Improved DNA extraction and quantitative real-time PCR for genotyping Erysiphe necator and detecting the DMI fungicide resistance marker A495T, using single ascocarps

DNA extraction from minute fungal samples is challenging in all genetic studies. Identification of genetic groups and population biology mostly rely on the laborious production of single conidium isolates or on field samples, including infected plant materials. This paper reports a simple and cost-effective protocol for DNA extraction from individual chasmothecia of Erysiphe necator for subsequent applications. It is a less laborious alternative for genotyping purposes than production and analysis of single conidium isolates or analysis of infected plant material from the field. Using the protocols described here for 186 E. necator samples tested, genetic groups A and B were assigned. Based on CYP51 sequences, all the samples belonged to group B, while TUB2 sequences exhibited SNPs also diagnostic for group A. Additionally, a quantitative real-time PCR detection method of single nucleotide polymorphism in the CYP51 gene associated with DMI fungicide resistance was applied. The A495T marker, associated with DMI resistance, and here reported for the first time from Hungary, was detected by quantitative real-time PCR assays and direct sequencing of CYP51. The methods developed in this study can be applied as routine tests to monitor powdery mildew populations for fungicide resistance and other genetic characteristics

Association Between the Epigenetic Lifespan Predictor GrimAge and History of Suicide Attempt in Bipolar Disorder

Bipolar disorder (BD) has been previously associated with premature mortality and aging, including acceleration of epigenetic aging. Suicide attempts (SA) are greatly elevated in BD and are associated with decreased lifespan, biological aging, and poorer clinical outcomes. We investigated the relationship between GrimAge, an epigenetic clock trained on time-to-death and associated with mortality and lifespan, and SA in two independent cohorts of BD individuals (discovery cohort - controls (n = 50), BD individuals with (n = 77, BD/SA) and without (n = 67, BD/non-SA) lifetime history of SA; replication cohort - BD/SA (n = 48) and BD/non-SA (n = 47)). An acceleration index for the GrimAge clock (GrimAgeAccel) was computed from blood DNA methylation (DNAm) and compared between groups with multiple general linear models. Differences in epigenetic aging from the discovery cohort were validated in the independent replication cohort. In the discovery cohort, controls, BD/non-SA, and BD/SA significantly differed on GrimAgeAccel (F = 5.424, p = 0.005), with the highest GrimAgeAccel in BD/SA (p = 0.004, BD/SA vs. controls). Within the BD individuals, BD/non-SA and BD/SA differed on GrimAgeAccel in both cohorts (p = 0.008) after covariate adjustment. Finally, DNAm-based surrogates revealed possible involvement of plasminogen activator inhibitor 1, leptin, and smoking pack-years in driving accelerated epigenetic aging. These findings pair with existing evidence that not only BD, but also SA, may be associated with an accelerated biological aging and provide putative biological mechanisms for morbidity and premature mortality in this population

Blood Epigenome-Wide Association Studies of Suicide Attempt in Adults With Bipolar Disorder

Suicide attempt (SA) risk is elevated in individuals with bipolar disorder (BD), and DNA methylation patterns may serve as possible biomarkers of SA. We conducted epigenome-wide association studies (EWAS) of blood DNA methylation associated with BD and SA. DNA methylation was measured at \u3e700,000 positions in a discovery cohort of n = 84 adults with BD with a history of SA (BD/SA), n = 79 adults with BD without history of SA (BD/non-SA), and n = 76 non-psychiatric controls (CON). EWAS revealed six differentially methylated positions (DMPs) and seven differentially methylated regions (DMRs) between BD/SA and BD/non-SA, with multiple immune-related genes implicated. There were no epigenome-wide significant differences when BD/SA and BD/non-SA were each compared to CON, and patterns suggested that epigenetics differentiating BD/SA from BD/non-SA do not differentiate BD/non-SA from CON. Weighted gene co-methylation network analysis and trait enrichment analysis of the BD/SA vs. BD/non-SA contrast further corroborated immune system involvement, while gene ontology analysis implicated calcium signalling. In an independent replication cohort of n = 48 BD/SA and n = 47 BD/non-SA, fold changes at the discovery cohort\u27s significant sites showed moderate correlation across cohorts and agreement on direction. In both cohorts, classification accuracy for SA history among individuals with BD was highest when methylation at the significant CpG sites as well as information from clinical interviews were combined, with an AUC of 88.8% (CI = 83.8-93.8%) and 82.1% (CI = 73.6-90.5%) for the combined epigenetic-clinical classifier in the discovery and replication cohorts, respectively. Our results provide novel insight to the role of immune system functioning in SA and BD and also suggest that integrating information from multiple levels of analysis holds promise to improve risk assessment for SA in adults with BD

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches

Frequency of fatigue and its changes in the first 6 months after traumatic brain injury: results from the CENTER-TBI study

Background: Fatigue is one of the most commonly reported subjective symptoms following traumatic brain injury (TBI). The aims were to assess frequency of fatigue over the first 6 months after TBI, and examine whether fatigue changes could be predicted by demographic characteristics, injury severity and comorbidities. Methods: Patients with acute TBI admitted to 65 trauma centers were enrolled in the study Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI). Subj