Biology subjects

Konwar, C.

Publications and source records attributed to Konwar, C..

5 recordsLinked to original sources

bioRxiv2026

Prenatal diet buffers infant epigenetic changes linked to pollution and transient wheeze

Prenatal air pollution exposure is associated with childhood asthma, particularly among biological males. The mechanisms remain unclear, but may involve lasting epigenetic changes, such DNA methylation (DNAm), that occur during gestation in response to oxidative stress and inflammation. Higher maternal intake of "protective" micronutrients, like antioxidants, could buffer pollution-induced oxidative stress and inflammation to mitigate potentially adverse DNAm differences contributing to asthma. Using data from 515 CANDLE participants, we examined associations between prenatal NO2, PM2.5, and PM10 and cord blood DNAm, evaluated DNAm mediation of pollution associations with childhood wheeze phenotypes (transient, persistent, and late-onset), and assessed buffering of DNAm by maternal polyunsaturated fatty acid, vitamin C, or folate intake, and overall diet quality measured by the Alternative Healthy Eating Index-Pregnancy (AHEI-P). We identified 19, seven, and five regional DNAm differences associated NO2, PM2.5, and PM10. Mediation analyses suggested a role for HLA-DPA1/DPB1 DNAm in NO2 and PM2.5 associations with transient wheeze. To assess buffering, we fit pollutant-by-diet interaction models, defining buffering as an interaction opposite in sign to the main pollutant effect. One or more micronutrients or AHEI-P attenuated pollutant effects at 16 of 19 NO2-associated DMRs, including HLA-DPA1/DPB1, and all PM2.5- and PM10-associated DMRs. However, attenuation of HLA-DPA1/DPB1 DNAm did not significantly reduce the indirect effect of NO2 on transient wheeze. In sex-stratified analyses, biological males exhibited lower PM2.5-associated DNAm in SERPINB9, a gene linked to lung function. These findings suggest prenatal air pollution alters DNAm, which may contribute to transient wheeze, with some differences partially buffered by maternal diet. Significance StatementPrenatal air pollution exposure contributes to child wheeze and asthma, potentially through the oxidative stress response and subsequent changes to infant DNA methylomes. Here, we used data from the CANDLE cohort to identify cord blood DNAm differences associated with NO2, PM2.5, or PM10. We examined if any alterations mediated the relationship between prenatal air pollution exposures and transient, persistent, or late-onset wheeze at age 4 to 6 years. Some of these DNAm differences appeared to be at least partially buffered by maternal micronutrients and/or overall diet quality.

genomics↗

bioRxiv2025

RAMEN: Dissecting individual, additive and interactive gene-environment contributions to DNA methylome variability in cord blood

DNA methylation (DNAme) is the most commonly studied epigenetic mark in human populations. DNAme has gained attention in the Developmental Origins of Health and Disease field due to its gene expression regulation and potential long-term stability. Genetic variation and environmental exposures are amongst the main factors influencing inter-individual DNAme variability. However, the proportion and genomic distribution of their individual, additive and interactive effects on the DNA methylome remains unclear. Here, we introduce RAMEN, a Findable, Accessible, Interoperable, and Reusable (FAIR) framework tailored for DNAme microarrays. Using machine learning and statistical techniques, RAMEN models and dissects gene-environment contributions to genome-wide Variably Methylated Regions (VMRs), while controlling for spurious associations. To comprehensively test the power of RAMEN, we analyzed and characterized VMRs from cord blood samples from two independent cohorts (CHILD and PREDO; overall n=1,662). We identified genetics as a consistent key contributor to DNAme variability, usually in additive and interactive combinations with the environment, with genetic terms explaining the largest proportion of DNAme variance, compared to environmental and interaction terms. Operationalizing RAMEN as an R package to conduct scalable genome-exposome contribution analyses, our results highlighted the importance of genetic variation in sculpting DNAme patterns in early life.

bioinformatics↗

bioRxiv2025

Advancing Pediatric and Longitudinal DNA Methylation Studies with CellsPickMe, an Integrated Blood Cell Deconvolution Method

Prospective birth cohorts offer the potential to interrogate the relation between early life environment and embedded biological processes such as DNA methylation (DNAme). These association studies are frequently conducted in the context of blood, a heterogeneous tissue composed of diverse cell types. Accounting for this cellular heterogeneity across samples is essential, as it is a main contributor to inter-individual DNAme variation. Integrated blood cell deconvolution of pediatric and longitudinal birth cohorts poses a major challenge, as existing methods fail to account for the distinct cell population shift between birth and adolescence. In this paper, we critically evaluated the reference-based deconvolution procedure and optimized its prediction accuracy for longitudinal birth cohorts using DNAme data from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort. The optimized algorithm, CellsPickMe, integrates cord and adult references and picks DNAme features for each population of cells with machine learning algorithms. It demonstrated improved deconvolution accuracy in cord, pediatric, and adult blood samples compared to existing benchmark methods. CellsPickMe supports blood cell deconvolution across early developmental periods under a single framework, enabling cross-time-point integration of longitudinal DNAme studies. Given the increased resolution of cell populations predicted by CellsPickMe, this R package empowers researchers to explore immune system dynamics using DNAme data in population studies across the life course.

bioinformatics↗

bioRxiv2024

Comparison of Infinium MethylationEPIC v2.0 to v1.0 for human population epigenetics: considerations for addressing EPIC version differences in DNA methylation-based tools

The recently launched Illumina Infinium MethylationEPIC v2.0 (EPICv2), successor of MethylationEPIC v1.0 (EPICv1), retains a majority of probes in EPICv1, while expanding coverage of regulatory elements. The concordance between the two EPIC versions in DNA methylation-based tools has not yet been investigated. To address this, DNA methylation was profiled on both versions using matched blood samples across four cohorts spanning early to late adulthood. High concordance between versions at the array level but variable agreement at the individual probe level was noted. A significant contribution of EPIC version to DNA methylation variation was observed, though it was to a smaller extent compared to sample relatedness and cell type composition. Modest but significant differences in DNA methylation-based estimates between versions were observed, irrespective of the data preprocessing method used. Adjustments for EPIC version or calculation of estimates separately for each version largely mitigated these version-specific discordances. This work emphasizes the importance of accounting for EPIC version differences in research scenarios, especially in meta-analyses and longitudinal studies that require data harmonization across versions. Summary blurbOur work illustrates the importance of accounting for EPIC version in DNA methylation-based tools, specifically in research scenarios when samples profiled across different versions are harmonized.

genomics↗

bioRxiv2021

A cross-cohort analysis of autosomal DNA methylation sex differences in the term placenta

BackgroundHuman placental DNA methylation (DNAme) data is a valuable resource for studying sex differences during gestation, as DNAme profiles after delivery reflect the cumulative effects of gene expression patterns and exposures across gestation. Here, we present an analysis of sex differences in autosomal patterns of DNAme in the uncomplicated term placenta (n=343) using the Illumina 450K array. ResultsUsing a false discovery rate < 0.05 and a mean sex difference in DNAme beta value of > 0.10, we identified 162 autosomal CpG sites that were differentially methylated by sex, and that replicated in an independent cohort of samples (n=293). Several of these differentially methylated CpG sites were part of larger correlated regions of differential DNAme, and many also exhibited sex-specific DNAme variability. Although global DNAme levels did not differ by sex, the majority of significantly differentially methylated CpGs were more highly methylated in male placentae, the opposite of what is seen in differential methylation analyses of somatic tissues. Interestingly, patterns of autosomal DNAme at these significantly differentially methylated CpGs organized placental samples along a continuum, rather than into discrete male and female clusters, and sample position along the continuum was significantly associated with maternal age and newborn birthweight standard deviation. ConclusionsOur results provide a comprehensive analysis of sex differences in autosomal DNAme in the term human placenta. We report a list of high-confidence autosomal sex-associated differentially methylated CpGs, and identify several key features of these loci that suggest their relevance to sex differences observed in normative and complicated pregnancies.

genomics↗