Nine Things Genomics Can Tell Us About Candida auris

FUNDING RF was supported by MRC grant MR/N006364/2 and a Wellcome Trust Seed Award (215239/Z/19/Z). ACKNOWLEDGMENTS We would like to give thanks to Carolina Coelho for helpful discussions and assistance with Figure 1 and Jose Muñoz for valuable feedback on the manuscript.Peer reviewedPublisher PD

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

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

Population genetics and microevolution of clinical <i>Candida glabrata</i> reveals recombinant sequence types and hyper-variation within mitochondrial genomes, virulence genes and drug-targets

Candida glabrata is the second most common etiological cause of worldwide systemic candidiasis in adult patients. Genome analysis of 68 isolates from 8 hospitals across Scotland, together with 83 global isolates, revealed insights into the population genetics and evolution of C. glabrata. Clinical isolates of C. glabrata from across Scotland are highly-genetically diverse, including at least 19 separate sequence types (STs) that have been recovered previously in globally diverse locations, and one newly discovered ST. Several STs had evidence for ancestral recombination, suggesting transmission between distinct geographical regions has coincided with genetic exchange arising in new clades. Three isolates were missing MATα1, potentially representing a second mating type. Signatures of positive selection were identified in every ST including enrichment for Epithelial Adhesins (EPA) thought to facilitate fungal adhesion to human epithelial cells. In patent microevolution was identified from seven sets of recurrent cases of candidiasis, revealing an enrichment for non-synonymous and frameshift indels in cell surface proteins. Microevolution within patients also affected EPA genes, and several genes involved in drug resistance including the ergosterol synthesis gene ERG4 and the echinocandin target FKS1/2, the latter coinciding with a marked drop in fluconazole MIC. In addition to nuclear genome diversity, the C. glabrata mitochondrial genome was particularly diverse, with reduced conserved sequence and conserved protein encoding genes in all non-reference ST15 isolates. Together, this study highlights the genetic diversity within the C. glabrata population that may impact virulence and drug resistance, and two major mechanisms generating this diversity: microevolution and genetic exchange/recombination

Blood-based epigenome-wide analyses of 19 common disease states: A longitudinal, population-based linked cohort study of 18,413 Scottish individuals.

BackgroundDNA methylation is a dynamic epigenetic mechanism that occurs at cytosine-phosphate-guanine dinucleotide (CpG) sites. Epigenome-wide association studies (EWAS) investigate the strength of association between methylation at individual CpG sites and health outcomes. Although blood methylation may act as a peripheral marker of common disease states, previous EWAS have typically focused only on individual conditions and have had limited power to discover disease-associated loci. This study examined the association of blood DNA methylation with the prevalence of 14 disease states and the incidence of 19 disease states in a single population of over 18,000 Scottish individuals.Methods and findingsDNA methylation was assayed at 752,722 CpG sites in whole-blood samples from 18,413 volunteers in the family-structured, population-based cohort study Generation Scotland (age range 18 to 99 years). EWAS tested for cross-sectional associations between baseline CpG methylation and 14 prevalent disease states, and for longitudinal associations between baseline CpG methylation and 19 incident disease states. Prevalent cases were self-reported on health questionnaires at the baseline. Incident cases were identified using linkage to Scottish primary (Read 2) and secondary (ICD-10) care records, and the censoring date was set to October 2020. The mean time-to-diagnosis ranged from 5.0 years (for chronic pain) to 11.7 years (for Coronavirus Disease 2019 (COVID-19) hospitalisation). The 19 disease states considered in this study were selected if they were present on the World Health Organisation's 10 leading causes of death and disease burden or included in baseline self-report questionnaires. EWAS models were adjusted for age at methylation typing, sex, estimated white blood cell composition, population structure, and 5 common lifestyle risk factors. A structured literature review was also conducted to identify existing EWAS for all 19 disease states tested. The MEDLINE, Embase, Web of Science, and preprint servers were searched to retrieve relevant articles indexed as of March 27, 2023. Fifty-four of approximately 2,000 indexed articles met our inclusion criteria: assayed blood-based DNA methylation, had >20 individuals in each comparison group, and examined one of the 19 conditions considered. First, we assessed whether the associations identified in our study were reported in previous studies. We identified 69 associations between CpGs and the prevalence of 4 conditions, of which 58 were newly described. The conditions were breast cancer, chronic kidney disease, ischemic heart disease, and type 2 diabetes mellitus. We also uncovered 64 CpGs that associated with the incidence of 2 disease states (COPD and type 2 diabetes), of which 56 were not reported in the surveyed literature. Second, we assessed replication across existing studies, which was defined as the reporting of at least 1 common site in >2 studies that examined the same condition. Only 6/19 disease states had evidence of such replication. The limitations of this study include the nonconsideration of medication data and a potential lack of generalizability to individuals that are not of Scottish and European ancestry.ConclusionsWe discovered over 100 associations between blood methylation sites and common disease states, independently of major confounding risk factors, and a need for greater standardisation among EWAS on human disease