovine monocytes. The libraries were then purified using genes, according to an annotation file downloaded from Agencourt Ampure beads (Beckman-Coulter) and se- the Johns Hopkins University Center for Computational quenced on an Illumina HiSeq2500 sequencer to pro- Biology FTP website (ftp://ftp.ccb.jhu.edu/pub/data/as- duce 75 bp paired-end reads (Integragen SA, France). sembly/Bos_taurus/Bos_taurus_UMD_3.0/annotation/; RRBS sequences were analyzed using an integrated accessed Aug. 2010). The microarray design and pipeline combining scripts developed in house in Py- hybridization protocol, as well as more details on the thon, R and Shell, together with external tools (https:// data analysis, can be found in . The identification of github.com/FAANG/faang-methylation/tree/master/ regions of interest containing clusters of probes RRBS-toolkit/). Details about the analysis and the identi- enriched in at least one tissue, the identification of dif- fication of differentially methylated cytosines (DMCs) ferentially methylated regions (DMRs) among these re- are provided in the Additional file 1: Supplementary gions of interest, as well as the calculation of mean methods. percentages of enriched probes (Pr) in each tissue for For each tissue, the mean methylation percentage was each region r (DMR or region of interest), are detailed calculated (mean of the methylation percentages ob- in the Additional file 1: Supplementary methods. Three tained in the two biological replicates) as well as the dif- Pr were obtained per region: Pr ,Pr and Pr ference between two tissues (Additional file 2, column sperm liver fibro- . The scatterplot shown in Fig. 3d illustrates the L). The scatterplot shown in Fig. 3e illustrates the differ- blasts Pr -Pr and Pr -Pr differences for ences between monocytes and sperm and between fibro- liver sperm fibroblasts sperm the regions of interest (in black) and for the DMRs spe- blasts and sperm for the 1,580,644 CpGs covered by 5 to cific to the comparison between sperm and somatic 500 uniquely mapped reads (CpGs 5-500) in all six sam- cells (in red). Positive values for both Pr -Pr ples (in black) and for the DMCs specific to the com- liver sperm and Pr -Pr indicated that the two somatic parison between sperm and somatic cells (in red). fibroblasts sperm cell types were more methylated than sperm in the re- Among these DMCs, those undermethylated in sperm gion considered, while negative values for both Pr - were selected based on a positive value for both differ- liver Pr and Pr -Pr indicated the contrary. ences. Together with the CpGs 5-500, they were then sperm fibroblasts sperm Similarly, an opposite sign for the values of Pr - annotated relative to gene features, CGIs and repeats as liver Pr and Pr -Pr reflected the explained for the in silico analyses. Genes containing sperm fibroblasts sperm Perrier et al. BMC Genomics (2018) 19:404 Page 7 of 18 DMCs were subjected to DAVID analysis using genes were weak (2.6% for sperm and 1.6% for PBMCs), dem- containing the 1,580,644 CpGs 5-500 as the background. onstrating the limited inter-individual variability within To better characterize repetitive elements, an artificial each cell type and the reliability of the technique to as- genome containing the consensus sequence of each bo- sess global DNA methylation. Because the sperm sam- vine repeat was constituted from the Repbase database ples were collected from Holstein, Montbéliarde, . Reads were aligned on this artificial genome as ex- Normande and Belgian White Blue bulls, we investigated plained above, and the average methylation percentage the effect of the breed on global sperm DNA methyla- was calculated for each repeat and each sample (average tion. Methylation was significantly lower in Belgian methylation percentage for all CpGs included in one White Blue than in any other breed and significantly genomic repeat and covered by either 5-500 reads or by lower in Holstein than in Normande and Montbéliarde > 500 reads). (Fig. 1b). A bootstrap analysis confirmed that these breed-related differences were not due to an unbalanced Bisulfite-pyrosequencing number of bulls from each breed (Additional file 1: Bisulfite conversion was performed on 1 μg genomic Figure S1). These results demonstrated that global DNA as described elsewhere . After ethanol precipi- methylation varied across different bovine breeds, sug- tation, the DNA pellet was re-suspended in 20 μlH O. gesting that the presence of DNA polymorphism could For LSM4 and BTSAT4, primers were designed using influence the global CCGG content and methylation. the MethPrimer program  and amplifications were However, the range of variation (from 42.6% in Belgian carried out from 1 μl treated DNA with Platinum Taq White Blue to 47.1% in Normande) was weak relative to DNA polymerase (Invitrogen), according to the manu- the 30% difference we observed between sperm and facturer’s instructions with variable MgCl concentra- PBMCs, suggesting that sperm weaker methylation was tions. The following program was used: 3 min. at 94 °C not breed-dependent. To determine whether the global followed by 50 cycles of 30 s. at 94 °C, 1 min. at variable DNA methylation of sperm was comparably low in an- hybridization temperatures, 1 min. at 72 °C, and finally other species, we collected paired semen and blood sam- 10 min. at 72 °C. For DDX4 and SYCP3, primers were ples from bulls and rams. While sperm was less designed using the Pyromark assay design software methylated than blood in both species (Fig. 1c), the dif- (Qiagen) and amplifications were performed using the ference between the two cell types was much greater for Pyromark PCR kit (Qiagen) according to the manufac- bulls (30% less methylation in sperm than in blood) than turer’s instructions. The primers used to amplify each for rams (only 10% less methylation in sperm than in region are listed in Additional file 1:Table S4,together blood). Differences between species were observed in with the hybridization temperatures and MgCl con- both cell types, but were broader for sperm (20% differ- centrations. The reverse primers were 5′-biotinylated. ence) than for blood (only 1.2% difference). In sperm, After denaturation and purification, the biotinylated the difference between species was independent of the antisense strand of PCR product was used as a template cryopreservation process (Fig. 1d). for pyrosequencing with 0.3 μM pyrosequencing primer, This lower methylation of sperm may have resulted using the Pyromark Q24 device and Pyromark Gold Q96 from a biased representation of the regions present in reagents (Qiagen). The pyrosequencing primers are our sperm genomic DNA. Indeed, the high level of listed in Additional file 1: Table S5. Each CpG was sperm chromatin compaction impeded the complete assayed in duplicate, and inconsistent duplicates (more extraction of genomic DNA using standard proce- than 5% difference) were repeated. The methylation per- dures, which were therefore optimized by the addition centage per CpG was then obtained by calculating the of reducing agents such as DTT (see for instance mean of all replicates that passed quality control by the ). The protocol we used during this study per- Pyromark Q24 software. The statistical analysis was per- formed well for DNA extraction from human sperm formed on the mean percentage per CpG using permu- (50 mM DTT, ). To investigate whether these con- tation tests as explained for LUMA. ditionswerealsosuitedtobovinesperm,weused genomic DNA from twopairedsperm andblood Results samples(thesamesamples as in Fig. 1c), both of Global DNA methylation level is low in bull sperm which were purified in the presence of 50 mM DTT, We first assessed the global level of DNA methylation in as a template for genotyping. As expected, the same bull sperm relative to somatic cells (PBMCs) using genotype was obtained from blood and sperm DNA LUMA [33, 34] on a large sample size. The average in each bull. Likewise, for each animal, CNV profiles methylation at CCGG sites was dramatically lower in were compared between tissues because any differ- sperm (45.5%, n = 185) than in PBMCs (74.8%, n = 73; ence might be indicative of preferential extraction. Fig. 1a). The standard deviations in this large sample The plots were similar and no gross discrepancies Perrier et al. BMC Genomics (2018) 19:404 Page 8 of 18 could be observed between tissues (Additional file 1: enzymatic digestion (MspI) and the size selection of Figure S2). This result therefore ruled out the possi- restriction fragments. Because no data were available bility that some specific regions of the bovine genome regarding the optimal size window for RRBS in cattle, failed to be extracted from sperm chromatin under we conducted an in silico prediction of the genome our experimental conditions. coverage by RRBS and compared the results with Because the genomic distribution of CCGG sites may those of MeDIP-chip (Additional file 1: Supplemen- display species-specific variations that might influence tary methods, Tables S2-S3). These in silico analyses the methylation results, we compared sperm CCGG suggested that the RRBS procedure could be success- methylation in several species relative to the genomic fully adapted to the bovine genome using MspI with features available. In the horse, pig, mouse, human and a size window of 40-290 bp, and would lead to a goat, sperm DNA methylation level was much higher base-resolution map of the methylome with coverage than that measured in bovine and closer to that deter- that would complement that of the MeDIP-chip. mined in sheep (Fig. 1e). The CCGG distribution in the Independent bovine samples were analyzed using bovine genome was within the range observed in other MeDIP-chip (sperm, liver and fibroblasts) and RRBS species for most of the features examined, except for (sperm, monocytes and fibroblasts) in order to determine CGIs that accounted for 14.2% of total CCGG sites in cell type-dependent variations of the methylome using bovine vs. 8.0 to 12.2% in other species. The important these two complementary technologies. For RRBS we se- representation of bovine CCGGs in CGIs was balanced lected CpGs covered by 5 to 500 uniquely mapped reads by a low representation in open sea (regions with a low for each sample (CpGs 5-500), from which an average CpG density). Most mammalian CGIs are unmethylated methylation rate was calculated. The average methylation in somatic cells and can become methylated during de- in sperm (51.8%) was higher than in fibroblasts (48%) velopment and disease . Because blood cells (in and lower than in monocytes (57.6%; Additional file 1: which most CGIs are supposed to be unmethylated) dis- Table S6). We next categorized the CpGs 5-500 into played a roughly similar methylation level in bovine and hypo- (< 20% methylation), intermediate (20–80% me- sheep, it is unlikely that the lower level of DNA methyla- thylation) and hypermethylated CpGs (> 80% methyla- tion in bull sperm resulted solely from the higher per- tion) and observed a larger proportion of hypo- and a centage of bovine CCGG sites in CGIs. Another smaller proportion of intermediate CpGs in sperm than in remarkable feature of the bovine CCGG sites was their somatic cells, which was counterbalanced by a large pro- strong enrichment in satellites, which represented 1.6% portion of hypermethylated CpGs (Fig. 2a). This bimodal of all CCGGs vs. 0.1 to 0.6% in other species. However, distribution of methylation in sperm probably explained whatever the species examined, the < 2% difference in the intermediate level of average methylation for CpGs 5- CCGG sites present in satellites could not account for 500. It was noted that when only CpGs covered by more the > 10% difference in sperm methylation (Discussion). than 500 uniquely mapped reads were considered (CpGs Taken together, these data demonstrated that compared > 500), average methylation increased in monocytes (81. to somatic cells, bull sperm displayed a dramatically lower 4%) and in fibroblasts (66.6%), but fell dramatically in level of CCGG methylation which seemed to be specific to sperm (22.5%; Additional file 1: Table S6). the bovine species. This lower methylation was neither re- We next conducted descriptive analyses of the MeDIP- lated to the process of semen cryopreservation nor to a chip and RRBS data. For MeDIP-chip, a normalized factor technical artefact, and could not be fully explained by the NEpi, representing the number of enriched probes at pro- genomic distribution of CCGG sites in the bovine species. moter p for sample i, was calculated for all promoters and samples, and hierarchical clustering and principal compo- The tissue/cell type is a major determinant of DNA nent analysis (PCA) were run on the resulting matrix. For methylation landscapes in cattle RRBS, PCA and hierarchical clustering were computed Because high-throughput analyses were necessary to from the matrix of methylation percentages obtained for identify regions that were hypomethylated in bovine each CpG 5-500 and each sample (Fig. 2b). With both sperm, we decided to assess two cost-effective ap- types of descriptive analysis, the samples were clearly proaches which are widely used to study DNA methy- clustered according to the tissue/cell type using both lation: MeDIP-chip  and RRBS . MeDIP-chip MeDIP-chip and RRBS. Interestingly, hierarchical cluster- enables precise targeting of specific regions in the ing revealed that the distance between sperm and other genome through custom design of the microarray (in cell types was more important than the distance between our case, 3360 bp spanning the promoter and up- liver and fibroblasts or monocytes and fibroblasts, stream region of each of the 21,296 bovine genes highlighting the methylation specificities of germinal cells ), while RRBS offers a base-resolution analysis of compared to somatic cells. This could also be seen in CpG-rich regions through the combined use of PCA, where dimension 1 opposed sperm to one or both Perrier et al. BMC Genomics (2018) 19:404 Page 9 of 18 of enriched probes between two tissues/cell types in 27,684 regions of interest containing clusters of probes enriched in at least one tissue/cell type (see Additional file 1: Supplementary methods). This led to the identifi- cation of 4329 DMRs between sperm and liver; 3780 DMRs between sperm and fibroblasts and 2803 DMRs between fibroblasts and liver. The features of each DMR and the corresponding promoter regions are summa- rized in Additional file 3. For RRBS, we identified 298,901 DMCs between monocytes and sperm; 450,971 DMCs between fibroblasts and sperm, and 239,036 DMCs between monocytes and fibroblasts using strin- gent criteria (Additional file 1: Supplementary methods, MeDIP Table S7 and Additional file 2). Fibroblasts Consistent with the results of clustering and PCA, the Liver Sperm number of DMRs/DMCs was higher between sperm and any somatic cell type/tissue than between two somatic cell types, for both MeDIP and RRBS. Fig. 3a-b shows the distribution of DMRs and DMCs in different Venn territories corresponding to pairwise comparisons under MeDIP (Fig. 3a) and RRBS (Fig. 3b). DMRs and DMCs specific to the comparison between sperm and somatic RRBS cells could be deduced from these territories (in red). Fibroblasts These 1678 DMRs and 174,103 DMCs were located at Monocytes Sperm the intersection between the “sperm vs. somatic cell type 1” and “sperm vs. somatic cell type 2” territories (yellow and orange) excluding the DMRs/DMCs also shared by the “somatic cell type 1 vs. somatic cell type 2” compari- son (three-color territory). Because the comparison be- tween sperm and fibroblasts was performed using both MeDIP and RRBS, the actual complementarity of the Fig. 2 Cell type is a major determinant of DNA methylation landscapes two technologies could be assessed using real data. As in cattle. a Proportion of hypo- (< 20% methylation), intermediate (20–80% methylation) and hypermethylated CpGs (> 80% methylation) shown in Fig. 3c, only a limited subset of CpGs was in each RRBS library, showing contrasted distributions between cell shared by DMRs identified using MeDIP and DMCs types. b Descriptive analyses. Upper panel: MeDIP-chip on sperm (n = identified using RRBS. This result, together with the in 4, red), fibroblasts (n = 3, green) and liver (n = 4, blue). For each sample silico analysis indicating that the targeted regions were i and each promoter p, a normalized number of enriched probed largely different under MeDIP-chip and RRBS, clearly NEpi was computed (see Additional file 1: Supplementary methods). Correlation clustering and PCA were then performed on the matrix demonstrated that we were able to identify two distinct of the normalized number of enriched probes. Lower panel: RRBS on subsets of sperm-specific DMRs/DMCs. sperm (n = 2, red), fibroblasts (n = 2, green) and monocytes (n =2, We next investigated whether these two subsets of blue). Correlation clustering and PCA were run on the totality of CpGs sperm-specific DMRs/DMCs might display similar fea- covered between 5 and 500 reads in the six samples tures in terms of variations in methylation. For both MeDIP (Fig. 3d) and RRBS (Fig. 3e), we plotted the dif- somatic cells/tissue, while the difference between the two ferences in methylation between each somatic cell type somatic cell types was more apparent along dimension 2. and sperm for sperm-specific DMRs/DMCs. The appro- Our results therefore showed that the tissue/cell type priate background was used for each technology: the represented the main source of variation in methylation, 27,684 regions of interest subjected to differential analysis and that sperm-specific methylation profiles could in MeDIP and the 1,580,644 CpGs 5-500 covered in all six emerge from our MeDIP and RRBS data. samples analyzed using RRBS. With both MeDIP and RRBS, background regions/CpGs (in black) were particu- Identification of regions and CpGs hypomethylated in larly concentrated around the center of the plot, illustrat- bull sperm ing that most of the regions/CpGs analyzed did not We next ran a differential analysis on each pair of tis- display cell type-dependent variations. By contrast, sperm- sue/cell types. For MeDIP, we compared the proportion specific DMRs/DMCs were located along a diagonal that spz84 spz32 spz34 spz34 spz55 spz81 mono1 F2251 F029 mono2 F5538 F5538 F029 5055 Perrier et al. BMC Genomics (2018) 19:404 Page 10 of 18 a b MeDIP (DMRs) RRBS (DMCs) Sperm vs Sperm Sperm vs Sperm Fibroblasts vs Liver Fibroblasts vs Monocytes 940 1,678 1,645 119,509 174,103 51,078 52 27,537 1,110 954 129,822 46,183 687 35,494 Fibroblasts vs Fibroblasts vs Liver Monocytes MeDIP RRBS 58,604 2,849 448,122 (61,453 CpGs in DMRs) (450,971 DMCs) d e MeDIP (1,678 DMRs) RRBS (174,103 DMCs) 1,144/1,678 (68%) 137,857/174,103 (79%) 5,660/27,684 (20%) 358,634/1,580,644 (23%) 36,243/174,103 (21%) 534/1,678 (32%) 495,627/1,580,644 (31%) 12,024/27,684 (43%) Liver-sperm (% methylation) Monocyte-sperm (% methylation) Fig. 3 Two distinct sets of differentially methylated regions and differentially methylated CpGs display undermethylation in sperm. a Venn diagram showing the DMRs identified using MeDIP-chip in sperm, fibroblasts and liver. A total of 1678 DMRs specific to the comparison between sperm and somatic cells was obtained (in red). b Venn diagram showing the DMCs identified using RRBS in sperm, fibroblasts and monocytes. A total of 174,103 DMCs specific to the comparison between sperm and somatic cells was obtained (in red). c The CpG positions included in the 3780 DMRs identified between sperm and fibroblasts using MeDIP were extracted. The Venn diagram shows the intersection between these CpGs and the 450,971 DMCs identified between sperm and fibroblasts using RRBS. d, e Scatterplots showing the methylation differences between two somatic tissues and sperm, for all regions or CpGs used during differential analysis (in black; background) and for DMRs/DMCs specific to the comparison between sperm and somatic cells (in red). The proportions of background regions/CpGs overmethylated (upper right edge) and undermethylated (lower left edge) in both somatic tissues compared to sperm are indicated in black. The proportions of DMRs/DMCs overmethylated and undermethylated in both somatic tissues compared to sperm are indicated in red. d Differences in methylation between liver and sperm (Pr -Pr ; x-axis) and between fibroblasts and sperm (Pr - liver sperm fibroblasts Pr ; y-axis) for regions and DMRs identified using MeDIP (see Additional file 1: Supplementary methods for the definition of Pr ,Pr and Pr ). sperm sperm liver fibroblasts e Differences in methylation between monocytes and sperm (x-axis) and between fibroblasts and sperm (y-axis) for CpGs and DMCs identified using RRBS ran from the lower left-hand corner to the upper right- comparison between sperm and somatic cells, most of hand corner, meaning that these DMRs/DMCs behaved them being less methylated in sperm. These hypomethy- similarly in the two somatic cell types when compared to lated sperm-specific DMRs/DMCs (hypo-DMRs/DMCs) sperm. Most strikingly, a great majority of sperm-specific could therefore partly explain the lower global DNA DMRs/DMCs (68% for MeDIP and 79% for RRBS) were methylation of sperm observed in LUMA which was par- grouped in the upper right-hand corner, indicating that ticularly marked in cattle. they were hypomethylated in sperm when compared to the two somatic cell types examined. Hypomethylation in bull sperm targets specific genomic Taken together, these results demonstrated that using features and functions two complementary technologies we were able to identify To determine whether specific gene ontology (GO) terms two distinct subsets of DMRs/DMCs specific to the were enriched in the sperm hypomethylated regions, we Fibroblast-sperm (% methylation) Fibroblast-sperm (% methylation) Perrier et al. BMC Genomics (2018) 19:404 Page 11 of 18 next annotated the hypo-DMRs identified by MeDIP rela- mapped reads (that had been used to identify hypo- tive to genes. Consistent with the microarray design, most DMC) and CpGs targeted by ambiguous reads, that of the 1144 hypo-DMRs were located in or close to genes specific families of repetitive elements were massively according to the criteria described in the Methods, result- hypomethylated in bovine sperm. ing in a list of 701 unique genes which were then sub- Regarding gene features with respect to RRBS data, the jected to DAVID analysis. Significant enrichments were proportion of genes containing hypo-DMCs was relatively found for biological processes such as sexual reproduction unchanged compared with background (Fig. 5a, left panel) (36 genes), fertilization (15 genes) and RNA transport (11 , but interestingly, exons were more frequently repre- genes). Further analysis using a more restrictive list of GO sented (26.8% vs. 13.8% in background) while promoter- terms led to the identification of functional clusters in- TSS were represented less than in background (4.8% vs. volved in mRNA processing (Fig. 4a) and meiosis/sperm- 13.1%). The distribution of hypo-DMCs in CGIs, shores atogenesis (Fig. 4b). and shelves was identical to that observed with the back- Because CpGs targeted by RRBS are scattered along ground (middle panel). We investigated whether specific the genome, we then started to characterize the hypo- GO terms were enriched in gene features displaying a dif- DMCs identified by RRBS relative to different genomic ferent representation in hypo-DMCs and background. For features (genes, CpG density and overlapping repeats; exonic hypo-DMCs, which accounted for 2713 unique Fig. 5a). The most remarkable observation was a dra- genes, significant enrichments were found for biological matic enrichment of hypo-DMCs for repeats (24.5% vs. processes such as cell adhesion (213 genes), the regulation 13.2% in background; right panel), and particularly for of signaling (382 genes) and cell migration (177 genes), satellites (64.7% vs. 17.8%). In order to get a more pre- and one main functional cluster related to cell adhesion cise picture of the methylation status of repetitive ele- could also be identified (Fig. 5b). For hypo-DMCs located in ments by rescuing some of the information included in promoter-TSS, which accounted for 1200 unique genes, the the ambiguous reads, we aligned the reads on a Repbase most enriched biological process was sexual reproduction artificial genome containing the consensus sequence of (44 genes), and two functional clusters were identified each bovine repeat (see Additional file 1: Supplementary (Fig. 5c) as being related to piRNA metabolism (left panel) methods for details, and Additional file 4 and Additional and to meiosis and spermatogenesis (right panel). file 1: Table S8 for data). The hypomethylation of sperm From an analysis of both hypo-DMRs obtained in was clear in satellites and also in rDNA repeats encoding MeDIP and hypo-DMCs identified through RRBS, we ribosomal RNAs (Additional file 1: Figure S3). We there- therefore concluded that undermethylation in sperm es- fore concluded from both CpGs targeted by uniquely sentially targeted repeats and the promoters of genes Fig. 4 Hypo-DMRs identified by MeDIP-chip target genes involved in mRNA processing and spermatogenesis. Genes containing the 1144 hypo-DMRs were subjected to DAVID analysis, with the regions of interest used as the background. Terms of gene ontology, pathways or Uniprot keywords enriched among the DMRs and their corresponding p-values are indicated, as are the genes present in each category. The green color on the heatmap represents a correspondence between a gene and a category. To limit the size of the heatmaps, only GO terms designated as DIRECT by DAVID were used for cluster generation. a Functional cluster related to mRNA processing. b Functional cluster related to meiosis and spermatogenesis Perrier et al. BMC Genomics (2018) 19:404 Page 12 of 18 Gene features CpG density Overlapping repeats Fig. 5 Hypo-DMCs identified by RRBS target specific genomic features and functions. The 137,861 hypo-DMCs and 1,580,644 CpGs (background) were annotated relative to gene features, CpG density and overlapping repeats. a Distribution of hypo-DMCs and background CpGs among these genomic features. b Genes with hypo-DMCs located in exons were subjected to DAVID analysis, with genes from some of the 1,580,644 CpGs in exons used as the background. The heatmap represents a functional cluster related to cell adhesion. c Genes with hypo-DMCs located in promoter-TSS were subjected to DAVID analysis, with genes containing some of the 1,580,644 CpGs in promoter-TSS used as the background. The heatmaps represent functional clusters related to piRNA metabolism (left panel) and to meiosis and spermatogenesis (right panel). To limit the size of the heatmaps, only GO terms designated as DIRECT by DAVID were used for cluster generation Hypo-DMCs Background Perrier et al. BMC Genomics (2018) 19:404 Page 13 of 18 important to spermatogenesis (which is the differenti- obtained by MeDIP analysis and DMCs obtained by ation process that eventually leads to the mature sperm RRBS analysis whenever possible, and their position rela- we analyzed), but also to genes involved in cell commu- tive to genes involved in sperm functions. Figure 6a nication, signaling and migration that may be essential shows the detailed localization of these regions together to both sperm functions and post-fertilization steps. with their coverage and individual methylation in both MeDIP and RRBS. LSM4, which contained a hypo-DMR, Hypomethylation of four regions is confirmed by is a gene involved in RNA processing . Genes SYCP3 bisulfite-pyrosequencing and DDX4, which contained both hypo-DMRs and Four regions were selected for validation, based on their hypo-DMCs identified in our study, play a major role in hypomethylation in sperm, the presence of both DMRs spermatogenesis insofar as either mutation or aberrant ab chr7:4,816,562-4,818,894 Liver LSM4 Fibro. Liver Fibro. Sperm Probes Mono. Fibro. Sperm Sperm CpGs LSM4 chr20:23,444,000-23,445,400 Liver DDX4 Liver Fibro. Fibro. Sperm Probes Mono. Fibro. Sperm Sperm CpGs DDX4 chr5:65,888,500-65,889,300 SYCP3 Liver Fibro. Liver Sperm Fibro. Probes Mono. Fibro. Sperm Sperm CpGs SYCP3 BTSAT4 chr9:45,022,400-45,022,645 Liver Mono. Fibro. Fibro. Sperm Sperm CpGs BTSAT4 Fig. 6 Validation by bisulfite-pyrosequencing. a IGV browser views of the gene regions targeted for pyrosequencing. In the MeDIP-chip panels, the “Probes” track indicates the probe positions on the microarray. The blue, green, and red bar charts represent probes with signal enrichment in the MeDIP fraction for liver, fibroblast and sperm samples, respectively. An absence of chart at a given probe position denotes that signal was not enriched in the MeDIP fraction. In the RRBS panels, the blue, green, and red bar charts represent the methylation percentages at each CpG 5- 500 position for monocyte, fibroblast and sperm samples, respectively. Individual CpGs are shown, as are the MeDIP probe classes based on CpG frequency (the upper, middle and lower bands represent high, intermediate and low class probes, respectively). The orange boxes delineate the regions analyzed by pyrosequencing. b Methylation percentages of the CpGs assayed by pyrosequencing in sperm (n = 6), fibroblasts (n = 3) and liver (n = 4). The difference between sperm and somatic cells is significant at every position (p < 0.05, permutation test) RRBS RRBS MeDIP RRBS MeDIP RRBS MeDIP Perrier et al. BMC Genomics (2018) 19:404 Page 14 of 18 methylation of these genes associate to male infertility reproduction, fertilization, cell adhesion and migration, [46, 47]. BTSAT4 was the most frequently represented meiosis, RNA transport and processing (including bovine satellite in our RRBS data and displayed under- piRNA metabolism), and the regulation of signaling. methylation in sperm (Additional file 4). Overall, the Interestingly, genes involved in these processes displayed four regions represented 42 analyzed CpGs. We used the highly dynamic expression in post-natal mouse sperm- pyrosequencing of bisulfite-converted DNA to atogonial stem cells . In human sperm, some of these quantify the absolute methylation percentage of individ- processes (cell adhesion, sexual reproduction, meiosis ual CpGs in sperm, liver and fibroblasts (Fig. 6b; gen- and piRNA metabolism) are enriched in hypomethylated omic DNA from monocytes was in limited amounts and promoters . Hypermethylation of the piRNA ma- was saved for separate investigations). Consistent with chinery in testes has also been associated with human the MeDIP data showing enriched probes along the spermatogenic disorders . In several species includ- LSM4 promoter in liver and fibroblasts but not in ing bovine, the undermethylation of mature spermatozoa sperm, the CpGs analyzed by pyrosequencing were all could therefore reflect a dynamic sequence of past tran- hypomethylated in sperm. In this region, the CpGs scriptional events in the male germline differentiation assessed by pyrosequencing were not covered by RRBS, program which are essential to sperm functions. but those in DDX4, SYCP3 and BTSAT4 were covered Another striking finding revealed by our RRBS data by the three techniques and displayed hypomethylation was the undermethylation of repetitive elements in bull in sperm whatever the technique used. By pyrosequenc- sperm, especially satellites. How to analyze repetitive ing four additional CpGs, we also checked that one re- sequences is still a matter of debate because of poten- gion specifically hypermethylated in sperm compared to tial mapping artefacts [52, 53]. We initially decided to somatic cells validated (Additional file 1: Figure S4). discard ambiguous reads, which may have led to an In conclusion, the results obtained using MeDIP, RRBS underestimation of the total contribution of repeats to and pyrosequencing were in excellent agreement, which hypomethylated loci in sperm. Alternatively, we aligned validated the high-throughput data and led to the the sequences on an artificial genome that contained characterization of gene regions with methylation pat- one copy of each bovine repetitive element, and were terns specific to bull sperm. able to confirm the undermethylation of satellites and rDNA repeats encoding ribosomal RNAs in bull sperm. The undermethylation of satellites in sperm has long Discussion been described in several species, including bovine, by During this study, we performed DNA methylation ana- analyses of candidate sequences [54–56]. More re- lyses at different genome scales in order to exhaustively cently, the undermethylated status of satellites in hu- characterize the bovine sperm methylome. Our main man and chimp sperm has been generalized to the findings were that global DNA methylation level was whole genome . Satellites are essential components low in bull sperm compared with other species, and that of the constitutive heterochromatin in mammals, and bull sperm was less methylated than bovine somatic cells this function is partly mediated by DNA methylation in the context of two genome-wide methylation assays . Satellites play key roles in chromosome structure, targeting distinct genomic regions, namely RRBS and stability and segregation. Through their high molecular MeDIP-chip, with a focus on gene promoters. dynamics and ability to drive chromosome rearrange- The undermethylation of bull sperm compared to ments, they are considered to be major actors in dis- somatic cells agreed well with findings in other mam- eases such as cancer, but also in genome evolution and mals and provides additional evidence of features of the speciation . The significance of satellite under- male germline differentiation program being conserved methylation in sperm could be related to the transcrip- across species. Hypomethylated loci have been identified tional burst that arises from paternal satellites in early for instance in the sperm of human and chimp using mouse development, which is necessary for normal for- whole genome bisulfite sequencing  or the Infinium mation of the heterochromatin in embryos and for de- 450 K methylation platform [19, 49]. Together with our velopmental progression . Consistent with this work, these reports demonstrate that the undermethy- important transcriptional activity, satellites remain lated status of sperm is independent of both the genome hypomethylated after fertilization in normal preimplan- coverage and resolution of the technology used to map tation embryos [56, 60]. In contrast, embryos resulting DNA methylation, and suggest that the presence of from somatic cell nuclear transfer (SCNT) and have a hypomethylated loci is conserved among mammals. We reduced development potential, display somatic-like found that promoters and exons of genes hypomethy- hypermethylated satellites in the mouse  and bovine lated in bull sperm were enriched for biological pro- [61–63]. Of note, the hypermethylation of satellites cesses essential to sperm functions, such as sexual seems to persist in the sperm of adult SCNT-derived Perrier et al. BMC Genomics (2018) 19:404 Page 15 of 18 bulls , suggesting that it has resisted the two waves precludes their correct integration in genome assem- of epigenetic reprogramming that occur during early blies, which probably led to an underestimation of their development and germ cell differentiation. contribution to global CCGG methylation. Although we Strikingly, the sperm-specific hypomethylated se- cannot rule out that the weaker global methylation in quences identified throughout this study, and which bull sperm is due to a higher representation of bovine were particularly enriched in genes related to germline satellites in CCGGs, an alternative explanation might be differentiation and in satellite and rDNA repeats, dis- that satellite methylation is quantitatively lower in bull played several common features. Firstly, these hypo- sperm than in the sperm of other species. This is sup- methylated sequences have previously been described as ported by an old report which demonstrated that relative targets of DNMT3B de novo DNA methyltransferase. In- to somatic tissues, most satellites are largely under- deed, the methylation of satellites is half-reduced in the methylated in the sperm of cattle while they are only germline of newborn male mice deficient for DNMT3B slightly undermethylated in mouse sperm . The . In humans, DNMT3B mutations lead to the ICF abundance of satellites in the bovine genome, together syndrome (Immunodeficiency Centromeric instability with their low methylation content in male germ cells, Facial anomalies). In somatic cells, this disease is associ- may contribute to explaining some of the bovine-specific ated with a hypomethylated status of germline genes and features of meiotic recombination. Crossing-over events centromeric satellites that closely mimics that of gam- are more frequently observed in the spermatocytes of etes, affecting nuclear organization and chromosome Bos taurus than in those of related Bovidae species (wil- stability . Another common feature shared by debeests; ). Moreover, the meiotic recombination sperm-specific hypomethylated sequences is that they rate in cattle is particularly elevated in males, while in partly remain associated to nucleosomes in mature most species it is higher in females [72, 73]. The fre- spermatozoa, as reported for genes involved in RNA quency of crossing-overs is usually low in repeat-rich processing and for repetitive elements (including centro- domains associated with heterochromatin ; however, meric satellites) in humans and bovine , and for sat- the weak methylation of satellites in bull germ cells ellites and rDNA repeats in bovine . In addition, probably reflects a particular chromatin structure that sperm histone retention particularly affects CpGs that may promote crossing-overs. The undermethylation of lack methylation in humans  and the mouse . bull spermatozoa may also have contributed to shaping The association with retained nucleosomes in sperm, to- the bovine genome. Indeed, segmental duplications in gether with the important function of paternal satellite the bovine reference genome are particularly enriched transcripts in early embryos and the hypermethylation of for satellite repeats that are undermethylated in bull satellites in SCNT embryos, support the hypothesis that sperm, including BTSAT4 . Segmental duplications regions which are hypomethylated in sperm play a fun- promote both chromosome rearrangements that drive damental role not only in germline differentiation but bovine genome evolution (as indicated by their enrich- also in post-fertilization epigenetic reprogramming. ment in the vicinity of cattle-specific evolutionary break- The final question arising from our study originates points ), and inter-individual variability through from the intriguing finding that global methylation at their ability to promote CNVs . The role of segmen- CCGG sites was more than 10% lower in bull sperm tal duplications in structural variations of the bovine than in any other species investigated (sheep, horse, pig, genome might be mediated by hypomethylated se- mouse, goat and human), which remains to be con- quences in the bull germline. In support of this hypoth- firmed using larger samples. Because satellites are under- esis, human-specific evolutionary rearrangements and methylated in the sperm of many species, the global CNVs associate not only with low copy repeats and the undermethylation of bull sperm could partly be ex- deletions/duplications they generate, but also with hypo- plained by the larger amount of satellite sequences methylated regions of human sperm , thus providing present in the bovine genome (eight satellite compo- a potential link between hypomethylation in the germ- nents representing 23% of the total genomic content; line and genome structural variation. ). We annotated the CCGG sites relative to the gen- omic features available in different species, and indeed observed that CCGGs overlapping satellites represented Conclusions 1.6% of all CCGGs in bovine vs. 0.1 to 0.6% in other spe- By means of a thorough characterization of bull cies. From a purely mathematical point of view, the low sperm DNA methylation at different genome scales, percentage of CCGG sites present in satellites whatever this study has provided evidence that bull spermato- the species probably did not accounted for the > 10% zoa are less methylated when compared to not only difference in sperm methylation. However, it should be bovine somatic cells but also the sperm of other kept in mind that the repetitive nature of satellites mammals. The sequences undermethylated in bull Perrier et al. BMC Genomics (2018) 19:404 Page 16 of 18 sperm are conserved across species, which may de- We are grateful to the Genotoul bioinformatics platform Toulouse Midi- Pyrenees (Bioinfo Genotoul) for providing computing and storage resources. note an important role in germline differentiation and in post-fertilization epigenetic reprogramming. The Funding cattle-specific lower methylation at CCGG sites may This work was supported by grants from the French National Research Agency (grant ANR-13-LAB3-0008-01 ‘SeQuaMol’ and grant ANR-11-INBS be partly related to the abundance of satellites in the -0003 in the framework of the ‘Investing for the Future’ program). SeQuaMol bovine genome and to their undermethylated status funding allowed the collection of large number of animal semen samples in the male germline. Given the potential evolutionary and molecular analyses (MeDIP-chip, RRBS, bisulfite conversion, pyrosequencing). The grant ANR-11-INBS -0003 supported molecular analyses such as LUMA. implications of these findings, it would be of consid- The funders had no role in the design of the study and collection, analysis, and erable interest to quantify DNA methylation at differ- interpretation of data and in writing the manuscript. JPP (phD student) was ent stages of bovine male germline differentiation in supported by the French Ministry of Higher Education and Research and was a fellow of the ABIES doctoral school. HAA was supported by the PremUp order to understand when and how this undermethy- foundation. lation takes place. Availability of data and materials Additional data files are provided (see above). The MeDIP-chip and RRBS Additional files datasets supporting the results of this article are available in the NCBI Gene Expression Omnibus database under accession numbers GSE102960 (MeDIP) Additional file 1: is a pdf file containing supplementary methods, and GSE102169 (RRBS). supplementary references, eight supplementary tables and four supplementary figures. Table S1. reference genomes used for in silico analyses and origin of Authors’ contributions the files used for annotation. Table S2. in silico characterization of JPP carried out the experiments, data analysis and drafting of the bovine reduced restriction (RR) genomes generated using different size manuscript. ES participated in conception of the study, collected the selection criteria. Table S3. comparison of RR genomes obtained with a samples and performed the experiments. AP performed the LUMA 40-290 bp selection size window in different species. Table S4. primers experiments and interpreted the data. LJ performed the bioinformatics and and PCR conditions used to generate the pyrosequencing templates. statistical analyses of MeDIP data. LJ, FP, HAA and MG developed the Table S5. pyrosequencing primers. Table S6. library characterization, bioinformatics pipeline for RRBS data analysis and contributed to critical mapping efficiency on the bovine genome (UMD3.1), coverage and revision of the manuscript. MW supervised the RRBS library preparations and average methylation in RRBS libraries. Table S7. results of comparisons contributed to critical revision of the manuscript. CLD, SF and DB between tissues by RRBS. Table S8. mapping efficiency on a Repbase contributed to sample collection and to critical revision of the manuscript. LS artificial bovine genome, coverage and average methylation in RRBS and HJ obtained funding and participated in the conception of the study libraries. Figure S1. Bootstrap analysis of global CCGG methylation in and editing of the manuscript. HK coordinated the study and carried out the bull sperm from four different breeds. Figure S2. Genotyping of bull experiments, data analysis and drafting of the manuscript. All authors have sperm and blood samples. Figure S3. Average methylation percentages for read and approved the final manuscript. CpGs 5-500 and CpGs > 500 in each cell type, in reads uniquely aligned on a Repbase bovine artificial genome. Figure S4. Pyrosequencing of CpGs Ethics approval and consent to participate hypermethylated in sperm. (PDF 1840 kb) All study methods were implemented in accordance with EU guidelines and regulations (Directive 2010/63/UE). For animals maintained in INRA facilities, Additional file 2: is a Microsoft Excel file listing the DMCs identified the experimental protocols were approved by the INRA local Ethics using RRBS. This file includes three datasheets corresponding to the Committee (COMETHEA, authorization numbers 12/160 and Méjusseaumes pairwise comparisons between sperm, fibroblasts and monocytes. Animal Care committee 0162503). Mice semen samples were collected from (XLSX 84756 kb) 7-week-old male C57Bl/6JOlaHsd mice supplied by Harlan Laboratory (Venray, Additional file 3: is a Microsoft Excel file listing the DMRs identified Netherlands) and euthanized by cervical dislocation. The bull samples were using MeDIP. This file includes three datasheets corresponding to the provided by commercial companies: Montbéliarde breed by GEN’IATEST pairwise comparisons between sperm, fibroblasts and liver. One DMR (Roulans, France) and UMOTEST (Ceyzeriat, France), Holstein and Normande may be present in several lanes if shared by several promoters. (XLSX breeds by EVOLUTION (Rennes, France) and Belgian White Blue Breed by AWE 821 kb) (Ciney, Belgium); the ram, goat and boar semen were also supplied by Additional file 4: is a Microsoft Excel file listing the consensus commercial companies, Oson (France), CAPGENES (France) and Laboratoire sequences of each bovine repetitive element as defined in Repbase National de Contrôle des Reproducteurs (LNCR, France), respectively. Stallion and the average methylation percentages for CpGs 5-500 and CpGs semen was supplied by Dr. M. Magistrini (UMR INRA 0085 PRC, Nouzilly, France). > 500 in each RRBS sample. (XLSX 16 kb) Human sperm samples from patients included in a PHRC METASPERME study, coordinated by Dr. R. Levy (Laboratoire d’Histologie Embryologie Cytogénétique CECOS, Hôpital Jean Verdier, Bondy, France); this study received the approval Abbreviations from French ethics board (Conseil d’évaluation éthique pour les recherches CGI: CpG island; CNV: Copy number variation; DAVID: Database for en santé, CERES) and all the patients gave their informed written consent to Annotation, Visualization and Integrated Discovery; DMC: Differentially participate. methylated CpG; DMR: Differentially methylated region; DTT: Dithiothreitol; GO: Gene ontology; IGV: Integrative Genomics Viewer; LUMA: Luminometric Competing interests methylation assay; MeDIP: Methylated DNA immunoprecipitation; The authors declare that they have no competing interests. PBMCs: Peripheral blood mononuclear cells; PBS: Phosphate buffer saline; PCA: Principal component analysis; RR genome: Reduced representation genome; RRBS: Reduced representation bisulfite sequencing; SCNT: Somatic Publisher’sNote cell nuclear transfer; TSS: Transcription start site; TTS: Transcription Springer Nature remains neutral with regard to jurisdictional claims in termination site published maps and institutional affiliations. Acknowledgements Author details We would like to thank Evolution, Umotest, Awe, GEN’IATEST, OSON, UMR BDR, INRA, ENVA, Université Paris Saclay, 78350 Jouy en Josas, France. Capgenes, LNCR, Michèle Magistrini and Rachel Lévy for providing semen Present Address: Laboratory of Animal Reproduction, Department of samples; Véronique Duranthon and Delphine Dubé for providing fibroblasts Biological Sciences, Faculty of Science and Engineering, University of and Chaneze Mehalla for help with the pyrosequencing of DDX4 and SYCP3. Limerick, Limerick, Ireland. ALLICE, 149 rue de Bercy, 75012 Paris, France. Perrier et al. BMC Genomics (2018) 19:404 Page 17 of 18 Present Address: Institut Curie, PSL Research University, CNRS, UMR3664, DNA methylation patterns. Fertil Steril. 2016; https://doi.org/10.1016/j. 75005 Paris, France. Present Address: Sorbonne Universités, UPMC Univ Paris fertnstert.2015.09.013. 06, CNRS, UMR3664, 75005 Paris, France. CNRS, Université de Strasbourg, 19. Urdinguio RG, Bayon GF, Dmitrijeva M, Torano EG, Bravo C, Fraga MF, Bassas UMR7242 Biotechnologie et signalisation cellulaire, 300 bd Sébastien Brant, L, Larriba S, Fernandez AF, Aberrant DNA. Methylation patterns of 67412 Illkirch cedex, France. UMR GABI, INRA, AgroParisTech, Université Paris spermatozoa in men with unexplained infertility. Hum Reprod. 2015;30(5): Saclay, 78350 Jouy en Josas, France. UMR CNRS/USTL 8576, UGSF, Villeneuve 1014–28. D’Ascq, France. 20. Bourc'his D, Bestor TH. Meiotic catastrophe and retrotransposon reactivation in male germ cells lacking Dnmt3L. Nat. 2004;431(7004):96–9. Received: 12 September 2017 Accepted: 7 May 2018 21. Song N, Endo D, Song B, Shibata Y, Koji T. 5-aza-2′-deoxycytidine impairs mouse spermatogenesis at multiple stages through different usage of DNA methyltransferases. Toxicol. 2016;361-362:62–72. 22. Barau J, Teissandier A, Zamudio N, Roy S, Nalesso V, Herault Y, Guillou F, References Bourc'his D. The DNA methyltransferase DNMT3C protects male germ cells 1. Carrell DT. Epigenetics of the male gamete. Fertil Steril. 2012;97(2):267–74. from transposon activity. Sci. 2016;354(6314):909–12. 2. Tang WW, Kobayashi T, Irie N, Dietmann S, Surani MA. Specification and 23. Guo F, Yang B, Ju ZH, Wang XG, Qi C, Zhang Y, Wang CF, Liu HD, Feng MY, epigenetic programming of the human germ line. Nat Rev Genet. 2016; Chen Y, et al. Alternative splicing, promoter methylation, and functional 17(10):585–600. SNPs of sperm flagella 2 gene in testis and mature spermatozoa of Holstein 3. Yao C, Liu Y, Sun M, Niu M, Yuan Q, Hai Y, Guo Y, Chen Z, Hou J, Liu Y, et al. bulls. Reprod. 2014;147(2):241–52. MicroRNAs and DNA methylation as epigenetic regulators of mitosis, 24. Jena SC, Kumar S, Rajput S, Roy B, Verma A, Kumaresan A, Mohanty TK, De meiosis and spermiogenesis. Reprod. 2015;150(1):R25–34. S, Kumar R, Datta TK. Differential methylation status of IGF2-H19 locus does 4. Bao J, Bedford MT. Epigenetic regulation of the histone-to-protamine not affect the fertility of crossbred bulls but some of the CTCF binding sites transition during spermiogenesis. Reprod. 2016;151(5):R55–70. could be potentially important. Mol Reprod Dev. 2014;81(4):350–62. 5. Gannon JR, Emery BR, Jenkins TG, Carrell DT. The sperm epigenome: 25. Yao W, Li Y, Li B, Luo H, Xu H, Pan Z, Xie Z, Li Q. Epigenetic regulation of implications for the embryo. Adv Exp Med Biol. 2014;791:53–66. bovine spermatogenic cell-specific gene boule. PLoS One. 2015;10(6): 6. Boissonnas CC, Jouannet P, Jammes H. Epigenetic disorders and male e0128250. subfertility. Fertil Steril. 2013;99(3):624–31. 26. Verma A, Rajput S, De S, Kumar R, Chakravarty AK, Datta TK. Genome-wide 7. Boissonnas CC, Abdalaoui HE, Haelewyn V, Fauque P, Dupont JM, Gut I, profiling of sperm DNA methylation in relation to buffalo (Bubalus bubalis) Vaiman D, Jouannet P, Tost J, Jammes H. Specific epigenetic alterations of bull fertility. Theriogenology. 2014;82(5):750–9. e751 IGF2-H19 locus in spermatozoa from infertile men. Eur J Human Genet : 27. Kropp J, Carrillo JA, Namous H, Daniels A, Salih SM, Song J, Khatib H. Male EJHG. 2010;18(1):73–80. fertility status is associated with DNA methylation signatures in sperm and 8. Nanassy L, Carrell DT. Abnormal methylation of the promoter of CREM is transcriptomic profiles of bovine preimplantation embryos. BMC Genomics. broadly associated with male factor infertility and poor sperm quality but is 2017;18(1):280. improved in sperm selected by density gradient centrifugation. Fertil Steril. 28. Shojaei Saadi HA, O'Doherty AM, Gagne D, Fournier E, Grant JR, Sirard MA, 2011;95(7):2310–4. Robert C. An integrated platform for bovine DNA methylome analysis 9. Lambrot R, Xu C, Saint-Phar S, Chountalos G, Cohen T, Paquet M, Suderman suitable for small samples. BMC Genomics. 2014;15:451. M, Hallett M, Kimmins S. Low paternal dietary folate alters the mouse sperm 29. Bovine Genome S, Analysis C, Elsik CG, Tellam RL, Worley KC, Gibbs RA, epigenome and is associated with negative pregnancy outcomes. Nat Muzny DM, Weinstock GM, Adelson DL, Eichler EE, et al. The genome Commun. 2013;4:2889. sequence of taurine cattle: a window to ruminant biology and evolution. 10. Martinez D, Pentinat T, Ribo S, Daviaud C, Bloks VW, Cebria J, Villalmanzo N, Science. 2009;324(5926):522–8. Kalko SG, Ramon-Krauel M, Diaz R, et al. In utero undernutrition in male 30. Kader F, Ghai M. DNA methylation-based variation between human mice programs liver lipid metabolism in the second-generation offspring populations. Mol Genet Genomics : MGG. 2017;292(1):5–35. involving altered Lxra DNA methylation. Cell Metab. 2014;19(6):941–51. 31. Ortiz I, Dorado J, Ramirez L, Morrell JM, Acha D, Urbano M, Galvez MJ, 11. Radford EJ, Ito M, Shi H, Corish JA, Yamazawa K, Isganaitis E, Seisenberger S, Carrasco JJ, Gomez-Arrones V, Calero-Carretero R, et al. Effect of single layer Hore TA, Reik W, Erkek S, et al. In utero effects. In utero undernourishment centrifugation using Androcoll-E-large on the sperm quality parameters of perturbs the adult sperm methylome and intergenerational metabolism. Sci. cooled-stored donkey semen doses. Animal. 2014;8(2):308–15. 2014;345(6198):1255903. 32. Kiefer H. Genome-wide analysis of methylation in bovine clones by methylated 12. Aston KI, Uren PJ, Jenkins TG, Horsager A, Cairns BR, Smith AD, Carrell DT. DNA immunoprecipitation (MeDIP). Methods Mol Biol. 2015;1222:267–80. Aberrant sperm DNA methylation predicts male fertility status and embryo 33. Karimi M, Johansson S, Stach D, Corcoran M, Grander D, Schalling M, quality. Fertil Steril. 2015; https://doi.org/10.1016/j.fertnstert.2015.08.019. Bakalkin G, Lyko F, Larsson C, Ekstrom TJ. LUMA (LUminometric methylation 13. Salas-Huetos A, Blanco J, Vidal F, Godo A, Grossmann M, Pons MC, assay)–a high throughput method to the analysis of genomic DNA Fernandez SF, Garrido N, Anton E. Spermatozoa from patients with seminal methylation. Exp Cell Res. 2006;312(11):1989–95. alterations exhibit a differential micro-ribonucleic acid profile. Fertil Steril. 34. Attig L, Vige A, Gabory A, Karimi M, Beauger A, Gross MS, Athias A, Gallou- 2015;104(3):591–601. Kabani C, Gambert P, Ekstrom TJ, et al. Dietary alleviation of maternal 14. Ni K,Spiess AN,Schuppe HC,Steger K.The impact of sperm obesity and diabetes: increased resistance to diet-induced obesity protamine deficiency and sperm DNA damage on human male transcriptional and epigenetic signatures. PLoS One. 2013;8(6):e66816. fertility: a systematic review and meta-analysis. Androl. 2016;4(5): 35. Kiefer H, Jouneau L, Campion E, Rousseau-Ralliard D, Larcher T, Martin- 789–99. Magniette ML, Balzergue S, Ledevin M, Prezelin A, Chavatte-Palmer P, et 15. de Castro Barbosa T, Ingerslev LR, Alm PS, Versteyhe S, Massart J, al. Altered DNA methylation associated with an abnormal liver phenotype Rasmussen M, Donkin I, Sjogren R, Mudry JM, Vetterli L, et al. High- in a cattle model with a high incidence of perinatal pathologies. Sci Rep. fat diet reprograms the epigenome of rat spermatozoa and 2016;6:38869. transgenerationally affects metabolism of the offspring. Mol Metab. 36. Huang d W, Sherman BT, Lempicki RA. Systematic and integrative analysis 2016;5(3):184–97. of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 16. Sharma U, Conine CC, Shea JM, Boskovic A, Derr AG, Bing XY, Belleannee C, 4(1):44–57. Kucukural A, Serra RW, Sun F, et al. Biogenesis and function of tRNA 37. Gu H, Smith ZD, Bock C, Boyle P, Gnirke A, Meissner A. Preparation of fragments during sperm maturation and fertilization in mammals. Sci. 2016; reduced representation bisulfite sequencing libraries for genome-scale DNA 351(6271):391–6. methylation profiling. Nat Protoc. 2011;6(4):468–81. 17. Kobayashi N, Miyauchi N, Tatsuta N, Kitamura A, Okae H, Hiura H, Sato A, Utsunomiya T, Yaegashi N, Nakai K, et al. Factors associated 38. Auclair G, Guibert S, Bender A, Weber M. Ontogeny of CpG island with aberrant imprint methylation and oligozoospermia. Sci Rep. methylation and specificity of DNMT3 methyltransferases during embryonic 2017;7:42336. development in the mouse. Genome Biol. 2014;15(12):545. 18. Jenkins TG, Aston KI, Meyer TD, Hotaling JM, Shamsi MB, Johnstone EB, Cox 39. Bao W, Kojima KK, Kohany O. Repbase update, a database of repetitive KJ, Stanford JB, Porucznik CA, Carrell DT. Decreased fecundity and sperm elements in eukaryotic genomes. Mob DNA. 2015;6:11. Perrier et al. BMC Genomics (2018) 19:404 Page 18 of 18 40. Dupont JM, Tost J, Jammes H, Gut IG. De novo quantitative bisulfite development of bovine somatic cell nuclear transfer embryos. J Reprod sequencing using the pyrosequencing technology. Anal Biochem. 2004; Dev. 2011;57(3):393–402. 333(1):119–27. 64. Couldrey C, Wells DN. DNA methylation at a bovine alpha satellite I repeat 41. Li LC, Dahiya R. MethPrimer: designing primers for methylation PCRs. CpG site during development following fertilization and somatic cell Bioinformatics. 2002;18(11):1427–31. nuclear transfer. PLoS One. 2013;8(2):e55153. 65. Kato Y, Kaneda M, Hata K, Kumaki K, Hisano M, Kohara Y, Okano M, Li E, 42. Weyrich A. Preparation of genomic DNA from mammalian sperm. Curr Protoc Mo Biol. 2012, Chapter 2:Unit 2;13:11–3. Nozaki M, Sasaki H. Role of the Dnmt3 family in de novo methylation of imprinted and repetitive sequences during male germ cell development in 43. Schubeler D. ESCI award lecture: regulation, function and biomarker the mouse. Hum Mol Genet. 2007;16(19):2272–80. potential of DNA methylation. Eur J Clin Investig. 2015;45(3):288–93. 66. Walton EL, Francastel C, Velasco G. Dnmt3b prefers germ line genes and 44. Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, Schubeler D. Centromeric regions: lessons from the ICF syndrome and Cancer and Chromosome-wide and promoter-specific analyses identify sites of implications for diseases. Biol. 2014;3(3):578–605. differential DNA methylation in normal and transformed human cells. Nat 67. Samans B, Yang Y, Krebs S, Sarode GV, Blum H, Reichenbach M, Wolf E, Genet. 2005;37(8):853–62. Steger K, Dansranjavin T, Schagdarsurengin U. Uniformity of nucleosome 45. Arribas-Layton M, Dennis J, Bennett EJ, Damgaard CK, Lykke-Andersen J. The preservation pattern in mammalian sperm and its connection to repetitive C-terminal RGG domain of human Lsm4 promotes processing body formation DNA elements. Dev Cell. 2014;30(1):23–35. stimulated by arginine Dimethylation. Mol Cell Biol. 2016;36(17):2226–35. 68. Sillaste G, Kaplinski L, Meier R, Jaakma U, Eriste E, Salumets A. A novel 46. Miyamoto T, Minase G, Okabe K, Ueda H, Sengoku K. Male infertility and its hypothesis for histone-to-protamine transition in Bos taurus spermatozoa. genetic causes. J Obstet Gynaecol Res. 2015;41(10):1501–5. Reprod. 2017;153(3):241–51. 47. Cheng YS, Lu CW, Lin TY, Lin PY, Lin YM. Causes and clinical features of 69. Erkek S, Hisano M, Liang CY, Gill M, Murr R, Dieker J, Schubeler D, van der infertile men with nonobstructive azoospermia and histopathologic Vlag J, Stadler MB, Peters AH. Molecular determinants of nucleosome diagnosis of Hypospermatogenesis. Urol. 2017; https://doi.org/10.1016/j. retention at CpG-rich sequences in mouse spermatozoa. Nat Struct Mol Biol. urology.2017.03.026. 2013;20(7):868–75. 48. Molaro A, Hodges E, Fang F, Song Q, McCombie WR, Hannon GJ, Smith AD. 70. Macaya G, Cortadas J, Bernardi G. An analysis of the bovine genome by Sperm methylation profiles reveal features of epigenetic inheritance and density-gradient centrifugation. Preparation of the dG+dC-rich DNA evolution in primates. Cell. 2011;146(6):1029–41. components. Eur J Biochem. 1978;84(1):179–88. 49. Krausz C, Sandoval J, Sayols S, Chianese C, Giachini C, Heyn H, Esteller M. 71. Vozdova M, Sebestova H, Kubickova S, Cernohorska H, Vahala J, Rubes J. A Novel insights into DNA methylation features in spermatozoa: stability and comparative study of meiotic recombination in cattle (Bos taurus) and three peculiarities. PLoS One. 2012;7(10):e44479. wildebeest species (Connochaetes gnou, C. Taurinus taurinus and C. t. 50. Hammoud SS, Low DH, Yi C, Lee CL, Oatley JM, Payne CJ, Carrell DT, Albojubatus). Cytogenet Genome Res. 2013;140(1):36–45. Guccione E, Cairns BR. Transcription and imprinting dynamics in developing 72. Ma L, O'Connell JR, VanRaden PM, Shen B, Padhi A, Sun C, Bickhart DM, postnatal male germline stem cells. Genes Dev. 2015;29(21):2312–24. Cole JB, Null DJ, Liu GE, et al. Cattle sex-specific recombination and genetic 51. Heyn H, Ferreira HJ, Bassas L, Bonache S, Sayols S, Sandoval J, Esteller M, control from a large pedigree analysis. PLoS Genet. 2015;11(11):e1005387. Larriba S. Epigenetic disruption of the PIWI pathway in human 73. Kadri NK, Harland C, Faux P, Cambisano N, Karim L, Coppieters W, Fritz S, spermatogenic disorders. PLoS One. 2012;7(10):e47892. Mullaart E, Baurain D, Boichard D, et al. Coding and noncoding variants in 52. Royo H, Stadler MB, Peters AH. Alternative computational analysis shows no HFM1, MLH3, MSH4, MSH5, RNF212, and RNF212B affect recombination rate evidence for nucleosome enrichment at repetitive sequences in in cattle. Genome Res. 2016;26(10):1323–32. mammalian spermatozoa. Dev Cell. 2016;37(1):98–104. 74. Termolino P, Cremona G, Consiglio MF, Conicella C. Insights into epigenetic 53. Dansranjavin T, Schagdarsurengin U. The rationale of the inevitable, or why landscape of recombination-free regions. Chromosoma. 2016;125(2):301–8. is the consideration of repetitive DNA elements indispensable in studies of 75. Liu GE, Ventura M, Cellamare A, Chen L, Cheng Z, Zhu B, Li C, Song J, sperm nucleosomes. Dev Cell. 2016;37(1):13–4. Eichler EE. Analysis of recent segmental duplications in the bovine genome. 54. Adams RL, Burdon RH, Fulton J. Methylation of satellite DNA. Biochem BMC Genomics. 2009;10:571. Biophys Res Commun. 1983;113(2):695–702. 76. Fadista J, Thomsen B, Holm LE, Bendixen C. Copy number variation in the 55. Feinstein SI, Racaniello VR, Ehrlich M, Gehrke CW, Miller DA, Miller OJ. bovine genome. BMC Genomics. 2010;11:284. Pattern of undermethylation of the major satellite DNA of mouse sperm. 77. Li J, Harris RA, Cheung SW, Coarfa C, Jeong M, Goodell MA, White LD, Patel Nucleic Acids Res. 1985;13(11):3969–78. A, Kang SH, Shaw C, et al. Genomic hypomethylation in the human 56. Yamagata K, Yamazaki T, Miki H, Ogonuki N, Inoue K, Ogura A, Baba T. germline associates with selective structural mutability in the human Centromeric DNA hypomethylation as an epigenetic signature discriminates genome. PLoS Genet. 2012;8(5):e1002692. between germ and somatic cell lineages. Dev Biol. 2007;312(1):419–26. 57. Nishibuchi G, Dejardin J. The molecular basis of the organization of repetitive DNA-containing constitutive heterochromatin in mammals. Chromosom Res. 2017;25(1):77–87. 58. Biscotti MA, Olmo E, Heslop-Harrison JS. Repetitive DNA in eukaryotic genomes. Chromosom Res. 2015;23(3):415–20. 59. Probst AV, Okamoto I, Casanova M, El Marjou F, Le Baccon P, Almouzni G. A strand-specific burst in transcription of pericentric satellites is required for chromocenter formation and early mouse development. Dev Cell. 2010; 19(4):625–38. 60. Kaneda M, Akagi S, Watanabe S, Nagai T. Comparison of DNA methylation levels of repetitive loci during bovine development. BMC Proc. 2011;5(Suppl 4):S3. 61. Kang YK, Lee HJ, Shim JJ, Yeo S, Kim SH, Koo DB, Lee KK, Beyhan Z, First NL, Han YM. Varied patterns of DNA methylation change between different satellite regions in bovine preimplantation development. Mol Reprod Dev. 2005;71(1):29–35. 62. Yamanaka K, Kaneda M, Inaba Y, Saito K, Kubota K, Sakatani M, Sugimura S, Imai K, Watanabe S, Takahashi M. DNA methylation analysis on satellite I region in blastocysts obtained from somatic cell cloned cattle. Anim Sci J. 2011;82(4):523–30. 63. Yamanaka K, Sakatani M, Kubota K, Balboula AZ, Sawai K, Takahashi M. Effects of downregulating DNA methyltransferase 1 transcript by RNA interference on DNA methylation status of the satellite I region and in vitro
BMC Genomics – Springer Journals
Published: May 29, 2018
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