DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines

A genetic-epigenetic interplay at 1q21.1 locus underlies CHD1L-mediated vulnerability to primary progressive multiple sclerosis

Multiple Sclerosis (MS) is a heterogeneous inflammatory and neurodegenerative disease with an unpredictable course towards progressive disability. Treating progressive MS is challenging due to limited insights into the underlying mechanisms. We examined the molecular changes associated with primary progressive MS (PPMS) using a cross-tissue (blood and post-mortem brain) and multilayered data (genetic, epigenetic, transcriptomic) from independent cohorts. In PPMS, we found hypermethylation of the 1q21.1 locus, controlled by PPMS-specific genetic variations and influencing the expression of proximal genes (CHD1L, PRKAB2) in the brain. Evidence from reporter assay and CRISPR/dCas9 experiments supports a causal link between methylation and expression and correlation network analysis further implicates these genes in PPMS brain processes. Knock-down of CHD1L in human iPSC-derived neurons and knock-out of chd1l in zebrafish led to developmental and functional deficits of neurons. Thus, several lines of evidence suggest a distinct genetic-epigenetic-transcriptional interplay in the 1q21.1 locus potentially contributing to PPMS pathogenesis.

BISCUIT: an efficient, standards-compliant tool suite for simultaneous genetic and epigenetic inference in bulk and single-cell studies

Data from both bulk and single-cell whole-genome DNA methylation experiments are under-utilized in many ways. This is attributable to inefficient mapping of methylation sequencing reads, routinely discarded genetic information, and neglected read-level epigenetic and genetic linkage information. We introduce the BISulfite-seq Command line User Interface Toolkit (BISCUIT) and its companion R/Bioconductor package, biscuiteer, for simultaneous extraction of genetic and epigenetic information from bulk and single-cell DNA methylation sequencing. BISCUIT's performance, flexibility and standards-compliant output allow large, complex experimental designs to be characterized on clinical timescales. BISCUIT is particularly suited for processing data from single-cell DNA methylation assays, with its excellent scalability, efficiency, and ability to greatly enhance mappability, a key challenge for single-cell studies. We also introduce the epiBED format for single-molecule analysis of coupled epigenetic and genetic information, facilitating the study of cellular and tissue heterogeneity from DNA methylation sequencing.

Genome-wide analysis of hepatic DNA methylation reveals impact of epigenetic aging on xenobiotic metabolism and transport genes in an aged mouse model

Hepatic xenobiotic metabolism and transport decline with age, while intact xenobiotic metabolism is associated with longevity. However, few studies have examined the genome-wide impact of epigenetic aging on these processes. We used reduced representation bisulfite sequencing (RRBS) to map DNA methylation changes in liver DNA from mice ages 4 and 24 months. We identified several thousand age-associated differentially methylated sites (a-DMS), many of which overlapped genes encoding Phase I and Phase II drug metabolizing enzymes, in addition to ABC and SLC classes of transporters. Notable genes harboring a-DMS were Cyp1a2, Cyp2d9, and Abcc2 that encode orthologs of the human drug metabolizing enzymes CYP1A2 and CYP2D6, and the multidrug resistance protein 2 (MRP2) transporter. Cyp2d9 hypermethylation with age was significantly associated with reduced gene expression, while Abcc2 expression was unchanged with age. Cyp1a2 lost methylation with age while, counterintuitively, its expression also reduced with age. We hypothesized that age-related dysregulation of the hepatic transcriptional machinery caused down-regulation of genes despite age-related hypomethylation. Bioinformatic analysis of hypomethylated a-DMS in our sample found them to be highly enriched for hepatic nuclear factor 4 alpha (HNF4α) binding sites. HNF4α promotes Cyp1a2 expression and is downregulated with age, which could explain the reduction in Cyp1a2 expression. Overall, our study supports the broad impact of epigenetic aging on xenobiotic metabolism and transport. Future work should evaluate the interplay between hepatic nuclear receptor function and epigenetic aging. These results may have implications for studies of longevity and healthy aging.

Genetic control of DNA methylation is largely shared across European and East Asian populations

DNA methylation is an ideal trait to study the extent of the shared genetic control across ancestries, effectively providing hundreds of thousands of model molecular traits with large QTL effect sizes. We investigate cis DNAm QTLs in three European (n = 3701) and two East Asian (n = 2099) cohorts to quantify the similarities and differences in the genetic architecture across populations. We observe 80,394 associated mQTLs (62.2% of DNAm probes with significant mQTL) to be significant in both ancestries, while 28,925 mQTLs (22.4%) are identified in only a single ancestry. mQTL effect sizes are highly conserved across populations, with differences in mQTL discovery likely due to differences in allele frequency of associated variants and differing linkage disequilibrium between causal variants and assayed SNPs. This study highlights the overall similarity of genetic control across ancestries and the value of ancestral diversity in increasing the power to detect associations and enhancing fine mapping resolution.

Removing unwanted variation between samples in Hi-C experiments

Hi-C data are commonly normalized using single sample processing methods, with focus on comparisons between regions within a given contact map. Here, we aim to compare contact maps across different samples. We demonstrate that unwanted variation, of likely technical origin, is present in Hi-C data with replicates from different individuals, and that properties of this unwanted variation change across the contact map. We present band-wise normalization and batch correction, a method for normalization and batch correction of Hi-C data and show that it substantially improves comparisons across samples, including in a quantitative trait loci analysis as well as differential enrichment across cell types.

Predicting locus-specific DNA methylation levels in cancer and paracancer tissues

Aim: To predict base-resolution DNA methylation in cancerous and paracancerous tissues. Material & methods: We collected six cancer DNA methylation datasets from The Cancer Genome Atlas and five cancer datasets from Gene Expression Omnibus and established machine learning models using paired cancerous and paracancerous tissues. Tenfold cross-validation and independent validation were performed to demonstrate the effectiveness of the proposed method. Results: The developed cross-tissue prediction models can substantially increase the accuracy at more than 68% of CpG sites and contribute to enhancing the statistical power of differential methylation analyses. An XGBoost model leveraging multiple correlating CpGs may elevate the prediction accuracy. Conclusion: This study provides a powerful tool for DNA methylation analysis and has the potential to gain new insights into cancer research from epigenetics.

Global characterization of RNA editing in genetic regulation of multiple ovarian cancer subtypes

RNA editing plays an extensive role in the initiation and progression of cancer. However, the overall profile and molecular functions of RNA editing in different ovarian cancer subtypes have not been fully characterized and elucidated. Here, we conducted a study on RNA editing in four cohorts of ovarian cancer subtypes through large-scale parallel reporting and bioinformatics analysis. Our findings revealed that RNA editing patterns exhibit subtype-specific characteristics within cancer subtypes. The expression pattern of ADAR and the number of differential editing sites varied under different conditions. CCOC and EOC exhibited significant editing deficiency, whereas HGSC and MOC displayed significant editing excess. The sites within the turquoise module of the coedited network also revealed their correlation with ovarian cancer. In addition, we identified an average of over 40,000 cis-edQTLs in the four subtypes. Finally, we explored the association between RNA editing and drug response, uncovering several potentially effective editing-drug pairs (EDP) and suggesting the conceivable utility of RNA editing sites as therapeutic targets for cancer treatment. Overall, our comprehensive study has identified and characterized RNA editing events in various subtypes of ovarian cancer, providing a new perspective for ovarian cancer research and facilitating the development of medical interventions and treatments.

Gbdmr: identifying differentially methylated CpG regions in the human genome via generalized beta regressions

BACKGROUND

DNA methylation is a biochemical process in which a methyl group is added to the cytosine-phosphate-guanine (CpG) site on DNA molecules without altering the DNA sequence. Multiple CpG sites in a certain genome region can be differentially methylated across phenotypes. Identifying these differentially methylated CpG regions (DMRs) associated with the phenotypes contributes to disease prediction and precision medicine development.

RESULTS

We propose a novel DMR detection algorithm, gbdmr. In contrast to existing methods under a linear regression framework, gbdmr assumes that DNA methylation levels follow a generalized beta distribution. We compare gbdmr to alternative approaches via simulations and real data analyses, including dmrff, a new DMR detection approach that shows promising performance among competitors, and the traditional EWAS that focuses on single CpG sites. Our simulations demonstrate that gbdmr is superior to the other two when the correlation between neighboring CpG sites is strong, while dmrff shows a higher power when the correlation is weak. We provide an explanation of these phenomena from a theoretical perspective. We further applied the three methods to multiple real DNA methylation datasets. One is from a birth cohort study undertaken on the Isle of Wight, United Kingdom, and the other two are from the Gene Expression Omnibus database repository. Overall, gbdmr identifies more DMR CpGs linked to phenotypes than dmrff, and the simulated results support the findings.

CONCLUSIONS

Gbdmr is an innovative method for detecting DMRs based on generalized beta regression. It demonstrated notable advantages over dmrff and traditional EWAS, particularly when adjacent CpGs exhibited moderate to strong correlations. Our real data analyses and simulated findings highlight the reliability of gbdmr as a robust DMR detection tool. The gbdmr approach is accessible and implemented by R on GitHub: https://github.com/chengzhouwu/gbdmr .

Genome-wide DNA methylation dynamics and RNA-seq analysis during grape (cv. 'Cabernet Franc') skin coloration

This study generated whole genome DNA methylation maps to characterize DNA methylomes of grape (cv. 'Cabernet Franc') skins and examine their functional significance during grape skin coloration. We sampled grape skin tissues at three key stages (the early stage of grape berry swelling, the late stage of grape berry swelling and the veraison) during which the color of grape berries changed from green to red. DNA methylation levels of grape skins at the three stages were higher in transposable element regions than in the genic regions, and the CG and CHG DNA methylation levels of the genic region were higher than the CHH DNA methylation levels. We identified differentially methylated regions (DMRs) in S2_vs_S1 and S3_vs_S1. The results indicated that DMRs predominantly occurred within the CHH context during grape skin coloration. Many gene ontology (GO)-enriched DMR-related genes were involved in "nucleotide binding," "catalytic activity" and "ribonucleotide binding" terms; however, many KEGG-enriched DMR-related genes were involved in the "flavonoid biosynthesis" pathway. Our results could provide an important foundation for future research on the development mechanism of grape berries.

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

Introduction

Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.

Methods

Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.

Results

Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.

Discussion

The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.

Metastable epialleles in humans

First identified in isogenic mice, metastable epialleles (MEs) are loci where the extent of DNA methylation (DNAm) is variable between individuals but correlates across tissues derived from different germ layers within a given individual. This property, termed systemic interindividual variation (SIV), is attributed to stochastic methylation establishment before germ layer differentiation. Evidence suggests that some putative human MEs are sensitive to environmental exposures in early development. In this review we introduce key concepts pertaining to human MEs, describe methods used to identify MEs in humans, and review their genomic features. We also highlight studies linking DNAm at putative human MEs to early environmental exposures and postnatal (including disease) phenotypes.

Genetic Susceptibility Modifies Relationships Between Air Pollutants and Stroke Risk: A Large Cohort Study

BACKGROUND

The extent to which genetic susceptibility modifies the associations between air pollutants and the risk of incident stroke is still unclear. This study was designed to investigate the separate and joint associations of long-term exposure to air pollutants and genetic susceptibility on stroke risk.

METHODS

The participants of this study were recruited by the UK Biobank between 2006 and 2010. These participants were followed up from the enrollment until the occurrence of stroke events or censoring of data. Hazard ratios (HRs) and 95% CIs for stroke events associated with long-term exposure to air pollutants were estimated by fitting both crude and adjusted Cox proportional hazards models. Additionally, the polygenic risk score was calculated to estimate whether the polygenic risk score modifies the associations between exposure to air pollutants and incident stroke.

RESULTS

A total of 502 480 subjects were included in this study. After exclusion, 452 196 participants were taken into the final analysis. During a median follow-up time of 11.7 years, 11 334 stroke events were observed, with a mean age of 61.60 years, and men accounted for 56.2% of the total cases. Long-term exposures to particulate matter with an aerodynamic diameter smaller than 2.5 µm (adjusted HR, 1.70 [95% CI, 1.43-2.03]) or particulate matter with an aerodynamic diameter smaller than 10 µm (adjusted HR, 1.50 [95% CI, 1.36-1.66]), nitrogen dioxide (adjusted HR, 1.10 [95% CI, 1.07-1.12]), and nitrogen oxide (adjusted HR, 1.04 [95% CI, 1.02-1.05]) were pronouncedly associated with increased risk of stroke. Meanwhile, participants with high genetic risk and exposure to high air pollutants had ≈45% (31%, 61%; particulate matter with an aerodynamic diameter smaller than 2.5 µm), 48% (33%, 65%; particulate matter with an aerodynamic diameter smaller than 10 µm), 51% (35%, 69%; nitrogen dioxide), and 39% (25%, 55%; nitrogen oxide) higher risk of stroke compared with those with low genetic risk and exposure to low air pollutants, respectively. Of note, we observed additive and multiplicative interactions between genetic susceptibility and air pollutants on stroke events.

CONCLUSIONS

Chronic exposure to air pollutants was associated with an increased risk of stroke, especially in populations at high genetic risk.

Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package

Analyzing different omics data types independently is often too restrictive to allow for detection of subtle, but consistent, variations that are coherently supported based upon different assays. Integrating multi-omics data in one model can increase statistical power. However, designing such a model is challenging because different omics are measured at different levels. We developed the iNETgrate package ( https://bioconductor.org/packages/iNETgrate/ ) that efficiently integrates transcriptome and DNA methylation data in a single gene network. Applying iNETgrate on five independent datasets improved prognostication compared to common clinical gold standards and a patient similarity network approach.

An epigenome-wide analysis of DNA methylation, racialized and economic inequities, and air pollution

Importance

DNA methylation (DNAm) provides a plausible mechanism by which adverse exposures become embodied and contribute to health inequities, due to its role in genome regulation and responsiveness to social and biophysical exposures tied to societal context. However, scant epigenome-wide association studies (EWAS) have included structural and lifecourse measures of exposure, especially in relation to structural discrimination.

Objective

Our study tests the hypothesis that DNAm is a mechanism by which racial discrimination, economic adversity, and air pollution become biologically embodied.

Design

A series of cross-sectional EWAS, conducted in My Body My Story (MBMS, biological specimens collected 2008-2010, DNAm assayed in 2021); and the Multi Ethnic Study of Atherosclerosis (MESA; biological specimens collected 2010-2012, DNAm assayed in 2012-2013); using new georeferenced social exposure data for both studies (generated in 2022).

Setting

MBMS was recruited from four community health centers in Boston; MESA was recruited from four field sites in: Baltimore, MD; Forsyth County, NC; New York City, NY; and St. Paul, MN.

Participants

Two population-based samples of US-born Black non-Hispanic (Black NH), white non-Hispanic (white NH), and Hispanic individuals (MBMS; n=224 Black NH and 69 white NH) and (MESA; n=229 Black NH, n=555 white NH and n=191 Hispanic).

Exposures

Eight social exposures encompassing racial discrimination, economic adversity, and air pollution.

Main outcome

Genome-wide changes in DNAm, as measured using the Illumina EPIC BeadChip (MBMS; using frozen blood spots) and Illumina 450k BeadChip (MESA; using purified monocytes). Our hypothesis was formulated after data collection.

Results

We observed the strongest associations with traffic-related air pollution (measured via black carbon and nitrogen oxides exposure), with evidence from both studies suggesting that air pollution exposure may induce epigenetic changes related to inflammatory processes. We also found suggestive associations of DNAm variation with measures of structural racial discrimination (e.g., for Black NH participants, born in a Jim Crow state; adult exposure to racialized economic residential segregation) situated in genes with plausible links to effects on health.

Conclusions and Relevance

Overall, this work suggests that DNAm is a biological mechanism through which structural racism and air pollution become embodied and may lead to health inequities.

A novel principal component based method for identifying differentially methylated regions in Illumina Infinium MethylationEPIC BeadChip data

Differentially methylated regions (DMRs) are genomic regions with methylation patterns across multiple CpG sites that are associated with a phenotype. In this study, we proposed a Principal Component (PC) based DMR analysis method for use with data generated using the Illumina Infinium MethylationEPIC BeadChip (EPIC) array. We obtained methylation residuals by regressing the M-values of CpGs within a region on covariates, extracted PCs of the residuals, and then combined association information across PCs to obtain regional significance. Simulation-based genome-wide false positive (GFP) rates and true positive rates were estimated under a variety of conditions before determining the final version of our method, which we have named DMRPC. Then, DMRPC and another DMR method, coMethDMR, were used to perform epigenome-wide analyses of several phenotypes known to have multiple associated methylation loci (age, sex, and smoking) in a discovery and a replication cohort. Among regions that were analysed by both methods, DMRPC identified 50% more genome-wide significant age-associated DMRs than coMethDMR. The replication rate for the loci that were identified by only DMRPC was higher than the rate for those that were identified by only coMethDMR (90% for DMRPC vs. 76% for coMethDMR). Furthermore, DMRPC identified replicable associations in regions of moderate between-CpG correlation which are typically not analysed by coMethDMR. For the analyses of sex and smoking, the advantage of DMRPC was less clear. In conclusion, DMRPC is a new powerful DMR discovery tool that retains power in genomic regions with moderate correlation across CpGs.

Age-related promoter-switch regulates Runx1 expression in adult rat hearts

BACKGROUND

Runt-related transcription factor-1 (RUNX1), a key member of the core-binding factor family of transcription factors, has emerged as a novel therapeutic target for cardiovascular disease. There is an urgent need to fully understand the expression pattern of Runx1 in the heart and the mechanisms by which it is controlled under normal conditions and in response to disease. The expression of Runx1 is regulated at the transcriptional level by two promoters designated P1 and P2. Alternative usage of these two promoters creates differential mRNA transcripts diversified in distribution and translational potential. While the significance of P1/P2 promoter-switch in the transcriptional control of Runx1 has been highlighted in the embryogenic process, very little is known about the level of P1- and P2-specific transcripts in adult hearts, and the underlying mechanisms controlling the promoter-switch.

METHODS

To amplify P1/P2 specific sequences in the heart, we used two different sense primers complementary to either P1 or P2 5'-regions to monitor the expression of P1/P2 transcripts. DNA methylation levels were assessed at the Runx1 promoter regions. Rats were grouped by age.

RESULTS

The expression levels of both P1- and P2-derived Runx1 transcripts were decreased in older rats when compared with that in young adults, paralleled with an age-dependent decline in Runx1 protein level. Furthermore, older rats demonstrated a higher degree of DNA methylation at Runx1 promoter regions. Alternative promoter usage was observed in hearts with increased age, as reflected by altered P1:P2 mRNA ratio.

CONCLUSION

Our data demonstrate that the expression of Runx1 in the heart is age-dependent and underscore the importance of gene methylation in the promoter-mediated transcriptional control of Runx1, thereby providing new insights to the role of epigenetic regulation in the heart.

Metabolic syndrome: Nutri-epigenetic cause or consequence?

Metabolic syndrome is a cluster of conditions that results from the interplay of genetic and environmental factors, which increase the comorbidity risk of obesity, hyperglycemia, dyslipidemia, arterial hypertension, stroke, and cardiovascular disease. In this article, we review various high-impact studies which link epigenetics with metabolic syndrome by comparing each study population, methylation effects, and strengths and weaknesses of each research. We also discuss world statistical data on metabolic syndrome incidence in developing countries where the metabolic syndrome is common condition that has significant public health implications.

Comprehensive Analyses Revealed Eight Immune Related Signatures Correlated With Aberrant Methylations as Prognosis and Diagnosis Biomarkers for Kidney Renal Papillary Cell Carcinoma

BACKGROUND

Kidney renal papillary cell carcinoma (KIRP) is a common type of renal cell carcinoma. DNA methylation plays an important role in the development of several cancers. The aim of our study was to identify differentially expressed genes associated with abnormal DNA methylation as biomarkers for predicting the outcome of KIRP.

METHOD

We downloaded KIRP methylation data, RNA sequencing (RNAseq) data, and their corresponding clinical information from the Cancer Genome Atlas (TCGA) database. ChAMP and DEGseq2 packages in R software were used to screen differentially methylated probes (DMPs) and differentially expressed genes (DEGs). Univariate and multivariate Cox regression analyses were used to identify suitable immune related genes correlated with aberrant methylations as prognosis biomarkers.

RESULTS

We identified 8 DEGs (Cysteine And Glycine Rich Protein 1 [CSRP1], major histocompatibility complex, Class II, DM Beta [HLA-DMB], LIF Receptor Subunit Alpha [LIFR], Leukotriene B4 receptor 2 [LTB4R2], Mitogen-Activated Protein Kinase Kinase Kinase 14 [MAP3K14], Nuclear Receptor Subfamily 2 Group F Member 1 [NR2F1], Secreted And Transmembrane 1 [SECTM1], and Vimentin [VIM]) that were independently associated with the overall survival (months) (OS) of KIRP. The time dependent area under the curve (AUC) for each receiver operating characteristic (ROC) of the risk assessment model at 1, 3, 5, and 10-years reached 0.8415, 0.8131, 0.7873, and 0.7667. The risk assessment model was correlated with several immune cells and factors. The AUC value of the diagnosis model using those 8 DEGs reached 0.99.

CONCLUSIONS

The risk assessment model constructed by those 8 DEGs was well able to predict the prognosis and diagnose of KIRP. However, whether the prognosis and diagnosis model could be applied in clinical practice requires further study.

Exploring the Functional Basis of Epigenetic Aging in Relation to Body Fat Phenotypes in the Norfolk Island Cohort

DNA methylation is an epigenetic factor that is modifiable and can change over a lifespan. While many studies have identified methylation sites (CpGs) related to aging, the relationship of these to gene function and age-related disease phenotypes remains unclear. This research explores this question by testing for the conjoint association of age-related CpGs with gene expression and the relation of these to body fat phenotypes. The study included blood-based gene transcripts and intragenic CpG methylation data from Illumina 450 K arrays in 74 healthy adults from the Norfolk Island population. First, a series of regression analyses were performed to detect associations between gene transcript level and intragenic CpGs and their conjoint relationship with age. Second, we explored how these age-related expression CpGs (eCpGs) correlated with obesity-related phenotypes, including body fat percentage, body mass index, and waist-to-hip ratio. We identified 35 age-related eCpGs associated with age. Of these, ten eCpGs were associated with at least one body fat phenotype. Collagen Type XI Alpha 2 Chain (COL11A2), Complement C1s (C1s), and four and a half LIM domains 2 (FHL2) genes were among the most significant genes with multiple eCpGs associated with both age and multiple body fat phenotypes. The COL11A2 gene contributes to the correct assembly of the extracellular matrix in maintaining the healthy structural arrangement of various components, with the C1s gene part of complement systems functioning in inflammation. Moreover, FHL2 expression was upregulated under hypermethylation in both blood and adipose tissue with aging. These results suggest new targets for future studies and require further validation to confirm the specific function of these genes on body fat regulation.

Insight into gene expression associated with DNA methylation and small RNA in the rhizome-root system of Moso bamboo

Moso bamboo (Phyllostachys edulis), typically a monopodial scattering bamboo, is famous for its rapid growth. The rhizome-root system of Moso bamboo plays a crucial role in its clonal growth and spatial distribution. However, few studies have focused on rhizome-root systems. Here we collected LBs, RTs, and RGFNSs, the most important parts of the rhizome-root system, to study the molecular basis of the rapid growth of Moso bamboo due to epigenetic changes, such as DNA modifications and small RNAs. The angle of the shoot apical meristem of LB gradually decreased with increasing distance from the mother plant, and the methylation levels of LB were much higher than those of RT and RGFNS. 24 nt small RNAs and mCHH exhibited similar distribution patterns in transposable elements, suggesting a potential association between these components. The miRNA abundance of LB gradually increased with increasing distance from the mother plant, and a negative correlation was observed between gene expression levels and mCG and mCHG levels in the gene body. This study paves the way for further exploring the effects of epigenetic factors on the physiology of Moso bamboo.

The Impact of Heavy Smoking on Male Infertility and Its Correlation with the Expression Levels of the PTPRN2 and PGAM5 Genes

Smoking has been linked to male infertility by affecting the sperm epigenome and genome. In this study, we aimed to determine possible changes in the transcript levels of PGAM5 (the phosphoglycerate mutase family member 5), PTPRN2 (protein tyrosine phosphatase, N2-type receptor), and TYRO3 (tyrosine protein kinase receptor) in heavy smokers compared to non-smokers, and to investigate their association with the fundamental sperm parameters. In total, 118 sperm samples (63 heavy-smokers (G1) and 55 non-smokers (G2)) were included in this study. A semen analysis was performed according to the WHO guidelines. After a total RNA extraction, RT-PCR was used to quantify the transcript levels of the studied genes. In G1, a significant decrease in the standard semen parameters in comparison to the non-smokers was shown (p < 0.05). Moreover, PGAM5 and PTPRN2 were differentially expressed (p ≤ 0.03 and p ≤ 0.01, respectively) and downregulated in the spermatozoa of G1 compared to G2. In contrast, no difference was observed for TYRO3 (p ≤ 0.3). In G1, the mRNA expression level of the studied genes was correlated negatively with motility, sperm count, normal form, vitality, and sperm membrane integrity (p < 0.05). Therefore, smoking may affect gene expression and male fertility by altering the DNA methylation patterns in the genes associated with fertility and sperm quality, including PGAM5, PTPRN2, and TYRO3.

Contrasting Association of Maternal Plasma Biomarkers of Smoking and 1-Carbon Micronutrients with Offspring DNA Methylation: Evidence of Aryl Hydrocarbon Receptor Repressor Gene-Smoking-Folate Interaction

BACKGROUND

Maternal prenatal smoking is known to alter offspring DNA methylation (DNAm). However, there are no effective interventions to mitigate smoking-induced DNAm alteration.

OBJECTIVES

This study investigated whether 1-carbon nutrients (folate, vitamins B6, and B12) can protect against prenatal smoking-induced offspring DNAm alterations in the aryl hydrocarbon receptor repressor (AHRR) (cg05575921), GFI1 (cg09935388), and CYP1A1 (cg05549655) genes.

METHODS

This study included mother-newborn dyads from a racially diverse US birth cohort. The cord blood DNAm at the above 3 sites were derived from a previous study using the Illumina Infinium MethylationEPIC BeadChip. Maternal smoking was assessed by self-report and plasma biomarkers (hydroxycotinine and cotinine). Maternal plasma folate, and vitamins B6 and B12 concentrations were obtained shortly after delivery. Linear regressions, Bayesian kernel machine regression, and quantile g-computation were applied to test the study hypothesis by adjusting for covariables and multiple testing.

RESULTS

The study included 834 mother-newborn dyads (16.7% of newborns exposed to maternal smoking). DNAm at cg05575921 (AHRR) and at cg09935388 (GFI1) was inversely associated with maternal smoking biomarkers in a dose-response fashion (all P < 7.01 × 10-13). In contrast, cg05549655 (CYP1A1) was positively associated with maternal smoking biomarkers (P < 2.4 × 10-6). Folate concentrations only affected DNAm levels at cg05575921 (AHRR, P = 0.014). Regression analyses showed that compared with offspring with low hydroxycotinine exposure (<0.494) and adequate maternal folate concentrations (quartiles 2-4), an offspring with high hydroxycotinine exposure (≥0.494) and low folate concentrations (quartile 1) had a significant reduction in DNAm at cg05575921 (M-value, ß ± SE = -0.801 ± 0.117, P = 1.44 × 10-11), whereas adequate folate concentrations could cut smoking-induced hypomethylation by almost half. Exposure mixture models further supported the protective role of adequate folate concentrations against smoking-induced aryl hydrocarbon receptor repressor (AHRR) hypomethylation.

CONCLUSIONS

This study found that adequate maternal folate can attenuate maternal smoking-induced offspring AHRR cg05575921 hypomethylation, which has been previously linked to a range of pediatric and adult diseases.

Y chromosome sequence and epigenomic reconstruction across human populations

Recent advances in long-read sequencing technologies have allowed the generation and curation of more complete genome assemblies, enabling the analysis of traditionally neglected chromosomes, such as the human Y chromosome (chrY). Native DNA was sequenced on a MinION Oxford Nanopore Technologies sequencing device to generate genome assemblies for seven major chrY human haplogroups. We analyzed and compared the chrY enrichment of sequencing data obtained using two different selective sequencing approaches: adaptive sampling and flow cytometry chromosome sorting. We show that adaptive sampling can produce data to create assemblies comparable to chromosome sorting while being a less expensive and time-consuming technique. We also assessed haplogroup-specific structural variants, which would be otherwise difficult to study using short-read sequencing data only. Finally, we took advantage of this technology to detect and profile epigenetic modifications among the considered haplogroups. Altogether, we provide a framework to study complex genomic regions with a simple, fast, and affordable methodology that could be applied to larger population genomics datasets.

Rare genetic variants underlie outlying levels of DNA methylation and gene-expression

Testing the effect of rare variants on phenotypic variation is difficult due to the need for extremely large cohorts to identify associated variants given expected effect sizes. An alternative approach is to investigate the effect of rare genetic variants on DNA methylation (DNAm) as effect sizes are expected to be larger for molecular traits compared with complex traits. Here, we investigate DNAm in healthy ageing populations-the Lothian Birth Cohorts of 1921 and 1936-and identify both transient and stable outlying DNAm levels across the genome. We find an enrichment of rare genetic single nucleotide polymorphisms (SNPs) within 1 kb of DNAm sites in individuals with stable outlying DNAm, implying genetic control of this extreme variation. Using a family-based cohort, the Brisbane Systems Genetics Study, we observed increased sharing of DNAm outliers among more closely related individuals, consistent with these outliers being driven by rare genetic variation. We demonstrated that outlying DNAm levels have a functional consequence on gene expression levels, with extreme levels of DNAm being associated with gene expression levels toward the tails of the population distribution. This study demonstrates the role of rare SNPs in the phenotypic variation of DNAm and the effect of extreme levels of DNAm on gene expression.

meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans

Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans.

Integrative Co-methylation Network Analysis Identifies Novel DNA Methylation Signatures and Their Target Genes in Alzheimer's Disease

BACKGROUND

DNA methylation is a key epigenetic marker, and its alternations may be involved in Alzheimer's disease (AD). CpGs sharing similar biological functions or pathways tend to be co-methylated.

METHODS

We performed an integrative network-based DNA methylation analysis on 2 independent cohorts (N = 941) using brain DNA methylation profiles and RNA-sequencing as well as AD pathology data.

RESULTS

Weighted co-methylation network analysis identified 6 modules as significantly associated with neuritic plaque burden. In total, 15 hub CpGs including 3 novel CpGs were identified and replicated as being significantly associated with AD pathology. Furthermore, we identified and replicated 4 target genes (ATP6V1G2, VCP, RAD52, and LST1) as significantly regulated by DNA methylation at hub CpGs. In particular, VCP gene expression was also associated with AD pathology in both cohorts.

CONCLUSIONS

This integrative network-based multiomics study provides compelling evidence for a potential role of DNA methylation alternations and their target genes in AD.

Association of APP gene polymorphisms and promoter methylation with essential hypertension in Guizhou: a case-control study

BACKGROUND

Single-nucleotide polymorphisms (SNPs) and DNA methylation are crucial regulators of essential hypertension (EH). Amyloid precursor protein (APP) mutations are implicated in hypertension development. Nonetheless, studies on the association of APP gene polymorphism and promoter methylation with hypertension are limited. Therefore, this case-control aims to evaluate the genetic association of APP gene polymorphism and promoter methylation with EH in Guizhou populations.

OBJECTIVE AND METHODS

We conducted a case-control study on 343 EH patients and 335 healthy controls (including Miao, Buyi, and Han populations) in the Guizhou province of China to analyze 11 single-nucleotide polymorphisms (rs2040273, rs63750921, rs2211772, rs2830077, rs467021, rs368196, rs466433, rs364048, rs364051, rs438031, rs463946) in the APP gene via MassARRAY SNP. The MassARRAY EpiTYPER was employed to detect the methylation levels of the promoters.

RESULTS

In the Han population, the rs2211772 genotype distribution was significantly different between disease and control groups (χ² = 6.343, P = 0.039). The CC genotype reduced the risk of hypertension compared to the TT or TC genotype (OR 0.105, 95%CI 0.012-0.914, P = 0.041). For rs2040273 in the Miao population, AG or GG genotype reduced the hypertension risk compared with the AA genotype (OR 0.533, 95%CI 0.294-0.965, P = 0.038). Haplotype TCC (rs364051-rs438031-rs463946) increased the risk of EH in Guizhou (OR 1.427, 95%CI 1.020-1.996, P = 0.037). Each 1% increase in CpG_19 (- 613 bp) methylation level was associated with a 4.1% increase in hypertension risk (OR 1.041, 95%CI 1.002-1.081, P = 0.039). Each 1% increase in CpG_1 (- 296 bp) methylation level was associated with an 8% decrease in hypertension risk in women (OR 0.920, 95%CI 0.860-0.984, P = 0.015). CpG_19 significantly correlated with systolic blood pressure (r = 0.2, P = 0.03). The methylation levels of CpG_19 in hypertensive patients with rs466433, rs364048, and rs364051 minor alleles were lower than that with wild-type alleles (P < 0.05). Moreover, rs467021 and rs364051 showed strong synergistic interaction with EH (χ² = 7.633, P = 0.006). CpG_11, CpG_19, and rs364051 showed weak synergistic interaction with EH (χ² = 19.874, P < 0.001).

CONCLUSION

In summary, rs2211772 polymorphism and promoter methylation level of APP gene may be linked to EH in Guizhou populations. Our findings will provide novel insights for genetic research of hypertension and Alzheimer's disease.

Inter-Individual Variation in DNA Methylation Patterns across Two Tissues and Leukocytes in Mature Brahman Cattle

Quantifying the natural inter-individual variation in DNA methylation patterns is important for identifying its contribution to phenotypic variation, but also for understanding how the environment affects variability, and for incorporation into statistical analyses. The inter-individual variation in DNA methylation patterns in female cattle and the effect that a prenatal stressor has on such variability have yet to be quantified. Thus, the objective of this study was to utilize methylation data from mature Brahman females to quantify the inter-individual variation in DNA methylation. Pregnant Brahman cows were transported for 2 h durations at days 60 ± 5; 80 ± 5; 100 ± 5; 120 ± 5; and 140 ± 5 of gestation. A non-transport group was maintained as a control. Leukocytes, amygdala, and anterior pituitary glands were harvested from eight cows born from the non-transport group (Control) and six from the transport group (PNS) at 5 years of age. The DNA harvested from the anterior pituitary contained the greatest variability in DNA methylation of cytosine-phosphate-guanine (mCpG) sites from both the PNS and Control groups, and the amygdala had the least. Numerous variable mCpG sites were associated with retrotransposable elements and highly repetitive regions of the genome. Some of the genomic features that had high variation in DNA methylation are involved in immune responses, signaling, responses to stimuli, and metabolic processes. The small overlap of highly variable CpG sites and features between tissues and leukocytes supports the role of variable DNA methylation in regulating tissue-specific gene expression. Many of the CpG sites that exhibited high variability in DNA methylation were common between the PNS and Control groups within a tissue, but there was little overlap in genomic features with high variability. The interaction between the prenatal environment and the genome could be responsible for the differences in location of the variable DNA methylation.

The impact of spatial correlation on methylation entropy with application to mouse brain methylome

BACKGROUND

With the advance of bisulfite sequencing technologies, massive amount of methylation data have been generated, which provide unprecedented opportunities to study the epigenetic mechanism and its relationship to other biological processes. A commonly seen feature of the methylation data is the correlation between nearby CpG sites. Although such a spatial correlation was utilized in several epigenetic studies, its interaction to other characteristics of the methylation data has not been fully investigated.

RESULTS

We filled this research gap from an information theoretic perspective, by exploring the impact of the spatial correlation on the methylation entropy (ME). With the spatial correlation taken into account, we derived the analytical relation between the ME and another key parameter, the methylation probability. By comparing it to the empirical relation between the two corresponding statistics, the observed ME and the mean methylation level, genomic loci under strong epigenetic control can be identified, which may serve as potential markers for cell-type specific methylation. The proposed method was validated by simulation studies, and applied to analyze a published dataset of mouse brain methylome.

CONCLUSIONS

Compared to other sophisticated methods developed in literature, the proposed method provides a simple but effective way to detect CpG segments under strong epigenetic control (e.g., with bipolar methylation pattern). Findings from this study shed light on the identification of cell-type specific genes/pathways based on methylation data from a mixed cell population.

Genome-wide DNA methylation predicts environmentally driven life history variation in a marine fish

Epigenetic modifications are thought to be one of the molecular mechanisms involved in plastic adaptive responses to environmental variation. However, studies reporting associations between genome-wide epigenetic changes and habitat-specific variations in life history traits (e.g., lifespan, reproduction) are still scarce, likely due to the recent application of methylome resequencing methods to non-model species. In this study, we examined associations between whole genome DNA methylation and environmentally driven life history variation in 2 lineages of a marine fish, the capelin (Mallotus villosus), from North America and Europe. In both lineages, capelin harbor 2 contrasting life history tactics (demersal vs. beach-spawning). Performing whole genome and methylome sequencing, we showed that life history tactics are associated with epigenetic changes in both lineages, though the effect was stronger in European capelin. Genetic differentiation between the capelin harboring different life history tactics was negligible, but we found genome-wide methylation changes in both lineages. We identified 9,125 European and 199 North American differentially methylated regions (DMRs) due to life history. Gene ontology (GO) enrichment analysis for both lineages revealed an excess of terms related to neural function. Our results suggest that environmental variation causes important epigenetic changes that are associated with contrasting life history tactics in lineages with divergent genetic backgrounds, with variable importance of genetic variation in driving epigenetic variation. Our study emphasizes the potential role of genome-wide epigenetic variation in adaptation to environmental variation.

Epigenome-wide association studies of meat traits in Chinese Yorkshire pigs highlights several DNA methylation loci and genes

In this study, we aimed to identified CpG sites at which DNA methylation levels are associated with meat quality traits in 140 Yorkshire pigs, including pH at 45 min (pH_45min), pH at 24 h (pH_24h), drip loss (DL), meat redness value (a*), yellowness (b*) and lightness (L*). Genome-wide methylation levels were measured in muscular tissue using reduced representation bisulfite sequencing (RRBS). Associations between DNA methylation levels and meat quality traits were examined using linear mixed-effect models that were adjusted for gender, year, month and body weight. A Bonferroni-corrected p-value lower than 7.79 × 10 - 8 was considered statistically significant threshold. Eight CpG sites were associated with DL, including CpG sites annotated to RBM4 gene (cpg301054, cpg301055, cpg301058, cpg301059, cpg301066, cpg301072 and cpg301073) and NCAM1 gene (cpg1802985). Two CpG sites were associated with b*, including RNFT1 and MED13 (cpg2272837) and TRIM37 gene (cpg2270611). Five CpG sites were associated with L*, including GSDMA and LRRC3C gene (cpg2252750) and ENSSSCG00000043539 and IRX1 gene (cpg2820178, cpg2820179, cpg2820181 and cpg2820182). No significant associations were observed with pH_45min, pH_24h or a*. We reported associations of meat quality traits with DNA methylation and identified some candidate genes associated with these traits, such as NCAM1, MED13 and TRIM37 gene. These results provide new insight into the epigenetic molecular mechanisms of meat quality traits in pigs.

An integrative framework and recommendations for the study of DNA methylation in the context of race and ethnicity

Human social epigenomics research is critical to elucidate the intersection of social and genetic influences underlying racial and ethnic differences in health and development. However, this field faces major challenges in both methodology and interpretation with regard to disentangling confounded social and biological aspects of race and ethnicity. To address these challenges, we discuss how these constructs have been approached in the past and how to move forward in studying DNA methylation (DNAm), one of the best-characterized epigenetic marks in humans, in a responsible and appropriately nuanced manner. We highlight self-reported racial and ethnic identity as the primary measure in this field, and discuss its implications in DNAm research. Racial and ethnic identity reflects the biological embedding of an individual's sociocultural experience and environmental exposures in combination with the underlying genetic architecture of the human population (i.e., genetic ancestry). Our integrative framework demonstrates how to examine DNAm in the context of race and ethnicity, while considering both intrinsic factors-including genetic ancestry-and extrinsic factors-including structural and sociocultural environment and developmental niches-when focusing on early-life experience. We reviewed DNAm research in relation to health disparities given its relevance to race and ethnicity as social constructs. Here, we provide recommendations for the study of DNAm addressing racial and ethnic differences, such as explicitly acknowledging the self-reported nature of racial and ethnic identity, empirically examining the effects of genetic variants and accounting for genetic ancestry, and investigating race-related and culturally regulated environmental exposures and experiences.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44155-023-00039-z.

DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits

Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.

Using epigenomics to understand cellular responses to environmental influences in diseases

It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.

Pharmacogenomic and Statistical Analysis

Genetic variants can alter response to drugs and other therapeutic interventions. The study of this phenomenon, called pharmacogenomics, is similar in many ways to other types of genetic studies but has distinct methodological and statistical considerations. Genetic variants involved in the processing of exogenous compounds exhibit great diversity and complexity, and the phenotypes studied in pharmacogenomics are also more complex than typical genetic studies. In this chapter, we review basic concepts in pharmacogenomic study designs, data generation techniques, statistical analysis approaches, and commonly used methods and briefly discuss the ultimate translation of findings to clinical care.

A Targeted Epigenetic Clock for the Prediction of Biological Age

Epigenetic clocks were initially developed to track chronological age, but accumulating evidence indicates that they can also predict biological age. They are usually based on the analysis of DNA methylation by genome-wide methods, but targeted approaches, based on the assessment of a small number of CpG sites, are advisable in several settings. In this study, we developed a targeted epigenetic clock purposely optimized for the measurement of biological age. The clock includes six genomic regions mapping in ELOVL2, NHLRC1, AIM2, EDARADD, SIRT7 and TFAP2E genes, selected from a re-analysis of existing microarray data, whose DNA methylation is measured by EpiTYPER assay. In healthy subjects (n = 278), epigenetic age calculated using the targeted clock was highly correlated with chronological age (Spearman correlation = 0.89). Most importantly, and in agreement with previous results from genome-wide clocks, epigenetic age was significantly higher and lower than expected in models of increased (persons with Down syndrome, n = 62) and decreased (centenarians, n = 106; centenarians' offspring, n = 143; nutritional intervention in elderly, n = 233) biological age, respectively. These results support the potential of our targeted epigenetic clock as a new marker of biological age and open its evaluation in large cohorts to further promote the assessment of biological age in healthcare practice.

Blood MAPT expression and methylation status in Alzheimer's disease

Aim

This study aimed to investigate the expression levels and methylation status of microtubule-associated protein tau (MAPT) in the blood of Alzheimer's disease (AD) patients and age- and sex-matched healthy controls.

Methods

Fifty AD outpatients and 50 healthy contorls were enrolled. Blood samples were collected for processing of complementary DNA and genomic DNA. MAPT messenger ribonucleic acid (mRNA) expression was analyzed by real-time quantitative polymerase chain reaction. The methylation rates of four cytosine-phosphate-guanine (CpG) sites in the upstream region of MAPT exon1 were evaluated by the pyrosequencing method.

Results

No significant differences in MAPT mRNA expression levels were found between AD and control subjects (AD 0.97 ± 0.49 vs. control 1.0 ± 0.64, p = 0.62). MAPT mRNA expression levels were not correlated with any other clinical characteristics or results of psychological tests. MAPT mRNA expression levels were significantly higher in AD subjects treated with acetylcholinesterase inhibitors (AchEIs) (n = 25) than in subjects not treated with AChEIs (n = 25) (unmedicated 0.83 ± 0.33 vs. medicated 1.12 ± 0.59, p = 0.049). The AD subjects did not differ from the control subjects in methylation rates at selected CpG sites. MAPT methylation status were not correlated with clinical characteristics, the results of psychological tests, or MAPT mRNA expression.

Conclusion

MAPT mRNA expression levels and methylation status in blood do not appear useful as biomarkers for AD or the examined CpG sites were not genetically significant for MAPT gene expression or AD pathology. However, AChEIs may alter MAPT mRNA expression. Further studies are needed to explore blood biomarkers that can discriminate AD patients from controls.

Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels

There is increasing appreciation that, in addition to being shaped by an individual's genotype and environment, most complex traits are also determined by poorly understood interactions between these two factors. So-called "genotype × environment" (G×E) interactions remain difficult to map at the organismal level but can be uncovered using molecular phenotypes. To do so at large scale, we used TM3'seq to profile transcriptomes across 12 cellular environments in 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression levels across environments and revealed a context-dependent genetic architecture: The average heritability of gene expression levels increased in treatment relative to control conditions, and on average, each treatment revealed new expression quantitative trait loci (eQTLs) at 11% of genes. Across our experiments, 22% of all identified eQTLs were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses suggested that positive selection has shaped G×E loci involved in responding to immune challenges and hormones but not to man-made chemicals. We hypothesize that this reflects a reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting G×E interactions, and provides new insight into the evolutionary mechanisms that maintain G×E loci in human populations.

MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition

The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by β diversity; that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome β diversity with or without interacting with an environmental factor. We show that the score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes p-value approximations based on asymptotic distributions unacceptably liberal. By correcting for skewness and kurtosis, we develop accurate p-value approximations, whose accuracy was verified by extensive simulations. We exemplify our methods by analyzing a set of 147 genotyped subjects with 16S rRNA microbiome profiles from non-malignant lung tissues. Correcting for skewness and kurtosis eliminated the dramatic deviation in the quantile–quantile plots. We provided preliminary evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for both unweighted (p = 0.0032) and weighted (p = 0.011) UniFrac distance matrices. In summary, our methods will facilitate analyzing large-scale genome-wide association studies of the human microbiome.

Tissue- and ethnicity-independent hypervariable DNA methylation states show evidence of establishment in the early human embryo

We analysed DNA methylation data from 30 datasets comprising 3474 individuals, 19 tissues and 8 ethnicities at CpGs covered by the Illumina450K array. We identified 4143 hypervariable CpGs ('hvCpGs') with methylation in the top 5% most variable sites across multiple tissues and ethnicities. hvCpG methylation was influenced but not determined by genetic variation, and was not linked to probe reliability, epigenetic drift, age, sex or cell heterogeneity effects. hvCpG methylation tended to covary across tissues derived from different germ-layers and hvCpGs were enriched for proximity to ERV1 and ERVK retrovirus elements. hvCpGs were also enriched for loci previously associated with periconceptional environment, parent-of-origin-specific methylation, and distinctive methylation signatures in monozygotic twins. Together, these properties position hvCpGs as strong candidates for studying how stochastic and/or environmentally influenced DNA methylation states which are established in the early embryo and maintained stably thereafter can influence life-long health and disease.

Manipulating base quality scores enables variant calling from bisulfite sequencing alignments using conventional bayesian approaches

Background

Calling germline SNP variants from bisulfite-converted sequencing data poses a challenge for conventional software, which have no inherent capability to dissociate true polymorphisms from artificial mutations induced by the chemical treatment. Nevertheless, SNP data is desirable both for genotyping and to understand the DNA methylome in the context of the genetic background. The confounding effect of bisulfite conversion however can be conceptually resolved by observing differences in allele counts on a per-strand basis, whereby artificial mutations are reflected by non-complementary base pairs.

Results

Herein, we present a computational pre-processing approach for adapting sequence alignment data, thus indirectly enabling downstream analysis on a per-strand basis using conventional variant calling software such as GATK or Freebayes. In comparison to specialised tools, the method represents a marked improvement in precision-sensitivity based on high-quality, published benchmark datasets for both human and model plant variants.

Conclusion

The presented “double-masking” procedure represents an open source, easy-to-use method to facilitate accurate variant calling using conventional software, thus negating any dependency on specialised tools and mitigating the need to generate additional, conventional sequencing libraries alongside bisulfite sequencing experiments. The method is available at https://github.com/bio15anu/revelioand an implementation with Freebayes is available at https://github.com/EpiDiverse/SNP

Mixed model-based eQTL analysis reveals lncRNAs associated with regulation of genes involved in sex determination and spermatogenesis: The key to understanding human gender imbalance

BACKGROUND

An imbalance in the prenatal sex ratio in humans may be due to several factors affecting sperm physiology, including genetic features. In this study, we conducted a transcriptome-wide analysis of expression quantitative trait loci (eQTLs) to identify target genes associated with previously described QTLs associated with gender imbalance.

METHODS

A mixed model explaining polygenic effects by genomic covariance among individuals was used to identify the eQTLs using gene expression and genotype data from 462 European/African individuals.

RESULTS

Eight eGenes were associated with four QTLs (P < 4.00 × 10^-5), with strong associations found (P < 4.00 × 10^-8) between rs2485007 and eGenes ANKRD26P3 (P = 3.40 × 10^-9) and LINC00421 (P = 1.35 × 10^-9). ANKRD26P3 and LINC00421 are both lncRNAs associated with the control of testis-dominant genes PELP1, TAF15, NANOG, TEX14, TCF3, ZNF433, ZNF555, TEX37, FATE1, TCP11, and CYLC2 and Y-linked genes SRY and ZFY, as well as several genes with roles in spermatogenesis (ODF1, SPATC1, SPATA3, SPATA31E1, SPERT, SPATA16, MOSPD1, SPATA24, and SPO11) and sex determination (SOX family genes).

CONCLUSIONS

The above eGenes contribute directly or indirectly to gene regulation for sex determination and spermatogenesis, thereby serving as important functional clues for gender-biased selection.

Antagonistic regulatory effects of a single cis-acting expression quantitative trait locus between transcription and translation of the MRPL43 gene

Background

Heterogeneity of expression quantitative trait locus (eQTL) effects have been shown across gene expression processes. Knowledge on how to produce the heterogeneity is quite limited. This study aims to examine fluctuations in differential gene expression by alleles of sequence variants across expression processes.

Results

Genome-wide eQTL analyses with transcriptome-wide gene expression data revealed 20 cis-acting eQTLs associated simultaneously with mRNA expression, ribosome occupancy, and protein abundance. A 97 kb-long eQTL signal for mitochondrial ribosomal protein L43 (MRPL43) covered the gene, showing a heterogeneous effect size on gene products across expression stages. One allele of the eQTL was associated with increased mRNA expression and ribosome occupancy but decreased protein abundance. We examined the heterogeneity and found that the eQTL can be attributed to the independent functions of three nucleotide variants, with a strong linkage. NC_000010.11:g.100987606G > T, upstream of MRPL43, may regulate the binding affinity of transcription factors. NC_000010.11:g.100986746C > G, 3 bp from an MRPL43 splice donor site, may alter the splice site. NC_000010.11:g.100978794A > G, in the isoform with a long 3′-UTR, may strengthen the binding affinity of the microRNA. Individuals with the TGG haplotype at these three variants had higher levels of mRNA expression and ribosome occupancy than individuals with the GCA haplotype but lower protein levels, producing the flipped effect throughout the expression process.

Conclusions

These findings suggest that multiple functional variants in a linkage exert their regulatory functions at different points in the gene expression process, producing a complexity of single eQTLs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01057-7.

The missing diversity in human epigenomic studies

Recent work has highlighted a lack of diversity in genomic studies. However, less attention has been given to epigenomics. Here, we show that epigenomic studies are lacking in diversity and propose several solutions to address this problem.

LARGE-SCALE INTEGRATION OF DNA METHYLATION AND GENE EXPRESSION ARRAY PLATFORMS IDENTIFIES BOTH cis AND trans RELATIONSHIPS

Although epigenome-wide association studies (EWAS) have been successful in identifying DNA methylation (DNAm) patterns associated with disease states, any further characterization of etiologic mechanisms underlying disease remains elusive. This knowledge gap does not originate from a lack of DNAm–trait associations, but rather stems from study design issues that affect the interpretability of EWAS results. Despite known limitations in predicting the function of a particular CpG site, most EWAS maintain the broad assumption that altered DNAm results in a concomitant change of transcription at the most proximal gene. This study integrated DNAm and gene expression (GE) measurements in two cohorts, the Adolescent and Young Adult Twin Study (AYATS) and the Pregnancy, Race, Environment, Genes (PREG) study, to improve the understanding of epigenomic regulatory mechanisms. CpG sites associated with GE in cis were enriched in areas of transcription factor binding and areas of intermediate-to-low CpG density. CpG sites associated with trans GE were also enriched in areas of known regulatory significance, including enhancer regions. These results highlight issues with restricting DNAm-transcript annotations to small genomic intervals and question the validity of assuming a cis DNAm–GE pathway. Based on these findings, the interpretation of EWAS results is limited in studies without multi-omic support and further research should identify genomic regions in which GE-associated DNAm is overrepresented. An in-depth characterization of GE-associated CpG sites could improve predictions of the downstream functional impact of altered DNAm and inform best practices for interpreting DNAm–trait associations generated by EWAS.

Regulation of glutamate transport and neuroinflammation in a term newborn rat model of hypoxic–ischaemic brain injury

In the newborn brain, moderate-severe hypoxia–ischaemia induces glutamate excitotoxicity and inflammation, possibly via dysregulation of candidate astrocytic glutamate transporter ( Glt1) and pro-inflammatory cytokines (e.g. Tnfα, Il1β, Il6). Epigenetic mechanisms may mediate dysregulation. Hypotheses: (1) hypoxia–ischaemia dysregulates mRNA expression of these candidate genes; (2) expression changes in Glt1 are mediated by DNA methylation changes; and (3) methylation values in brain and blood are correlated. Seven-day-old rat pups ( n = 42) were assigned to nine groups based on treatment (for each timepoint: naïve ( n = 3), sham ( n = 3), hypoxia–ischaemia ( n = 8) and timepoint for tissue collection (6, 12 and 24 h post-hypoxia). Moderate hypoxic–ischemic brain injury was induced via ligation of the left common carotid artery followed by 100 min hypoxia (8% O2, 36°C). mRNA was quantified in cortex and hippocampus for the candidate genes, myelin ( Mbp), astrocytic ( Gfap) and neuronal ( Map2) markers (qPCR). DNA methylation was measured for Glt1 in cortex and blood (bisulphite pyrosequencing). Hypoxia–ischaemia induced pro-inflammatory cytokine upregulation in both brain regions at 6 h. This was accompanied by gene expression changes potentially indicating onset of astrogliosis and myelin injury. There were no significant changes in expression or promoter DNA methylation of Glt1. This pilot study supports accumulating evidence that hypoxia–ischaemia causes neuroinflammation in the newborn brain and prioritises further expression and DNA methylation analyses focusing on this pathway. Epigenetic blood biomarkers may facilitate identification of high-risk newborns at birth, maximising chances of neuroprotective interventions.

The Prospective Application of Melatonin in Treating Epigenetic Dysfunctional Diseases

In the past, different human disorders were described by scientists from the perspective of either environmental factors or just by genetically related mechanisms. The rise in epigenetic studies and its modifications, i.e., heritable alterations in gene expression without changes in DNA sequences, have now been confirmed in diseases. Modifications namely, DNA methylation, posttranslational histone modifications, and non-coding RNAs have led to a better understanding of the coaction between epigenetic alterations and human pathologies. Melatonin is a widely-produced indoleamine regulator molecule that influences numerous biological functions within many cell types. Concerning its broad spectrum of actions, melatonin should be investigated much more for its contribution to the upstream and downstream mechanistic regulation of epigenetic modifications in diseases. It is, therefore, necessary to fill the existing gaps concerning corresponding processes associated with melatonin with the physiological abnormalities brought by epigenetic modifications. This review outlines the findings on melatonin’s action on epigenetic regulation in human diseases including neurodegenerative diseases, diabetes, cancer, and cardiovascular diseases. It summarizes the ability of melatonin to act on molecules such as proteins and RNAs which affect the development and progression of diseases.

Placental multi-omics integration identifies candidate functional genes for birthweight

Abnormal birthweight is associated with increased risk for cardiometabolic diseases in later life. Although the placenta is critical to fetal development and later life health, it has not been integrated into largescale functional genomics initiatives, and mechanisms of birthweight-associated variants identified by genome wide association studies (GWAS) are unclear. The goal of this study is to provide functional mechanistic insight into the causal pathway from a genetic variant to birthweight by integrating placental methylation and gene expression with established GWAS loci for birthweight. We identify placental DNA methylation and gene expression targets for several birthweight GWAS loci. The target genes are broadly enriched in cardiometabolic, immune response, and hormonal pathways. We find that methylation causally influences WNT3A, CTDNEP1, and RANBP2 expression in placenta. Multi-trait colocalization identifies PLEKHA1, FES, CTDNEP1, and PRMT7 as likely functional effector genes. These findings reveal candidate functional pathways that underpin the genetic regulation of birthweight via placental epigenetic and transcriptomic mechanisms. Clinical trial registration; ClinicalTrials.gov, NCT00912132.

Making sense of the ageing methylome

Over time, the human DNA methylation landscape accrues substantial damage, which has been associated with a broad range of age-related diseases, including cardiovascular disease and cancer. Various age-related DNA methylation changes have been described, including at the level of individual CpGs, such as differential and variable methylation, and at the level of the whole methylome, including entropy and correlation networks. Here, we review these changes in the ageing methylome as well as the statistical tools that can be used to quantify them. We detail the evidence linking DNA methylation to ageing phenotypes and the longevity strategies aimed at altering both DNA methylation patterns and machinery to extend healthspan and lifespan. Lastly, we discuss theories on the mechanistic causes of epigenetic ageing.

Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

BACKGROUND

Conflicting results regarding alterations to sperm DNA methylation in cases of spermatogenesis defects, male infertility and poor developmental outcomes have been reported in humans. Bulls used for artificial insemination represent a relevant model in this field, as the broad dissemination of bull semen considerably alleviates confounding factors and enables the precise assessment of male fertility. This study was therefore designed to assess the potential for sperm DNA methylation to predict bull fertility.

RESULTS

A unique collection of 100 sperm samples was constituted by pooling 2-5 ejaculates per bull from 100 Montbéliarde bulls of comparable ages, assessed as fertile (n = 57) or subfertile (n = 43) based on non-return rates 56 days after insemination. The DNA methylation profiles of these samples were obtained using reduced representation bisulfite sequencing. After excluding putative sequence polymorphisms, 490 fertility-related differentially methylated cytosines (DMCs) were identified, most of which were hypermethylated in subfertile bulls. Interestingly, 46 genes targeted by DMCs are involved in embryonic and fetal development, sperm function and maturation, or have been related to fertility in genome-wide association studies; five of these were further analyzed by pyrosequencing. In order to evaluate the prognostic value of fertility-related DMCs, the sperm samples were split between training (n = 67) and testing (n = 33) sets. Using a Random Forest approach, a predictive model was built from the methylation values obtained on the training set. The predictive accuracy of this model was 72% on the testing set and 72% on individual ejaculates collected from an independent cohort of 20 bulls.

CONCLUSION

This study, conducted on the largest set of bull sperm samples so far examined in epigenetic analyses, demonstrated that the sperm methylome is a valuable source of male fertility biomarkers. The next challenge is to combine these results with other data on the same sperm samples in order to improve the quality of the model and better understand the interplay between DNA methylation and other molecular features in the regulation of fertility. This research may have potential applications in human medicine, where infertility affects the interaction between a male and a female, thus making it difficult to isolate the male factor.