Epigenomics

Abstract. Introduction: Preterm birth drives adverse perinatal maternal and infant health outcomes through heterogeneous symptoms, severity, and etiologies. Delivery prior to reaching 37 weeks of gestation may result from medically indicated intervention for pregnancy complications or spontaneously in the absence of prior symptoms. Placental tissue collected following preterm birth exhibits differential DNA methylation compared to full-term placentas and may indicate pregnancy health during gestation. Placental DNA currently has limited utility for assessing health of ongoing pregnancy, as sampling placental tissue during gestation increases the risk of infection and miscarriage. Risks associated with placental sampling during pregnancy limit the use of DNA methylation in clinical preterm birth prediction. Assessing preterm birth risk during gestation requires non-invasive methods for characterizing placental DNA methylation. Results: We quantified genome-wide DNA methylation patterns of hypermethylated cell-free DNA in pregnant (n = 99) and non-pregnant (n = 93) plasma using cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq). In each sample, we assessed DNA methylation status in 300-bp genomic windows, examining both sequencing read counts and calculated absolute molar DNA amount. Known hypermethylated placental regions, including RASSF1, STAT5A, and ERG promoters showed significantly increased odds of detection in pregnant samples, suggesting enrichment of cell-free placental DNA. Of the 536,444 300-bp windows examined, 173,071 (32%) showed significant enrichment in pregnant plasma. Linear modeling identified 107,505 differentially methylated regions (DMRs) associated with pregnancies later diagnosed with intrauterine growth restriction (IUGR) (n = 22). Alu elements showed increased representation in these DMRs than expected, while other repetitive elements exhibited underrepresentation. Discussion: These results demonstrate cfMeDIP-seq's ability to enrich for cell-free placental DNA and characterize cell-free DNA methylation signatures of pregnancies complicated by IUGR. Enrichment of cell-free placental DNA enables non-invasive profiling of placental DNA methylation from maternal plasma. Detectable epigenetic signatures in maternal plasma may identify pregnancies at elevated risk for preterm birth before clinical symptoms appear. Our findings further highlight the potential of cell-free placental DNA for monitoring pregnancy health.

Background. DNA methylation patterns in cord blood are robustly associated with birthweight in the general population. However, it remains unknown whether these associations extend to clinically relevant populations, such as preterm neonates or those born small for gestational age, and whether they directly reflect birthweight or are driven indirectly by genetic, familial, maternal, and obstetric factors. Methods. We calculated a birthweight methylation profile score (MPSBW) using weights of 835 CpGs previously associated with birthweight in the general population and evaluated its association with birthweight in 67 monochorionic (MC) twin pairs including 134 neonates (97% born preterm) from the Twinlife study. MC twin pairs are identical twins sharing a single placenta, often unequally, which can result in unequal resource distribution and differential fetal growth. Results. We examined the association between within-pair differences in birthweight and MPSBW, thereby estimating the association independent of factors shared equally by co-twins. A 500-gram increase in birthweight was associated with a 0.256 SD increase in MPSBW (p<0.005) in this population of preterm neonates. Adjustment for polygenic score for birthweight (PGSBW) confirmed that the observed epigenetic associations were not driven by common genetic variation underlying birthweight. Interestingly, a similar effect size (0.226 SD per 500 g birthweight increase; p<0.05) was observed in the within-pair analysis, which controls for all shared influences within a twin pair. Conclusion DNA methylation is associated with individual differences in birthweight in a high-risk clinical population of MC twins, independent of shared genetic, familial or maternal influences.

Objective: Maternal body mass index (BMI) is often used as a measure of metabolic status and increased or decreased maternal BMI is associated with a heightened risk of cardiometabolic diseases across generations. The placenta mediates these maternal metabolic cues; however, its genome wide transcriptional adaptations in response to maternal BMI remain incompletely defined. Methods: To delineate placental genes, pathways, and interaction clusters whose transcript abundance varies with maternal prepregnancy BMI through a genome wide meta analysis of human placental RNA sequencing datasets. Placental RNA seq reads from four publicly available cohorts (n=146) were mapped to the GRCh38 reference genome and differentially expressed genes were identified. An independent microarray cohort (n=19) was reanalysed separately to facilitate cross platform comparison. Functional enrichment employed GO, KEGG, and STRING protein interaction resources. Results: Meta-analysis of 146 RNA seq samples identified eight genes with genome-wide significance in placentae from underweight pregnancies including inflammatory signaling gene MAP4K1 and metabolic enzyme PSPH, while overweight and obese categories revealed nominally significant differential expression. KEGG analysis demonstrated significant downregulation of oxidative phosphorylation with increasing maternal BMI, and protein-protein interaction networks revealed inflammatory mediators as central nodes in overweight and obese groups. Independent microarray validation corroborated key findings, including consistent downregulation of oxidative phosphorylation in obesity. Conclusion: Maternal BMI is associated with placental transcriptomic signatures involving inflammatory, metabolic, and hormonal pathways, with consistent downregulation of oxidative phosphorylation across platforms. This genome-wide meta-analysis provides a reproducible catalogue of BMI-responsive placental transcripts that may contribute to developmental programming of offspring health.

The early onset of labour increases mortality and developmental risks for a human newborn. Key genes in human labour have been investigated using multiple modalities, but their regulation by non-coding RNA (e.g. lncRNA and miRNA) remains incomplete. This study explores the three-way relationship between labour-associated transcription factors (TFs), miRNA and lncRNA suggested by the competing endogenous RNA (ceRNA) hypothesis, to understand the underlying regulatory framework. Experimentally validated miRNA-lncRNA interactions are modelled using five distinct machine learning (ML) architectures to predict 20469 labour-linked miRNA-lncRNA interactions. Known mRNA-ncRNA interactions from databases were included to construct a tripartite network, and a subset of 9989 labour-linked network motifs containing TFs were isolated and analysed. Gene enrichment of nodes in TF-lncRNA-miRNA network, as well as validation from public myometrial datasets indicate high significance in contractile pathways including immune signalling. Experimentally unconfirmed tripartite network motifs have been found, and we elaborate on their potential regulation in labour using 8 TF-lncRNA-miRNA network motifs. A unified ncRNA-TF regulatory atlas in labour has been synthesized, and a complete summary of the tripartite network motifs can be accessed and visualised using the user-friendly, public database.

BACKGROUNDSpontaneous preterm birth (sPTB) remains the foremost cause of neonatal morbidity and mortality worldwide. Although histologic chorioamnionitis (HCA) and placental vascular abnormalities are frequently observed in sPTB, the molecular cascades linking these lesions to labor initiation remain poorly understood. Emerging evidence implicates circadian dysregulation and trophoblast dysfunction as additional drivers of sPTB. OBJECTIVEThis study aims to map placental pathology to distinct transcriptomic functional signatures that may precipitate sPTB, delineate the contribution of circadian regulation - both core-clock genes and circadian transcription-factor target sets (TFTs) - to sPTB, and identify placental cell-type-enriched and developmental pathway signatures that differ between sPTB and term deliveries. STUDY DESIGNWe performed bulk RNA sequencing on 32 formalin fixed, paraffin embedded placental specimens from 12 selected women (9 sPTB and 3 Term) in the POUCH Study cohort. Samples were selected for white ethnicity, maternal age 23-33years, and parity 1-4 to reduce heterogeneity within groups. An extraction-free HTG transcriptome panel assayed 19,398 protein-coding genes. Log2-fold changes of all genes were computed with limma adjusted for maternal age, gestational age, parity, placental region, placental pathology, and POUCHID (a clustering variable) for sPTB vs. Term and HCA/vascular lesion vs. no pathology (no placental pathology adjustment). Gene-set enrichment used 50 Hallmark sets (MSigDB) plus curated placental circadian, circadian TFT, cell-type, and developmental pathways or gene sets. RESULTSsPTB placentas displayed a global suppression of metabolic, secretory, and immune pathways (e.g., protein secretion, oxidative phosphorylation, Interferon responses, Complement, ROS, MYC Targets, TGF {beta}, mTORC1, and Coagulation) while KRAS Signaling Down and EMT were up-regulated. HCA-enriched sets (TNF/NF-{kappa}B, ROS, KRAS Up, IL-2/STAT5, Hypoxia, Interferon-{gamma}) were up-regulated, with EMT and Notch remaining down. Vascular abnormalities alone showed up-regulation of 12 Hallmark sets - including TGF-{beta}, TNF/NF-{kappa}B, ROS, pancreatic {beta}-cell stress, Hypoxia, Oxidative Phosphorylation, EMT, and mTORC1 - while Notch was down-regulated. When HCA co-exists with vascular abnormalities, the Hallmark profile becomes more inflammatory highlighting a synergistic exacerbation of innate immunity, oxidative stress, and programmed cell death with the 12 up-regulated sets (Complement, Interferon /{gamma}, TNF, ROS, Apoptosis, and Heme Metabolism). The exclusive downregulation of DNA Repair suggests compromised genomic integrity. Circadian gene-sets analysis revealed an up-regulated Regulation of Circadian Sleep Wake Cycle in sPTB but down-regulation of core clock pathway and suppressed circadian TF targets. Cell-type enrichment reveals increased trophoblast giant cells and IGFBP1-DKK1 positive fetal cells, with marked suppression of extravillous trophoblasts, syncytiotrophoblasts, villous cytotrophoblasts, and fetal myeloid cells. Placental developmental pathways were downregulated, indicating arrested trophoblast maturation. CONCLUSIONOur pilot analysis demonstrates sPTB placentas exhibit a global suppression of metabolic, secretory, and immune-modulatory programs and maladaptive trophoblast remodeling, whereas HCA and vascular abnormalities drove distinct inflammatory or hypoxic signatures. The shared and opposing Hallmark pathways across phenotypes highlight distinct yet overlapping pathogenic mechanisms. Dysregulated circadian pathways, consistent downregulated transcription factor target gene sets, and trophoblast-specific signatures implicate circadian misalignment and impaired placental maturation as key contributors to preterm parturition. These findings provide a mechanistic atlas linking placental pathology to sPTB and highlight potential targets for chronotherapeutic and cell-type-specific interventions. AJOG at a GlanceO_ST_ABSWhy was this study conducted?C_ST_ABSSpontaneous preterm birth remains a leading cause of neonatal morbidity. Histopathologic lesions of the placenta, particularly chorioamnionitis and vascular abnormalities, are common in preterm deliveries, yet the underlying molecular pathways are poorly understood. We sought to integrate functioning pathway profiles of placental histology, circadian biology, and cell types to identify mechanistic drivers of sPTB. Key findingsO_LIsPTB placentas showed widespread down-regulation of oxidative phosphorylation, mTORC1, hypoxia, interferon, and TNF/NF-{kappa}B pathways. C_LIO_LIHCA placentas up-regulated the same pathways (except androgen response), revealing a reciprocal inflammatory-hypoxic signature. C_LIO_LIVascular abnormalities displayed a distinct mix of up- and down-regulated pathways, suggesting divergent reparative responses. C_LIO_LIPlacentas with co-existing HCA and vascular abnormalities enriched more inflammatory Hallmark pathways: the 12 up-regulated sets (Complement, Interferon /{gamma}, TNF, ROS, Apoptosis, and Heme Metabolism) highlight a synergistic exacerbation of innate immunity, oxidative stress, and programmed cell death and the exclusive down-regulation of DNA Repair suggests compromised genomic integrity, which can contribute to premature placental senescence and preterm labor. C_LIO_LICircadian clock and multiple transcription-factor targets were enriched in sPTB, and trophoblast-specific signatures (giant, extravillous, syncytiotrophoblast) were prominent. C_LI What does this add to what is known?The study demonstrates a clear dichotomy between inflammatory and hypoxic molecular programs in sPTB and HCA, identifies circadian dysregulation as a potential contributor, and highlights trophoblast subpopulations as key players. These insights open avenues for targeted biomarkers and chronotherapy in preterm birth prevention.

ObjectiveTo develop and validate a cell-free DNA (cfDNA) fragmentomic classifier for the early prediction of spontaneous preterm birth (PTB) using routine first-trimester non-invasive prenatal testing (NIPT) data. MethodsA nested case-control study was conducted within a prospective multicenter Vietnamese cohort comprising 286 pregnancies, including 82 spontaneous PTB cases and 204 term controls. Maternal plasma cfDNA collected during routine first-trimester NIPT (median gestational age, 12 weeks) was sequenced to a depth of approximately 20 million reads per sample. Five fragmentomic feature categories including copy number alterations, end-motif composition, nucleosome distance, fragment length, and joint fragment-lengthxend-motif were evaluated for PTB prediction. Machine learning classifiers were developed in a training cohort (n = 228, 65 PTB vs 163TB) and tested in a validation cohort (n = 58, 17 PTB vs 41 TB). ResultsAmong the five fragmentomic feature classes evaluated, 4-mer end-motif (EM) profiles exhibited the most pronounced differences between PTB and term control samples. Consistent with these findings, the EM-based classifier demonstrated the highest discriminative performance in the validation cohort, achieving an AUC of 0.970 (95% CI, 0.912-1.000). At a specificity >90%, the model achieved a sensitivity of 94% (95% CI, 78-100%). ConclusionThese findings demonstrate that cfDNA EM signatures derived from routine first-trimester NIPT can accurately identify pregnancies at risk of spontaneous preterm birth, without additional blood collection or sequencing, thereby extending the clinical utility of existing prenatal screening infrastructure. KEY POINTSO_ST_ABSWhat is already known about this topic?C_ST_ABSO_LICurrent first-trimester prediction strategies based on maternal characteristics, cervical length, and biochemical markers have limited predictive accuracy, particularly in nulliparous women. C_LIO_LIExisting cfDNA-based approaches have shown only modest performance or require additional assays, limiting clinical applicability. C_LI What does this study add?O_LIExisting NIPT sequencing data can be repurposed (without additional blood sampling or sequencing) for accurate prediction of spontaneous preterm birth (AUC=0.970). C_LIO_LIA classifier employing 4-mer end-motif (EM) profiles achieved an AUC of 0.970. At a specificity >90%, the model achieved a sensitivity of 94%. C_LI

DNA methylation is a key epigenetic mechanism regulating the expression of genes involved in numerous developmental and pathological processes. However, the contribution of DNA methylation of microRNA genes to atherosclerosis remains poorly understood. In this study, we profiled DNA methylation patterns of both the regulatory elements and gene bodies of five microRNA genes (MIR10B, MIR21, MIR100, MIR127, and MIR143) in vascular tissues and paired peripheral blood cells (PBC) of 92 patients with advanced carotid atherosclerosis and 32 PBC of control participants by targeted bisulfite sequencing. We identified distinct tissue-specific DNA methylation patterns for all five microRNA genes in patients with advanced carotid atherosclerosis. The regulatory regions of MIR10B, MIR127, and MIR100 were moderately hypomethylated in carotid atherosclerotic plaques compared with intact vascular tissues. We further integrated our findings with lab-internal and publicly available epigenome-wide methylation datasets and evaluated the influence of vascular and blood cell composition using computational deconvolution approaches. After adjustment for cellular heterogeneity in vascular tissues, DNA methylation at CpG sites in MIR100 and MIR127 remained independently associated with atherosclerosis. Increased DNA methylation at a single CpG site (chr11:122025143, GRCh37/hg19) located within the MIR100 E-box region was associated with metabolic syndrome. Moreover, DNA methylation levels of MIR10B, MIR21, and MIR127 in atherosclerotic plaques were linked with indicators of histological instability and history of acute cerebrovascular events. In peripheral blood, we observed moderate hypomethylation of the regulatory regions of MIR10B, MIR21, and the MIR100 E-box region in patients compared with the control group. However, only the MIR10B remained robust against blood cell composition. In blood, MIR10B and MIR143 methylation correlated with lipid metabolism and carotid stenosis, while the MIR21 CpG island showed strong blood-plaque concordance, confirming its potential as a surrogate biomarker. Overall, advanced carotid atherosclerosis is characterized by specific tissue-altering DNA methylation patterns of microRNA genes, where alterations mainly occur in the regulatory regions, predominantly featuring hypomethylation. The results underscore the complex, cell- and tissue-specific nature of DNA methylation of microRNA genes in both the regulatory elements and gene bodies in vascular tissue and blood, highlighting the critical need to decipher these intricate epigenetic landscapes to identify reliable, robust biomarkers for assessing plaque instability and cardiovascular risk.

Preeclampsia (PE) is a hypertensive pregnancy-specific disorder and a leading cause of maternal and fetal mortality. A common feature of PE placentas and maternal plasma is dyslipidemia, or abnormal lipid levels, which can increase oxidative stress and endothelial dysfunction. However, the precise transcriptional, post-transcriptional, and epigenetic mechanisms underlying these abnormalities remain poorly characterized. Identifying such changes may clarify disease mechanisms and identify lipid-related PE biomarkers. We conducted a large-scale meta-analysis integrating public placental datasets from NCBI GEO, comprising four DNA methylation (DNAm) datasets (n = 172), three RNA-sequencing datasets (n = 92), and an independent RNA microarray validation cohort (n =146). We evaluated differential DNAm (limma), gene expression (DESeq2), transcript-level shifts (Swish), and alternative splicing (rMATS) in PE versus control placentas, with all analyses stratified by fetal sex via an interaction term model. We also performed placental cell-type deconvolution to quantify PE-associated cell-type proportion changes. Our results demonstrated that lipid-related regulation changes in PE placentas occur primarily at the gene and transcript level, with DNAm showing no changes. We also identified significant isoform switching in PE that were undetected by differential gene expression analysis, and primarily driven by alternative transcription initiation and termination sites rather than alternative splicing. A subset of these isoform switches mapped to pathways dysregulated in PE and were predicted to cause functional protein changes. An interaction term model identified several sex-specific differentially expressed genes (DEGs) in PE, including a subset of male-specific downregulated genes involved in oxidative metabolism. However, many of the remaining sex-specific DEGs across both sexes were previously uncharacterized in the literature. These findings suggest that transcriptional and isoform-level regulation play a role in PE-associated dyslipidemia, with certain regulatory pathways displaying fetal sex-specific patterns. Highlights- Preeclampsia-associated dyslipidemia manifests at the gene and transcript level - Reciprocal isoform switches were missed by standard gene-level analyses - Alternative transcript initiation and termination drove isoform switching - Sex-interaction modeling identified sex-specific transcriptional shifts in PE

The existence and functional relevance of mitochondrial DNA methylation remain controversial. Here, we systematically profiled cytosine methylation and hydroxymethylation across human brain and blood tissues spanning healthy and malignant states using orthogonal sequencing approaches that avoid chemical conversion during library preparation. While nuclear DNA exhibited canonical methylation patterns, mitochondrial DNA consistently showed negligible signal, indistinguishable from background technical noise. By mapping cytosine-guanine sites between mitochondrial DNA and nuclear-embedded mitochondrial sequences, we demonstrate the potential of these nuclear counterparts to confound not only cytosine methylation but also hydroxymethylation measurements, corroborating and extending prior findings implicating nuclear contamination as a potential source of apparent mitochondrial epigenetic signals. Additional technical factors that inflate apparent mtDNA methylation signals were identified, including sequence context biases, flow cell chemistries, and coverage-dependent discrepancies between the heavy and light strands. Collectively, these results provide convergent evidence against the presence of biologically meaningful cytosine methylation or hydroxymethylation in mitochondrial DNA. These findings caution against interpreting apparent mtDNA methylation signals in human adult tissues as meaningful without rigorous orthogonal validation and comprehensive consideration of technical and analytical confounding factors.

IDH1 R132 mutations are among the most frequent hotspot mutations in cancer, but their mutational origins have remained unclear. Here, we provide evidence that IDH1 R132C, the predominant IDH1 mutation in cholangiocarcinoma, acute myeloid leukemia, and melanoma, likely arises through APOBEC3-mediated mutagenesis. IDH1 R132C is a TpC>TpT substitution on the lagging-strand DNA template within a hairpin-forming sequence context, consistent with APOBEC3 susceptibility. In vitro assays showed that APOBEC3A can deaminate the relevant cytosine, and APOBEC3A and APOBEC3B were relatively highly expressed in tumor types with recurrent IDH1 R132C mutations. IDH1 R132G, a TpC>TpG substitution at the same site, may similarly result from APOBEC3 activity. By contrast, IDH1 R132H, the predominant IDH1 mutation in lower grade glioma and glioblastoma, is a CpG>TpG substitution at a methylated cytosine on the leading-strand DNA template, a pattern more consistent with DNA polymerase epsilon replication error. Concordantly, tumor types enriched for IDH1 R132H showed relatively low POLE expression. Together, these in vitro and bioinformatic analyses provide insight into the distinct mutational mechanisms that likely underlie recurrent IDH1 hotspot mutations in cancer.

Background: Psychosocial stress is a key risk factor for coronary heart disease (CHD), particularly in postmenopausal women who face both a high stress burden and elevated cardiovascular risk. DNA methylation (DNAm), a critical epigenetic modification bridging environment and health, remains understudied as a contributor to stress-related CHD. Methods: We conducted an epigenome-wide association study (EWAS) of stress in the Women's Health Initiative (WHI), an ancestrally diverse cohort of postmenopausal women (n=3,857). At screening visit, participants completed a questionnaire assessing stressful life events and provided whole blood for DNAm. Incident CHD was then longitudinally ascertained (follow-up mean/SD: 16.7/8.4 years), and DNAm signatures were evaluated as CHD predictors using Cox regression. Predictive models were independently validated in the Jackson Heart Study (JHS; n=3,053) and Multi-Ethnic Study of Atherosclerosis (MESA; n=870). The bulk-level DNAm associations were computationally deconvolved at the cell-type-specific level using tensor composition analysis (TCA). Results: The EWAS in WHI identified 841 stress-related DNAm sites (99 hypermethylated, 742 hypomethylated with stress) after FDR correction, with 13 significant after Bonferroni correction, including sites located on immune and CHD-related genes (e.g., TNF, ALDH2). Methylation risk scores (MRSs) integrating the 841 FDR-significant sites (MRS841) and 13 Bonferroni-significant sites (MRS13) predicted incident CHD (HR=1.33-1.37; p[&le;]0.0008) and mediated 16.5-17.7% of the association between stress and CHD. In JHS and MESA, MRS13 independently predicted CHD (HR=1.34; p=0.036), whereas MRS841 was suggestively associated with CHD (HR=1.27; p=0.087). TCA indicated that the greatest number of stress-related sites predictive of CHD was specifically in monocytes (133 total), with directions consistent with bulk-level associations (9 hypermethylated, 124 hypomethylated with stress). Conclusion: Our study supports methylation risk scores as novel biomarkers of stress-related CHD and uncovers epigenetic regulation in monocytes as a potential underlying mechanism. These findings highlight biological pathways linking stress and disease and may promote personalized interventions in high-risk populations.

Background Complex traits are influenced by numerous variants, most of which have regulatory effects on gene expression that can be mediated by DNA methylation. Molecular QTL mapping is an approach that aims to dissect these effects. However, obtaining molecular phenotypes on a large scale is challenging, particularly in livestock species. In cattle, an epigenotyping array called EpiChip has recently been developed in the European RUMIGEN project. The EpiChip, which contains 43,317 CpG sites distributed all over the bovine genome, enables large-scale measurement of DNA methylation. This study aims to characterize the genetic determinism of blood DNA methylation in cows by estimating heritability and mapping cis- and trans-methylation QTLs (meQTLs). Results Whole blood samples from 4,457 genotyped Holstein cows were epigenotyped. Across all CpG sites, the heritability estimates averaged 24.6%. The local meQTL mapping at sequence-level for variable CpG sites (SD > 2.5%; n = 28,806) detected cis-meQTLs for 80.1% of the CpG sites, with sentinel SNPs located close to their associated CpGs. A two-step analysis was also conducted to identify long-range associations, with a particular focus on trans-meQTL hotspots. First, we identified CpG-SNP trans-associations using medium-density genotypes (50k SNPs) that revealed 31,846 SNPs with significant effects on 1 to 530 trans-CpG sites. Then, regions associated with at least 34 independent trans-CpGs were retained defining 31 hotpots. For each hotspot, a local sequence-level GWAS was conducted using the first principal component derived from the associated trans-CpGs. Out of the 31 detected hotspots, three were located close to transcription factor genes (RUNX1, NFIC and FOXA3) for which the associated trans-CpGs were enriched for the corresponding binding motif. Two other hotspots were located within KDM5A and KDM5B, and their corresponding trans-CpGs were strongly overrepresented in H3K4me3 narrow peaks in blood as well as in other tissues. Conclusions By identifying functional candidate genes associated with blood DNA methylation in cattle, these findings provide new insights into the regulatory architecture of DNA methylation in mammals, highlighting the value of large-scale molecular data from livestock populations.

Objective: Antipsychotic medications are a cornerstone in the treatment of many psychiatric disorders, but they are associated with adverse metabolic effects including weight gain and hypercholesterolemia. To investigate the underlying genetic architecture of these effects, we conducted the largest-by-far and most ethnically diverse genome-wide association study (GWAS) of longitudinal changes in total cholesterol (TC) and body mass index (BMI) using an antipsychotic-treated cohort from the Million Veteran Program (MVP). Methods: The study included 59,372 participants for TC and 39,112 for BMI across European (EUR), African American (AFR), and Hispanic (HIS) ancestries. GWAS, trans-ancestry meta-analysis, functional annotation, and summary-data-based Mendelian randomization (SMR) analyses were performed to identify associated loci, enriched biological pathways, and gene expression signals. Results: We identified genome-wide significant and suggestive loci for both traits, with stronger associations for TC and clinically significant weight gain (BMI > 1.5 kg/m2) than for overall BMI change. Significant loci included genes involved in cholesterol metabolism and lipid homeostasis for TC and genes previously implicated in BMI-related traits for BMI > 1.5 kg/m2. Trans-ancestry meta-analysis highlighted suggestive loci shared across ancestries, and functional annotation demonstrated significant enrichment of protein homeostasis and synaptic function gene sets for BMI > 1.5 kg/m2. SMR analyses further identified suggestive gene expression associations in whole blood and liver. Conclusions: These findings implicate lipid metabolism, body weight regulation, and central nervous system mechanisms in antipsychotic-associated metabolic changes. This work advances understanding of genetic susceptibility to metabolic adverse effects of antipsychotic treatment and may inform future precision medicine approaches to risk prediction and treatment selection.

IntroductionFetal development represents a critical window during which genetic and environmental influences shape lifelong health. DNA methylation (DNAm) is a candidate underlying mechanism. While individual prenatal exposures have been related to DNAm, no studies have investigated the broader prenatal exposome, nor incorporated genetics with the exposome. Here, we integrated the prenatal exposome and genetics as predictors of DNAm at birth. MethodsWe used data from the Dutch Generation R (n=2282) and English Avon Longitudinal Study of Parents and Children (ALSPAC; n=809) cohorts. We performed epigenome-wide elastic net regression, using Generation R for model development/internal validation and ALSPAC for external validation, to predict DNAm at each CpG site. We used three models: Model 1 included 42 prenatal exposures, Model 2 additionally included child sex, gestational age and birth weight, and Model 3 further included meQTLs. ResultsIn Model 1, the prenatal exposome explained on average 0.7% of DNAm variation across 347 validated CpGs (0.1% of tested CpGs). This increased to 40,044 CpGs (10.2%) with 1.3% of variation explained in Model 2, and 91,305 CpGs (23.2%) with 3.0% of variation explained in Model 3. In Model 1, prenatal smoking was the largest predictor, followed by delivery characteristics, among which meconium-stained amniotic fluid was a novel finding. In Model 3, typically both SNPs and multiple prenatal exposures were selected. DiscussionWe find that genomic associations with cord blood DNAm are stronger and more widespread than prenatal exposures, although typically, the prenatal exposome explains additional variation in DNAm beyond genetic influences.

Epigenetic dysregulation is a defining feature of cancer, but it remains poorly understood how this is coordinated across the genome. In this study we focusd on DNA methylation (DNAm) in acute myeloid leukemia (AML). Despite highly heterogeneous and largely patient-specific patterns, we identified co-regulated clusters of CpGs that could be assembled into reproducible epigenetic networks. Multilinear regression models accurately predicted the patient-specific DNAm deviations, even for CpGs located on different chromosomes. The alterations were mirrored on homologous chromosomes and there was no clear association with epigenetic driver mutations. Furthermore, we found very similar co-regulation patterns in acute lymphoblastic leukemia (ALL); with AML-derived models successfully predicting the ALL-associated DNAm changes. Notably, the top 1000 AML-associated CpGs showed also pronounced aberrations in DNAm levels across 46 other cancer types, whereas this was hardly observed across multiple non-malignant cell types. Co-regulation analysis realed very similar patterns in non-malignant blood and pan-cancer analysis, albeit the DNAm levels remained overall consistent in the controls. Collectively, our findings demonstrate that the complex, patient-specific DNAm landscapes observed in leukemia are not random. Instead, they are orchestrated within expanded epigenetic networks, which also exist in non-maligant cells, highlighting a higher-order regulatory layer in cancer epigenomics.

The Developmental Origins of Health and Disease (DOHaD) hypothesis proposes that the perinatal environment shapes susceptibility to complex traits across life [1]. The placenta, a transient organ mediating maternal-fetal exchange, plays a central role in this process and has emerged as a key molecular archive in utero [2-4]. Placental DNA methylation (DNAm) is a unique mediator between prenatal exposures, fetal genetics and later-life outcomes [5-9]. DNAm quantitative trait loci (mQTL) have helped disentangling causal mechanisms underlying GWAS loci for complex diseases [10-15]. Despite growing evidence that placental genomic regulation has broad and profound effects on the developmental programming of early- and later-life health outcomes [17], existing placental studies remain limited in scale and largely focused on growth- and neuro-related traits [12-16]. Here, we construct a high-resolution placental mQTL resource and systematically investigate how placental DNAm relates to early- and later-life traits, and to shared vulnerability and complex interactions among them.

Pulmonary hypertension (BPD-PH) associated with bronchopulmonary dysplasia (BPD) in preterm infants associates with high morbidity and mortality within the first two years of life. In a previous unbiased study, we identified a panel miRNAs in tracheal aspirates (TA) that were differentially expressed in extremely low gestational age newborns (ELGANs) with BPD-PH compared to those with BPD but no PH. To explore the predictive potential of these miRNAs, we studied TA exosomes from 7 days old ELGANs and analysed a curated panel of 16 miRNAs through logistic regression and calculated the predictive AUROC to diagnose BPD-PH at 36 weeks PMA. AUROC of TA miRNAs was 0.76 with sensitivity and specificity of 53% and 93%, respectively. Adding sex and gestational age to the variables improved the AUROC to 0.78 with sensitivity and specificity of 61 and 87% respectively. Due to challenges of obtaining TA in non-invasively ventilated infants, we collected saliva samples from ELGANs at 7 days of age and compared the log expression of these 16 miRNAs in both biofluids and found significant correlation in their expression (pearson r=0.92, p<0.001). We calculated the predictive AUROC of the same miRNAs to diagnose BPD-PH at 36 weeks PMA. AUROC of these miRNAs in saliva was = 0.85 with sensitivity and specificity of 82% and 72%, respectively; addition of biological sex and gestational age improved AUROC to 0.86 with sensitivity and specificity of 79% and 76% respectively. Leave-one-sample-out sensitivity analysis demonstrated stable training performance with reduced performance in testing samples, supporting the need for validation in larger independent cohorts. In conclusion, early salivary miRNAs have great potential for risk stratification of ELGANs to develop BPD-PH, while also providing the opportunity to identify target molecules and mechanisms that modulate molecular function.

Prostate cancer remains one of the most common cancers among men globally. While significant strides have been made in diagnosis and treatment, understanding the complex genetic and epigenetic underpinnings of the disease remains crucial for guiding intervention and developing more personalized and effective therapies. The importance of DNA methylation in prostate cancer has been known for some time, but important facets of the modulation of the epigenome during carcinogenesis remain obscure, partly because the bulk of cancer methylation data have been produced using microarray technologies. Here we utilise the TruSeq methyl capture method (EPICseq) to profile the, previously defined, UK Prostate ICGC cohort of well-annotated primary prostate cancers. To this we add methylation sequencing of benign tissue from the same men. These data allow us to identify differentially methylated regions distinguishing cancerous and non-cancerous prostate tissue, while identifying numerous genes whose methylation profiles can perform that task as well as distinguishing between classes of prostate cancer. We describe a describe a methylation-based control mechanism for prostate-cancer-associated SNPs, and show that this seems a likely mechanism of action for a SNP near the MMP7 gene. We describe three novel molecular signatures that arise from different aspects of the biology of prostate cancer revealed by sequencing. Each is shown to be an independent classifier of cancers into groups with different expected times to relapse. These consist of patterns in driver gene methylation, strand-specific methylation, and signal arising in mitochondrial reads. We show that these signatures, combined with existing molecular tools, provide a powerful predictor of time to recurrence. By substantially enhancing understanding of prostate cancer risk, detection, and prognosis, we pave the way for the development of clinical practices that will benefit patients and improve outcomes.

MotivationEpigenome-wide association studies (EWAS) have identified numerous DNA methylation (DNAm) CpG sites associated with complex traits and diseases, but interpretation of those CpG sites remains challenging because in EWAS, CpGs are mostly linked to nearby genes based only on genomic proximity. Expression quantitative trait methylation (eQTM) analyses connect DNAm CpGs with statistically associated gene expression levels. However, a comprehensive, searchable resource integrating eQTMs across diverse tissues and disease contexts has been lacking. ResultsWe developed the eQTM Atlas, a web-based resource that manually curates more than 11 million DNAm-gene expression associations from eight cohorts, covering 11 tissue types, four broad disease contexts, 173,886 unique CpG probes and 20,231 unique genes. The Atlas supports gene- or CpG-searches by tissue or disease type and finding associated CpG or genes, visualization of cis- and trans-eQTMs through genome browser, heatmap interfaces across various tissues, and cohort-level data downloads. By integrating eQTM results with EWAS resources, the eQTM Atlas enables users to connect disease- or trait-associated CpGs to statistically associated genes rather than relying solely on proximity-based gene annotation, supporting functional interpretation of EWAS findings and generation of disease-specific regulatory hypotheses. Availability and implementationThe eQTM Atlas is freely available at https://shiny.crc.pitt.edu/eqtm_browser/. The web interface is implemented in R Shiny and hosted through the University of Pittsburgh Center for Research Computing (CRC). Source code is available at https://github.com/ads303/eQTM-Atlas.

Serous tubal intraepithelial carcinoma (STIC) is a known precursor of high-grade serous ovarian cancer (HGSOC). Yet, molecular events driving progression from STIC to HGSOC remain poorly defined. Aberrant DNA methylation is a hallmark of cancer, yet its role in early HGSOC remains unclear. We performed a comprehensive meta-analysis of publicly available Illumina Infinium DNA methylation EPIC array datasets assessing 255 samples comprising STIC, HGSOC, and histologically normal fallopian tube tissues. We mapped DNA methylation alterations during early tumorigenesis, identified conserved methylation patterns across STIC and HGSOC, and assessed RNA-sequencing data to define transcriptional consequences within genomic and epigenomic landscapes. STIC and HGSOC exhibited widespread DNA hypomethylation relative to normal tissue, accompanied by focal hypermethylation in CpG islands and 5' regulatory regions. DNA Hypomethylation intensifies during progression from STIC to HGSOC, particularly in cis-regulatory enhancer domains and intergenic regions. We identified 11,660 CpG sites and 447 genomic regions with conserved DNA methylation patterns across STIC and HGSOC. Within these, 70 genes showed coordinated DNA methylation and expression changes, including TRIM15, NKAPL, and RIPPLY3. These findings reveal that epigenetic remodelling occurs in STIC lesions, prior to malignant transformation. DNA methylation alterations at regulatory regions may drive invasion and offer novel avenues for early detection and targeted intervention.