Therese M. Murphy

Esophageal cancer (OC) is currently the eighth most common form of cancer worldwide with a 5‐year survival rate of 10%–20%, with the primary risk factor of esophageal adenocarcinoma (OAC) being the development of Barrett's Esophagus (BO). Despite its clinical significance, the molecular pathogenesis underlying both BO and OC is not well understood. In recent years, epigenetic dysregulation, particularly aberrant DNA methylation, has emerged as a critical area of investigation, given its potential utility in the identification of diagnostic, prognostic, and therapeutic biomarkers. This review examines the evolving epigenetic landscape of esophageal cancer, including a focus on its origins in BO with a particular emphasis on DNA methylation, the most extensively researched epigenetic mechanism. Key DNA methylation‐associated alterations involved in OAC and OSCC initiation and progression are discussed, alongside their potential clinical application as biomarkers for early detection, prognosis, and risk stratification in BO populations. Furthermore, the role of these epigenetically regulated genes in the disruption of Wnt signaling and cell cycle control pathways implicated in esophageal carcinogenesis is explored. The review concludes by outlining future research directions, current challenges, and the promise of epigenetic studies in advancing our understanding of OC pathogenesis and improving patient outcomes.

Inflammatory bowel diseases (IBDs) are chronic inflammatory disorders influenced by environmental factors and characterised by a dysregulated immune response. DNA methylation (DNAm) a key epigenetic mechanism plays a role in the etiology of complex diseases like IBD. Epigenetic clocks which estimate biological aging through DNAm patterns have also been linked to various health states, including IBD. Previously, we profiled DNA methylation in peripheral blood from adult IBD patients and controls using the Illumina 450K microarray ( n = 184). We now expand this dataset with 8-year clinical follow-up data, including disease progression and treatment response. Additionally, we generate second and third-generation epigenetic clock measures in this cohort to investigate if IBD patients exhibit epigenetic age acceleration compared to healthy controls. We identified one CpG site (cg03583111) significantly differentially methylated in IBD patients with long-term clinical recurrence (after the first year of study) compared to non-recurrence (no treatment escalation after 8 years). We assessed DNAm aging signatures in IBD patients versus controls, finding evidence of significant epigenetic age acceleration, as measured by three epigenetic clocks (GrimAge, GrimAge2, and DunedinPACE), in IBD patients compared to controls. These associations were replicated in two independent IBD cohorts: adult (GSE87648, n = 377) and paediatric (GSE112611, n = 238). Moreover, we observed higher age acceleration (GrimAge, U = 669, p = 0.003) and a faster pace of aging (DunedinPACE, t = 3.233, p = 0.002) in patients with active UC compared to inactive disease, but not for CD. These findings suggest that blood-based DNAm signatures could serve as biomarkers for detecting, monitoring, and classifying IBD.

Major depression (MD) is a leading cause of global disease burden, and both experimental and population-based studies suggest that differences in DNA methylation may be associated with the condition. However, previous DNA methylation studies have, so far, not been widely replicated, suggesting a need for larger meta-analysis studies. Here we conducted a meta-analysis of methylome-wide association analysis for lifetime MD across 18 studies of 24,754 European-ancestry participants (5,443 MD cases) and an East Asian sample (243 cases, 1,846 controls). We identified 15 CpG sites associated with lifetime MD with methylome-wide significance. The methylation score created using the methylome-wide association analysis summary statistics was significantly associated with MD status in an out-of-sample classification analysis (area under the curve 0.53). Methylation score was also associated with five inflammatory markers, with the strongest association found with tumor necrosis factor beta. Mendelian randomization analysis revealed 23 CpG sites potentially causally linked to MD, with 7 replicated in an independent dataset. Our study provides evidence that variations in DNA methylation are associated with MD, and further evidence supporting involvement of the immune system.

Background Inflammatory bowel diseases (IBDs) are chronic inflammatory disorders with a dysregulated immune response partly influenced by environmental factors. DNA methylation (DNAm), a key epigenetic mechanism, is implicated in the etiology of complex diseases, including IBD. Epigenetic clocks, which use DNAm patterns to estimate biological aging, have been increasingly linked to various health and disease states. Previous studies have associated DNAm with IBD, and first- and second-generation epigenetic clocks with IBD subtypes. Results In a discovery IBD cohort (n=149) with 8-year clinical follow-up data, we explored the relationship between DNAm variation, second- and third-generation epigenetic clocks, and IBD clinicopathological outcomes, including disease subtype, activity, and recurrence. One CpG site was significantly differentially methylated (Benjamini-Hochberg adjusted p-valueConclusions Overall, we present strong evidence that dynamic age-related DNAm changes can be used to differentiate between IBD (including separately by subtype) and controls. Furthermore, our study provides important new evidence that DunedinPACE may have utility as a biomarker for monitoring disease recurrence in IBD patients and may be a strong marker of disease activity in UC patients. Overall, this suggests that blood-based DNAm signatures could serve as biomarkers for detection and monitoring of IBD.

Major Depression (MD) is a leading cause of global disease burden, and both experimental and population-based studies suggest that differences in DNA methylation (DNAm) may be associated with the condition. However, previous DNAm studies have not so far been widely replicated, suggesting a need for larger meta-analysis studies. In the present study, the Psychiatric Genomics Consortium Major Depressive Disorder working group conducted a meta-analysis of methylome-wide association analysis (MWAS) for life-time MD across 18 studies of 24,754 European-ancestry participants (5,443 MD cases) and an East Asian sample (243 cases, 1846 controls). We identified fifteen CpG sites associated with lifetime MD with methylome-wide significance (p < 6.42×10 ^{- 8} ). Top CpG effect sizes in European ancestries were positively correlated with those from an independent East Asian MWAS (r = 0.482 and p = 0.068 for significant CpG sites, r = 0.261 and p = 0.009 for the top 100 CpG sites). Methylation score (MS) created using the MWAS summary statistics was significantly associated with MD status in an out-of-sample classification analysis (β = 0.122, p = 0.005, AUC = 0.53). MS was also associated with five inflammatory markers, with the strongest association found with Tumor Necrosis Factor Beta (β=-0.154, p=1.5×10 ^{- 5} ). Mendelian randomisation (MR) analysis demonstrated that 23 CpG sites were potentially causally associated with MD and six of those were replicated in an independent mQTL dataset (Wald’s ratio test, absolute β ranged from 0.056 to 0.932, p ranged from 7×10 ^{- 3} to 4.58×10 ^{- 6} ). CpG sites located in the Major Histocompatibility complex (MHC) region showed the strongest evidence from MR analysis of being associated with MD. Our study provides evidence that variations in DNA methylation are associated with MD, and further evidence supporting involvement of the immune system. Larger sample sizes in diverse ancestries are likely to reveal replicable associations to improve mechanistic inferences with the potential to inform molecular target identification.

Background: The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine–guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation and two independent test sets for the comparisons.Results: We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. This approach achieved a correlation between estimated and actual TL of 0.295 (83.4% CI [0.201, 0.384]) on the EXTEND test data set. Contrastingly, the baseline model of elastic net regression with no prior feature reduction stage performed less well in general—suggesting a prior feature-selection stage may have important utility. A previously developed TL estimator, DNAmTL, achieved a correlation of 0.216 (83.4% CI [0.118, 0.310]) on the EXTEND data. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities.Conclusions: The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

Suicide is the second leading cause of death globally among young people representing a significant global health burden. Although the molecular correlates of suicide remains poorly understood, it has been hypothesised that epigenomic processes may play a role. The objective of this study was to identify suicide-associated DNA methylation changes in the human brain by utilising previously published and unpublished methylomic datasets. We analysed prefrontal cortex (PFC, n = 211) and cerebellum (CER, n = 114) DNA methylation profiles from suicide completers and non-psychiatric, sudden-death controls, meta-analysing data from independent cohorts for each brain region separately. We report evidence for altered DNA methylation at several genetic loci in suicide cases compared to controls in both brain regions with suicide-associated differentially methylated positions enriched among functional pathways relevant to psychiatric phenotypes and suicidality, including nervous system development (PFC) and regulation of long-term synaptic depression (CER). In addition, we examined the functional consequences of variable DNA methylation within a PFC suicide-associated differentially methylated region (PSORS1C3 DMR) using a dual luciferase assay and examined expression of nearby genes. DNA methylation within this region was associated with decreased expression of firefly luciferase but was not associated with expression of nearby genes, PSORS1C3 and POU5F1. Our data suggest that suicide is associated with DNA methylation, offering novel insights into the molecular pathology associated with suicidality.

Telomeres are TTAGGG repeats located at the end of chromosomes that maintain DNA stability. Telomere length (TL) has been widely implicated as a marker of biological age, and is associated with several human diseases, including depression, cardiovascular disease and cancer. Twin studies and cohort studies estimate heritability of TL between 78-82%. Moreover, several genomic loci which influence TL have been identified. Despite the success of genetic studies in furthering our understanding of telomere biology, identified variants account for only a small proportion of the estimated heritability. Over the last decade, epigenetic regulation of mammalian telomeres has become apparent. These epigenetic mechanisms, which act to regulate gene expression via modifications to DNA, histone proteins and chromatin, change with age and in response to specific environmental and psychosocial factors —providing a mechanism for the interaction between genotype and the environment. The present review examines the evidence for genetic and epigenetic regulation of TL and discusses their role in human disease, before outlining some of the methodological limitations of these studies. Finally, the review defines what the ‘epigenetic clock’ is and evaluates its relationship with TL.

Suicide is the second leading cause of death globally among young people and the tenth leading cause of death across all ages. Approximately 800,000 people die by suicide every year representing a significant global health burden. Despite this burden, the molecular pathology of suicide remains poorly understood. A number of recent studies have shown that epigenetic alterations are associated with suicidal behaviour. These epigenetic mechanisms, which act to regulate gene expression via modifications to DNA, histone proteins and chromatin, change with age and in response to specific environmental and psychosocial factors —providing a mechanism for the interaction between genotype and the environment. The present review briefly outlines the main epigenetic mechanisms involved in gene regulation and discusses recent findings of epigenetic alterations in suicidal behaviour, their caveats and the future direction of this emerging field of research.

Depression is a common and disabling disorder, representing a major social and economic health issue. Moreover, depression is associated with the progression of diseases with an inflammatory etiology including many inflammatory-related disorders. At the molecular level, the mechanisms by which depression might promote the onset of these diseases and associated immune-dysfunction are not well understood. In this study we assessed genome-wide patterns of DNA methylation in whole blood-derived DNA obtained from individuals with a self-reported history of depression (n = 100) and individuals without a history of depression (n = 100) using the Illumina 450K microarray. Our analysis identified six significant (Šidák corrected P < 0.05) depression-associated differentially methylated regions (DMRs); the top-ranked DMR was located in exon 1 of the LTB4R2 gene (Šidák corrected P = 1.27 × 10⁻¹⁴). Polygenic risk scores (PRS) for depression were generated and known biological markers of inflammation, telomere length (TL) and IL-6, were measured in DNA and serum samples, respectively. Next, we employed a systems-level approach to identify networks of co-methylated loci associated with a history of depression, in addition to depression PRS, TL and IL-6 levels. Our analysis identified one depression-associated co-methylation module (P = 0.04). Interestingly, the depression-associated module was highly enriched for pathways related to immune function and was also associated with TL and IL-6 cytokine levels. In summary, our genome-wide DNA methylation analysis of individuals with and without a self-reported history of depression identified several candidate DMRs of potential relevance to the pathogenesis of depression and its associated immune-dysfunction phenotype.