Digital-scRRBS: A Cost-Effective, Highly Sensitive, and Automated Single-Cell Methylome Analysis Platform via Digital Microfluidics.

Single-cell DNA methylation sequencing is highly effective for identifying cell subpopulations and constructing epigenetic regulatory networks. Existing methylome analyses require extensive starting materials and are costly, complex, and susceptible to contamination, thereby impeding the development of single-cell methylome technology. In this work, we report digital microfluidics-based single-cell reduced representation bisulfite sequencing (digital-scRRBS), the first microfluidics-based single-cell methylome library construction platform, which is an automatic, effective, reproducible, and reagent-efficient technique to dissect the single-cell methylome. Using our digital microfluidic chip, we isolated single cells in 15 s and successfully constructed single-cell methylation sequencing libraries with a unique genome mapping rate of up to 53.6%, covering up to 2.26 million CpG sites. Digital-scRRBS demonstrates a high capacity for distinguishing cell identity and tracking DNA methylation during drug administration. Digital-scRRBS expands the applicability of single-cell methylation methods as a versatile tool for epigenetic analysis of rare cells and populations with high levels of heterogeneity.

Socioeconomic changes predict genome-wide DNA methylation in childhood.

Childhood socioeconomic position (SEP) is a major determinant of health and well-being across the entire life course. To effectively prevent and reduce health risks related to SEP, it is critical to better understand when and under what circumstances socioeconomic adversity shapes biological processes. DNA methylation (DNAm) is one such mechanism for how early life adversity 'gets under the skin'. In this study, we evaluated the dynamic relationship between SEP and DNAm across childhood using data from 946 mother-child pairs in the Avon Longitudinal Study of Parents and Children. We assessed six SEP indicators spanning financial, occupational and residential domains during very early childhood (ages 0-2), early childhood (ages 3-5) and middle childhood (ages 6-7). Epigenome-wide DNAm was measured at 412 956 cytosine-guanines (CpGs) from peripheral blood at age 7. Using an innovative two-stage structured life-course modeling approach, we tested three life-course hypotheses for how SEP shapes DNAm profiles-accumulation, sensitive period and mobility. We showed that changes in the socioeconomic environment were associated with the greatest differences in DNAm, and that middle childhood may be a potential sensitive period when socioeconomic instability is especially important in shaping DNAm. Top SEP-related DNAm CpGs were overrepresented in genes involved in pathways important for neural development, immune function and metabolic processes. Our findings highlight the importance of socioeconomic stability during childhood and if replicated, may emphasize the need for public programs to help children and families experiencing socioeconomic instability and other forms of socioeconomic adversity.

A trans-omics assessment of gene-gene interaction in early-stage NSCLC.

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with PBonferroni ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (ßinteraction  = 0.018, P = 1.87 × 10-12 ), which mapped to RELA × HLA-G (ßinteraction  = 0.218, P = 8.82 × 10-11 ) and cg08872738 × cg27077312 (ßinteraction  = -0.010, P = 1.16 × 10-11 ), which mapped to TUBA1B × TOMM40 (ßinteraction =-0.250, P = 3.83 × 10-10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer.

Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow.

HIMA2: high-dimensional mediation analysis and its application in epigenome-wide DNA methylation data.

Mediation analysis plays a major role in identifying significant mediators in the pathway between environmental exposures and health outcomes. With advanced data collection technology for large-scale studies, there has been growing research interest in developing methodology for high-dimensional mediation analysis. In this paper we present HIMA2, an extension of the HIMA method (Zhang in Bioinformatics 32:3150-3154, 2016). First, the proposed HIMA2 reduces the dimension of mediators to a manageable level based on the sure independence screening (SIS) method (Fan in J R Stat Soc Ser B 70:849-911, 2008). Second, a de-biased Lasso procedure is implemented for estimating regression parameters. Third, we use a multiple-testing procedure to accurately control the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. We demonstrate its practical performance using Monte Carlo simulation studies and apply our method to identify DNA methylation markers which mediate the pathway from smoking to reduced lung function in the Coronary Artery Risk Development in Young Adults (CARDIA) Study.

Influences of polycyclic aromatic hydrocarbon on the epigenome toxicity and its applicability in human health risk assessment.

The existence of polycyclic aromatic hydrocarbons (PAHs) in ambient air is an escalating concern worldwide because of their ability to cause cancer and induce permanent changes in the genetic material. Growing evidence implies that during early life-sensitive stages, the risk of progression of acute and chronic diseases depends on epigenetic changes initiated by the influence of environmental cues. Several reports deciphered the relationship between exposure to environmental chemicals and epigenetics, and have known toxicants that alter the epigenetic states. Amongst PAHs, benzo[a]pyrene (B[a]P) is accepted as a group 1 cancer-causing agent by the International Agency for the Research on Cancer (IARC). B[a]P is a well-studied pro-carcinogen that is metabolically activated by the aryl hydrocarbon receptor (AhR)/cytochrome P450 pathway. Cytochrome P450 plays a pivotal role in the stimulation step, which is essential for DNA adduct formation. Accruing evidence suggests that epigenetic alterations assume a fundamental part in PAH-promoted carcinogenesis. This interaction between PAHs and epigenetic factors results in an altered profile of these marks, globally and locus-specific. Some of the epigenetic changes due to exposure to PAHs lead to increased disease susceptibility and progression. It is well understood that exposure to environmental carcinogens, such as PAH triggers disease pathways through changes in the genome. Several evidence reported due to the epigenome-wide association studies, that early life adverse environmental events may trigger widespread and persistent variations in transcriptional profiling. Moreover, these variations respond to DNA damage and/or a consequence of epigenetic modifications that need further investigation. Growing evidence has associated PAHs with epigenetic variations involving alterations in DNA methylation, histone modification, and micro RNA (miRNA) regulation. Epigenetic alterations to PAH exposure were related to chronic diseases, such as pulmonary disease, cardiovascular disease, endocrine disruptor, nervous system disorder, and cancer. This hormetic response gives a novel perception concerning the toxicity of PAHs and the biological reaction that may be a distinct reliance on exposure. This review sheds light on understanding the latest evidence about how PAHs can alter epigenetic patterns and human health. In conclusion, as several epigenetic change mechanisms remain unclear yet, further analyses derived from PAHs exposure must be performed to find new targets and disease biomarkers. In spite of the current limitations, numerous evidence supports the perception that epigenetics grips substantial potential for advancing our knowledge about the molecular mechanisms of environmental toxicants, also for predicting health-associated risks due to environmental circumstances exposure and individual susceptibility.

A multi-omics approach to investigate the inflammatory response to life course socioeconomic position.

Aim: Inflammation represents a potential pathway through which socioeconomic position (SEP) is biologically embedded. Materials & methods: We analyzed inflammatory biomarkers in response to life course SEP by integrating multi-omics DNA-methylation, gene expression and protein level in 178 European Prospective Investigation into Cancer and Nutrition-Italy participants. Results & conclusion: We identified 61 potential cis acting CpG loci whose methylation levels were associated with gene expression at a Bonferroni correction. We examined the relationships between life course SEP and these 61 cis-acting regulatory methylation sites individually and jointly using several scores. Less-advantaged SEP participants exhibit, later in life, a lower inflammatory methylome score, suggesting an overall increased expression of the corresponding inflammatory genes or proteins, supporting the hypothesis that SEP impacts adult physiology through inflammation.

Epigenomic Assessment of Cardiovascular Disease Risk and Interactions With Traditional Risk Metrics.

Background Epigenome-wide association studies for cardiometabolic risk factors have discovered multiple loci associated with incident cardiovascular disease (CVD). However, few studies have sought to directly optimize a predictor of CVD risk. Furthermore, it is challenging to train multivariate models across multiple studies in the presence of study- or batch effects. Methods and Results Here, we analyzed existing DNA methylation data collected using the Illumina HumanMethylation450 microarray to create a predictor of CVD risk across 3 cohorts: Women's Health Initiative, Framingham Heart Study Offspring Cohort, and Lothian Birth Cohorts. We trained Cox proportional hazards-based elastic net regressions for incident CVD separately in each cohort and used a recently introduced cross-study learning approach to integrate these individual scores into an ensemble predictor. The methylation-based risk score was associated with CVD time-to-event in a held-out fraction of the Framingham data set (hazard ratio per SD=1.28, 95% CI, 1.10-1.50) and predicted myocardial infarction status in the independent REGICOR (Girona Heart Registry) data set (odds ratio per SD=2.14, 95% CI, 1.58-2.89). These associations remained after adjustment for traditional cardiovascular risk factors and were similar to those from elastic net models trained on a directly merged data set. Additionally, we investigated interactions between the methylation-based risk score and both genetic and biochemical CVD risk, showing preliminary evidence of an enhanced performance in those with less traditional risk factor elevation. Conclusions This investigation provides proof-of-concept for a genome-wide, CVD-specific epigenomic risk score and suggests that DNA methylation data may enable the discovery of high-risk individuals who would be missed by alternative risk metrics.

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biology. Here, we present Sub-Compartment Identifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to predict sub-compartments using Hi-C chromatin interaction data. We find that the network topological centrality and clustering performance of SCI sub-compartment predictions are superior to those of hidden Markov model (HMM) sub-compartment predictions. Moreover, using orthogonal Chromatin Interaction Analysis by in-situ Paired-End Tag Sequencing (ChIA-PET) data, we confirmed that SCI sub-compartment prediction outperforms HMM. We show that SCI-predicted sub-compartments have distinct epigenetic marks, transcriptional activities, and transcription factor enrichment. Moreover, we present a deep neural network to predict sub-compartments using epigenome, replication timing, and sequence data. Our neural network predicts more accurate sub-compartment predictions when SCI-determined sub-compartments are used as labels for training.

Replication and expansion of epigenome-wide association literature in a black South African population.

BACKGROUND: DNA methylation is associated with non-communicable diseases (NCDs) and related traits. Methylation data on continental African ancestries are currently scarce, even though there are known genetic and epigenetic differences between ancestral groups and a high burden of NCDs in Africans. Furthermore, the degree to which current literature can be extrapolated to the understudied African populations, who have limited resources to conduct independent large-scale analysis, is not yet known. To this end, this study examines the reproducibility of previously published epigenome-wide association studies of DNA methylation conducted in different ethinicities, on factors related to NCDs, by replicating findings in 120 South African Batswana men aged 45 to 88 years. In addition, novel associations between methylation and NCD-related factors are investigated using the Illumina EPIC BeadChip. RESULTS: Up to 86% of previously identified epigenome-wide associations with NCD-related traits (alcohol consumption, smoking, body mass index, waist circumference, C-reactive protein, blood lipids and age) overlapped with those observed here and a further 13% were directionally consistent. Only 1% of the replicated associations presented with effects opposite to findings in other ancestral groups. The majority of these inconcistencies were associated with population-specific genomic variance. In addition, we identified eight new 450K array CpG associations not previously reported in other ancestries, and 11 novel EPIC CpG associations with alcohol consumption. CONCLUSIONS: The successful replication of existing EWAS findings in this African population demonstrates that blood-based 450K EWAS findings from commonly investigated ancestries can largely be extrapolated to ethnicities for which epigenetic data are not yet available. Possible population-specific differences in 14% of the tested associations do, however, motivate the need to include a diversity of ethnic groups in future epigenetic research. The novel associations found with the enhanced coverage of the Illumina EPIC array support its usefulness to expand epigenetic literature.

Smoking and blood DNA methylation: an epigenome-wide association study and assessment of reversibility.

We conducted a genome-wide association study of blood DNA methylation and smoking, attempted replication of previously discovered associations, and assessed the reversibility of smoking-associated methylation changes. DNA methylation was measured in baseline peripheral blood samples for 5,044 participants in the Melbourne Collaborative Cohort Study. For 1,032 participants, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. A cross-sectional analysis of the association between smoking and DNA methylation and a longitudinal analysis of changes in smoking status and changes in DNA methylation were conducted. We used our cross-sectional analysis to replicate previously reported associations for current (N = 3,327) and former (N = 172) smoking. A comprehensive smoking index accounting for the biological half-life of smoking compounds and several aspects of smoking history was constructed to assess the reversibility of smoking-induced methylation changes. This measure of lifetime exposure to smoking allowed us to detect more associations than comparing current with never smokers. We identified 4,496 cross-sectional associations at P < 10-7, including 3,296 annotated to 1,326 genes that were not previously implicated in smoking-associated DNA methylation changes at this significance threshold. We replicated the majority of previously reported associations (P < 10-7) for current and former smokers. In our data, we observed for former smokers a substantial degree of return to the methylation levels of never smokers, compared with current smokers (median: 74%, IQR = 63-86%), corresponding to small values (median: 2.75, IQR = 1.5-5.25) for the half-life parameter of the comprehensive smoking index. Longitudinal analyses identified 368 sites at which methylation changed upon smoking cessation. Our study demonstrates the usefulness of the comprehensive smoking index to detect associations between smoking and DNA methylation at CpGs across the genome, replicates the vast majority of previously reported associations, and quantifies the reversibility of smoking-induced methylation changes.

Can social support during pregnancy affect maternal DNA methylation? Findings from a cohort of African-Americans.

BACKGROUND: While stress and the absence of social support during pregnancy have been linked to poor health outcomes, the underlying biological mechanisms are unclear. METHODS: We examined whether adverse experiences during pregnancy alter DNA methylation (DNAm) in maternal epigenomes. Analyses included 250 African-American mothers from the Boston Birth Cohort. Genome-wide DNAm profiling was performed in maternal blood collected after delivery, using the Infinium HumanMethylation450 Beadchip. Linear regression models, with adjustment of pertinent covariates, were applied. RESULTS: While self-reported maternal psychosocial lifetime stress and stress during pregnancy was not associated with DNAm alterations, we found that absence of support from the baby's father was significantly associated with maternal DNAm changes in TOR3A, IQCB1, C7orf36, and MYH7B and that lack of support from family and friends was associated with maternal DNA hypermethylation on multiple genes, including PRDM16 and BANKL. CONCLUSIONS: This study provides intriguing results suggesting biological embedding of social support during pregnancy on maternal DNAm, warranting additional investigation, and replication.