Epigenetic Pattern on the Human Y Chromosome Is Evolutionarily Conserved

Who's afraid of the X? Incorporating the X and Y chromosomes into the analysis of DNA methylation array data

BACKGROUND

Many human disease phenotypes manifest differently by sex, making the development of methods for incorporating X and Y-chromosome data into analyses vital. Unfortunately, X and Y chromosome data are frequently excluded from large-scale analyses of the human genome and epigenome due to analytical complexity associated with sex chromosome dosage differences between XX and XY individuals, and the impact of X-chromosome inactivation (XCI) on the epigenome. As such, little attention has been given to considering the methods by which sex chromosome data may be included in analyses of DNA methylation (DNAme) array data.

RESULTS

With Illumina Infinium HumanMethylation450 DNAme array data from 634 placental samples, we investigated the effects of probe filtering, normalization, and batch correction on DNAme data from the X and Y chromosomes. Processing steps were evaluated in both mixed-sex and sex-stratified subsets of the analysis cohort to identify whether including both sexes impacted processing results. We found that identification of probes that have a high detection p-value, or that are non-variable, should be performed in sex-stratified data subsets to avoid over- and under-estimation of the quantity of probes eligible for removal, respectively. All normalization techniques investigated returned X and Y DNAme data that were highly correlated with the raw data from the same samples. We found no difference in batch correction results after application to mixed-sex or sex-stratified cohorts. Additionally, we identify two analytical methods suitable for XY chromosome data, the choice between which should be guided by the research question of interest, and we performed a proof-of-concept analysis studying differential DNAme on the X and Y chromosome in the context of placental acute chorioamnionitis. Finally, we provide an annotation of probe types that may be desirable to filter in X and Y chromosome analyses, including probes in repetitive elements, the X-transposed region, and cancer-testis gene promoters.

CONCLUSION

While there may be no single "best" approach for analyzing DNAme array data from the X and Y chromosome, analysts must consider key factors during processing and analysis of sex chromosome data to accommodate the underlying biology of these chromosomes, and the technical limitations of DNA methylation arrays.

Age-related DNA methylation on Y chromosome and their associations with total mortality among Chinese males

In view of the sex differences in aging‐related diseases, sex chromosomes may play a critical role during aging process. This study aimed to identify age‐related DNA methylation changes on Y chromosome (ChrY). A two‐stage study design was conducted in this study. The discovery stage contained 419 Chinese males, including 205 from the Wuhan‐Zhuhai cohort panel, 107 from the coke oven workers panel, and 107 from the Shiyan panel. The validation stage contained 587 Chinese males from the Dongfeng‐Tongji sub‐cohort. We used the Illumina HumanMethylation BeadChip to determine genome‐wide DNA methylation in peripheral blood of the study participants. The associations between age and methylation levels of ChrY CpGs were investigated by using linear regression models with adjustment for potential confounders. Further, associations of age‐related ChrY CpGs with all‐cause mortality were tested in the validation stage. We identified the significant associations of 41 ChrY CpGs with age at false discovery rate (FDR) <0.05 in the discovery stage, and 18 of them were validated in the validation stage (p < 0.05). Meta‐analysis of both stages confirmed the robust positive associations of 14 CpGs and negative associations of 4 CpGs with age (FDR<0.05). Among them, cg03441493 and cg17816615 were significantly associated with all‐cause mortality risk [HR(95% CI) = 1.37 (1.04, 1.79) and 0.70 (0.54, 0.93), respectively]. Our results highlighted the importance of ChrY CpGs on male aging.

Male-specific age estimation based on Y-chromosomal DNA methylation

Although DNA methylation variation of autosomal CpGs provides robust age predictive biomarkers, no male-specific age predictor exists based on Y-CpGs yet. Since sex chromosomes play an important role in aging, a Y-chromosome-based age predictor would allow studying male-specific aging effects and would also be useful in forensics. Here, we used blood-based DNA methylation microarray data of 1,057 males from six cohorts aged 15-87 and identified 75 Y-CpGs with an interquartile range of ≥0.1. Of these, 22 and six were significantly hyper- and hypomethylated with age (p(cor)<0.05, Bonferroni), respectively. Amongst several machine learning algorithms, a model based on support vector machines with radial kernel performed best in male-specific age prediction. We achieved a mean absolute deviation (MAD) between true and predicted age of 7.54 years (cor=0.81, validation) when using all 75 Y-CpGs, and a MAD of 8.46 years (cor=0.73, validation) based on the most predictive 19 Y-CpGs. The accuracies of both age predictors did not worsen with increased age, in contrast to autosomal CpG-based age predictors that are known to predict age with reduced accuracy in the elderly. Overall, we introduce the first-of-its-kind male-specific epigenetic age predictor for future applications in aging research and forensics.

Sex-specific plasticity across generations II: Grandpaternal effects are lineage specific and sex specific

Transgenerational plasticity (TGP) occurs when the environment encountered by one generation (F0) alters the phenotypes of one or more future generations (e.g. F1 and F2). Sex selective TGP, via specific lineages or to only male or female descendants, has been underexplored in natural systems, and may be adaptive if it allows past generations to fine-tune the phenotypes of future generations in response to sex-specific life-history strategies. We sought to understand if exposing males to predation risk can influence grandoffspring via sperm in three-spined stickleback Gasterosteus aculeatus. We specifically tested the hypothesis that grandparental effects are transmitted in a sex-specific way down the male lineage, from paternal grandfathers to F2 males. We reared F1 offspring of unexposed and predator-exposed F0 males under 'control' conditions and used them to generate F2s with control grandfathers, a predator-exposed maternal grandfather (i.e. predator-exposed F0 males to F1 daughters to F2s), a predator-exposed paternal grandfather (i.e. predator-exposed F0 males to F1 sons to F2s) or two predator-exposed grandfathers. We then assayed male and female F2s for a variety of traits related to antipredator defence. We found little evidence that transgenerational effects were mediated to only male descendants via the paternal lineage. Instead, grandpaternal effects depended on lineage and were mediated largely across sexes, from F1 males to F2 females and from F1 females to F2 males. When their paternal grandfather was exposed to predation risk, female F2s were heavier and showed a reduced change in behaviour in response to a simulated predator attack relative to grandoffspring of control, unexposed grandparents. In contrast, male F2s showed reduced antipredator behaviour when their maternal grandfather was exposed to predation risk. However, these patterns were only evident when one grandfather, but not both grandfathers, was exposed to predation risk, suggesting the potential for non-additive interactions across lineages. If sex-specific and lineage effects are common, then grandparental effects are likely underestimated in the literature. These results draw attention to the importance of sex-selective inheritance of environmental effects and raise new questions about the proximate and ultimate causes of selective transmission across generations.

The Y Chromosome: A Complex Locus for Genetic Analyses of Complex Human Traits

The Human Y chromosome (ChrY) has been demonstrated to be a powerful tool for phylogenetics, population genetics, genetic genealogy and forensics. However, the importance of ChrY genetic variation in relation to human complex traits is less clear. In this review, we summarise existing evidence about the inherent complexities of ChrY variation and their use in association studies of human complex traits. We present and discuss the specific particularities of ChrY genetic variation, including Y chromosomal haplogroups, that need to be considered in the design and interpretation of genetic epidemiological studies involving ChrY.

Genes located in Y-chromosomal regions important for male fertility show altered transcript levels in cryptorchidism and respond to curative hormone treatment

Background

Undescended (cryptorchid) testes in patients with defective mini-puberty and low testosterone levels contain gonocytes that fail to differentiate normally, which impairs the development of Ad spermatogonia and ultimately leads to adult infertility. Treatment with the gonadotropin-releasing hormone agonist GnRHa increases luteinizing hormone and testosterone and rescues fertility in the majority of pathological cryptorchid testes. Several Y-chromosomal genes in the male-specific Y region (MSY) are essential for spermatogenesis, testis development and function, and are associated with azoospermia, infertility and cryptorchidism. In this study, we analyzed the expression of MSY genes in testes with Ad spermatogonia (low infertility risk patients) as compared to testes lacking Ad spermatogonia (high infertility risk) before and after curative GnRHa treatment, and in correlation to their location on the Y-chromosome.

Results

Twenty genes that are up- or down-regulated in the Ad- group are in the X-degenerate or the ampliconic region, respectively. GnRHa treatment increases mRNA levels of 14 genes in the ampliconic region and decreases mRNA levels of 10 genes in the X-degenerate region.

Conclusion

Our findings implicate Y-chromosomal genes, including USP9Y, UTY, TXLNGY, RBMY1B, RBMY1E, RBMY1J and TSPY4, some of which are known to be important for spermatogenesis, in the curative hormonal treatment of cryptorchidism-induced infertility.

Maternal trans-general analysis of the human mitochondrial DNA pattern

There is an intimate connection between mitochondrial DNA (mtDNA) methylation and some diseases, such as cancer. MtDNA is almost strictly maternally inherited. However, whether the aberrant mtDNA methylation involved in breast cancer progression and whether mtDNA methylation can be transmitted through maternal line are poorly understood. Here we applied bisulfite sequencing to global mitochondrial DNA and whole genomic DNA methylation array from fifteen members of five three-female-generation families with one breast cancer patient in each family. We found that mtDNA methylation was maternally inherited in D-loop region and eight aberrant mtDNA methylation sites were correlated with breast cancer. Furthermore, conjoint analysis showed that mtDNA methylation sites could be potential biomarkers combined with nuclear DNA methylation sites for breast cancer risk prediction.

The NCI-60 Methylome and Its Integration into CellMiner

A unique resource for systems pharmacology and genomic studies is the NCI-60 cancer cell line panel, which provides data for the largest publicly available library of compounds with cytotoxic activity (∼21,000 compounds), including 108 FDA-approved and 70 clinical trial drugs as well as genomic data, including whole-exome sequencing, gene and miRNA transcripts, DNA copy number, and protein levels. Here, we provide the first readily usable genome-wide DNA methylation database for the NCI-60, including 485,577 probes from the Infinium HumanMethylation450k BeadChip array, which yielded DNA methylation signatures for 17,559 genes integrated into our open access CellMiner version 2.0 (https://discover.nci.nih.gov/cellminer). Among new insights, transcript versus DNA methylation correlations revealed the epithelial/mesenchymal gene functional category as being influenced most heavily by methylation. DNA methylation and copy number integration with transcript levels yielded an assessment of their relative influence for 15,798 genes, including tumor suppressor, mitochondrial, and mismatch repair genes. Four forms of molecular data were combined, providing rationale for microsatellite instability for 8 of the 9 cell lines in which it occurred. Individual cell line analyses showed global methylome patterns with overall methylation levels ranging from 17% to 84%. A six-gene model, including PARP1, EP300, KDM5C, SMARCB1, and UHRF1 matched this pattern. In addition, promoter methylation of two translationally relevant genes, Schlafen 11 (SLFN11) and methylguanine methyltransferase (MGMT), served as indicators of therapeutic resistance or susceptibility, respectively. Overall, our database provides a resource of pharmacologic data that can reinforce known therapeutic strategies and identify novel drugs and drug targets across multiple cancer types. Cancer Res; 77(3); 601-12. ©2016 AACR.