Molecular basis for increased susceptibility of Indigenous North Americans to seropositive rheumatoid arthritis

Objective The pathogenetic mechanisms by which HLA-DRB1 alleles are associated with anticitrullinated peptide antibody (ACPA)-positive rheumatoid arthritis (RA) are incompletely understood. RA high-risk HLA-DRB1 alleles are known to share a common motif, the ‘shared susceptibility epitope (SE)’. Here, the electropositive P4 pocket of HLA-DRB1 accommodates self-peptide residues containing citrulline but not arginine. HLA-DRB1 His/Phe13β stratifies with ACPA-positive RA, while His13βSer polymorphisms stratify with ACPA-negative RA and RA protection. Indigenous North American (INA) populations have high risk of early-onset ACPA-positive RA, whereby HLA-DRB1*04:04 and HLA-DRB1*14:02 are implicated as risk factors for RA in INA. However, HLA-DRB1*14:02 has a His13βSer polymorphism. Therefore, we aimed to verify this association and determine its molecular mechanism. Methods HLA genotype was compared in 344 INA patients with RA and 352 controls. Structures of HLA-DRB1*1402-class II loaded with vimentin-64Arg59-71, vimentin-64Cit59-71 and fibrinogen β−74Cit69-81 were solved using X-ray crystallography. Vimentin-64Cit59-71-specific and vimentin59-71-specific CD4+ T cells were characterised by flow cytometry using peptide-histocompatibility leukocyte antigen (pHLA) tetramers. After sorting of antigen-specific T cells, TCRα and β-chains were analysed using multiplex, nested PCR and sequencing. Results ACPA+ RA in INA was independently associated with HLA-DRB1*14:02. Consequent to the His13βSer polymorphism and altered P4 pocket of HLA-DRB1*14:02, both citrulline and arginine were accommodated in opposite orientations. Oligoclonal autoreactive CD4+ effector T cells reactive with both citrulline and arginine forms of vimentin59-71 were observed in patients with HLA-DRB1*14:02+ RA and at-risk ACPA- first-degree relatives. HLA-DRB1*14:02-vimentin59-71-specific and HLA-DRB1*14:02-vimentin-64Cit59-71-specific CD4+ memory T cells were phenotypically distinct populations. Conclusion HLA-DRB1*14:02 broadens the capacity for citrullinated and native self-peptide presentation and T cell expansion, increasing risk of ACPA+ RA

Training compliance control yields improvements in drawing as a function of beery scores

Many children have difficulty producing movements well enough to improve in sensori-motor learning. Previously, we developed a training method that supports active movement generation to allow improvement at a 3D tracing task requiring good compliance control. Here, we tested 7–8 year old children from several 2nd grade classrooms to determine whether 3D tracing performance could be predicted using the Beery VMI. We also examined whether 3D tracing training lead to improvements in drawing. Baseline testing included Beery, a drawing task on a tablet computer, and 3D tracing. We found that baseline performance in 3D tracing and drawing co-varied with the visual perception (VP) component of the Beery. Differences in 3D tracing between children scoring low versus high on the Beery VP replicated differences previously found between children with and without motor impairments, as did post-training performance that eliminated these differences. Drawing improved as a result of training in the 3D tracing task. The training method improved drawing and reduced differences predicted by Beery scores

Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk.

Variation in the human leukocyte antigen (HLA) genes accounts for one-half of the genetic risk in type 1 diabetes (T1D). Amino acid changes in the HLA-DR and HLA-DQ molecules mediate most of the risk, but extensive linkage disequilibrium complicates the localization of independent effects. Using 18,832 case-control samples, we localized the signal to 3 amino acid positions in HLA-DQ and HLA-DR. HLA-DQβ1 position 57 (previously known; P = 1 × 10(-1,355)) by itself explained 15.2% of the total phenotypic variance. Independent effects at HLA-DRβ1 positions 13 (P = 1 × 10(-721)) and 71 (P = 1 × 10(-95)) increased the proportion of variance explained to 26.9%. The three positions together explained 90% of the phenotypic variance in the HLA-DRB1-HLA-DQA1-HLA-DQB1 locus. Additionally, we observed significant interactions for 11 of 21 pairs of common HLA-DRB1-HLA-DQA1-HLA-DQB1 haplotypes (P = 1.6 × 10(-64)). HLA-DRβ1 positions 13 and 71 implicate the P4 pocket in the antigen-binding groove, thus pointing to another critical protein structure for T1D risk, in addition to the HLA-DQ P9 pocket.This research utilizes resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD), and Juvenile Diabetes Research Foundation International (JDRF) and supported by U01 DK062418. This work is supported in part by funding from the National Institutes of Health (5R01AR062886-02 (PIdB), 1R01AR063759 (SR), 5U01GM092691-05 (SR), 1UH2AR067677-01 (SR), R01AR065183 (PIWdB)), a Doris Duke Clinical Scientist Development Award (SR), the Wellcome Trust (JAT) and the National Institute for Health Research (JAT and JMMH), and a Vernieuwingsimpuls VIDI Award (016.126.354) from the Netherlands Organization for Scientific Research (PIWdB). TLL was supported by the German Research Foundation (LE 2593/1-1 and LE 2593/2-1).This is the accepted manuscript. The final version is available at http://www.nature.com/ng/journal/v47/n8/full/ng.3353.html

Shocked monazite chronometry: integrating microstructural and in situ isotopic age data for determining precise impact ages

Monazite is a robust geochronometer and occurs in a wide range of rock types. Monazite also records shock deformation from meteorite impact but the effects of impact-related microstructures on the U–Th–Pb systematics remain poorly constrained. We have, therefore, analyzed shock-deformed monazite grains from the central uplift of the Vredefort impact structure, South Africa, and impact melt from the Araguainha impact structure, Brazil, using electron backscatter diffraction, electron microprobe elemental mapping, and secondary ion mass spectrometry (SIMS). Crystallographic orientation mapping of monazite grains from both impact structures reveals a similar combination of crystal-plastic deformation features, including shock twins, planar deformation bands and neoblasts. Shock twins were documented in up to four different orientations within individual monazite grains, occurring as compound and/or type one twins in (001), (100), (10 1 ¯) , {110}, { 212 } , and type two (irrational) twin planes with rational shear directions in [ 0 1 ¯ 1 ¯ ] and [ 1 ¯ 1 ¯ 0 ]. SIMS U–Th–Pb analyses of the plastically deformed parent domains reveal discordant age arrays, where discordance scales with increasing plastic strain. The correlation between discordance and strain is likely a result of the formation of fast diffusion pathways during the shock event. Neoblasts in granular monazite domains are strain-free, having grown during the impact events via consumption of strained parent grains. Neoblastic monazite from the Inlandsee leucogranofels at Vredefort records a 207Pb/206Pb age of 2010 ± 15 Ma (2σ, n = 9), consistent with previous impact age estimates of 2020 Ma. Neoblastic monazite from Araguainha impact melt yield a Concordia age of 259 ± 5 Ma (2σ, n = 7), which is consistent with previous impact age estimates of 255 ± 3 Ma. Our results demonstrate that targeting discrete microstructural domains in shocked monazite, as identified through orientation mapping, for in situ U–Th–Pb analysis can date impact-related deformation. Monazite is, therefore, one of the few high-temperature geochronometers that can be used for accurate and precise dating of meteorite impacts

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors

Genotype-stratified treatment for monogenic insulin resistance: a systematic review

This is the final version. Available from Nature Research via the DOI in this record. Data availability: All data used in this review is available from publicly available and herein referenced sources. A list of included studies is provided in Supplementary Data 1. All data generated or analyzed during this study are included in this published article and its supplementary information files. Source data for the figures are available as Supplementary Data 2.BACKGROUND: Monogenic insulin resistance (IR) includes lipodystrophy and disorders of insulin signalling. We sought to assess the effects of interventions in monogenic IR, stratified by genetic aetiology. METHODS: Systematic review using PubMed, MEDLINE and Embase (1 January 1987 to 23 June 2021). Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual data were extracted and duplicates were removed. Outcomes were analysed for each gene and intervention, and in aggregate for partial, generalised and all lipodystrophy. RESULTS: 10 non-randomised experimental studies, 8 case series, and 23 case reports meet inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin use is associated with the lowering of triglycerides and haemoglobin A1c (HbA1c) in all lipodystrophy (n = 111), partial (n = 71) and generalised lipodystrophy (n = 41), and in LMNA, PPARG, AGPAT2 or BSCL2 subgroups (n = 72,13,21 and 21 respectively). Body Mass Index (BMI) is lowered in partial and generalised lipodystrophy, and in LMNA or BSCL2, but not PPARG or AGPAT2 subgroups. Thiazolidinediones are associated with improved HbA1c and triglycerides in all lipodystrophy (n = 13), improved HbA1c in PPARG (n = 5), and improved triglycerides in LMNA (n = 7). In INSR-related IR, rhIGF-1, alone or with IGFBP3, is associated with improved HbA1c (n = 17). The small size or absence of other genotype-treatment combinations preclude firm conclusions. CONCLUSIONS: The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to improve metabolic markers in lipodystrophy, and rhIGF-1 appears to lower HbA1c in INSR-related IR. For other interventions, there is insufficient evidence to assess efficacy and risks in aggregated lipodystrophy or genetic subgroups.Wellcome TrustWellcome Trus

Widespread Non-Additive and Interaction Effects Within Human Leukocyte Antigen Loci Modulate the Risk of Autoimmune Diseases

Autoimmune diseases occur when the immune system mistakenly targets normal tissues. These diseases comprise a wide range of conditions, such as systemic lupus erythematosus, inflammatory bowel disease, and multiple sclerosis. For most autoimmune diseases, the strongest genetic risk is conferred by the human leukocyte antigen (HLA) genes, which are located within the major histocompatibility complex (MHC). The proteins encoded by HLA genes present peptide antigens to T cells, which then determine if a given antigen represents a self-peptide or a foreign peptide. In certain cases, this process can go awry, leading to T cell-mediated destruction of healthy tissues. Importantly, each HLA molecule can present a different set of peptide antigens, and patients who are heterozygous at a certain HLA gene possess a larger antigen-binding repertoire. Currently, most models that investigate the relationship between HLA alleles and autoimmunity assume that genetic variants combine on a log-additive scale (i.e. the logarithm of the odds ratio is additive). Under a log-additive model, the log odds for a patient with one copy of a risk allele (heterozygous) is exactly half that of a patient with two copies of the allele (homozygous). However, because patients with heterozygous HLA genotypes can present a broader array of antigens, they may be more likely to present a pathogenic autoantigen that leads to autoimmunity. Therefore, I hypothesized that a non-additive model accounting for heterozygous effects among HLA alleles might yield a more accurate estimate of autoimmune disease risk. To test this hypothesis, I examined a total of 25,835 patients across five autoimmune conditions: rheumatoid arthritis (RA), type 1 diabetes (T1D), psoriasis, achalasia, and celiac disease. I used an imputation algorithm developed by our lab to infer classical HLA alleles from dense genotype data across the MHC region. In four out of five diseases, I observed that accounting for non-additive effects significantly improved the fit of a logistic regression model (RA, P = 2.5 × 10-12; T1D, P = 2.4 × 10-10; psoriasis, P = 5.9 × 10-6; celiac disease, P = 1.2 × 10-87). In three of these diseases, the models were further improved after accounting for interactions between specific HLA alleles (RA, P = 1.8 × 10-3; T1D, P = 8.6 × 10-27; celiac disease, P = 6.0 × 10-100). These allelic interactions generally increased disease risk for heterozygous patients, and they explained moderate but significant fractions of phenotypic variance (1.4% in RA, 4.0% in T1D, and 4.1% in celiac disease) beyond an additive model. These novel findings substantially revise our understanding of the genetic architecture of autoimmunity and may provide insight into the pathogenic mechanism of these diseases

Phenotypic and genetic classification of diabetes

The historical subclassification of diabetes into predominantly types 1 and 2 is well appreciated to inadequately capture the heterogeneity seen in patient presentations, disease course, response to therapy and disease complications. This review summarises proposed data-driven approaches to further refine diabetes subtypes using clinical phenotypes and/or genetic information. We highlight the benefits as well as the limitations of these subclassification schemas, including practical barriers to their implementation that would need to be overcome before incorporation into clinical practice. Graphical abstract: [Figure not available: see fulltext.]