Candida auris is a recently emerged multidrug-resistant fungal pathogen causing severe illness in hospitalized patients. C. auris is most closely related to a few environmental or rarely observed but cosmopolitan Candida species. However, C. auris is unique in the concern it is generating among public health agencies for its rapid emergence, difficulty to treat, and the likelihood for further and more extensive outbreaks and spread. To date, five geographically distributed and genetically divergent lineages have been identified, none of which includes isolates that were collected prior to 1996. Indeed, C. auris' ecological niche(s) and emergence remain enigmatic, although a number of hypotheses have been proposed. Recent genomic and transcriptomic work has also identified a variety of gene and chromosomal features that may have conferred C. auris with several important clinical phenotypes including its drug-resistance and growth at high temperatures. In this review we discuss nine major lines of enquiry into C. auris that big-data technologies and analytical approaches are beginning to answer.

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.

Background DNA 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 findings DNA 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. Conclusions We 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.

BackgroundBlood DNA methylation can inform us about the biological mechanisms that underlie common disease states. Previous epigenome–wide analyses of common diseases often focus solely on the prevalence or incidence of individual conditions and rely on small sample sizes, which may limit power to discover disease–associated loci.ResultsWe conduct blood–based epigenome-wide association studies on the prevalence of 14 common disease states in Generation Scotland (nindividuals≤18,413, nCpGs=752,722). We also utilise health record linkage to perform epigenome–wide analyses on the incidence of 19 disease states. We present a structured literature review on existing epigenome–wide analyses for all 19 disease states to assess the degree of replication within the existing literature and the novelty of the present findings. We identify 69 associations between CpGs and the prevalence of four disease states at baseline, of which 58 are novel. We also uncover 64 CpGs that associate with the incidence of two disease states (COPD and type 2 diabetes), of which 56 are novel. These associations were independent from common lifestyle risk factors. We highlight poor replication across the existing literature. Here, replication was defined by the reporting of at least one common gene in >2 studies examining the same disease state. Existing blood–based epigenome–wide analyses showed evidence of replication for only 4/19 disease states (with up–to–15% of unique genes replicated for lung cancer).ConclusionsOur summary data and structured review of the literature provide an important platform to guide future studies that examine the role of blood DNA methylation in complex disease states.

Background and AimsCardiovascular disease (CVD) is among the leading causes of death worldwide. Therefore, it is important to identify CVD risk as early as possible and to translate this knowledge into effective preventative strategies. Here, ASSIGN – a cardiovascular risk calculator recommended for use in Scotland – was examined in tandem with epigenetic and proteomic features in risk prediction models in 12,790 participants in the Generation Scotland cohort.MethodsPreviously generated DNA methylation-derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cardiac troponins. The associations between potentially informative multi-omic features and the disease status were examined using univariate Cox PH models (ncases=1,288). Splitting the cohort into independent training (n≥3,681) and test (n≥1,996) subsets composed of unrelated individuals, two composite scores were developed: CVD TroponinScore and CVD EpiScore.ResultsThere were 65 protein EpiScores that associated with incident CVD (ncases=1,288) independently of ASSIGN (PBonferroni<0.05), over a follow up of up to 15 years of electronic health record linkage. The CVD TroponinScore (based on the concentration of two cardiac troponins) and CVD EpiScore (based on 52 protein EpiScores) were both associated with CVD risk in Cox models in test sets (Hazard Ratio HR=1.28 and 1.23, P=5.9×10−4and 3.8×10−3, ntest=1,996 and 3,711, respectively).ConclusionsEpiScores for circulating protein levels have the potential to improve the prediction of CVD and be useful tools for precision medicine.Structured graphical abstractStructural graphical abstractCVD – Cardiovascular Disease, EpiScore – Epigenetic Score, Cox PH – Cox Proportional Hazards Regression, DNAm – DNA methylationStructured abstract textKey QuestionCan we augment CVD prediction beyond clinical risk scores?Key FindingNewly derived multi-omic scores (CVD TroponinScore and CVD EpiScore) were both associated with CVD risk in holdout test sets.Take-home MessageMulti-omic data have the potential to augment CVD risk prediction tools such as ASSIGN or SCORE2.