Epigenetics

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.

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.

BackgroundPrenatal growth restriction has been associated with adverse neonatal and long- term health outcomes, yet the epigenetic mechanisms by which an adverse intrauterine environment shapes fetal immune development remain incompletely understood. Monozygotic dichorionic-diamniotic twins with selective fetal growth restriction (sFGR) provide a unique human model for investigating environmentally driven developmental programming independent of genetic variation and inter-twin placental vascular anastomoses. MethodsUmbilical cord blood buffy coat samples were collected from three sFGR and two gestational age-matched concordant control twin pairs. Bulk RNA sequencing and genome-wide DNA methylation analysis were performed, followed by differential expression, pathway enrichment, hematopoietic and immune module analyses, differential methylation, and integrative transcriptomic-epigenomic analyses. ResultsCompared with concordant control twin pairs, discordant twins exhibited broad attenuation of immune and inflammatory transcriptional programs alongside enrichment of erythroid- and hypoxia-related pathways, consistent with adaptive hematopoietic responses to intrauterine stress. Within discordant twin pairs, the growth-restricted co-twins displayed marked transcriptional asymmetry characterized by selective enrichment of cytotoxic lymphoid signatures despite global suppression of myeloid and antigen-presenting cell-associated programs. Integrated transcriptomic and epigenomic analyses further revealed coordinated epigenetic remodeling, with hypomethylated regions in growth-restricted twins enriched for immune regulatory pathways, including T cell differentiation and leukocyte activation. At selected loci, concordant hypomethylation and increased gene expression suggested a potential epigenetic basis for the observed immune remodeling. ConclusionsThese findings suggest that intrauterine growth restriction is associated with coordinated hematopoietic and immune reprogramming at birth, consistent with both compositional and cell-intrinsic alterations. In genetically identical twins, relative growth divergence was associated with polarized transcriptional states, highlighting how intrauterine environmental differences may shape early immune development independent of genetic background.

Mammalian preimplantation development requires precisely coordinated lineage decisions to establish the trophectoderm (TE), inner cell mass (ICM), epiblast (EPI), and primitive endoderm (PrE). Glucose metabolism and epigenetic regulation are increasingly recognised as key determinants of lineage specification during preimplantation development. However, how glucose-dependent metabolic cues interface with epigenetic mechanisms to regulate embryonic cell fate remains poorly understood. Here, we investigated the role of glucose in regulating protein methylation by protein arginine methyltransferases (PRMT) in bovine preimplantation development. PRMT1 and its associated histone mark H4R3me2a were detected throughout bovine oocyte maturation and embryo development. Pharmacological inhibition of Type I PRMTs using two structurally distinct inhibitors, GSK3368715 and MS023, markedly reduced global protein asymmetric dimethylarginine (ADMA) and H4R3me2a levels. PRMT inhibition impaired blastocyst cell proliferation, reduced total cell number, and disrupted both first and second lineage decisions, as demonstrated by decreased CDX2- and SOX2-positive TE and ICM cells and reduced NANOG- and GATA6-positive EPI and PrE cell allocation. Mechanistically, Type I PRMT inhibition downregulated key components of the Hippo-associated TE programme, including YAP, TEAD4, and TFAP2C. Consistent effects were observed in mouse embryos, where MS023 treatment reduced ADMA, CDX2, YAP, and TFAP2C expression and impaired TE and ICM allocation. Collectively, our findings identify Type I PRMT-mediated ADMA as an essential epigenetic regulator of early mammalian lineage specification and support a conserved ADMA-Hippo regulatory axis linking arginine methylation to embryonic cell fate decisions. In briefType I protein arginine methyltransferase (PRMT)-mediated asymmetric dimethylarginine (ADMA) is required for proper lineage specification during mammalian preimplantation development. ADMA depletion disrupts Hippo signalling, cell proliferation, and trophectoderm and inner cell mass allocation in bovine and mouse embryos.

Background: Contemporary cardiovascular disease (CVD) risk equations may not fully capture cumulative biological aging or long-term exposure burden. DNA methylation (DNAm) biomarkers may capture aging- and exposure-related biology, but their incremental prognostic value beyond clinical risk-factor models like PREVENT remains uncertain. To our knowledge, no prior study has benchmarked DNAm-based biomarkers with PREVENT. Methods: In a population-based cohort study, we analyzed NHANES 1999-2002 participants with DNAm biomarkers and mortality follow-up. We derived a DNAmScore from candidate DNAm biomarkers using elastic-net Cox regression with repeated nested cross-validation. A PREVENT-like clinical model was defined as a Cox model fit in NHANES using PREVENT predictors. Weighted Cox models estimated the association between DNAmScore and mortality after adjustment for PREVENT-like clinical predictors. We then compared the PREVENT-like clinical model, DNAmScore alone, and a combined model (PREVENT-like clinical predictors plus DNAmScore) using cross-fitted C-index, time-dependent AUC, calibration, and Brier score. Results: Our cohort included 2,282 participants; 597 and 937 deaths occurred by 10 and 15 years, respectively. After adjustment for PREVENT-like clinical predictors, the cross-fitted DNAmScore was strongly associated with all-cause mortality (HR per 1-SD increase, 2.43; 95% CI, 1.97?2.99). At 10 years, AUCs were 0.791 for the PREVENT-like model, 0.791 for DNAmScore, and 0.803 for the combined model. At 15 years, corresponding AUCs were 0.825, 0.822, and 0.835. Compared with the PREVENT-like model, the combined model improved AUC by 0.013 (95% CI, 0.006?0.020) at 10 years and 0.010 (95% CI, 0.004?0.015) at 15 years. The combined model had lower Brier scores at all three horizons with similar calibration. DNAmScore remained associated with CVD mortality after clinical adjustment. Conclusions: DNAmScore identified residual biological risk beyond PREVENT-like clinical predictors, with strong independent mortality associations and modest, consistent improvements in cross-fitted prediction performance. These findings support development and external validation of CVD-specific DNAm biomarkers.

Background: The placenta has a unique transcriptomic profile, including microRNAs that are secreted into maternal circulation throughout pregnancy. MicroRNAs are small, non-coding RNA that post-transcriptionally regulate gene expression. Spontaneous preterm birth (sPTB) is associated with substantial differences in both placental pathophysiology and placental gene expression compared to term birth. We aimed to generate microRNA signatures of sPTB and map them to target genes using a microRNA-mRNA network. Methods: This study was conducted within the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study. Placental samples were collected at delivery, and RNA was isolated for mRNA and microRNA sequencing. To investigate sPTB, this study excluded placental samples of participants with iatrogenic indications for PTB or induced labor. We examined differences in microRNA expression in participants who delivered before 37 weeks (N=35) compared to term participants (N=404) in a series of covariate-adjusted linear regression models. We used paired placental microRNA and mRNA expression data from this cohort to validate associations between computationally predicted microRNA-mRNA pairs and establish a microRNA-mRNA network. Results: Expression of 7 microRNAs were increased in sPTB (FDR<0.05) and were inversely correlated with sPTB-associated genes involved in immune signaling. Expression of 12 microRNAs were decreased in sPTB, including 4 members of the maternally expressed chromosome 14 microRNA cluster (miR-376a-3p, miR-376c-3p, miR-377-3p, and miR-381-3p). These microRNAs were predicted to negatively regulate oxidative phosphorylation genes that were increased in sPTB. The associations between miR-376c-3p and miR-377-3p and oxidative phosphorylation were confirmed in microRNA knockdown experiments. Conclusions: This study highlights potential biological mechanisms by which placental microRNA dysfunction might contribute to sPTB and highlights putative sPTB biomarkers that may be detectable in maternal circulation.

Accurate epigenetic age prediction from DNA methylation profiles is intrinsically high-dimensional, creating a need for parsimonious models that preserve predictive performance while reducing the number of assayed cytosine-phosphate-guanine (CpG) loci. This study introduces MOSurvivor, a population-based multi-objective search framework that jointly optimizes a weight-threshold CpG selector and eight XGBoost hyperparameters. Experiments used the GSE40279 whole-blood cohort (656 individuals profiled on the Illumina HumanMethylation450 platform). After retaining 1,000 age-correlated CpGs, five strategies were evaluated on the same 30 seeded 80:20 train/test splits: fixed-parameter XGBoost using all 1,000 CpGs, random search, a genetic algorithm, particle swarm optimization, and MOSurvivor. Internal fitness was estimated using three-fold cross-validation on each training set. Across the 30 held-out test sets, MOSurvivor achieved a mean absolute error (MAE) of 4.149 {+/-} 0.300 years, root mean squared error of 5.545 {+/-} 0.392 years, and R2 of 0.855{+/-} 0.027 while retaining 211.6 {+/-} 54.8 CpGs. Relative to full-feature XGBoost (MAE 4.095 {+/-} 0.285 years), MOSurvivor reduced the feature set by 78.8% at an MAE increase of only 0.054 years (1.3%). Paired Wilcoxon tests found no significant accuracy difference between MOSurvivor and any comparator (all unadjusted p > 0.05; all Holm-adjusted p [&ge;] 0.476). The most recurrent locus, cg16867657, appeared in 29 runs, whereas mean pairwise Jaccard similarity was 0.124, indicating a small stable core embedded in multiple near-equivalent feature subsets. MOSurvivor thus offers a competitive accuracy-parsimony trade-off rather than superior absolute accuracy. External validation and leakage-free nested feature preselection remain necessary before biological or clinical translation. Keywords: epigenetic clock, DNA methylation, feature selection, multi-objective optimization, XGBoost, metaheuristics, biological aging.

BackgroundDNA methylation-based biomarkers have enabled robust estimation of biological age across tissues, and longitudinally trained measures such as DunedinPACE provide estimates of the pace of aging from blood methylomes. However, longitudinal methylation data are often unavailable, particularly for minimally invasive tissues such as buccal mucosa. Here, we developed a pseudo-longitudinal framework to estimate a buccal mucosa-derived PACE-like aging-rate score from cross-sectional methylome data. MethodsWe used a buccal biological age estimator as an internal pseudo-time axis. Methylation beta-values were transformed to M-values, and CpG-specific smooth functions of biological age were fitted in cross-validation. Local derivatives of these functions were used to project each individuals buccal methylome forward by a small time step. The projected methylome was converted back to beta-values, biological age was recalculated, and the change in biological age per unit time was defined as a pseudo-aging velocity. This raw velocity was transformed to a non-negative PACE-like score centered at 1.0. We then trained cross-fitted models to predict the derived score from buccal CpG methylation profiles. ResultsIn 151 individuals, the proposed score was reproducibly predicted from buccal methylomes in out-of-fold analysis, with a Pearson correlation of 0.706 and Spearman correlation of 0.710 between observed and predicted PACE-like scores. Sensitivity analyses across CpG selection size and regression models showed broadly consistent performance. In contrast, the proposed buccal PACE-like score showed only modest association with measured DunedinPACE, and alternative attempts to reconstruct DunedinPACE from buccal methylomes, including supervised proxy modeling and buccal-to-blood CpG imputation, showed limited sample-level performance. ConclusionsThese results support the feasibility of deriving a tissue-specific PACE-like aging-rate score from cross-sectional buccal methylome data by treating biological age as a pseudo-time axis. The proposed score should not be interpreted as a replacement for blood-derived DunedinPACE, but rather as an exploratory buccal methylome dynamics index that may capture tissue-specific aging-related variation.

Keloids are benign fibroproliferative disorders majorly characterized by excessive extracellular matrix deposition, with recurrence rates exceeding 80% following conventional therapy. Although epigenetic dysregulation has been implicated in keloid pathogenesis, whether genome-wide DNA methylation actively drives pathological cellular reprogramming, and whether this state is therapeutically reversible, remains unclear. We performed genome-wide DNA methylation profiling on keloid tissues, matched primary keloid fibroblasts, and normal controls. Our analysis revealed a shared DNA hypermethylation pattern between keloid tissues and fibroblasts, which was validated by three independent public cohorts. By integrating DNA methylome and transcriptome, we demonstrated that DNA methylation-regulated genes were enriched in osteochondrogenesis-related pathways, such as cartilage and bone development pathways. Furthermore, pharmacologic inhibition of DNA hypermethylation by DNA demethylating agent decitabine reduced the expression of osteochondrogenic markers and inhibited collagen deposition and keloid growth in primary keloid fibroblasts and patient-derived xenograft (PDX) model, offering a potential therapeutic strategy of keloid.

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

DNA methylation undergoes predictable changes with age, and preimplantation embryos are known to undergo global epigenetic reprogramming. However, the specific fate of age associated methylation signatures during early development has not been systematically quantified. Using published human sperm age associated differentially methylated regions (DMRs) as a feature space, we integrated single cell methylome and transcriptome data to develop the Transgenerational Reset Operator (TRO), a computational framework for profiling preimplantation stages. We found that the morula stage represents the nadir of age associated methylation entropy while retaining high developmental potency, distinguishing it from a simple demethylation endpoint. Dynamical modelling revealed that independent DMR drift fails to recapitulate the morula state, requiring a coordinated, structured correction concentrated in specific DMR subsets and modules with marked directional sensitivity. Independent chromatin accessibility data supported a stage specific methylation accessibility coupling at morula, albeit with modest effect sizes. Cross species mouse and orthogonal multiomic evidence suggested partial conservation but with weight dependence and heterogeneity. Collectively, our study defines morula as a computational "ground zero" candidate for age associated methylation features and proposes a testable hypothesis of developmental regulation, while emphasizing that matched parental offspring perturbation experiments are needed to establish causal mechanisms.

Epigenetic clocks have transformed the study of biological aging in epidemiology and clinical trials. However, the utility of these measures in clinical settings is limited by a lack of population-based norms that clinicians, patients, and researchers can use to understand and communicate how fast an individual is aging relative to same-aged peers. Here, we developed age norms for DunedinPACE, an epigenetic Pace of Aging measure derived from DNA methylation. To do so, we meta-analyzed data from 11 cohorts (N = 37,855 individuals, ages 17-99 years) to characterize the association between chronological age and DunedinPACE. We investigated sex differences and nonlinearity, confirmed results using longitudinal data, verified that age-normed DunedinPACE scores predict clinical outcomes, and illustrated how norms support the needs of clinical aging research. The age norms reported here will help integrate biomarkers of aging, such as DunedinPACE, into precision public health and medicine.

Background: Human aging does not follow a single trajectory. Epigenetic changes offer insight into the heterogeneity in aging by reflecting the combined influence of genetic, environmental, and lifestyle factors on the timing and progression of age-related changes beyond what chronological age alone can explain. Recent studies in cancer and aging underscore the importance of methylation variability as a marker of biological dysregulation. Methods: We investigated the role of DNA methylation in aging heterogeneity by performing epigenome-wide differential methylation and variance association analyses in blood samples from 1,445 Canadians aged 45 to 85 from the Canadian Longitudinal Study on Aging. Results: We identified 448 differentially methylated regions and 488 differentially variable regions associated with health decline as measured by the health deficit accumulation Frailty Index, cognitive function, and physical function. These two classes of regions showed minimal overlap, with distinct gene coverage, suggesting that variability contributes a complementary signal to aging heterogeneity. Genes overlapped by differentially methylated regions were enriched for immune and inflammation-related pathways, whereas differentially variable regions highlighted additional localized, CpG-island-enriched signals shared across health domains, consistent with regionally structured rather than diffuse dysregulation. By integrating significant CpGs from both analyses, we constructed an epigenetic biomarker. The biomarker was associated with all-cause mortality and showed higher discrimination than biomarkers constructed from differential methylation or variability alone, with a similar pattern reproduced in the Baltimore Longitudinal Study of Aging. Conclusions: These findings suggest that DNA methylation variability may provide a complementary dimension of epigenetic aging and support further evaluation in larger cohorts with more mortality events.

DNA methylation changes accumulate with age through both regulated and stochastic processes, yet the determinants of epigenetic information loss remain poorly defined. Using genome-wide DNA methylation profiles from 1,531 healthy human samples spanning 14 tissues, we quantified epigenetic noise by Shannon entropy and corrected it for cellular and tissue heterogeneity. Adjusted entropy was consistently low in promoters, first exons and CpG islands, and high in CpG-poor and intergenic regions. Cumulative mitotic history showed a stronger association with epigenetic noise than chronological age, explaining most of its variance particularly within CpG-rich regulatory regions. By contrast, age-related, replication-independent effects predominated outside CpG islands and in low-proliferative tissues such as the brain. Moreover, biological age acceleration was largely attributable to cell division in a tissue-specific manner. Collectively, mitotic history emerges as a major determinant of epigenetic noise accumulation across human tissues, while genomic context modulates regional vulnerability to methylation information loss during aging.

DNA methylation-based epigenetic clocks estimate biological age from methylation profiles, and the difference between predicted biological age and chronological age is commonly described as age acceleration (AA). We compared AA across eight cancer types, lung, colorectal, breast, thyroid, bone marrow and blood, kidney, uterus, and head and neck, using seven epigenetic clocks and 5,528 publicly available samples. Across the 56 cancer type clock combinations, tumor tissues showed higher average AA than normal tissues in 44 comparisons. The uterus cohort showed the clearest deviation from this overall trend, with normal samples exhibiting higher AA for six of seven clocks. Analyses of paired normal and tumor samples generally showed higher predicted ages and greater variability in tumor samples. We additionally examined age-associated methylation changes and the ability of clock CpGs to distinguish tumor from normal tissue. Several discriminatory CpGs were shared across cancer types and frequently showed tumor-associated hypermethylation at cancer-related loci. Small subsets of top-ranked CpGs captured substantial discriminatory information. Age-stratified subsampling preserved the main AA patterns, suggesting that chronological-age differences did not explain the observed tumor-normal differences. Overall, these findings highlight broad cancer-associated alterations in epigenetic aging together with substantial cancer type- and clock-specific heterogeneity.

Chromatin accessibility has long been used as a marker for regions of DNA with regulatory potential. Fiber-seq detects chromatin accessibility on individual DNA fibers, enabling analyses beyond the identification of the accessible chromatin regions (ACRs). By providing single molecule level high resolution, Fiber-seq provides unprecedented qualitative descriptions, including potential categorizations of ACRs, identification of internal transcription factor footprints and nucleosome positioning within individual DNA fibers. As with all tools, the power of this technique depends on careful experimental design and data analysis -- incorrect usage will result in incorrect conclusions. Here we offer guidelines and flag potential pitfalls when generating and analyzing Fiber-seq data, such as (1) the optimum levels of adenosine methylation per-fiber, (2) the power of per-fiber state inference, (3) the importance of controlling for read depth and methylation rates when comparing across samples, (4) the limitations of long-read sequence mapping, and (5) suggestions for identification of differentially accessible peaks across samples.

Wildfire smoke is an escalating global public health threat exposing millions of people, including children, to hazardous air pollution each year. Although wildfire smoke toxicants have been linked to a range of adverse health outcomes, including immune dysregulation, the long-term consequences of real-world pediatric wildfire smoke exposure on health and development remain largely unknown. To investigate the persistent effects of early-life exposure on immune health, here we leveraged a cohort of rhesus macaques that experienced nine consecutive days of hazardous wildfire smoke exposure in infancy during the 2020 Oregon Labor Day wildfires. By integrating ex vivo immune stimulations, multiplex cytokine profiling, single-cell transcriptomics, and genome-wide DNA methylation profiling, we identified persistent immunological consequences across molecular and functional levels. We found that a single severe postnatal exposure, in the first three months of life, was associated with persistent change in the innate immune response, including reduced pro-inflammatory cytokine response to a bacterial endotoxin, with subtle but consistent transcriptional changes in myeloid cells, particularly among males. Wildfire smoke exposure was also associated with changes in proportion of B and T/NK cells, and within the T/NK cell compartment, exposed animals exhibited an expansion of cytotoxic cells. Consistent with this, CD8+ T cells displayed extensive transcriptional remodeling and shifted toward more differentiated effector states, with the greatest differentiation observed in animals exposed at the youngest ages. Genome-wide DNA methylation profiling identified smoke-associated methylation changes consistent with acceleration of epigenetic aging, as well as persistent epigenetic alterations impacting genes involved in oxidative stress responses, innate immunity, T cell differentiation, and hematopoiesis. These findings demonstrate that a single severe wildfire smoke exposure during a critical developmental window is associated with extensive immune and epigenetic remodeling that persist years after exposure, providing new insight into the long-term biological consequences of early-life wildfire smoke exposure.

Epigenetic clocks are powerful tools for estimating both chronological and biological age, enabling the integration of age information into population monitoring, demographic modelling, and research on the ecophysiology and evolution of ageing. Most epigenetic clocks so far have been developed for mammals: here, we present the Bird Epigenetic Ageing Clock (BEAC) for estimating chronological age in avian species. BEAC was established based on genome-wide enzymatic methylation sequencing data of known-age king penguins (Aptenodytes patagonicus), and validated in nine other bird species. The BEAC collects age-informative signals into a bisulfite amplicon sequencing panel of 24 primer pairs, providing a highly accurate and cost-effective alternative to sequencing-intensive approaches. It achieved strong predictive performance in independent king penguin training (R{superscript 2}=0.88; MAE=1.7 years, n=78) and testing data (R{superscript 2}=0.79; MAE=2.3 years, n=41), with negligible batch effects, high longitudinal consistency, and resilience to reduced sample size or missing loci. Importantly, cross-species validation across 180 samples showed that BEAC reliably captures age-associated methylation signals in nine additional bird species across seven clades, demonstrating that a single set of loci can be predictive of ageing across multiple different bird species. BEAC offers a flexible, empirically validated tool and a transferable framework for developing epigenetic clocks in avian species, providing a highly valuable resource for eco-evolutionary studies of ageing in wild species.

Epigenetic clocks based on DNA methylation patterns are among the most accurate molecular correlates of chronological age, yet widely used clocks are predominantly empirical models with limited explicit characterization of the underlying methylation variability, lacking a direct connection to the physical mechanisms of aging. In this work, we bridge this gap by introducing an information-theoretic framework for DNA methylation dynamics combined with nonlinear machine learning to develop a competitive and interpretable age predictor. We model the population distribution of methylation {beta}-values at each CpG site using a reparameterized three-parameter Generalized Gamma Distribution (GGD) and derive a closed-form expression for its differential Shannon entropy. The resulting CpG-level entropy is used to characterize methylation variability and as a criterion for locus filtering. We introduce the Stacy Gradient Boosting Clock (Stacy-GB), which combines this GGD-based representation with a LightGBM regressor. The model was evaluated across independent cohorts using the ComputAgeBench epigenetic clock benchmark. Stacy-GB achieved a mean absolute error (MAE) of 3.74 years and a median error (bias) of 2.41 years, significantly outperforming state-of-the-art epigenetic clock baselines. Furthermore, age acceleration estimated by Stacy-GB was associated with several clinical pathologies, including ischemic heart disease, HIV infection, multiple sclerosis, and Werner syndrome, supporting its potential as an accurate and biophysically grounded tool for clinical aging research.