Association with HLA-DRβ1 position 37 distinguishes juvenile Dermatomyositis from adult-onset myositis

Decoding the genetic landscape of juvenile dermatomyositis: insights from phosphorylation-associated single nucleotide polymorphisms

Genome-wide association studies (GWASs) have identified genetic susceptibility loci associated with juvenile dermatomyositis (JDM). Single nucleotide polymorphisms related to phosphorylation (phosSNPs) are critical nonsynonymous mutations exerting substantial influence on gene expression regulation. The aim of this study was to identify JDM susceptibility genes in the GWAS loci by the use of phosSNPs. We explored quantitative trait loci (QTLs) among the phosSNPs associated with JDM using data from eQTL (bulk tissues and single-cell) and pQTL studies. For gene expression and protein levels significantly influenced by JDM-associated phosSNPs, we assessed their associations with JDM through MR analyses. Additionally, we conducted differential expression gene analyses, incorporating single-cell transcriptomic profiling of 6 JDM cases and 11 juvenile controls (99,396 cells). We identified 31 phosSNPs situated in the 6p21 locus that were associated with JDM. Half of these phosSNPs showed effects on gene expression in various cells and circulating protein levels. In MR analyses, we established associations between the expression levels of pivotal JDM-associated genes, including MICB, C4A, HLA-DRB1, HLA-DRB5, and PSMB9, in skin, muscle, or blood cells and circulating levels of C4A, with JDM. Utilizing single-cell eQTL data, we identified a total of 276 association signals across 14 distinct immune cell types for 28 phosSNPs. Further insights were gained through single-cell differential expression analysis, revealing differential expression of PSMB9, HLA-A, HLA-B, HLA-C, HLA-DPB1, HLA-DQA1, HLA-DQB1, and HLA-DRB1 in immune cells. The present study pinpointed phosSNPs within susceptibility genes for JDM and unraveled the intricate relationships among these SNPs, gene expression levels, and JDM.

Non-coding variants impact cis-regulatory coordination in a cell type-specific manner

BACKGROUND

Interactions among cis-regulatory elements (CREs) play a crucial role in gene regulation. Various approaches have been developed to map these interactions genome-wide, including those relying on interindividual epigenomic variation to identify groups of covariable regulatory elements, referred to as chromatin modules (CMs). While CM mapping allows to investigate the relationship between chromatin modularity and gene expression, the computational principles used for CM identification vary in their application and outcomes.

RESULTS

We comprehensively evaluate and streamline existing CM mapping tools and present guidelines for optimal utilization of epigenome data from a diverse population of individuals to assess regulatory coordination across the human genome. We showcase the effectiveness of our recommended practices by analyzing distinct cell types and demonstrate cell type specificity of CRE interactions in CMs and their relevance for gene expression. Integration of genotype information revealed that many non-coding disease-associated variants affect the activity of CMs in a cell type-specific manner by affecting the binding of cell type-specific transcription factors. We provide example cases that illustrate in detail how CMs can be used to deconstruct GWAS loci, assess variable expression of cell surface receptors in immune cells, and reveal how genetic variation can impact the expression of prognostic markers in chronic lymphocytic leukemia.

CONCLUSIONS

Our study presents an optimal strategy for CM mapping and reveals how CMs capture the coordination of CREs and its impact on gene expression. Non-coding genetic variants can disrupt this coordination, and we highlight how this may lead to disease predisposition in a cell type-specific manner.

Borrelia burgdorferi and autoimmune mechanisms: implications for mimicry, misdiagnosis, and mismanagement in Lyme disease and autoimmune disorders

The genus Borrelia encompasses a diverse group of spirochetes transmitted primarily by ticks, with Borrelia burgdorferi causing Lyme disease, which is prevalent in North America and Europe. Borrelia's structural adaptations and ability to persist in diverse host tissues underscore its pathogenic potential. Beyond traditional infectious responses, Borrelia engages in complex interactions with the host immune system, contributing to autoimmune mechanisms such as molecular mimicry and persistent infections. This intricate interplay manifests in symptoms resembling various autoimmune diseases, including systemic lupus erythematosus, dermatomyositis, local scleroderma, and systemic sclerosis. However, these associations lack a precise explanation, emphasizing the need for further investigation. The cases of misdiagnosis between Lyme borreliosis and autoimmune diseases highlight the critical importance of accurate diagnostics and adherence to guidelines. Understanding Borrelia's impact on immune responses is pivotal for advancing diagnostics and targeted therapeutic interventions in Lyme borreliosis and its potential autoimmune implications.

Artificial intelligence for nailfold capillaroscopy analyses - a proof of concept application in juvenile dermatomyositis

BACKGROUND

Biomarkers for idiopathic inflammatory myopathies are difficult to identify and may involve expensive laboratory tests. We assess the potential for artificial intelligence (AI) to differentiate children with juvenile dermatomyositis (JDM) from healthy controls using nailfold capillaroscopy (NFC) images. We also assessed the potential of NFC images to reflect the range of disease activity with JDM.

METHODS

A total of 1,120 NFC images from 111 children with active JDM, diagnosed between 1990 and 2020, and 321 NFC images from 31 healthy controls were retrieved from the CureJM JDM Registry. We built a lightweight and explainable deep neural network model called NFC-Net. Images were downscaled by interpolation techniques to reduce the computational cost.

RESULTS

NFC-Net achieved high performance in differentiating patients with JDM from controls, with an area under the ROC curve (AUROC) of 0.93 (0.84, 0.99) and accuracy of 0.91 (0.82, 0.92). With sensitivity (0.85) and specificity (0.90) resulted in model precision of 0.95. The AUROC and accuracy for predicting clinical disease activity from inactivity were 0.75 (0.61, 0.81) and 0.74 (0.65, 0.79).

CONCLUSION

The good performance of the NFC-Net demonstrates that NFC images are sufficient for detecting often unrecognized JDM disease activity, providing a reliable indicator of disease status.

IMPACT

Proposed NFC-Net can accurately predict children with JDM from healthy controls using nailfold capillaroscopy (NFC) images. Additionally, it predicts the scores to JDM disease activity versus no activity. Equipped with gradients, NFC-Net is explainable and gives visual information beside the reported accuracies. NFC-Net is computationally efficient since it is applied to substantially downscaled NFC images. Furthermore, the model can be wrapped within an edge-based device like a mobile application that is accessible to both clinicians and patients.

[Advances in the diagnostics and treatment of juvenile dermatomyositis]

Juvenile dermatomyositis (JDM) is by far the most frequent inflammatory myopathy in childhood and adolescence. It is clinically characterized by inflammatory changes of the skin and muscles but as a multisystemic disease can also affect the skeletal system, the gastrointestinal tract, lungs and heart. Intrinsic (multigenetic risk) and extrinsic factors (triggers) are involved in the pathogenesis resulting in endothelial damage, involvement of fascies, activation of the interferon system and autoimmune reactions including formation of myositis-specific autoantibodies (MSA). In contrast to dermatomyositis in adults, in children and adolescents there are no associations with malignant diseases. The variable expression, the rarity of the disease and the risk of long-term damage and complications necessitate pediatric rheumatological experience in the diagnostics and treatment. Recently, new approaches in drug treatment have substantially improved the outcome and prognosis but a multidisciplinary treatment (including physicians, physiotherapists, psychologists, social workers) is mandatory, especially in the first phases of the disease. Particularly important is a professionally correct treatment of the functional sequelae, which are a particular focus of this article.

A rigorous benchmarking of alignment-based HLA typing algorithms for RNA-seq data

Accurate identification of human leukocyte antigen (HLA) alleles is essential for various clinical and research applications, such as transplant matching and drug sensitivities. Recent advances in RNA-seq technology have made it possible to impute HLA types from sequencing data, spurring the development of a large number of computational HLA typing tools. However, the relative performance of these tools is unknown, limiting the ability for clinical and biomedical research to make informed choices regarding which tools to use. Here we report the study design of a comprehensive benchmarking of the performance of 12 HLA callers across 682 RNA-seq samples from 8 datasets with molecularly defined gold standard at 5 loci, HLA-A, -B, -C, -DRB1, and -DQB1. For each HLA typing tool, we will comprehensively assess their accuracy, compare default with optimized parameters, and examine for discrepancies in accuracy at the allele and loci levels. We will also evaluate the computational expense of each HLA caller measured in terms of CPU time and RAM. We also plan to evaluate the influence of read length over the HLA region on accuracy for each tool. Most notably, we will examine the performance of HLA callers across European and African groups, to determine discrepancies in accuracy associated with ancestry. We hypothesize that RNA-Seq HLA callers are capable of returning high-quality results, but the tools that offer a good balance between accuracy and computational expensiveness for all ancestry groups are yet to be developed. We believe that our study will provide clinicians and researchers with clear guidance to inform their selection of an appropriate HLA caller.

High-resolution HLA genotyping in inclusion body myositis refines 8.1 ancestral haplotype association to DRB1*03:01:01 and highlights pathogenic role of arginine-74 of DRβ1 chain

OBJECTIVES

Inclusion body myositis (IBM) is a progressive inflammatory-degenerative muscle disease of older individuals, with some patients producing anti-cytosolic 5'-nucleotidase 1A (NT5C1A, aka cN1A) antibodies. Human Leukocyte Antigens (HLA) is the highest genetic risk factor for developing IBM. In this study, we aimed to further define the contribution of HLA alleles to IBM and the production of anti-cN1A antibodies.

METHODS

We HLA haplotyped a Western Australian cohort of 113 Caucasian IBM patients and 112 ethnically matched controls using Illumina next-generation sequencing. Allele frequency analysis and amino acid alignments were performed using the Genentech/MiDAS bioinformatics package. Allele frequencies were compared using Fisher's exact test. Age at onset analysis was performed using the ggstatsplot package. All analysis was carried out in RStudio version 1.4.1717.

RESULTS

Our findings validated the independent association of HLA-DRB1*03:01:01 with IBM and attributed the risk to an arginine residue in position 74 within the DRβ1 protein. Conversely, DRB4*01:01:01 and DQA1*01:02:01 were found to have protective effects; the carriers of DRB1*03:01:01 that did not possess these alleles had a fourteenfold increased risk of developing IBM over the general Caucasian population. Furthermore, patients with the abovementioned genotype developed symptoms on average five years earlier than patients without. We did not find any HLA associations with anti-cN1A antibody production.

CONCLUSIONS

High-resolution HLA sequencing more precisely characterised the alleles associated with IBM and defined a haplotype linked to earlier disease onset. Identification of the critical amino acid residue by advanced biostatistical analysis of immunogenetics data offers mechanistic insights and future directions into uncovering IBM aetiopathogenesis.

Juvenile idiopathic inflammatory myositis: an update on pathophysiology and clinical care

The childhood-onset or juvenile idiopathic inflammatory myopathies (JIIMs) are a heterogenous group of rare and serious autoimmune diseases of children and young people that predominantly affect the muscles and skin but can also involve other organs, including the lungs, gut, joints, heart and central nervous system. Different myositis-specific autoantibodies have been identified that are associated with different muscle biopsy features, as well as with different clinical characteristics, prognoses and treatment responses. Thus, myositis-specific autoantibodies can be used to subset JIIMs into sub-phenotypes; some of these sub-phenotypes parallel disease seen in adults, whereas others are distinct from adult-onset idiopathic inflammatory myopathies. Although treatments and management have much improved over the past decade, evidence is still lacking for many of the current treatments and few validated prognostic biomarkers are available with which to predict response to treatment, comorbidities (such as calcinosis) or outcome. Emerging data on the pathogenesis of the JIIMs are leading to proposals for new trials and tools for monitoring disease.

Update on dermatomyositis

PURPOSE OF REVIEW

This review summarizes and comments on current knowledge in dermatomyositis.

RECENT FINDINGS

The 2018 European Neuromuscular Centre classification of dermatomyositis has been challenging by the discovery of clinicopathological features associated with dermatomyositis-specific antibody (DMSA) that were not incorporated in the original criteria. These features include but may not be limited to the presence of perifascicular necrosis in anti-Mi-2 dermatomyositis; presence of diffuse nonperifascicular sarcoplasmic myxovirus resistance protein A expression in anti-MDA5 dermatomyositis; and dermatomyositis sine dermatitis in anti-NXP-2 dermatomyositis. Variations and subclassifications within the same DMSA subtypes are observed: anti-MDA5 dermatomyositis is clinically subcategorized into good, intermediate, and poor prognostic subgroups; concurrent anti-CCAR1 and anti-TIF1-γ positivity identify anti-TIF1-γ-positive patient with a lower risk for cancer-associated myositis. Owing to distinct IFN1-signaling pathway activation in dermatomyositis, JAK-STAT inhibitor - the pathway-targeted therapy, have been studied with promising results in refractory dermatomyositis and some new-onset dermatomyositis. In addition, the potential serum biomarkers for IFN1 pathway activation are being investigated for their performance in monitoring the disease activity and the efficacy of the treatment.

SUMMARY

DMSA, evidence of prominent IFN1 pathway activation, and risk/severity-associated biomarkers would likely play major roles in future dermatomyositis classification, disease monitoring, and treatment decision.

Anti-FHL1 autoantibodies in juvenile myositis are associated with anti-Ro52 autoantibodies but not with severe disease features

OBJECTIVES

Four-and-a-half LIM domains 1 (FHL1) is a muscle-specific protein. Autoantibodies against FHL1 were recently discovered in adults with idiopathic inflammatory myopathies (IIMs) and were found to be associated with clinical features and outcomes indicative of increased disease severity. Anti-FHL1 autoantibodies have not been described in children. Here, the prevalence and clinical features associated with anti-FHL1 autoantibodies were examined in a large North American cohort of juvenile patients with IIM.

METHODS

Sera from 338 juvenile IIM patients and 91 juvenile healthy controls were screened for anti-FHL1 autoantibodies by ELISA. Clinical characteristics and HLA alleles of those with and without anti-FHL1 autoantibodies were compared among those with juvenile IIM.

RESULTS

Anti-FHL1 autoantibodies were present in 10.9% of juvenile IIM patients and 1.1% of controls. The frequency of anti-FHL1 autoantibodies among clinical and serologic subgroups did not differ. A higher percentage of Asian patients had anti-FHL1 autoantibodies (11% vs 0.7%; P = 0.002). Myositis-associated autoantibodies (MAAs) [odds ratio (OR) 2.09 (CI 1.03, 4.32)], anti-Ro52 autoantibodies specifically [OR 4.17 (CI 1.83, 9.37)] and V-sign rash [OR 2.59 (CI 1.22, 5.40)] were associated with anti-FHL1 autoantibodies. There were no differences in other features or markers of disease severity. No HLA associations with anti-FHL1 autoantibodies in Caucasian myositis patients were identified.

CONCLUSION

Anti-FHL1 autoantibodies are present in ∼11% of juvenile IIM patients and commonly co-occur with MAAs, including anti-Ro52 autoantibodies. In contrast to adult IIM, anti-FHL1 autoantibodies in juvenile myositis are associated with V-sign rash but not with other distinctive clinical features or worse outcomes.

The Genetics of Autoimmune Myositis

The idiopathic inflammatory myopathies (IIM) are rare, heterogeneous systemic autoimmune disorders, characterized by inflammation of skeletal muscle and multi-organ involvement. Studies to identify genetic risk factors and dysregulated gene expression in IIM aim to increase our understanding of disease pathogenesis. Genome-wide association studies have confirmed the HLA region as the most strongly associated region in IIM, with different associations between clinically-defined subgroups. Associated genes are involved in both the innate and adaptive immune response, while identification of variants reported in other autoimmune disorders suggests shared biological pathways. Targeted imputation analysis has identified key associated amino acid residues within HLA molecules that may influence antigen recognition. These amino acids increase risk for specific clinical phenotypes and autoantibody subgroups, and suggest that serology-defined subgroups may be more homogeneous. Recent data support the contribution of rare genetic variation to disease susceptibility in IIM, including mitochondrial DNA variation in sporadic inclusion body myositis and somatic mutations and loss of heterozygosity in cancer-associated myositis. Gene expression studies in skeletal muscle, blood and skin from individuals with IIM has confirmed the role of interferon signalling and other dysregulated pathways, and identified cell-type specific signatures. These dysregulated genes differentiate IIM subgroups and identify potential biomarkers. Here, we review recent genetic studies in IIM, and how these inform our understanding of disease pathogenesis and provide mechanistic insights into biological pathways.