Altered DNA methylation profiles in blood from patients with sporadic Creutzfeldt-Jakob disease

Deciphering the role of immune cell composition in epigenetic age acceleration: Insights from cell-type deconvolution applied to human blood epigenetic clocks

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health-related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell-type deconvolution with EAA predicted by six widely-used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging-related diseases, precision medicine, and therapeutic interventions.

Identifying epigenetic aging moderators using the epigenetic pacemaker

Epigenetic clocks are DNA methylation-based chronological age prediction models that are commonly employed to study age-related biology. The difference between the predicted and observed age is often interpreted as a form of biological age acceleration, and many studies have measured the impact of environmental and disease-associated factors on epigenetic age. Most epigenetic clocks are fit using approaches that minimize the error between the predicted and observed chronological age, and as a result, they may not accurately model the impact of factors that moderate the relationship between the actual and epigenetic age. Here, we compare epigenetic clocks that are constructed using penalized regression methods to an evolutionary framework of epigenetic aging with the epigenetic pacemaker (EPM), which directly models DNA methylation as a function of a time-dependent epigenetic state. In simulations, we show that the value of the epigenetic state is impacted by factors such as age, sex, and cell-type composition. Next, in a dataset aggregated from previous studies, we show that the epigenetic state is also moderated by sex and the cell type. Finally, we demonstrate that the epigenetic state is also moderated by toxins in a study on polybrominated biphenyl exposure. Thus, we find that the pacemaker provides a robust framework for the study of factors that impact epigenetic age acceleration and that the effect of these factors may be obscured in traditional clocks based on linear regression models.

Association of Blood-Based DNA Methylation Markers With Late-Onset Alzheimer Disease: A Potential Diagnostic Approach

BACKGROUND AND OBJECTIVES

There is an urgent need to identify novel noninvasive biomarkers for Alzheimer disease (AD) diagnosis. Recent advances in blood-based measurements of phosphorylated tau (pTau) species are promising but still insufficient to address clinical needs. Epigenetics has been shown to be helpful to better understand AD pathogenesis. Epigenetic biomarkers have been successfully implemented in other medical disciplines, such as oncology. The objective of this study was to explore the diagnostic accuracy of a blood-based DNA methylation marker panel as a noninvasive tool to identify patients with late-onset Alzheimer compared with age-matched controls.

METHODS

A case-control study was performed. Blood DNA methylation levels at 46 cytosine-guanine sites (21 genes selected after a comprehensive literature search) were measured by bisulfite pyrosequencing in patients with "probable AD dementia" following National Institute on Aging and the Alzheimer's Association guidelines (2011) and age-matched and sex-matched controls recruited at Neurology Department-University Hospital of Navarre, Spain, selected by convenience sampling. Plasma pTau181 levels were determined by Simoa technology. Multivariable logistic regression analysis was performed to explore the optimal model to discriminate patients with AD from controls. Furthermore, we performed a stratified analysis by sex.

RESULTS

The final study cohort consisted of 80 patients with AD (age: median [interquartile range] 79 [11] years; 58.8% female) and 100 cognitively healthy controls (age 77 [10] years; 58% female). A panel including DNA methylation levels at NXN, ABCA7, and HOXA3 genes and plasma pTau181 significantly improved (area under the receiver operating characteristic curve 0.93, 95% CI 0.89-0.97) the diagnostic performance of a single pTau181-based model, adjusted for age, sex, and APOE ɛ4 genotype. The sensitivity and specificity of this panel were 83.30% and 90.00%, respectively. After sex-stratified analysis, HOXA3 DNA methylation levels showed consistent association with AD.

DISCUSSION

These results highlight the potential translational value of blood-based DNA methylation biomarkers for noninvasive diagnosis of AD.

REGISTRATION INFORMATION

Research Ethics Committee of the University Hospital of Navarre (PI17/02218).

Identification of gene-level methylation for disease prediction

DNA methylation is an epigenetic alteration that plays a fundamental part in governing gene regulatory processes. The DNA methylation mechanism affixes methyl groups to distinct cytosine residues, influencing chromatin architectures. Multiple studies have demonstrated that DNA methylation's regulatory effect on genes is linked to the beginning and progression of several disorders. Researchers have recently uncovered thousands of phenotype-related methylation sites through the epigenome-wide association study (EWAS). However, combining the methylation levels of several sites within a gene and determining the gene-level DNA methylation remains challenging. In this study, we proposed the supervised UMAP Assisted Gene-level Methylation method (sUAGM) for disease prediction based on supervised UMAP (Uniform Manifold Approximation and Projection), a manifold learning-based method for reducing dimensionality. The methylation values at the gene level generated using the proposed method are evaluated by employing various feature selection and classification algorithms on three distinct DNA methylation datasets derived from blood samples. The performance has been assessed employing classification accuracy, F-1 score, Mathews Correlation Coefficient (MCC), Kappa, Classification Success Index (CSI) and Jaccard Index. The Support Vector Machine with the linear kernel (SVML) classifier with Recursive Feature Elimination (RFE) performs best across all three datasets. From comparative analysis, our method outperformed existing gene-level and site-level approaches by achieving 100% accuracy and F1-score with fewer genes. The functional analysis of the top 28 genes selected from the Parkinson's disease dataset revealed a significant association with the disease.

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods' performance and DM signatures' quality in the absence of gold standard data solving a long-existing problem in DM analysis.

5-Methylcytosine and 5-Hydroxymethylcytosine in Scrapie-Infected Sheep and Mouse Brain Tissues

Scrapie is a neurodegenerative disorder belonging to the group of transmissible spongiform encephalopathies or prion diseases, which are caused by an infectious isoform of the innocuous cellular prion protein (PrP^C) known as PrP^Sc. DNA methylation, one of the most studied epigenetic mechanisms, is essential for the proper functioning of the central nervous system. Recent findings point to possible involvement of DNA methylation in the pathogenesis of prion diseases, but there is still a lack of knowledge about the behavior of this epigenetic mechanism in such neurodegenerative disorders. Here, we evaluated by immunohistochemistry the 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) levels in sheep and mouse brain tissues infected with scrapie. Expression analysis of different gene coding for epigenetic regulatory enzymes (DNMT1, DNMT3A, DNMT3B, HDAC1, HDAC2, TET1, and TET2) was also carried out. A decrease in 5mC levels was observed in scrapie-affected sheep and mice compared to healthy animals, whereas 5hmC displayed opposite patterns between the two models, demonstrating a decrease in 5hmC in scrapie-infected sheep and an increase in preclinical mice. 5mC correlated with prion-related lesions in mice and sheep, but 5hmC was associated with prion lesions only in sheep. Differences in the expression changes of epigenetic regulatory genes were found between both disease models, being differentially expressed Dnmt3b, Hdac1, and Tet1 in mice and HDAC2 in sheep. Our results support the evidence that DNA methylation in both forms, 5mC and 5hmC, and its associated epigenetic enzymes, take part in the neurodegenerative course of prion diseases.

Epigenetic Changes in Prion and Prion-like Neurodegenerative Diseases: Recent Advances, Potential as Biomarkers, and Future Perspectives

Prion diseases are transmissible spongiform encephalopathies (TSEs) caused by a conformational conversion of the native cellular prion protein (PrP^C) to an abnormal, infectious isoform called PrP^Sc. Amyotrophic lateral sclerosis, Alzheimer’s, Parkinson’s, and Huntington’s diseases are also known as prion-like diseases because they share common features with prion diseases, including protein misfolding and aggregation, as well as the spread of these misfolded proteins into different brain regions. Increasing evidence proposes the involvement of epigenetic mechanisms, namely DNA methylation, post-translational modifications of histones, and microRNA-mediated post-transcriptional gene regulation in the pathogenesis of prion-like diseases. Little is known about the role of epigenetic modifications in prion diseases, but recent findings also point to a potential regulatory role of epigenetic mechanisms in the pathology of these diseases. This review highlights recent findings on epigenetic modifications in TSEs and prion-like diseases and discusses the potential role of such mechanisms in disease pathology and their use as potential biomarkers.

Genotype by environment interactions for chronic wasting disease in farmed US white-tailed deer

Despite implementation of enhanced management practices, chronic wasting disease in US white-tailed deer (Odocoileus virginianus) continues to expand geographically. Herein, we perform the largest genome-wide association analysis to date for chronic wasting disease (n = 412 chronic wasting disease-positive; n = 758 chronic wasting disease-nondetect) using a custom Affymetrix Axiom single-nucleotide polymorphism array (n = 121,010 single-nucleotide polymorphisms), and confirm that differential susceptibility to chronic wasting disease is a highly heritable (h2= 0.611 ± 0.056) polygenic trait in farmed US white-tailed deer, but with greater trait complexity than previously appreciated. We also confirm PRNP codon 96 (G96S) as having the largest-effects on risk (P ≤ 3.19E-08; phenotypic variance explained ≥ 0.025) across 3 US regions (Northeast, Midwest, South). However, 20 chronic wasting disease-positive white-tailed deer possessing codon 96SS genotypes were also observed, including one that was lymph node and obex positive. Beyond PRNP, we also detected 23 significant single-nucleotide polymorphisms (P-value ≤ 5E-05) implicating ≥24 positional candidate genes; many of which have been directly implicated in Parkinson’s, Alzheimer’s and prion diseases. Genotype-by-environment interaction genome-wide association analysis revealed a single-nucleotide polymorphism in the lysosomal enzyme gene ARSB as having the most significant regional heterogeneity of effects on chronic wasting disease (P ≤ 3.20E-06); with increasing copy number of the minor allele increasing susceptibility to chronic wasting disease in the Northeast and Midwest; but with opposite effects in the South. In addition to ARSB, 38 significant genotype-by-environment single-nucleotide polymorphisms (P-value ≤ 5E-05) were also detected, thereby implicating ≥ 36 positional candidate genes; the majority of which have also been associated with aspects of Parkinson’s, Alzheimer’s, and prion diseases.

Prion protein gene mutation detection using long-read Nanopore sequencing

Prion diseases are fatal neurodegenerative conditions that affect humans and animals. Rapid and accurate sequencing of the prion gene PRNP is paramount to human prion disease diagnosis and for animal surveillance programmes. Current methods for PRNP genotyping involve sequencing of small fragments within the protein-coding region. The contribution of variants in the non-coding regions of PRNP including large structural changes is poorly understood. Here, we used long-range PCR and Nanopore sequencing to sequence the full length of PRNP, including its regulatory region, in 25 samples from blood and brain of individuals with inherited or sporadic prion diseases. Nanopore sequencing detected the same variants as identified by Sanger sequencing, including repeat expansions/deletions. Nanopore identified additional single-nucleotide variants in the non-coding regions of PRNP, but no novel structural variants were discovered. Finally, we explored somatic mosaicism of PRNP's octapeptide repeat region, which is a hypothetical cause of sporadic prion disease. While we found changes consistent with somatic mutations, we demonstrate that they may have been generated by the PCR. Our study illustrates the accuracy of Nanopore sequencing for rapid and field prion disease diagnosis and highlights the need for single-molecule sequencing methods for the detection of somatic mutations.

Gene expression and epigenetic markers of prion diseases

Epigenetics, meaning the variety of mechanisms underpinning gene regulation and chromatin states, plays a key role in normal development as well as in disease initiation and progression. Epigenetic mechanisms like alteration of DNA methylation, histone modifications, and non-coding RNAs, have been proposed as biomarkers for diagnosis, classification, or monitoring of responsiveness to treatment in many diseases. In prion diseases, the profound associations with human aging, the effects of cell type and differentiation on in vitro susceptibility, and recently identified human risk factors, all implicate causal epigenetic mechanisms. Here, we review the current state of the art of epigenetics in prion diseases and its interaction with genetic determinants. In particular, we will review recent advances made by several groups in the field profiling DNA methylation and microRNA expression in mammalian prion diseases and the potential for these discoveries to be exploited as biomarkers.

Pseudotime Analysis Reveals Exponential Trends in DNA Methylation Aging with Mortality Associated Timescales

The epigenetic trajectory of DNA methylation profiles has a nonlinear relationship with time, reflecting rapid changes in DNA methylation early in life that progressively slow with age. In this study, we use pseudotime analysis to determine the functional form of these trajectories. Unlike epigenetic clocks that constrain the functional form of methylation changes with time, pseudotime analysis orders samples along a path, based on similarities in a latent dimension, to provide an unbiased trajectory. We show that pseudotime analysis can be applied to DNA methylation in human blood and brain tissue and find that it is highly correlated with the epigenetic states described by the Epigenetic Pacemaker. Moreover, we show that the pseudotime trajectory can be modeled with respect to time, using a sum of two exponentials, with coefficients that are close to the timescales of human age-associated mortality. Thus, for the first time, we can identify age-associated molecular changes that appear to track the exponential dynamics of mortality risk.

Genome-Wide Methylation Profiling in the Thalamus of Scrapie Sheep

Scrapie is a neurodegenerative disorder belonging to the group of transmissible spongiform encephalopathy (TSE). Scrapie occurs in sheep and goats, which are considered good natural animal models of these TSE. Changes in DNA methylation occur in the central nervous system (CNS) of patients suffering from prion-like neurodegenerative diseases, such as Alzheimer's disease. Nevertheless, potential DNA methylation alterations have not yet been investigated in the CNS of any prion disease model or naturally infected cases, neither in humans nor in animals. Genome-wide DNA methylation patterns were studied in the thalamus obtained from sheep naturally infected with scrapie at a clinical stage (n = 4) and from controls (n = 4) by performing a whole-genome bisulfite sequencing (WGBS) analysis. Ewes carried the scrapie-susceptible ARQ/ARQ PRNP genotype and were sacrificed at a similar age (4–6 years). Although the average genomic methylation levels were similar between the control and the scrapie animals, we identified 8,907 significant differentially methylated regions (DMRs) and 39 promoters (DMPs). Gene Ontology analysis revealed that hypomethylated DMRs were enriched in genes involved in transmembrane transport and cell adhesion, whereas hypermethylated DMRs were related to intracellular signal transduction genes. Moreover, genes highly expressed in specific types of CNS cells and those previously described to be differentially expressed in scrapie brains contained DMRs. Finally, a quantitative PCR (qPCR) validation indicated differences in the expression of five genes (PCDH19, SNCG, WDR45B, PEX1, and CABIN1) that matched the methylation changes observed in the genomic study. Altogether, these results suggest a potential regulatory role of DNA methylation in prion neuropathology.

Epigenetics in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease with unknown pathogenesis and complex pathological manifestations. At present, a large number of studies on targeted drugs for the typical pathological phenomenon of AD (Aβ) have ended in failure. Although there are some drugs on the market that indirectly act on AD, their efficacy is very low and the side effects are substantial, so there is an urgent need to develop a new strategy for the treatment of AD. An increasing number of studies have confirmed epigenetic changes in AD. Although it is not clear whether these epigenetic changes are the cause or result of AD, they provide a new avenue of treatment for medical researchers worldwide. This article summarizes various epigenetic changes in AD, including DNA methylation, histone modification and miRNA, and concludes that epigenetics has great potential as a new target for the treatment of AD.