Defining the Role of the MHC in Autoimmunity: A Review and Pooled Analysis

The major histocompatibility complex (MHC) is one of the most extensively studied regions in the human genome because of the association of variants at this locus with autoimmune, infectious, and inflammatory diseases. However, identification of causal variants within the MHC for the majority of these diseases has remained difficult due to the great variability and extensive linkage disequilibrium (LD) that exists among alleles throughout this locus, coupled with inadequate study design whereby only a limited subset of about 20 from a total of approximately 250 genes have been studied in small cohorts of predominantly European origin. We have performed a review and pooled analysis of the past 30 years of research on the role of the MHC in six genetically complex disease traits – multiple sclerosis (MS), type 1 diabetes (T1D), systemic lupus erythematosus (SLE), ulcerative colitis (UC), Crohn's disease (CD), and rheumatoid arthritis (RA) – in order to consolidate and evaluate the current literature regarding MHC genetics in these common autoimmune and inflammatory diseases. We corroborate established MHC disease associations and identify predisposing variants that previously have not been appreciated. Furthermore, we find a number of interesting commonalities and differences across diseases that implicate both general and disease-specific pathogenetic mechanisms in autoimmunity

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Rising rural body-mass index is the main driver of the global obesity epidemic in adults

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.Peer reviewe

Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms

Abstract Background The risk factors of diabetic retinopathy (DR) were investigated extensively in the past studies, but it remains unknown which risk factors were more associated with the DR than others. If we can detect the DR related risk factors more accurately, we can then exercise early prevention strategies for diabetic retinopathy in the most high-risk population. The purpose of this study is to build a prediction model for the DR in type 2 diabetes mellitus using data mining techniques including the support vector machines, decision trees, artificial neural networks, and logistic regressions. Results Experimental results demonstrated that prediction performance by support vector machines performed better than the other machine learning algorithms and achieved 79.5% and 0.839 in accuracy and area under the receiver operating characteristic curve using percentage split (i.e., data set divided into 80% as trainning and 20% as test), respectively. Evaluated by three-way data split scheme (i.e., data set divided into 60% as training, 20% as validation, and 20% as independent test), our method obtained slightly lower performance compared to percentage split, which suggested that three-way data split is a better way to evaluate the real performance and prevent overestimation. Moreover, we incorporated approaches proposed in previous studies to evaluate our data set and our prediction performance outperformed the other previous studies in most evaluation measures. This lends support to our assumption that appropriate machine learning algorithms combined with discriminative clinical features can effectively detect diabetic retinopathy. Conclusions Our method identifies use of insulin and duration of diabetes as novel interpretable features to assist with clinical decisions in identifying the high-risk populations for diabetic retinopathy. If duration of DM increases by 1 year, the odds ratio to have DMR is increased by 9.3%. The odds ratio to have DR is increased by 3.561 times for patients who use insulin compared to patients who do not use insulin. Our results can be used to facilitate development of clinical decision support systems for clinical practice in the future

Analysis of post-deployment cognitive performance and symptom recovery in U.S. Marines.

BACKGROUND: Computerized neurocognitive testing (NCAT) has been proposed to be useful as a screening tool for post-deployment cognitive deficits in the setting of mild traumatic brain injury (mTBI). We assessed the clinical utility of post-injury/post-deployment Automated Neurocognitive Assessment Metric (ANAM) testing, using a longitudinal design to compare baseline ANAM tests with two post-deployment ANAM tests in a group of Marines who experienced combat during deployment. METHODS AND FINDINGS: Post-deployment cognitive performance and symptom recovery were compared in a subsample of 1324 U.S. Marines with high rates of combat exposure during deployment. Of the sample, 169 Marines had available baseline and twice repeated post-deployment ANAM results. A retrospective analysis of the ANAM data, which consisted of a self-report questionnaire about deployment-related blast exposure, recent history of mTBI, current clinical symptoms, and cognitive performance. Self-reported concussion sustained anytime during deployment was associated with a decrease in cognitive performance measured between 2-8 weeks post-deployment. At the second post-deployment test conducted on average eight months later, performance on the second simple reaction time test, in particular, remained impaired and was the most consistent and sensitive indicator of the cognitive decrements. Additionally, post-concussive symptoms were shown to persist in injured Marines with a self-reported history of concussion for an additional five months after most cognitive deficits resolved. Results of this study showed a measurable deployment effect on cognitive performance, although this effect appears to resolve without lasting clinical sequelae in those without history of deployment-related concussion. CONCLUSIONS: These results highlight the need for a detailed clinical examination for service members with history of concussion and persistent clinical symptoms. Reliance solely upon computerized neurocognitive testing as a method for identifying service members requiring clinical follow-up post-concussion is not recommended, as cognitive functioning only slowly returned to baseline levels in the setting of persistent clinical symptoms

The coordinating role of IQGAP1 in the regulation of local, endosome-specific actin networks

IQGAP1 is a large, multi-domain scaffold that helps orchestrate cell signaling and cytoskeletal mechanics by controlling interactions among a spectrum of receptors, signaling intermediates, and cytoskeletal proteins. While this coordination is known to impact cell morphology, motility, cell adhesion, and vesicular traffic, among other functions, the spatiotemporal properties and regulatory mechanisms of IQGAP1 have not been fully resolved. Herein, we describe a series of super-resolution and live-cell imaging analyses that identified a role for IQGAP1 in the regulation of an actin cytoskeletal shell surrounding a novel membranous compartment that localizes selectively to the basal cortex of polarized epithelial cells (MCF-10A). We also show that IQGAP1 appears to both stabilize the actin coating and constrain its growth. Loss of compartmental IQGAP1 initiates a disassembly mechanism involving rapid and unconstrained actin polymerization around the compartment and dispersal of its vesicle contents. Together, these findings suggest IQGAP1 achieves this control by harnessing both stabilizing and antagonistic interactions with actin. They also demonstrate the utility of these compartments for image-based investigations of the spatial and temporal dynamics of IQGAP1 within endosome-specific actin networks

Throughput Means (SE) for Non-Concussed Service Members.

<p>Note: No significant findings. PT =  performance test. T1 =  pre-deployment. T2 =  post-deployment assessment. T3 =  second post-deployment assessment. Δ (T2-T1) =  change from T1 to T2. Δ (T3-T1) =  change from T1 to T3. cdd =  coded substitution delayed. cds =  coded substitution. m2s =  matching to sample. pro =  procedural reaction time. srt =  simple reaction time. srt =  simple reaction time repeated.</p