The role of melanins in melanotic fungi for pathogenesis and environmental survival.

Melanins provide fungi protection from environmental stressors, support their ecological roles, and can confer virulence in pathogens. While the function, structure, and synthesis of melanins in fungi are not fully understood, they have been shown to have varied roles. Recent research has revealed a wide range of functions, from radiation resistance to increasing virulence, shedding light on fungal diversity. Understanding fungal melanins can provide useful information, from harnessing the properties of these various melanins to targeting fungal infections.Key Points• Melanotic fungi are widespread in nature. • Melanin functions to protect fungi in the environment from a range of stresses. • Melanin contributes to pathogenesis and drug resistance of pathogenic fungi.

Genetic association and machine learning improves discovery and prediction of type 1 diabetes.

Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can lead to novel biological and therapeutic discovery and improved risk prediction. In this study, we performed genetic association and fine-mapping analyses in 817,718 European ancestry samples genome-wide and 29,746 samples at the MHC locus, which identified 165 independent risk signals for T1D of which 19 were novel. We used risk variants to train a machine learning model (named T1GRS) to predict T1D, which highly differentiated T1D from non-disease and type 2 diabetes (T2D) in Europeans as well as African Americans at or beyond the level of current standards. We identified extensive non-linear interactions between risk loci in T1GRS, for example between HLA-DQB1*57 and INS, coding and non-coding HLA alleles, and DEXI, INS and other beta cell loci, that provided mechanistic insight and improved risk prediction. T1D individuals formed distinct clusters based on genetic features from T1GRS which had significant differences in age of onset, HbA1c, and renal disease severity. Finally, we provided T1GRS in formats to enhance accessibility of risk prediction to any user and computing environment. Overall, the improved genetic discovery and prediction of T1D will have wide clinical, therapeutic, and research applications.

Circulating pancreatic enzyme levels are a causal biomarker of type 1 diabetes.

Novel biomarkers of type 1 diabetes (T1D) are needed for earlier detection of disease and identifying therapeutic targets. We identified biomarkers of T1D by combining plasma cis and trans protein QTLs (pQTLs) for 2,922 proteins in the UK Biobank with a T1D genome-wide association study (GWAS) in 157k samples. T1D risk variants at over 20% of known loci colocalized with cis or trans pQTLs, and distinct sets of T1D loci colocalized with immune, pancreatic secretion, or gut-related proteins. We identified 23 proteins with evidence for a causal role in using pQTLs as genetic instruments in Mendelian Randomization which included multiple sensitivity analyses. Proteins increasing T1D risk were involved in immune processes (e.g. HLA-DRA) and, more surprisingly, T1D protective proteins were enriched in pancreatic secretions (e.g. CPA1), cholesterol metabolism (e.g. APOA1), and gut homeostasis. Genetic variants associated with plasma levels of T1D-protective pancreatic enzymes such as CPA1 were enriched in cis-regulatory elements in pancreatic exocrine and gut enteroendocrine cells, and the protective effects of CPA1 and other enzymes on T1D were consistent when using instruments specific to acinar cells. Finally, pancreatic enzymes had decreased acinar expression in T1D, including CPA1 which was altered prior to onset. Together, these results reveal causal biomarkers and highlight processes in the exocrine pancreas, immune system, and gut that modulate T1D risk.

Multi-ancestry polygenic mechanisms of type 2 diabetes.

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Genetic discovery and risk prediction for type 1 diabetes in individuals without high-risk HLA-DR3/DR4 haplotypes.

Over 10% of type 1 diabetes (T1D) cases do not have high-risk HLA-DR3 or DR4 haplotypes with distinct clinical features such as later onset and reduced insulin dependence. To identify genetic drivers of T1D in the absence of DR3/DR4, we performed association and fine-mapping analyses in 12,316 non-DR3/DR4 samples. Risk variants at the MHC and other loci genome-wide had heterogeneity in effects on T1D dependent on DR3/DR4, and non-DR3/DR4 T1D had evidence for a greater polygenic burden. T1D-assocated variants in non-DR3/DR4 were more enriched for loci, regulatory elements, and pathways for antigen presentation, innate immunity, and beta cells, and depleted in T cells, compared to DR3/DR4. Non-DR3/DR4 T1D cases were poorly classified based on an existing genetic risk score GRS2, and we created a new GRS which highly discriminated non-DR3/DR4 T1D from both non-diabetes and T2D. In total we identified heterogeneity in T1D genetic risk and disease mechanisms dependent on high-risk HLA haplotype and which enabled accurate classification of T1D across HLA background.

Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity.

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.

Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity.

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.

Nutrient regulation of the islet epigenome controls adaptive insulin secretion.

Adaptation of the islet ß cell insulin-secretory response to changing insulin demand is critical for blood glucose homeostasis, yet the mechanisms underlying this adaptation are unknown. Here, we have shown that nutrient-stimulated histone acetylation plays a key role in adapting insulin secretion through regulation of genes involved in ß cell nutrient sensing and metabolism. Nutrient regulation of the epigenome occurred at sites occupied by the chromatin-modifying enzyme lysine-specific demethylase 1 (Lsd1) in islets. ß Cell-specific deletion of Lsd1 led to insulin hypersecretion, aberrant expression of nutrient-response genes, and histone hyperacetylation. Islets from mice adapted to chronically increased insulin demand exhibited shared epigenetic and transcriptional changes. Moreover, we found that genetic variants associated with type 2 diabetes were enriched at LSD1-bound sites in human islets, suggesting that interpretation of nutrient signals is genetically determined and clinically relevant. Overall, these studies revealed that adaptive insulin secretion involves Lsd1-mediated coupling of nutrient state to regulation of the islet epigenome.

Examination of Resident Physician Quality Improvement/Patient Safety Project Confidence Levels from Multiple Programs.

CONTEXT: It is now increasingly recognized that physicians should be engaged in quality improvement/patient safety (QIPS) activities to make their patient care systems perform more reliably and safely. In order to ensure that our nation's physicians embed this aspect of practice into their work, there also is a growing expectation for effective integration of QIPS training into graduate medical education. This exploratory pilot study was conducted to identify how residents' personal and residency program characteristics might be related to their perceived confidence to develop and conduct prospective QIPS projects. METHODS: A total non-probability convenience sample of 43 DO resident physicians from five residency programs (Family Medicine, Internal Medicine, Obstetrics and Gynecology, Pediatrics, and Psychiatry) at Authority Health were surveyed from 09/28/2015 to 01/06/2016 using online Survey Monkey software. A 38-item survey asked residents about their personal and residency program characteristics, as well as their current overall perceived confidence to develop and conduct QIPS projects. RESULTS: Two model terms that proved non-significant during analyses were residents' age category and year in residency training. In the final stepwise multinomial regression model, however, three covariates including: a) sex (p=0.045), b) being in a primary care residency program (p=0.038) and c) having had prior QIPS project experience (p=0.049) were each found to be statistically significant predictors of respondents' perceived comfort level categories. Male residents and those who were in a primary care residency program (i.e., Family Medicine, Internal Medicine or Pediatrics), and/or reported having had prior QIPS project experience, reported significantly higher confidence levels. CONCLUSIONS: Somewhat similar to earlier studies, these results suggest the need to incorporate QIPS education for resident trainees across the nation. Ideally, the findings from larger resident studies will enable GME leaders to develop and deliver evidence-based QIPS curricula that are better oriented to resident physicians' personal characteristics and preferences.