Comparison of pre-processing methodologies for Illumina 450k methylation array data in familial analyses

CpG methylation changes in human mesenchymal and neural stem cells in response to in vitro niche modifications

Stem cell therapies hold promise in addressing the burden of neurodegenerative diseases with human embryonic neural stem cells (hNSC-H9s) and bone marrow-derived human mesenchymal stem cells (hMSCs) as viable candidates. The induction of hMSC neurospheres (hMSC-IN) generate a more lineage-restricted common neural progenitor-like cell population, potentially tunable by heparan sulfate proteoglycans (HSPGs). We examined CpG (5 mC) site methylation patterns using Illumina Infinium 850 K EPIC arrays in hNSC-H9, hMSCs and hMSC-IN cultures with HSPG agonist heparin at early and late phases of growth. We identified key regulatory CpG sites in syndecans (SDC2; SDC4) that potentially regulate gene expression in monolayers. Unique hMSC-IN hypomethylation in glypicans (GPC3; GPC4) underscore their significance in neural lineages with Sulfatase 1 and 2 (SULF1 &2) CpG methylation changes potentially driving the neurogenic shift. hMSC-INs methylation levels at SULF1 CpG sites and SULF2:cg25401628 were more closely aligned with hNSC-H9 cells than with hMSCs. We further suggest SOX2 regulation governed by lncSOX2-Overall Transcript (lncSOX2-OT) methylation changes with preferential activation of ENO2 over other neuronal markers within hMSC-INs. Our findings illuminate epigenetic dynamics governing neural lineage commitment of hMSC-INs offering insights for targeted mechanisms for regenerative medicine and therapeutic strategies.

Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies

Biomedical research has grown increasingly cooperative through the sharing of consortia-level epigenetic data. Since consortia preprocess data prior to distribution, new processing pipelines can lead to different versions of the same dataset. Similarly, analytic frameworks evolve to incorporate cutting-edge methods and best practices. However, it remains unknown how different data and analytic versions alter the results of epigenome-wide analyses, which could influence the replicability of epigenetic associations. Thus, we assessed the impact of these changes using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. We analysed DNA methylation from two data versions, processed using separate preprocessing and analytic pipelines, examining associations between seven childhood adversities or prenatal smoking exposure and DNA methylation at age 7. We performed two sets of analyses: (1) epigenome-wide association studies (EWAS); (2) Structured Life Course Modelling Approach (SLCMA), a two-stage method that models time-dependent effects. SLCMA results were also compared across two analytic versions. Data version changes impacted both EWAS and SLCMA analyses, yielding different associations at conventional p-value thresholds. However, the magnitude and direction of associations was generally consistent between data versions, regardless of p-values. Differences were especially apparent in analyses of childhood adversity, while smoking associations were more consistent using significance thresholds. SLCMA analytic versions similarly altered top associations, but time-dependent effects remained concordant. Alterations to data and analytic versions influenced the results of epigenome-wide analyses. Our findings highlight that magnitude and direction are better measures for replication and stability than p-value thresholds.

Controlling Batch Effect in Epigenome-Wide Association Study

Methylation data, similar to other omics data, is susceptible to various technical issues that are potentially associated with unexplained or unrelated factors. Any difference in the measurement of DNA methylation, such as laboratory operation and sequencing platform, may lead to batch effects. With the accumulation of large-scale omics data, scientists are making joint efforts to generate and analyze omics data to answer various scientific questions. However, batch effects are inevitable in practice, and careful adjustment is needed. Multiple statistical methods for controlling bias and inflation between batches have been developed either by correcting based on known batch factors or by estimating directly from the output data. In this chapter, we will review and demonstrate several popular methods for batch effect correction and make practical recommendations in epigenome-wide association studies (EWAS).

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics-including transcriptomic, methylation, metabolomic-data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.

Multi-generational impacts of arsenic exposure on genome-wide DNA methylation and the implications for arsenic-induced skin lesions

As a nonmutagenic human carcinogen, arsenic (As)'s carcinogenic activity is likely the result of epigenetic changes, particularly alterations in DNA methylation. While increasing studies indicate a potentially important role for timing of As exposure on DNA methylation patterns and the subsequent differential risks for As toxicity and carcinogenesis, there is a lack of research that tackles these critical questions, particularly in human based populations. Here we reported a family-based study including three generations, in which each generation living in the same household had a distinctive timing of As exposure: in adulthood, in utero and during early childhood, and in germlines exposure for grandparents, parents, and grandchildren, respectively. We generated genome-wide DNA methylation data for 18 As-exposed families, nine control families, as well as 18 arsenical skin lesion patients. Our analysis showed that As exposure may leave detectable DNA methylation changes even though exposure occurred decades ago, and the most significant changes of global DNA methylation were observed among patients afflicted with arsenical skin lesions. As exposure across generations shared common differentially methylated DNA loci and regions (744 DML and 15 DMRs) despite the distinctive exposure timing in each generation. Importantly, based on these DML, clustering analysis grouped skin lesion patients together with grandparents in exposed families in the same cluster, separated from grandparents in control families. Further analysis identified a number of DML and several molecular pathways that were significantly distinguished between controls, exposed populations, as well as skin lesion patients. Finally, our exploratory analysis suggested that some of these DML altered by As exposure, may have the potential to be inherited affecting not only those directly exposed but also later generations. Together, our results suggest that common DML and/or DMRs associated with an increased risk for disease development could be identified regardless of when exposure to As occurred during their life span, and thus may be able to serve as biomarkers for identifying individuals at risk for As-induced skin lesions and possible cancers.

Positional effects revealed in Illumina methylation array and the impact on analysis

AIM

We aimed to prove the existence of positional effects in the Illumina methylation beadchip data and to find an optimal correction method.

MATERIALS & METHODS

Three HumanMethylation450, three HumanMethylation27 datasets and two EPIC datasets were analyzed. ComBat, linear regression, functional normalization and single-sample Noob were used for minimizing positional effects. The corrected results were evaluated by four methods.

RESULTS

We detected 52,988 CpG loci significantly associated with sample positions, 112 remained after ComBat correction in the primary dataset. The pre- and postcorrection comparisons indicate the positional effects could alter the measured methylation values and downstream analysis results.

CONCLUSION

Positional effects exist in the Illumina methylation array and may bias the analyses. Using ComBat to correct positional effects is recommended.

Epigenome-wide study for the offspring exposed to maternal HBV infection during pregnancy, a pilot study

BACKGROUND & AIM

Hepatitis B virus (HBV) can be transmitted to infants, and is related to infants' later disease risk. Epigenetic change (such as DNA methylation) may be mechanism underlying the relationship. In this study, we aimed to investigate whether prenatal HBV infection could alter DNA methylation status in newborns.

METHOD

We selected 12 neonates with intrauterine HBV infection whose mothers were HBsAg-positive during pregnancy, relative to 12 HBV-free neonates with HBsAg-negative mothers. The pattern of genome-wide DNA methylation in the umbilical cord blood was investigated by Illumina Infinium Human Methylation 450K BeadChip.

RESULT

The average level of global methylation in infected neonates exposed to maternal HBV infection was not significantly different from controls. However, after adjusting for multiple comparisons, we found differential significance in the cases group compared to the controls for 663 CpG sites, associated with 534 genes. Among these sites, 53.85% (357/663) had decreased methylation (ΔM < 0) and 46.15% (306/663) had increased methylation (ΔM > 0). The average percentage change (Δβ) in methylation ranged from -46% to 36%. Validated by pyrosequencing, we identified 4 significantly differentially methylated CpG sites in the KLHL35 gene and additional CpGs for the CPT1B gene. These genes play a role in the development of hepatocellular and colorectal carcinoma and fatty acid oxidation, suggesting the candidature of these genes in HBV related disease.

CONCLUSION

Prenatal HBV exposure, even without malformation or preterm birth, may alter the epigenome profile in newborns. We identified a set of genes with differentially methylated CpG sites presented in the cord blood of HBV-infected newborns with HBsAg-positive mothers, demonstrating that DNA methylation status at birth can be used as a biomarker of prenatal exposure. These DNA methylation differences suggest a possible role for epigenetic processes in neonatal development in response to prenatal HBV exposure.

Preclinical and Clinical Evidence of DNA Methylation Changes in Response to Trauma and Chronic Stress

Exposure to chronic stress, either repeated severe acute or moderate sustained stress, is one of the strongest risk factors for the development of psychopathologies such as post-traumatic stress disorder and depression. Chronic stress is linked with several lasting biological consequences, particularly to the stress endocrine system but also affecting intermediate phenotypes such as brain structure and function, immune function, and behavior. Although genetic predisposition confers a proportion of the risk, the most relevant molecular mechanisms determining those susceptible and resilient to the effects of stress and trauma may be epigenetic. Epigenetics refers to the mechanisms that regulate genomic information by dynamically changing the patterns of transcription and translation of genes. Mounting evidence from preclinical rodent and clinical population studies strongly support that epigenetic modifications can occur in response to traumatic and chronic stress. Here, we discuss this literature examining stress-induced epigenetic changes in preclinical models and clinical cohorts of stress and trauma occurring early in life or in adulthood. We highlight that a complex relationship between the timing of environmental stressors and genetic predispositions likely mediate the response to chronic stress over time, and that a better understanding of epigenetic changes is needed by further investigations in longitudinal and postmortem brain clinical cohorts.