Identify Methylation Sites Research Articles

Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework

DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci’s biological significance.

Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach.

DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OCpatients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC. Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified. A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC. This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understandthe mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach. The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression

BackgroundChronological and biological age correlate with DNA methylation levels at specific sites in the genome. Linear combinations of multiple methylation sites, termed epigenetic clocks, can inform us the chronological age and predict multiple health-related outcomes. However, why some sites correlating with lifespan, healthspan, or specific medical conditions remain poorly understood. Kidney fibrosis is the common pathway for chronic kidney disease, which affects 10% of European and US populations.ResultsHere we identify epigenetic clocks and methylation sites that correlate with kidney function. Moreover, we identify methylation sites that have a unique methylation signature in the kidney. Methylation levels in majority of these sites correlate with kidney state and function. When kidney function deteriorates, all of these sites regress toward the common methylation pattern observed in other tissues. Interestingly, while the majority of sites are less methylated in the kidney and become more methylated with loss of function, a fraction of the sites are highly methylated in the kidney and become less methylated when kidney function declines. These methylation sites are enriched for specific transcription-factor binding sites. In a large subset of sites, changes in methylation patterns are accompanied by changes in gene expression in kidneys of chronic kidney disease patients.ConclusionsThese results support the information theory of aging, and the hypothesis that the unique tissue identity, as captured by methylation patterns, is lost as tissue function declines. However, this information loss is not random, but guided toward a baseline that is dependent on the genomic loci.Significance statementDNA methylation at specific sites accurately reflects chronological and biological age. We identify sites that have a unique methylation pattern in the kidney. Methylation levels in the majority of these sites correlate with kidney state and function. Moreover, when kidney function deteriorates, all of these sites regress toward the common methylation pattern observed in other tissues. Thus, the unique methylation signature of the kidney is degraded, and epigenetic information is lost, when kidney disease progresses. These methylation sites are enriched for specific and methylation-sensitive transcription-factor binding sites, and associated genes show disease-dependent changes in expression. These results support the information theory of aging, and the hypothesis that the unique tissue identity, as captured by methylation patterns, is lost as tissue function declines.

Value of altered methylation patterns of genes RANBP3, LCP2 and GRAP2 in cfDNA in breast cancer diagnosis.

The purpose of this study was to investigate the potential of plasma cfDNA methylation patterns in reflecting tumour methylation changes, focusing on three candidate sites, cg02469161, cg11528914, and cg20131654. These sites were selected for verification, with a particular emphasis on their association with breast cancer. We conducted a comprehensive analysis of 850k whole-methylation sequencing data to identify potential markers for breast cancer detection. Subsequently, we investigated the methylation status of the genes Ran-binding protein 3 (RANBP3), Lymphocyte cytoplasmic protein 2 (LCP2), and GRB2 related adaptor protein 2 (GRAP2), situated at the specified sites, using cancer and canceradjacent tissues from 17 breast cancer patients. We also examined the methylation patterns in different molecular subtypes and pathological grades of breast cancer. Additionally, we compared the methylation levels of these genes in plasma cfDNA to their performance in tissues. Our analysis revealed that RANBP3, LCP2, and GRAP2 genes exhibited significant methylation differences between cancer and cancer-adjacent tissues. In breast cancer, these genes displayed diagnostic efficiencies of 91.0%, 90.6%, and 92.2%, respectively. Notably, RANBP3 showed a tendency towards lower methylation in HR+ breast cancer, and LCP2 methylation was correlated with tumour malignancy. Importantly, the methylation levels of these three genes in plasma cfDNA closely mirrored their tissue counterparts, with diagnostic efficiencies of 83.3%, 83.9%, and 77.6% for RANBP3, LCP2, and GRAP2, respectively. Our findings propose that the genes RANBP3, LCP2, and GRAP2, located at the identified methylation sites, hold significant potential as molecular markers in blood for the supplementary diagnosis of breast cancer. This study lays the groundwork for a more in-depth investigation into the changes in gene methylation patterns in circulating free DNA (cfDNA) for the early detection not only of breast cancer but also for various other types of cancer.

Exploring the Functional Basis of Epigenetic Aging in Relation to Body Fat Phenotypes in the Norfolk Island Cohort.

DNA methylation is an epigenetic factor that is modifiable and can change over a lifespan. While many studies have identified methylation sites (CpGs) related to aging, the relationship of these to gene function and age-related disease phenotypes remains unclear. This research explores this question by testing for the conjoint association of age-related CpGs with gene expression and the relation of these to body fat phenotypes. The study included blood-based gene transcripts and intragenic CpG methylation data from Illumina 450 K arrays in 74 healthy adults from the Norfolk Island population. First, a series of regression analyses were performed to detect associations between gene transcript level and intragenic CpGs and their conjoint relationship with age. Second, we explored how these age-related expression CpGs (eCpGs) correlated with obesity-related phenotypes, including body fat percentage, body mass index, and waist-to-hip ratio. We identified 35 age-related eCpGs associated with age. Of these, ten eCpGs were associated with at least one body fat phenotype. Collagen Type XI Alpha 2 Chain (COL11A2), Complement C1s (C1s), and four and a half LIM domains 2 (FHL2) genes were among the most significant genes with multiple eCpGs associated with both age and multiple body fat phenotypes. The COL11A2 gene contributes to the correct assembly of the extracellular matrix in maintaining the healthy structural arrangement of various components, with the C1s gene part of complement systems functioning in inflammation. Moreover, FHL2 expression was upregulated under hypermethylation in both blood and adipose tissue with aging. These results suggest new targets for future studies and require further validation to confirm the specific function of these genes on body fat regulation.

Quantitative analysis of a novel DNA hypermethylation panel using bronchial specimen for lung cancer diagnosis.

Non-diagnostic findings are common in transbronchial lung biopsy (TBLB) and endobronchial ultrasound-guided transbronchial lung biopsy (EBUS-TBLB). One of the challenges is to improve the detection of lung cancer using these techniques. To address this issue, we utilized an 850 K methylation chip to identify methylation sites that distinguish malignant from benign lung nodules. Our study found that a combination of HOXA7, SHOX2 and SCT methylation analysis has the best diagnostic yield in bronchial washing (sensitivity: 74.1%; AUC: 0.851) and brushing samples (sensitivity: 86.1%; AUC: 0.915). We developed a kit comprising these three genes and validated it in 329 unique bronchial washing samples, 397 unique brushing samples and 179 unique patients with both washing and brushing samples. The panel's accuracy in lung cancer diagnosis was 86.9%, 91.2% and 95% in bronchial washing, brushing and washing + brushing samples, respectively. When combined with cytology, rapid on-site evaluation (ROSE), and histology, the panel's sensitivity in lung cancer diagnosis was 90.8% and 95.8% in bronchial washing and brushing samples, respectively, and 100% in washing + brushing samples. Our findings suggest that quantitative analysis of the three-gene panel can improve the diagnosis of lung cancer using bronchoscopy.

Research on the pathological mechanism of rectal adenocarcinoma based on DNA methylation.

Colorectal cancer is one of the 3 most common cancers worldwide. In this study, a weighted network-based analysis method was proposed to explore the pathological mechanisms and prognostic targets of rectal adenocarcinoma (READ) at the deoxyribonucleic acid (DNA) methylation level. In this study, we downloaded clinical information and DNA methylation data from The Cancer Genome Atlas database. Differentially methylated gene analysis was used to identify the differential methylated genes in READ. Canonical correlation analysis was used to construct the weighted gene regulatory network for READ. Multilevel analysis and association analyses between gene modules and clinical information were used to mine key modules related to tumor metastasis evaluation. Genetic significance analysis was used to identify methylation sites in key modules. Finally, the importance of these methylation sites was confirmed using survival analysis. DNA methylation datasets from 90 cancer tissue samples and 6 paracancerous tissue samples were selected. A weighted gene regulatory network was constructed, and a multilevel algorithm was used to divide the gene co-expression network into 20 modules. From gene ontology enrichment analysis, characteristic M was related to biological processes such as the chemotaxis of fibroblast growth factors and the activation and regulation of immune cells etc and characteristic N was associated with the regulation of cytoskeleton formation, mainly microtubules and flagella, regulation of synapses, and regulation of cell mitosis. Based on the results of survival analysis, 7 key methylation sites were found closely correlated to the survival rate of READ, such as cg04441191 (microtubule-associated protein 4 [MAP4]), cg05658717 (KSR2), cg09622330 (GRIN2A), cg10698404 (YWHAG), cg17047993 (SPAG9), cg24504843 (CEP135), and cg24531267 (CEP250). Mutational and transcriptomic level studies revealed significant differences in DNA methylation, single nucleotide polymorphism, and transcript levels between YWHAG and MAP4 in normal tissues compared to tumor tissues, and differential expression of the 2 proteins in immunohistochemistry. Therefore, potential targeting drugs were screened for these 2 proteins for molecular docking, and artenimol was found to bind to MAP4 protein and 27-hydroxycholesterol to YWHAG. Our study found that key methylation sites played an important role in tumor metastasis and were associated with the prognosis of READ. Mutations and methylation may jointly regulate the transcription and translation of related genes, which in turn affect cancer progression. This may provide some new potential therapeutic targets and thoughts for the prognosis of READ.

MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction.

Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism, and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning-based methods have been proposed to identify DNA methylation, and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep learning framework for predicting DNA methylation sites, which is based on 5 popular transformer-based language models. The framework identifies methylation sites for 3 different types of DNA methylation: N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the "pretrain and fine-tune" paradigm. Pretraining is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA methylation status of each type. The 5 models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we argue that the model captures characteristic differences between different species that are relevant for methylation. This work demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source, and we provide a web server that implements the approach.

Genome-wide screening identifies DNA methylation sites that regulate the blood proteome.

Background: Identifying DNA methylation sites that regulate the blood proteome is important for biomedical purposes. Materials & methods: Herethe authors performed a genome-wide search to find DNA methylation sites that impact proteins. Results:The authors identified 165 methylation sites associated with 138 proteins. The authors noted hotspot genomic regions that control the levels of several proteins. For example, methylation of the ABO locus impacted37proteins and contributedto cardiometabolic comorbidities, including the severity of SARS-CoV-2 infection. The authors made these findings publicly available as a Unix softwarethat identifies methylation sites that cause disease and reveals the underlying proteins. The authors underlined the software applicationby showing that components of innate immunity contribute to systolic blood pressure. Conclusion: This study provides a catalog of DNA methylation sites that regulate the proteome, and the results are available as freeware for biological insight.

A brief review of machine learning methods for RNA methylation sites prediction.

Thanks to the tremendous advancement of deep sequencing and large-scale profiling, epitranscriptomics has become a rapidly growing field. As one of the most important parts of epitranscriptomics, ribonucleic acid (RNA) methylation has been focused on for years for its fundamental role in regulating the many aspects of RNA function. Thanks to the big data generated in sequencing, machine learning methods have been developed for efficiently identifying methylation sites. In this review, we comprehensively explore machine learning based approaches for predicting 10 types of methylation of RNA, which include m6A, m5C, m7G, 5hmC, m1A, m5U, m6Am, and so on. Firstly, we reviewed three main aspects of machine learning which are data, features and learning algorithms. Then, we summarized all the methods that have been used to predict the 10 types of methylation. Furthermore, the emergent methods which were designed to predict multiple types of methylation were also reviewed. Finally, we discussed the future perspectives for RNA methylation sites prediction.

Epigenetic aging: Biological age prediction and informing a mechanistic theory of aging.

Numerous studies have shown that epigenetic age-an individual's degree of aging based on patterns of DNA methylation-can be computed and is associated with an array of factors including diet, lifestyle, genetics, and disease. One can expect that still further associations will emerge with additional aging research, but to what end? Prediction of age was an important first step, but-in our view-the focus must shift from chasing increasingly accurate age computations to understanding the links between the epigenome and the mechanisms and physiological changes of aging. Here, we outline emerging areas of epigenetic aging research that prioritize biological understanding and clinical application. First, we survey recent progress in epigenetic clocks, which are beginning to predict not only chronological age but aging outcomes such as all-cause mortality and onset of disease, or which integrate aging signals across multiple biological processes. Second, we discuss research that exemplifies how investigation of the epigenome is building a mechanistic theory of aging and informing clinical practice. Such examples include identifying methylation sites and the genes most strongly predictive of aging-a subset of which have shown strong potential as biomarkers of neurodegenerative disease and cancer; relating epigenetic clock predictions to hallmarks of aging; and using longitudinal studies of DNA methylation to characterize human disease, resulting in the discovery of epigenetic indications of type 1 diabetes and the propensity for psychotic experiences.

DNA Methylation Mediates the Association Between Individual and Neighborhood Social Disadvantage and Cardiovascular Risk Factors.

Low socioeconomic status (SES) and living in a disadvantaged neighborhood are associated with poor cardiovascular health. Multiple lines of evidence have linked DNA methylation to both cardiovascular risk factors and social disadvantage indicators. However, limited research has investigated the role of DNA methylation in mediating the associations of individual- and neighborhood-level disadvantage with multiple cardiovascular risk factors in large, multi-ethnic, population-based cohorts. We examined whether disadvantage at the individual level (childhood and adult SES) and neighborhood level (summary neighborhood SES as assessed by Census data and social environment as assessed by perceptions of aesthetic quality, safety, and social cohesion) were associated with 11 cardiovascular risk factors including measures of obesity, diabetes, lipids, and hypertension in 1,154 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). For significant associations, we conducted epigenome-wide mediation analysis to identify methylation sites mediating the relationship between individual/neighborhood disadvantage and cardiovascular risk factors using the JT-Comp method that assesses sparse mediation effects under a composite null hypothesis. In models adjusting for age, sex, race/ethnicity, smoking, medication use, and genetic principal components of ancestry, epigenetic mediation was detected for the associations of adult SES with body mass index (BMI), insulin, and high-density lipoprotein cholesterol (HDL-C), as well as for the association between neighborhood socioeconomic disadvantage and HDL-C at FDR q < 0.05. The 410 CpG mediators identified for the SES-BMI association were enriched for CpGs associated with gene expression (expression quantitative trait methylation loci, or eQTMs), and corresponding genes were enriched in antigen processing and presentation pathways. For cardiovascular risk factors other than BMI, most of the epigenetic mediators lost significance after controlling for BMI. However, 43 methylation sites showed evidence of mediating the neighborhood socioeconomic disadvantage and HDL-C association after BMI adjustment. The identified mediators were enriched for eQTMs, and corresponding genes were enriched in inflammatory and apoptotic pathways. Our findings support the hypothesis that DNA methylation acts as a mediator between individual- and neighborhood-level disadvantage and cardiovascular risk factors, and shed light on the potential underlying epigenetic pathways. Future studies are needed to fully elucidate the biological mechanisms that link social disadvantage to poor cardiovascular health.

A methylation study implicates the rewiring of brain neural circuits during puberty in the emergence of sex differences in depression symptoms.

Women are 1.5-3 times more likely to suffer from depression than men. This sex bias first emerges during puberty and then persists across the reproductive years. As the cause remains largely elusive, we performed a methylation-wide association study (MWAS) to generate novel hypotheses. We assayed nearly all 28 million possible methylation sites in blood in 595 blood samples from 487 participants aged 9-17. MWASs were performed to identify methylation sites associated with increasing sex differences in depression symptoms as a function of pubertal stage. Epigenetic deconvolution was applied to perform analyses on a cell-type specific level. In monocytes, a substantial number of significant associations were detected after controlling the FDR at 0.05. These results could not be explained by plasma testosterone/estradiol or current/lifetime trauma. Our top results in monocytes were significantly enriched (ratio of 2.48) for genes in the top of a large genome-wide association study (GWAS) meta-analysis of depression and neurodevelopment-related Gene Ontology (GO) terms that remained significant after correcting for multiple testing. Focusing on our most robust findings (70 genes overlapping with the GWAS meta-analysis and the significant GO terms), we find genes coding for members of each of the major classes of axon guidance molecules (netrins, slits, semaphorins, ephrins, and cell adhesion molecules). Many of these genes were previously implicated in rodent studies of brain development and depression-like phenotypes, as well as human methylation, gene expression and GWAS studies. Our study suggests that the emergence of sex differences in depression may be related to the differential rewiring of brain circuits between boys and girls during puberty.

Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity.

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

HSM6AP: a high-precision predictor for the Homo sapiens N6-methyladenosine (m^6 A) based on multiple weights and feature stitching

ABSTRACT Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information about human genome and machine learning, this paper discusses the effect of the fusion sequence and gene-level feature extraction on the accuracy of methylation site recognition. The significant limitation of existing computing tools was exposed by discovered of new features. (1) Most prediction models are based solely on sequence features and use SVM or random forest as classification methods. (2) Limited by the number of samples, the model may not achieve good performance. In order to establish a better prediction model for methylation sites, we must set specific weighting strategies for training samples and find more powerful and informative feature matrices to establish a comprehensive model. In this paper, we present HSM6AP, a high-precision predictor for the Homo sapiens N6-methyladenosine () based on multiple weights and feature stitching. Compared with existing methods, HSM6AP samples were creatively weighted during training, and a wide range of features were explored. Max-Relevance-Max-Distance (MRMD) is employed for feature selection, and the feature matrix is generated by fusing a single feature. The extreme gradient boosting (XGBoost), an integrated machine learning algorithm based on decision tree, is used for model training and improves model performance through parameter adjustment. Two rigorous independent data sets demonstrated the superiority of HSM6AP in identifying methylation sites. HSM6AP is an advanced predictor that can be directly employed by users (especially non-professional users) to predict methylation sites. Users can access our related tools and data sets at the following website: http://lab.malab.cn/~lijing/HSM6AP.html The codes of our tool can be publicly accessible at https://github.com/lijingtju/HSm6AP.git

Epigenetic profiling of Italian patients identified methylation sites associated with hereditary transthyretin amyloidosis

Hereditary transthyretin (TTR) amyloidosis (hATTR) is a rare life-threatening disorder caused by amyloidogenic coding mutations located in TTR gene. To understand the high phenotypic variability observed among carriers of TTR disease-causing mutations, we conducted an epigenome-wide association study (EWAS) assessing more than 700,000 methylation sites and testing epigenetic difference of TTR coding mutation carriers vs. non-carriers. We observed a significant methylation change at cg09097335 site located in Beta-secretase 2 (BACE2) gene (standardized regression coefficient = −0.60, p = 6.26 × 10–8). This gene is involved in a protein interaction network enriched for biological processes and molecular pathways related to amyloid-beta metabolism (Gene Ontology: 0050435, q = 0.007), amyloid fiber formation (Reactome HSA-977225, q = 0.008), and Alzheimer’s disease (KEGG hsa05010, q = 2.2 × 10–4). Additionally, TTR and BACE2 share APP (amyloid-beta precursor protein) as a validated protein interactor. Within TTR gene region, we observed that Val30Met disrupts a methylation site, cg13139646, causing a drastic hypomethylation in carriers of this amyloidogenic mutation (standardized regression coefficient = −2.18, p = 3.34 × 10–11). Cg13139646 showed co-methylation with cg19203115 (Pearson’s r2 = 0.32), which showed significant epigenetic differences between symptomatic and asymptomatic carriers of amyloidogenic mutations (standardized regression coefficient = −0.56, p = 8.6 × 10–4). In conclusion, we provide novel insights related to the molecular mechanisms involved in the complex heterogeneity of hATTR, highlighting the role of epigenetic regulation in this rare disorder.

Epigenome-wide association scan identifies methylation sites associated with HIV infection.

Aim: To investigate the role of epigenetics in HIV pathophysiology. Materials & methods: We conducted an epigenome-wide association scan on HIV infection status among people who inject drugs in the AIDS Linked to the IntraVenous Experience study with primary (n=397) and validation samples (n=390). DNA methylation from blood was measured by the Illumina EPIC BeadChip. We controlled for cell type heterogeneity by HIV status. Results: HIV infection status was associated (p<10-8) with DNA methylation at 49 CpG sites. Sites were enriched in response to virus, interferon signaling pathway, etc. Among these sites, discovery and validation t-statistics were highly correlated (r=0.96). Conclusion: In a cohort of people who inject drugs, HIV status was associated with differential DNA methylation at biologically meaningful sites.

Abstract 149: Non-small cell lung cancer (NSCLC) detection by DNA methylation profiling and computational deconvolution

Abstract Purpose: Early detection of pulmonary malignancies is critical to achieving positive treatment outcomes. However, due to the minimal symptom burden often associated with early-stage neoplasms, in addition to the cost and limitations of advanced imaging modalities, detection of disease is often delayed until nodal or metastatic spread has occurred. Ongoing cellular turnover releases DNA into systemic circulation where it is able to achieve a steady state level. We postulated that we could identify methylation sites specific to pulmonary Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AD) and then computationally determine the presence or absence of neoplastic signatures from a heterogeneous tissue sample, such as is found in cell free DNA isolated from plasma. Methods: We designed a computational framework for the detection of informative methylation sites based on an iterative Euclidean Distance algorithm. After generation of a reference matrix comprised of 25 cells of origin, SCC and AD (via 444 biopsy-derived methylomes), we sought informative methylation sites which had unique expression patterns among the tested cell types. To quantify the reliability of this approach, we generated synthetic In sillico “plasma” samples comprised of varying compositions of SCC, AD and whole blood methylation data which we then deconvolved using a NNLS algorithm. Results: We identified 200 informative sites out of 390,000 tested. Based on the 200 identified informative sites, we were able to define a Euclidean Space with robust separation of individual cell types (L2&amp;gt;5). Pairings of CD4+/CD8+ T-Cells and SCC/AD had the closest Euclidean distances, likely related to cellular origin and biologic similarities. However, we accurately distinguished between these cell type pairs in &amp;gt;95% of In silico experiments. Additionally, we demonstrated accurate detection of neoplastic signature within synthetic heterogeneous mixtures ranging from .1 to 100% of neoplastic “spike-in” (PearsonR =.92, p=1.5e-42). Going forward, we hope to further define the operating characteristics of this approach In vivo and develop its capability as we look to recapitulate our results with plasma samples from patients with and without pulmonary malignancies. Conclusion: Here, we describe a computational framework for the detection of pulmonary malignancy via methylation signatures. Using this framework we identified 200 informative methylation sites which allow for In silico deconvolution and detection of NSCLC within heterogeneously complex samples. Citation Format: Nicholas J. Hornstein, Zorawar S. Noor, Matteo Pellegrini, Edward Garon. Non-small cell lung cancer (NSCLC) detection by DNA methylation profiling and computational deconvolution [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 149.

CpG Methylation in TGFβ1 and IL-6 Genes as Surrogate Biomarkers for Diagnosis of IBD in Children.

Diagnostic markers for distinguishing between Crohn disease (CD) and ulcerative colitis (UC) remain elusive. We studied whether methylation marks across the promoters of the transforming growth factor beta 1 (TGFβ1) and interleukin-6 genes have diagnostic utility. A case-control study was carried out. Cases were treatment-naïve, diagnosed before age 20, and recruited from 3 pediatric gastroenterology clinics across Canada. Control patients did not have inflammatory bowel disease and were recruited from orthopedic clinics within the same hospitals as the gastroenterology clinics. Patient DNA from peripheral blood was processed to identify methylation sites (CpG) across the promoter regions of the TGFβ1 and interleukin-6 genes. After initial nonparametric univariate analyses, multivariate logistic regression models were fit. Models with the best fit (Akaike information criteria) and strongest discriminatory capabilities (area under the curve [AUC]) were identified, and P values were adjusted for multiple comparisons using the false discovery rate method. A total of 67 CD, 31 UC, and 43 control patients were included. The age distribution of the 3 groups was similar. Most CD patients had ileocolonic disease (44.8%) and inflammatory disease (88.1%). Most UC patients had extensive (71%) and moderate disease (51.6%). Logistic regression analysis revealed the following: 14 TGFβ1 CpG sites discriminated between CD and control patients (AUC = 0.94), 9 TGFβ1 CpG sites discriminated between UC and control patients (AUC = 0.99), 3 TGFβ1 CpG sites discriminated between CD and UC (AUC = 0.81), and 6 TGFβ1 CpG sites distinguished colonic CD from UC (AUC = 0.91). We found that CpG methylation in the promoter of the TGFβ1 gene has high discriminative power for identifying CD and UC and could serve as an important diagnostic marker.

CpG methylation in the TGFB1 and IL-6 gene promoters serve as surrogate biomarkers for diagnosis of IBD in children

Abstract Crohn’s disease (CD) and ulcerative colitis (UC) are recurrent, chronic forms of inflammatory bowel diseases (IBD), the diagnosis of which can entail long delays. The delays can lead to disease presentation with complications and high morbidity. Therefore, better diagnostic markers for IBD are required. We investigated whether methylation marks in the TGFB1 and IL-6 gene promoters could be of diagnostic utility in IBD. To this end, we conducted a case-control study using peripheral blood DNA samples from 67 CD and 37 UC pediatric patients, and from 43 age-matched healthy controls. The patients were recruited from three pediatric gastroenterology clinics across Canada. The DNA samples were processed to identify methylation sites (CpG) across the promoter regions of the TGFB1 and IL-6 genes using the Sequenom technology. After implementing quality control measures, and initial non-parametric univariate (Man-Whitney U test) analyses, multivariate logistic regression analyses was carried out to identify models with the best fit (Akaike Information Criteria) and the greatest discriminatory capabilities (Area Under the Receiver Operating Curve). Logistic regression analysis showed that a model comprising 14 TGFB1 CpG sites had high discriminatory ability to separate CD from controls (AUC=0.94). Similarly, a logistic model comprising 9 CpG sites in the TGFB1 gene had near perfect ability to discriminate between UC and controls (AUC=0.99). A logistic model comprising three CpG sites in the TGFB1 gene had moderate ability (AUC=0.81) to discriminate between CD and UC. In conclusion, CpG methylation in the TGFB1 gene promoter has high discriminative power for identifying CD and UC, and could serve as an important diagnostic markers.