Epigenetic outlier profiles in depression: A genome-wide DNA methylation analysis of monozygotic twins

Epigenetics and the role of nutraceuticals in health and disease

In the post-genomic era, the data provided by complete genome sequencing could not answer several fundamental questions about the causes of many noninfectious diseases, diagnostic biomarkers, and novel therapeutic approaches. The rapidly expanding understanding of epigenetic mechanisms, as well as widespread acceptance of their hypothesized role in disease induction, facilitated the development of a number of novel diagnostic markers and therapeutic concepts. Epigenetic aberrations are reversible in nature, which enables the treatment of serious incurable diseases. Therefore, the interest in epigenetic modulatory effects has increased over the last decade, so about 60,000 publications discussing the expression of epigenetics could be detected in the PubMed database. Out of these, 58,442 were published alone in the last 10 years, including 17,672 reviews (69 historical articles), 314 clinical trials, 202 case reports, 197 meta-analyses, 156 letters to the editor, 108 randomized controlled trials, 87 observation studies, 40 book chapters, 22 published lectures, and 2 clinical trial protocols. The remaining publications are either miscellaneous or a mixture of the previously mentioned items. According to the species and gender, the publications included 44,589 human studies (17,106 females, 14,509 males, and the gender is not mentioned in the remaining papers) and 30,253 animal studies. In the present work, the role of epigenetic modulations in health and disease and the influencing factors in epigenetics are discussed.

Assessing Differential Variability of High-Throughput DNA Methylation Data

PURPOSE OF REVIEW

DNA methylation (DNAm) is essential to human development and plays an important role as a biomarker due to its susceptibility to environmental exposures. This article reviews the current state of statistical methods developed for differential variability analysis focusing on DNAm data.

RECENT FINDINGS

With the advent of high-throughput technologies allowing for highly reliable and cost-effective measurements of DNAm, many epigenome studies have analyzed DNAm levels to uncover biological mechanisms underlying past environmental exposures and subsequent health outcomes. These studies typically focused on detecting sites or regions which differ in their mean DNAm levels among exposure groups. However, more recent studies highlighted the importance of identifying differentially variable sites or regions as biologically relevant features. Currently, the analysis of differentially variable DNAm sites has not yet gained widespread adoption in environmental studies; yet, it is important to examine the effects of environmental exposures on inter-individual epigenetic variability. In this article, we describe six of the most widely used statistical approaches for analyzing differential variability of DNAm levels and provide a discussion of their advantages and current limitations.

Gene expression profiling in peripheral blood lymphocytes for major depression: preliminary cues from Chinese discordant sib-pair study

The etiology of major depressive disorder (MDD) involves many factors such as heredity and environment. There are very few MDD-related studies in Chinese population using twin or sib-pairs for depression-control samples. Here we used the microarray approach and compared gene expression profiling of peripheral blood lymphocytes from 6 sib-pairs discordant on lifetime history of MDD. Within sib-pair differentially expressed genes are obvious fewer in the 1st, 2nd, and 5th compared with those in the 3rd, 4th, and 6th sib-pairs. Gene expression pattern of these DEGs distinguished MDD individuals from the normal one in 3rd, 4th, and 6th sib-pair but not in the 1st, 2nd, and 5th pair, suggesting heterogeneity of different sib-pairs and somewhat commonalities among the 3rd, 4th, and 6th sib-pairs. Comprehensive protein interaction network analysis revealed two key genes PTH and FGF2 in a dominant network where the majority of the genes were significantly down-regulated. PTH was significantly down-regulated in all the sib-pairs while FGF2 was in the 3rd, 4th, and 6th sib-pairs. KEGG enrichment analysis of all the DEGs in networks showed that PTH and related genes were significantly enriched in the pathway of parathyroid hormone secretion, synthesis, and action while FGF2 and related genes were significantly enriched in the pathways of cancer and specifically breast cancer. Generally reduced expression of these genes in peripheral blood lymphocytes of MDD individuals implied their functional repression associated with MDD. Pending validation in more samples, the findings in this study provided valuable cues for understanding the potential mechanism of MDD, as well as potential markers for the diagnosis and treatment of depression in the Chinese population.

The link among microbiota, epigenetics, and disease development

The microbiome is a community of various microorganisms that inhabit or live on the skin of humans/animals, sharing the body space with their hosts. It is a sort of complex ecosystem of trillions of commensals, symbiotic, and pathogenic microorganisms, including trillions of bacteria, archaea, protozoa, fungi, and viruses. The microbiota plays a role in the health and disease status of the host. Their number, species dominance, and viability are dynamic. Their long-term disturbance is usually accompanied by serious diseases such as metabolic disorders, cardiovascular diseases, or even cancer. While epigenetics is a term that refers to different stimuli that induce modifications in gene expression patterns without structural changes in the inherited DNA sequence, these changes can be reversible or even persist for several generations. Epigenetics can be described as cell memory that stores experience against internal and external factors. Results from multiple institutions have contributed to the role and close interaction of both microbiota and epigenetics in disease induction. Understanding the mechanisms of both players enables a better understanding of disease induction and development and also opens the horizon to revolutionary therapeutic approaches. The present review illustrates the roles of diet, microbiome, and epigenetics in the induction of several chronic diseases. In addition, it discusses the application of epigenetic data to develop diagnostic biomarkers and therapeutics and evaluate their safety for patients. Understanding the interaction among all these elements enables the development of innovative preventive/therapeutic approaches for disease control.

An introduction to new robust linear and monotonic correlation coefficients

BACKGROUND

The most common measure of association between two continuous variables is the Pearson correlation (Maronna et al. in Safari an OMC. Robust statistics, 2019. https://login.proxy.bib.uottawa.ca/login?url=https://learning.oreilly.com/library/view/-/9781119214687/?ar&orpq&email=^u). When outliers are present, Pearson does not accurately measure association and robust measures are needed. This article introduces three new robust measures of correlation: Taba (T), TabWil (TW), and TabWil rank (TWR). The correlation estimators T and TW measure a linear association between two continuous or ordinal variables; whereas TWR measures a monotonic association. The robustness of these proposed measures in comparison with Pearson (P), Spearman (S), Quadrant (Q), Median (M), and Minimum Covariance Determinant (MCD) are examined through simulation. Taba distance is used to analyze genes, and statistical tests were used to identify those genes most significantly associated with Williams Syndrome (WS).

RESULTS

Based on the root mean square error (RMSE) and bias, the three proposed correlation measures are highly competitive when compared to classical measures such as P and S as well as robust measures such as Q, M, and MCD. Our findings indicate TBL2 was the most significant gene among patients diagnosed with WS and had the most significant reduction in gene expression level when compared with control (P value = 6.37E-05).

CONCLUSIONS

Overall, when the distribution is bivariate Log-Normal or bivariate Weibull, TWR performs best in terms of bias and T performs best with respect to RMSE. Under the Normal distribution, MCD performs well with respect to bias and RMSE; but TW, TWR, T, S, and P correlations were in close proximity. The identification of TBL2 may serve as a diagnostic tool for WS patients. A Taba R package has been developed and is available for use to perform all necessary computations for the proposed methods.

The Ghrelin/Growth Hormone Secretagogue Receptor System Is Involved in the Rapid and Sustained Antidepressant-Like Effect of Paeoniflorin

Major depressive disorder (MDD) is a debilitating mental illness affecting people worldwide. Although significant progress has been made in the development of therapeutic agents to treat this condition, fewer than half of all patients respond to currently available antidepressants, highlighting the urgent need for the development of new classes of antidepressant drugs. Here, we found that paeoniflorin (PF) produced rapid and sustained antidepressant-like effects in multiple mouse models of depression, including the forced swimming test and exposure to chronic mild stress (CMS). Moreover, PF decreased the bodyweight of mice without affecting food intake and glucose homeostasis, and also reduced the plasma levels of total ghrelin and the expression of ghrelin O-acyltransferase in the stomach; however, the plasma levels of ghrelin and the ghrelin/total ghrelin ratio were unaffected. Furthermore, PF significantly increased the expression of growth hormone secretagogue receptor 1 alpha (GHSR1α, encoded by the Ghsr gene) in the intestine, whereas the levels of GHSR1α in the brain were only marginally downregulated following subchronic PF treatment. Finally, the genetic deletion of Ghsr attenuated the antidepressant-like effects of PF in mice exposed to CMS. These results suggested that increased GHSR1α expression in the intestine mediates the antidepressant-like effects of PF. Understanding peripheral ghrelin/GHSR signaling may provide new insights for the screening of antidepressant drugs that produce fast-acting and sustained effects.

Twin study designs as a tool to identify new candidate genes for depression: A systematic review of DNA methylation studies

Monozygotic (MZ) twin studies constitute a key resource for the dissection of environmental and biological risk factors for human complex disorders. Given that epigenetic differences accumulate throughout the lifespan, the assessment of MZ twin pairs discordant for depression offers a genetically informative design to explore DNA methylation while accounting for the typical confounders of the field, shared by co-twins of a pair. In this review, we systematically evaluate all twin studies published to date assessing DNA methylation in association with depressive phenotypes. However, difficulty to recruit large numbers of MZ twin pairs fails to provide enough sample size to develop genome-wide approaches. Alternatively, region and pathway analysis revealed an enrichment for nervous system related functions; likewise, evidence supports an accumulation of methylation variability in affected subjects when compared to their co-twins. Nevertheless, longitudinal studies incorporating known risk factors for depression such as childhood trauma are required for understanding the role that DNA methylation plays in the etiology of depression.

Longitudinal DNA methylation changes at MET may alter HGF/c-MET signalling in adolescents at risk for depression

Unrecognized depression during adolescence can result in adult suicidal behaviour. The aim of this study was to identify, replicate and characterize DNA methylation (DNAm) shifts in depression aetiology, using a longitudinal, multi-tissue (blood and brain) and multi-layered (genetics, epigenetics, transcriptomics) approach. We measured genome-wide blood DNAm data at baseline and one-year follow-up, and imputed genetic variants, in 59 healthy adolescents comprising the discovery cohort. Depression and suicidal symptoms were determined using the Development and Well-Being Assessment (DAWBA) depression band, Montgomery-Åsberg Depression Rating Scale-Self (MADRS-S) and SUicide Assessment Scale (SUAS). DNAm levels at follow-up were regressed against depression scores, adjusting for sex, age and the DNAm residuals at baseline. Higher methylation levels of 5% and 13% at cg24627299 within the MET gene were associated with higher depression scores (p_raw<1e-4) and susceptibility for suicidal symptoms (p_adj.<0.005). The nearby rs39748 was discovered to be a methylation and expression quantitative trait locus in blood cells. mRNA levels of hepatocyte growth factor (HGF) expression, known to strongly interact with MET, were inversely associated with methylation levels at cg24627299, in an independent cohort of 1180 CD14+ samples. In an open-access dataset of brain tissue, lower methylation at cg24627299 was found in 45 adults diagnosed with major depressive disorder compared with matched controls (p_adj.<0.05). Furthermore, lower MET expression was identified in the hippocampus of depressed individuals compared with controls in a fourth, independent cohort. Our findings reveal methylation changes at MET in the pathology of depression, possibly involved in downregulation of HGF/c-MET signalling the hippocampal region.

Twin Registries Moving Forward and Meeting the Future: A Review

Twin registries have developed as a valuable resource for the study of many aspects of disease and society over the years in many different countries. A number of these registries include large numbers of twins with data collected at varying information levels for twin cohorts over the past several decades. More recent expansion of twin datasets has allowed for the collection of genetic data, together with many other levels of 'omic' information along with multiple demographic, physiological, health outcomes and other measures typically used in epidemiologic research. Other twin data sources outside these registries reflect research interests in particular aspects of disease or specific phenotypic assessment. Twin registries have the potential to play a key role in many aspects of the artificial intelligence/machine learning-driven projects of the future and will continue to keep adapting to the changing research landscape.

Genome-wide profiling of DNA methylome and transcriptome in peripheral blood monocytes for major depression: A Monozygotic Discordant Twin Study

DNA methylation plays an important role in major depressive disorder (MDD), but the specific genes and genomic regions associated with MDD remain largely unknown. Here we conducted genome-wide profiling of DNA methylation (Infinium MethylationEPIC BeadChip) and gene expression (RNA-seq) in peripheral blood monocytes from 79 monozygotic twin pairs (mean age 38.2 ± 15.6 years) discordant on lifetime history of MDD to identify differentially methylated regions (DMRs) and differentially expressed genes (DEGs) associated with MDD, followed by replication in brain tissue samples. Integrative DNA methylome and transcriptome analysis and network analysis was performed to identify potential functional epigenetic determinants for MDD. We identified 39 DMRs and 30 DEGs associated with lifetime history of MDD. Some genes were replicated in postmortem brain tissue. Integrative DNA methylome and transcriptome analysis revealed both negative and positive correlations between DNA methylation and gene expression, but the correlation pattern varies greatly by genomic locations. Network analysis revealed distinct gene modules enriched in signaling pathways related to stress responses, neuron apoptosis, insulin receptor signaling, mTOR signaling, and nerve growth factor receptor signaling, suggesting potential functional relevance to MDD. These results demonstrated that altered DNA methylation and gene expression in peripheral blood monocytes are associated with MDD. Our results highlight the utility of using peripheral blood epigenetic markers and demonstrate that a monozygotic discordant co-twin control design can aid in the discovery of novel genes associated with MDD. If validated, the newly identified genes may serve as novel biomarkers or druggable targets for MDD and related disorders.

Human epigenetic ageing is logarithmic with time across the entire lifespan

Epigenetic changes during ageing have been characterized by multiple epigenetic clocks that allow the prediction of chronological age based on methylation status. Despite their accuracy and utility, epigenetic age biomarkers leave many questions about epigenetic ageing unanswered. Specifically, they do not permit the unbiased characterization of non-linear epigenetic ageing trends across entire life spans, a critical question underlying this field of research. Here we provide an integrated framework to address this question. Our model, inspired from evolutionary models, is able to account for acceleration/deceleration in epigenetic changes by fitting an individual's model age, the epigenetic age, which is related to chronological age in a non-linear fashion. Application of this model to DNA methylation data measured across broad age ranges, from before birth to old age, and from two tissue types, suggests a universal logarithmic trend characterizes epigenetic ageing across entire lifespans.