Targeted bisulfite sequencing for biomarker discovery

Correlation of ChREBP Gene Methylation with Pathological Characteristics of Type 2 Diabetes Mellitus

The objective of this study is to investigate the expression of the carbohydrate response element binding protein (ChREBP) gene in type 2 diabetes mellitus (T2DM) and its correlation with pathological features. For obtaining and exploring the pathological features in patients, sixty T2DM patients (the research group) and thirty healthy controls (the control group) presented to our hospital between January 2019 and June 2019 were selected as the research participants. After DNA extraction from peripheral blood mononuclear cells (PBMCs) and modification of target gene methylation with bisulfite, differences in methylation were verified, and the correlation of ChREBP methylation level with T2DM pathological features and single nucleotide polymorphism (SNP) typing was discussed. According to the prediction results of UCSC Genome Browser Home, there were two CpG islands in the promoter region of the ChREBP gene, and the first exon was selected for research. The ChREBP methylation rate was statistically higher in the research group versus the control group (P < 0.05). Age, FPG, TC, and TG were confirmed by the multiple linear regression analysis to be correlated with the ChREBP methylation rate (P < 0.05). Finally, there was no difference in ChREBP methylation level between CT- and CC-type patients at rs17145750 and rs1051921 loci (P > 0.05). Peripheral blood ChREBP methylation is elevated in T2DM patients and is closely related to age, blood glucose, and blood-lipid level, which is expected to be a new direction for future T2DM diagnosis and treatment.

The response to influenza vaccination is associated with DNA methylation-driven regulation of T cell innate antiviral pathways

Background

The effect of vaccination on the epigenome remains poorly characterized. In previous research, we identified an association between seroprotection against influenza and DNA methylation at sites associated with the RIG-1 signaling pathway, which recognizes viral double-stranded RNA and leads to a type I interferon response. However, these studies did not fully account for confounding factors including age, gender, and BMI, along with changes in cell type composition.

Results

Here, we studied the influenza vaccine response in a longitudinal cohort vaccinated over two consecutive years (2019-2020 and 2020-2021), using peripheral blood mononuclear cells and a targeted DNA methylation approach. To address the effects of multiple factors on the epigenome, we designed a multivariate multiple regression model that included seroprotection levels as quantified by the hemagglutination-inhibition (HAI) assay test.

Conclusions

Our findings indicate that 179 methylation sites can be combined as potential signatures to predict seroprotection. These sites were not only enriched for genes involved in the regulation of the RIG-I signaling pathway, as found previously, but also enriched for other genes associated with innate immunity to viruses and the transcription factor binding sites of BRD4, which is known to impact T cell memory. We propose a model to suggest that the RIG-I pathway and BRD4 could potentially be modulated to improve immunization strategies.

Tissue informative cell-free DNA methylation sites in amyotrophic lateral sclerosis

Cell-free DNA (cfDNA) is increasingly recognized as a promising biomarker candidate for disease monitoring. However, its utility in neurodegenerative diseases, like amyotrophic lateral sclerosis (ALS), remains underexplored. Existing biomarker discovery approaches are tailored to a specific disease context or are too expensive to be clinically practical. Here, we address these challenges through a new approach combining advances in molecular and computational technologies. First, we develop statistical tools to select tissue-informative DNA methylation sites relevant to a disease process of interest. We then employ a capture protocol to select these sites and perform targeted methylation sequencing. Multi-modal information about the DNA methylation patterns are then utilized in machine learning algorithms trained to predict disease status and disease progression. We applied our method to two independent cohorts of ALS patients and controls (n=192). Overall, we found that the targeted sites accurately predicted ALS status and replicated between cohorts. Additionally, we identified epigenetic features associated with ALS phenotypes, including disease severity. These findings highlight the potential of cfDNA as a non-invasive biomarker for ALS.

BayesAge: A maximum likelihood algorithm to predict epigenetic age

Introduction: DNA methylation, specifically the formation of 5-methylcytosine at the C5 position of cytosine, undergoes reproducible changes as organisms age, establishing it as a significant biomarker in aging studies. Epigenetic clocks, which integrate methylation patterns to predict age, often employ linear models based on penalized regression, yet they encounter challenges in handling missing data, count-based bisulfite sequence data, and interpretation. Methods: To address these limitations, we introduce BayesAge, an extension of the scAge methodology originally designed for single-cell DNA methylation analysis. BayesAge employs maximum likelihood estimation (MLE) for age inference, models count data using binomial distributions, and incorporates LOWESS smoothing to capture non-linear methylation-age dynamics. This approach is tailored for bulk bisulfite sequencing datasets. Results: BayesAge demonstrates superior performance compared to scAge. Notably, its age residuals exhibit no age association, offering a less biased representation of epigenetic age variation across populations. Furthermore, BayesAge facilitates the estimation of error bounds on age inference. When applied to down-sampled data, BayesAge achieves a higher coefficient of determination between predicted and actual ages compared to both scAge and penalized regression. Discussion: BayesAge presents a promising advancement in epigenetic age prediction, addressing key challenges encountered by existing models. By integrating robust statistical techniques and tailored methodologies for count-based data, BayesAge offers improved accuracy and interpretability in predicting age from bulk bisulfite sequencing datasets. Its ability to estimate error bounds enhances the reliability of age inference, thereby contributing to a more comprehensive understanding of epigenetic aging processes.

Mini-review of DNA Methylation Detection Techniques and Their Potential Applications in Disease Diagnosis, Prognosis, and Treatment

DNA methylation is the dominant epigenetic mechanism for regulating gene expression in mammals, playing crucial roles in development, differentiation, and tissue homeostasis. Aberrations in DNA methylation are closely associated with the potential onset of various diseases. Consequently, numerous DNA methylation detection techniques have been successively developed. These methods not only facilitate the exploration of disease mechanisms but also hold significant promise for the development of diagnostic and prognostic strategies. In this Perspective, we present a comprehensive overview of commonly employed DNA methylation detection techniques as well as biosensing based on their underlying analytical techniques. For its medical applications, we begin by examining the pathogenesis of different diseases and then proceed to discuss how relevant technologies are applied in the context of these specific medical conditions. Additionally, we briefly discuss the current limitations of these techniques and highlight future challenges in advancing methylation detection and analysis methodologies.

Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED)

BACKGROUND

Cell-free DNA (cfDNA) contains a large amount of molecular information that can be used for multi-cancer early detection (MCED), including changes in epigenetic status of cfDNA, such as cfDNA fragmentation profile. The fragmentation of cfDNA is non-random and may be related to cfDNA methylation. This study provides clinical evidence for the feasibility of inferring cfDNA methylation levels based on cfDNA fragmentation patterns. We performed whole-genome bisulfite sequencing and whole-genome sequencing (WGS) on both healthy individuals and cancer patients. Using the information of whole-genome methylation levels, we investigated cytosine-phosphate-guanine (CpG) cleavage profile and validated the method of predicting the methylation level of individual CpG sites using WGS data.

RESULTS

We conducted CpG cleavage profile biomarker analysis on data from both healthy individuals and cancer patients. We obtained unique or shared potential biomarkers for each group and built models accordingly. The modeling results proved the feasibility to predict the methylation status of single CpG sites in cfDNA using cleavage profile model from WGS data.

CONCLUSION

By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites. Therefore, methylation profile, a widely used epigenetic biomarker, can be obtained from a single WGS assay for MCED.

Longitudinal analysis of influenza vaccination implicates regulation of RIG-I signaling by DNA methylation

Influenza virus infection alters the promoter DNA methylation of key immune response-related genes, including type-1 interferons and proinflammatory cytokines. However, less is known about the effect of the influenza vaccine on the epigenome. We utilized a targeted DNA methylation approach to study the longitudinal effects (day 0 pre-vaccination and day 28 post-vaccination) on influenza vaccination responses in peripheral blood mononuclear cells. We found that baseline, pre-vaccination methylation profiles are associated with pre-existing, protective serological immunity. Additionally, we identified 481 sites that were differentially methylated between baseline and day 28 post-vaccination. These were enriched for genes involved in the regulation of the RIG-I signaling pathway, an important regulator of viral responses. Our results suggest that DNA methylation changes to components of the RIG-I pathway are associated with vaccine effectiveness. Therefore, immunization strategies that target this pathway may improve serological responses to influenza vaccination.

Epigenetic changes to gene pathways linked to male fertility in ex situ black-footed ferrets

Environmental variation can influence the reproductive success of species managed under human care and in the wild, yet the mechanisms underlying this phenomenon remain largely mysterious. Molecular mechanisms such as epigenetic modifiers are important in mediating the timing and progression of reproduction in humans and model organisms, but few studies have linked epigenetic variation to reproductive fitness in wildlife. Here, we investigated epigenetic variation in black-footed ferrets (Mustela nigripes), an endangered North American mammal reliant on ex situ management for survival and persistence in the wild. Despite similar levels of genetic diversity in human-managed and wild-born populations, individuals in ex situ facilities exhibit reproductive problems, such as poor sperm quality. Differences across these settings suggest that an environmentally driven decline in reproductive capacity may be occurring in this species. We examined the role of DNA methylation, one well-studied epigenetic modifier, in this emergent condition. We leveraged blood, testes, and semen samples from male black-footed ferrets bred in ex situ facilities and found tissue-type specificity in DNA methylation across the genome, although 1360 Gene Ontology terms associated with male average litter size shared functions across tissues. We then constructed gene networks of differentially methylated genomic sites associated with three different reproductive phenotypes to explore the putative biological impact of variation in DNA methylation. Sperm gene networks associated with average litter size and sperm count were functionally enriched for candidate genes involved in reproduction, development, and its regulation through transcriptional repression. We propose that DNA methylation plays an important role in regulating these reproductive phenotypes, thereby impacting the fertility of male ex situ individuals. Our results provide information into how DNA methylation may function in the alteration of reproductive pathways and phenotypes in artificial environments. These findings provide early insights to conservation hurdles faced in the protection of this rare species.

Age-associated DNA methylation changes in Xenopus frogs

Age-associated changes in DNA methylation have been characterized across various animals, but not yet in amphibians, which are of particular interest because they include widely studied model organisms. In this study, we present clear evidence that the aquatic vertebrate species Xenopus tropicalis displays patterns of age-associated changes in DNA methylation. We have generated whole-genome bisulfite sequencing (WGBS) profiles from skin samples of nine frogs representing young, mature, and old adults and characterized the gene- and chromosome-scale DNA methylation changes with age. Many of the methylation features and changes we observe are consistent with what is known in mammalian species, suggesting that the mechanism of age-related changes is conserved. Moreover, we selected a few thousand age-associated CpG sites to build an assay based on targeted DNA methylation analysis (TBSseq) to expand our findings in future studies involving larger cohorts of individuals. Preliminary results of a pilot TBSeq experiment recapitulate the findings obtained with WGBS setting the basis for the development of an epigenetic clock assay. The results of this study will allow us to leverage the unique resources available for Xenopus to study how DNA methylation relates to other hallmarks of ageing.

The methylome of buccal epithelial cells is influenced by age, sex, and physiological properties

Epigenetic modifications, particularly DNA methylation, have emerged as regulators of gene expression and are implicated in various biological processes and disease states. Understanding the factors influencing the epigenome is essential for unraveling its complexity. In this study, we aimed to identify how the methylome of buccal epithelial cells, a noninvasive and easily accessible tissue, is associated with demographic and health-related variables commonly used in clinical settings, such as age, sex, blood immune composition, hemoglobin levels, and others. We developed a model to assess the association of multiple factors with the human methylome and identify the genomic loci significantly impacted by each trait. We demonstrated that DNA methylation variation is accurately modeled by several factors. We confirmed the well-known impact of age and sex and unveiled novel clinical factors associated with DNA methylation, such as blood neutrophils, hemoglobin, red blood cell distribution width, high-density lipoprotein cholesterol, and urea. Genomic regions significantly associated with these traits were enriched in relevant transcription factors, drugs, and diseases. Among our findings, we showed that neutrophil-impacted loci were involved in neutrophil functionality and maturation. Similarly, hemoglobin-influenced sites were associated with several diseases, including aplastic anemia, and the genomic loci affected by urea were related to congenital anomalies of the kidney and urinary tract. Our findings contribute to a better understanding of the human methylome plasticity and provide insights into novel factors shaping DNA methylation patterns, highlighting their potential clinical implications as biomarkers and the importance of considering these physiological traits in future medical epigenomic investigations.NEW & NOTEWORTHY We have developed a quantitative model to assess how the human methylome is associated with several factors and to identify the genomic loci significantly impacted by each trait. We reported novel health-related factors driving DNA methylation patterns and new site-specific regulations that further elucidate methylome dynamics. Our study contributes to a better understanding of the plasticity of the human methylome and unveils novel physiological traits with a potential role in future medical epigenomic investigations.

Unlocking the secrets: the power of methylation-based cfDNA detection of tissue damage in organ systems

BACKGROUND

Detecting organ and tissue damage is essential for early diagnosis, treatment decisions, and monitoring disease progression. Methylation-based assays offer a promising approach, as DNA methylation patterns can change in response to tissue damage. These assays have potential applications in early detection, monitoring disease progression, evaluating treatment efficacy, and assessing organ viability for transplantation. cfDNA released into the bloodstream upon tissue or organ injury can serve as a biomarker for damage. The epigenetic state of cfDNA, including DNA methylation patterns, can provide insights into the extent of tissue and organ damage.

CONTENT

Firstly, this review highlights DNA methylation as an extensively studied epigenetic modification that plays a pivotal role in processes such as cell growth, differentiation, and disease development. It then presents a variety of highly precise 5-mC methylation detection techniques that serve as powerful tools for gaining profound insights into epigenetic alterations linked with tissue damage. Subsequently, the review delves into the mechanisms underlying DNA methylation changes in organ and tissue damage, encompassing inflammation, oxidative stress, and DNA damage repair mechanisms. Next, it addresses the current research status of cfDNA methylation in the detection of specific organ tissues and organ damage. Finally, it provides an overview of the multiple steps involved in identifying specific methylation markers associated with tissue and organ damage for clinical trials. This review will explore the mechanisms and current state of research on cfDNA methylation-based assay detecting organ and tissue damage, the underlying mechanisms, and potential applications in clinical practice.

Introduction of a multiplex amplicon sequencing assay to quantify DNA methylation in target cytosine markers underlying four selected epigenetic clocks

BACKGROUND

DNA methylation analysis has proven to be a powerful tool for age assessment. However, the implementation of epigenetic age prediction in diagnostics or routine forensic casework requires appropriate laboratory methods. In this study, we aimed to compare the performance of large-scale DNA methylation analysis protocols that show promise in terms of accuracy, throughput, multiplexing capacity, and high sensitivity.

RESULTS

The protocols were designed to target a predefined panel of 161 genomic CG/CA sites from four known estimators of epigenetic age-related parameters, optimized and validated using artificially methylated controls or blood samples. We successfully targeted 96% of these loci using two enrichment protocols: Ion AmpliSeq™, an amplicon-based method integrated with Ion Torrent S5, and SureSelectXT Methyl-Seq, a hybridization-based method followed by MiSeq FGx sequencing. Both protocols demonstrated high accuracy and robustness. Although hybridization assays have greater multiplexing capabilities, the best overall performance was observed for the amplicon-based protocol with the lowest variability in DNA methylation at 25 ng of starting DNA, mean observed marker coverage of ~ 6.7 k reads, and accuracy of methylation quantification with a mean absolute difference between observed and expected methylation beta value of 0.054. The Ion AmpliSeq method correlated strongly with genome-scale EPIC microarray data (R = 0.91) and showed superiority in terms of methylation measurement accuracy. Method-to-method bias was accounted for by the use of linear transformation, which provided a highly accurate prediction of calendar age with a mean absolute error of less than 5 years for the VISAGE and Hannum age clocks used. The pace of aging (PoAm) and the mortality risk score (MRS) estimators included in our panel represent next-generation clocks, were found to have low to moderate correlations with the VISAGE and Hannum models (R < 0.75), and thus may capture different aspects of epigenetic aging.

CONCLUSIONS

We propose a laboratory tool that allows the quantification of DNA methylation in cytosines underlying four different clocks, thus providing broad information on epigenetic aging while maintaining a reasonable number of CpG markers, opening the way to a wide range of applications in forensics, medicine, and healthcare.

Exploring the crop epigenome: a comparison of DNA methylation profiling techniques

Epigenetic modifications play a vital role in the preservation of genome integrity and in the regulation of gene expression. DNA methylation, one of the key mechanisms of epigenetic control, impacts growth, development, stress response and adaptability of all organisms, including plants. The detection of DNA methylation marks is crucial for understanding the mechanisms underlying these processes and for developing strategies to improve productivity and stress resistance of crop plants. There are different methods for detecting plant DNA methylation, such as bisulfite sequencing, methylation-sensitive amplified polymorphism, genome-wide DNA methylation analysis, methylated DNA immunoprecipitation sequencing, reduced representation bisulfite sequencing, MS and immuno-based techniques. These profiling approaches vary in many aspects, including DNA input, resolution, genomic region coverage, and bioinformatics analysis. Selecting an appropriate methylation screening approach requires an understanding of all these techniques. This review provides an overview of DNA methylation profiling methods in crop plants, along with comparisons of the efficacy of these techniques between model and crop plants. The strengths and limitations of each methodological approach are outlined, and the importance of considering both technical and biological factors are highlighted. Additionally, methods for modulating DNA methylation in model and crop species are presented. Overall, this review will assist scientists in making informed decisions when selecting an appropriate DNA methylation profiling method.

Association of DNA methylation with energy and fear-related behaviors in canines

Introduction

Behavioral traits are influenced by gene by environment interactions. To study the genetic and epigenetic components of behavior, we analyzed whether dog behavioral traits could be predicted by their DNA methylation and genotypes.

Methods

We conducted an analysis on dog behaviors such as sociability, trainability and energy as measured by Canine Behavioral and Research Assessment Questionnaire (C-BARQ) behavioral surveys paired with buccal swabs from 46 dogs. Previously we used targeted bisulfite sequencing to analyze DNA methylation and collected genotype data from over 1,500 single nucleotide polymorphisms (SNPs). Owner-reported C-BARQ responses were used to quantify 14 behavioral trait values.

Results

Using Partial Least Squares (PLS) Regression analysis we found behavioral traits such as energy, attachment/attention-seeking, non-social fear, and stranger-directed fear to be significantly associated with DNA methylation across 3,059 loci. After we adjusted for age as a confounding variable, energy and stranger-directed fear remained significantly associated with methylation. We found that most behavioral traits were not predictable by our limited set of SNPs.

Discussion

By identifying individual genes whose methylation is significantly associated with behavioral traits, we generate hypotheses about possible mechanisms involved in behavioral regulation. Overall, our study extends previous work in behavioral epigenetics, shows that canine behaviors are predictable by DNA methylation, and serves as a proof of concept for future studies in behavioral epigenetics.

DNA methylation profiles in pneumonia patients reflect changes in cell types and pneumonia severity

Immune cell-type composition changes with age, potentially weakening the response to infectious diseases. Profiling epigenetics marks of immune cells can help us understand the relationship with disease severity. We therefore leveraged a targeted DNA methylation method to study the differences in a cohort of pneumonia patients (both COVID-19 positive and negative) and unaffected individuals from peripheral blood.This approach allowed us to predict the pneumonia diagnosis with high accuracy (AUC = 0.92), and the PCR positivity to the SARS-CoV-2 viral genome with moderate, albeit lower, accuracy (AUC = 0.77). We were also able to predict the severity of pneumonia (PORT score) with an R² = 0.69. By estimating immune cellular frequency from DNA methylation data, patients under the age of 65 positive to the SARS-CoV-2 genome (as revealed by PCR) showed an increase in T cells, and specifically in CD8+ cells, compared to the negative control group. Conversely, we observed a decreased frequency of neutrophils in the positive compared to the negative group. No significant difference was found in patients over the age of 65. The results suggest that this DNA methylation-based approach can be used as a cost-effective and clinically useful biomarker platform for predicting pneumonias and their severity.

Age group DNA methylation differences in lemon sharks ( Negaprion brevirostris ): Implications for future age estimation tools

Age information is often non‐existent for most shark populations due to a lack of measurable physiological and morphological traits that can be used to estimate age. Recently, epigenetic clocks have been found to accurately estimate age for mammals, birds, and fish. However, since these clocks rely, among other things, on the availability of reference genomes, their application is hampered in non‐traditional model organisms lacking such molecular resources. The technique known as Methyl‐Sensitive Amplified Polymorphism (MSAP) has emerged as a valid alternative for studying DNA methylation biomarkers when reference genome information is missing, and large numbers of samples need to be processed. Accordingly, the MSAP technique was used in the present study to characterize global DNA methylation patterns in lemon sharks from three different age groups (juveniles, subadults, and adults). The obtained results reveal that, while MSAP analyses lack enough resolution as a standalone approach to infer age in these organisms, the global DNA methylation patterns observed using this technique displayed significant differences between age groups. Overall, these results confer that DNA methylation does change with age in sharks like what has been seen for other vertebrates and that MSAP could be useful as part of an epigenetics pipeline to infer the broad range of ages found in large samples sizes.

Novel multiplex stool-based assay for the detection of early-stage colon cancer in a Chinese population

BACKGROUND

Stool DNA (sDNA) methylation analysis is a promising, noninvasive approach for colorectal cancer screening; however, reliable biomarkers for detecting early-stage colon cancer (ECC) are lacking, particularly in the Chinese population.

AIM

To identify a novel stool-based assay that can improve the effectiveness of ECC screening.

METHODS

A blinded case-control study was performed using archived stool samples from 125 ECC patients, and 125 control subjects with normal colonoscopy. The cohort was randomly divided into training and test sets at a 1.5:1 ratio. Targeted bisulfite sequencing (TBSeq) was conducted on five pairs of preoperative and postop-erative sDNA samples from ECC patients to identify DNA methylation biomarkers, which were validated using pyrosequencing. By logistic regression analysis, a multiplex stool-based assay was developed in the training set, and the detection performance was further assessed in the test set and combined set. The χ² test was used to investigate the association of detection sensitivity with clinico-pathological features.

RESULTS

Following TBSeq, three hypermethylated cytosine-guanine sites were selected as biomarkers, including paired box 8, Ras-association domain family 1 and secreted frizzled-related protein 2, which differed between the groups and were involved in important cancer pathways. An sDNA panel containing the three biomarkers was constructed with a logistic model. Receiver operating characteristic (ROC) analysis revealed that this panel was superior to the fecal immunochemical test (FIT) or serum carcinoembryonic antigen for the detection of ECC. We further found that the combination of the sDNA panel with FIT could improve the screening effectiveness. In the combined set, the sensitivity, specificity and area under the ROC curve for this multiplex assay were 80.0%, 93.6% and 0.918, respectively, and the performance remained excellent in the subgroup analysis by tumor stage. In addition, the detection sensitivity did not differ with tumor site, tumor stage, histological differentiation, age or sex, but was significantly higher in T4 than in T1-3 stage tumors (P = 0.041).

CONCLUSION

We identified a novel multiplex stool-based assay combining sDNA methylation biomarkers and FIT, which could detect ECC with high sensitivity and specificity throughout the colon, showing a promising application perspective.

DNA Methylation-Based Prediction of Post-operative Atrial Fibrillation

Background

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. In this pilot study of epigenetic precision medicine in the perioperative period, we carried out bisulfite sequencing to measure DNA methylation status in blood collected from patients prior to cardiac surgery to identify biosignatures of POAF.

Methods

We enrolled 221 patients undergoing cardiac surgery in this prospective observational study. DNA methylation measurements were obtained from blood samples drawn from awake patients prior to surgery. After controlling for clinical and methylation covariates, we analyzed DNA methylation loci in the discovery cohort of 110 patients for association with POAF. We also constructed predictive models for POAF using clinical and DNA methylation data. We subsequently performed targeted analyses of a separate cohort of 101 cardiac surgical patients to measure the methylation status solely of significant methylation loci in the discovery cohort.

Results

A total of 47 patients in the discovery cohort (42.7%) and 43 patients in the validation cohort (42.6%) developed POAF. We identified 12 CpGs that were statistically significant in the discovery cohort after correcting for multiple hypothesis testing. Of these sites, 6 were amenable to targeted bisulfite sequencing and chr16:24640902 was statistically significant in the validation cohort. In addition, the methylation POAF prediction model had an AUC of 0.79 in the validation cohort.

Conclusions

We have identified DNA methylation biomarkers that can predict future occurrence of POAF associated with cardiac surgery. This research demonstrates the use of precision medicine to develop models combining epigenomic and clinical data to predict disease.

The effects of age, sex, weight, and breed on canid methylomes

Unlike genomes, which are static throughout the lifespan of an organism, DNA methylomes are dynamic. To study these dynamics, we developed quantitative models that measure the effect of multiple factors on DNA methylomes including, age, sex, weight, and genetics. We conducted our study in canids, which prove to be an ideal species to assess epigenetic moderators due to their extreme variability in size and well-characterized genetic structure. We collected buccal swabs from 217 canids (207 domestic dogs and 10 grey wolves) and used targeted bisulphite sequencing to measure methylomes. We also measured genotypes at over one thousand single nucleotide polymorphisms (SNPs). As expected, we found that DNA methylomes are strongly associated with age, enabling the construction of epigenetic clocks. However, we also identify novel associations between methylomes and sex, weight, and sterilization status, leading to accurate models that predict these factors. Methylomes are also affected by genetics, and we observe multiple associations between SNP loci and methylated CpGs. Finally, we show that several factors moderate the relationship between epigenetic ages and real ages, such as body weight, which increases epigenetic ageing. In conclusion, we demonstrate that the plasticity of DNA methylomes is impacted by myriad genetics and physiological factors, and that DNA methylation biomarkers are accurate predictors of age, sex and sterilization status.

Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier

We systemically identified tuberculosis (TB)-related DNA methylation biomarkers and further constructed classifiers for TB diagnosis. TB-related DNA methylation datasets were searched through October 3, 2020. Limma and DMRcate were employed to identify differentially methylated probes (DMPs) and regions (DMRs). Machine learning methods were used to construct classifiers. The performance of the classifiers was evaluated in discovery datasets and a prospective independent cohort. Eighty-nine DMPs and 24 DMRs were identified based on 67 TB patients and 45 healthy controls from 4 datasets. Nine and three DMRs were selected by elastic net regression and logistic regression, respectively. Among the selected DMRs, two regions (chr3: 195635643-195636243 and chr6: 29691631-29692475) were differentially methylated in the independent cohort (p = 4.19 × 10^-5 and 0.024, respectively). Among the ten classifiers, the 3-DMR logistic regression classifier exhibited the strongest performance. The sensitivity, specificity, and area under the curve were, respectively, 79.1%, 84.4%, and 0.888 in the discovery datasets and 64.5%, 90.3%, and 0.838 in the independent cohort. The differential diagnostic ability of this classifier was also assessed. Collectively, these data showed that DNA methylation might be a promising TB diagnostic biomarker. The 3-DMR logistic regression classifier is a potential clinical tool for TB diagnosis, and further validation is needed.

A mammalian methylation array for profiling methylation levels at conserved sequences

Infinium methylation arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the mammalian methylation array, a single custom array that measures up to 36k CpGs per species that are well conserved across many mammalian species. We designed a set of probes that can tolerate specific cross-species mutations. We annotate the array in over 200 species and report CpG island status and chromatin states in select species. Calibration experiments demonstrate the high fidelity in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of conserved cytosines facilitating the development of epigenetic biomarkers, and it increases the probability that biological insights gained in one species will translate to others.

Methods to probe DNA methylation in the majority of non-human mammals are lacking. Here the authors developed a Mammalian Methylation Array that includes 36k well-conserved CpGs in mammals which will facilitate cross-species comparisons. They annotate the conserved CpGs in > 200 species. The array allows one to measure methylation in all mammalian species including unsequenced ones.

Can Forest Trees Cope with Climate Change?-Effects of DNA Methylation on Gene Expression and Adaptation to Environmental Change

Epigenetic modifications, including chromatin modifications and DNA methylation, play key roles in regulating gene expression in both plants and animals. Transmission of epigenetic markers is important for some genes to maintain specific expression patterns and preserve the status quo of the cell. This article provides a review of existing research and the current state of knowledge about DNA methylation in trees in the context of global climate change, along with references to the potential of epigenome editing tools and the possibility of their use for forest tree research. Epigenetic modifications, including DNA methylation, are involved in evolutionary processes, developmental processes, and environmental interactions. Thus, the implications of epigenetics are important for adaptation and phenotypic plasticity because they provide the potential for tree conservation in forest ecosystems exposed to adverse conditions resulting from global warming and regional climate fluctuations.

BiSulfite Bolt: A bisulfite sequencing analysis platform

Background

Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries.

Findings

We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy.

Conclusion

BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

Human DNA methylation signatures differentiate persistent from resolving MRSA bacteremia

Persistent methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is life threatening and occurs in up to 30% of MRSA bacteremia cases despite appropriate antimicrobial therapy. Isolates of MRSA that cause antibiotic-persistent methicillin-resistant S. aureus bacteremia (APMB) typically have in vitro antibiotic susceptibilities equivalent to those causing antibiotic-resolving methicillin-resistant S. aureus bacteremia (ARMB). Thus, persistence reflects host-pathogen interactions occurring uniquely in context of antibiotic therapy in vivo. However, host factors and mechanisms involved in APMB remain unclear. We compared DNA methylomes in circulating immune cells from patients experiencing APMB vs. ARMB. Overall, methylation signatures diverged in the distinct patient cohorts. Differentially methylated sites intensified proximate to transcription factor binding sites, primarily in enhancer regions. In APMB patients, significant hypomethylation was observed in binding sites for CCAAT enhancer binding protein-β (C/EBPβ) and signal transducer/activator of transcription 1 (STAT1). In contrast, hypomethylation in ARMB patients localized to glucocorticoid receptor and histone acetyltransferase p300 binding sites. These distinct methylation signatures were enriched in neutrophils and achieved a mean area under the curve of 0.85 when used to predict APMB using a classification model. These findings validated by targeted bisulfite sequencing (TBS-seq) differentiate epigenotypes in patients experiencing APMB vs. ARMB and suggest a risk stratification strategy for antibiotic persistence in patients treated for MRSA bacteremia.