Epigenetic Contributions to Clinical Risk Prediction of Cardiovascular Disease

BACKGROUND: Cardiovascular disease (CVD) is among the leading causes of death worldwide. The discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN-a cardiovascular risk prediction tool recommended for use in Scotland-was examined in tandem with epigenetic and proteomic features in risk prediction models in ≥12 657 participants from the Generation Scotland cohort.METHODS: Previously generated DNA methylation-derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cTnI (cardiac troponin I). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (n cases≥1274; n controls≥11 383) and visualized in a tailored R application. Splitting the cohort into independent training (n=6880) and test (n=3659) subsets, a composite CVD EpiScore was then developed. RESULTS: Sixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI ( P&lt;0.05), over a follow-up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response, and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (hazard ratio, 1.32; P=3.7×10 - 3; 0.3% increase in C-statistic). CONCLUSIONS: EpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the etiology of the disease.</p

The circulating proteome and brain health:Mendelian randomisation and cross-sectional analyses

Decline in cognitive function is the most feared aspect of ageing. Poorer midlife cognitive function is associated with increased dementia and stroke risk. The mechanisms underlying variation in cognitive function are uncertain. Here, we assessed associations between 1160 proteins' plasma levels and two measures of cognitive function, the digit symbol substitution test (DSST) and the Montreal Cognitive Assessment in 1198 PURE-MIND participants. We identified five DSST performance-associated proteins (NCAN, BCAN, CA14, MOG, CDCP1), with NCAN and CDCP1 showing replicated association in an independent cohort, GS (N = 1053). MRI-assessed structural brain phenotypes partially mediated (8-19%) associations between NCAN, BCAN, and MOG, and DSST performance. Mendelian randomisation analyses suggested higher CA14 levels might cause larger hippocampal volume and increased stroke risk, whilst higher CDCP1 levels might increase intracranial aneurysm risk. Our findings highlight candidates for further study and the potential for drug repurposing to reduce the risk of stroke and cognitive decline.</p

Transmission and accumulation of CTL escape variants drive negative associations between HIV polymorphisms and HLA

Human immunodeficiency virus (HIV)-1 amino acid sequence polymorphisms associated with expression of specific human histocompatibility leukocyte antigen (HLA) class I alleles suggest sites of cytotoxic T lymphocyte (CTL)-mediated selection pressure and immune escape. The associations most frequently observed are between expression of an HLA class I molecule and variation from the consensus sequence. However, a substantial number of sites have been identified in which particular HLA class I allele expression is associated with preservation of the consensus sequence. The mechanism behind this is so far unexplained. The current studies, focusing on two examples of “negatively associated” or apparently preserved epitopes, suggest an explanation for this phenomenon: negative associations can arise as a result of positive selection of an escape mutation, which is stable on transmission and therefore accumulates in the population to the point at which it defines the consensus sequence. Such negative associations may only be in evidence transiently, because the statistical power to detect them diminishes as the mutations accumulate. If an escape variant reaches fixation in the population, the epitope will be lost as a potential target to the immune system. These data help to explain how HIV is evolving at a population level. Understanding the direction of HIV evolution has important implications for vaccine development

Development and validation of DNA Methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes

This is the author accepted manuscriptAvailability of Data and Material: According to the terms of consent for Generation Scotland participants, access to data must be reviewed by the Generation Scotland Access Committee. Applications should be made to [email protected]. All code is available with open access at the following Gitlab repository: https://github.com/marioni-group MethylPipeR (version 1.0.0) is available at: https://github.com/marioni-group/MethylPipeR MethylPipeR-UI is available at: https://github.com/marioni-group/MethylPipeR-UI. The informed consents given by KORA study participants do not cover data posting in public databases. However, data are available upon request from KORA Project Application Self Service Tool (https://epi.helmholtz-muenchen.de/). Data requests can be submitted online and are subject to approval by the KORA Board.Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction and intervention. While standard risk factors have shown good predictive performance, we show that the use of blood-based DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk. Previous studies have been largely constrained by linear assumptions, the use of CpGs one-at43 a-time, and binary outcomes. We present a flexible approach (via an R package, MethylPipeR) based on a range of linear and tree-ensemble models that incorporate time-to-event data for prediction. Using the Generation Scotland cohort (training set ncases=374, ncontrols=9,461; test set ncases=252, ncontrols=4,526) our best-performing model (Area Under the Curve (AUC)=0.872, Precision Recall AUC (PRAUC)=0.302) showed notable improvement in 10-year onset prediction beyond standard risk factors (AUC=0.839, PRAUC=0.227). Replication was observed in the German-based KORA study (n=1,451, ncases = 142, p=1.6x10-5 49 ).Wellcome TrustChief Scientist Office of the Scottish Government Health DirectoratesScottish Funding Counci

Epigenetic scores for the circulating proteome as tools for disease prediction

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification

Blood-based epigenome-wide analyses of cognitive abilities

BACKGROUND: Blood-based markers of cognitive functioning might provide an accessible way to track neurodegeneration years prior to clinical manifestation of cognitive impairment and dementia. RESULTS: Using blood-based epigenome-wide analyses of general cognitive function, we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function (g). A DNAm predictor explains ~4% of the variance, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology- and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes. CONCLUSIONS: As sample sizes increase, the ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02596-5

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, we report an integrated DNA methylation and phenotypic study of the circulating proteome in relation to brain health. Methylome-wide association studies of 4058 plasma proteins are performed (N = 774), identifying 2928 CpG-protein associations after adjustment for multiple testing. These are independent of known genetic protein quantitative trait loci (pQTLs) and common lifestyle effects. Phenome-wide association studies of each protein are then performed in relation to 15 neurological traits (N = 1,065), identifying 405 associations between the levels of 191 proteins and cognitive scores, brain imaging measures or APOE e4 status. We uncover 35 previously unreported DNA methylation signatures for 17 protein markers of brain health. The epigenetic and proteomic markers we identify are pertinent to understanding and stratifying brain health

Epigenetic contributions to clinical risk prediction of cardiovascular disease

BACKGROUND: Cardiovascular disease (CVD) is among the leading causes of death worldwide. The discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN—a cardiovascular risk prediction tool recommended for use in Scotland—was examined in tandem with epigenetic and proteomic features in risk prediction models in ≥12 657 participants from the Generation Scotland cohort. METHODS: Previously generated DNA methylation–derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cTnI (cardiac troponin I). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (ncases≥1274; ncontrols≥11 383) and visualized in a tailored R application. Splitting the cohort into independent training (n=6880) and test (n=3659) subsets, a composite CVD EpiScore was then developed. RESULTS: Sixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI (P&lt;0.05), over a follow-up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response, and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (hazard ratio, 1.32; P=3.7×10−3; 0.3% increase in C-statistic). CONCLUSIONS: EpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the etiology of the disease

Skunk River Fall 1998

https://openspace.dmacc.edu/skunkriver/1019/thumbnail.jp

Immunodominant HIV-1 Cd4+ T Cell Epitopes in Chronic Untreated Clade C HIV-1 Infection

Background: A dominance of Gag-specific CD8+ T cell responses is significantly associated with a lower viral load in individuals with chronic, untreated clade C human immunodeficiency virus type 1 (HIV-1) infection. This association has not been investigated in terms of Gag-specific CD4+ T cell responses, nor have clade C HIV-1–specific CD4+ T cell epitopes, likely a vital component of an effective global HIV-1 vaccine, been identified. Methodology/Principal Findings: Intracellular cytokine staining was conducted on 373 subjects with chronic, untreated clade C infection to assess interferon-gamma (IFN-γ) responses by CD4+ T cells to pooled Gag peptides and to determine their association with viral load and CD4 count. Gag-specific IFN-γ–producing CD4+ T cell responses were detected in 261/373 (70%) subjects, with the Gag responders having a significantly lower viral load and higher CD4 count than those with no detectable Gag response (p<0.0001 for both parameters). To identify individual peptides targeted by HIV-1–specific CD4+ T cells, separate ELISPOT screening was conducted on CD8-depleted PBMCs from 32 chronically infected untreated subjects, using pools of overlapping peptides that spanned the entire HIV-1 clade C consensus sequence, and reconfirmed by flow cytometry to be CD4+ mediated. The ELISPOT screening identified 33 CD4+ peptides targeted by 18/32 patients (56%), with 27 of the 33 peptides located in the Gag region. Although the breadth of the CD4+ responses correlated inversely with viral load (p = 0.015), the magnitude of the response was not significantly associated with viral load. Conclusions/Significance: These data indicate that in chronic untreated clade C HIV-1 infection, IFN-γ–secreting Gag-specific CD4+ T cell responses are immunodominant, directed at multiple distinct epitopes, and associated with viral control