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Objective: Inclusion body myositis (IBM) is a progressive late‐onset muscle disease characterised by preferential weakness of quadriceps femoris and finger flexors, with elusive causes involving immune, degenerative, genetic and age‐related factors. Overlapping with normal muscle ageing makes diagnosis and prognosis problematic. Methods: We characterised peripheral blood leucocytes in 81 IBM patients and 45 healthy controls using flow cytometry. Using a random forest classifier, we identified immune changes in IBM compared to HC. K‐means clustering and the random forest one‐versus‐rest model classified patients into three immunophenotypic clusters. Functional outcome measures including mTUG, 2MWT, IBM‐FRS, EAT‐10, knee extension and grip strength were assessed across clusters. Results: The random forest model achieved a 94% AUC ROC with 82.76% specificity and 100% sensitivity. Significant differences were found in IBM patients, including increased CD8+ T‐bet+ cells, CD4+ T cells skewed towards a Th1 phenotype and altered γδ T cell repertoire with a reduced proportion of Vγ9+Vδ2+ cells. IBM patients formed three clusters: (i) activated and inflammatory CD8+ and CD4+ T‐cell profile and the highest proportion of anti‐cN1A‐positive patients in cluster 1; (ii) limited inflammation in cluster 2; (iii) highly differentiated, pro‐inflammatory T‐cell profile in cluster 3. Additionally, no significant differences in patients' age and gender were detected between immunophenotype clusters; however, worsening trends were detected with several functional outcomes. Conclusion: These findings unveil distinct immune profiles in IBM, shedding light on underlying pathological mechanisms for potential immunoregulatory therapeutic development. [ABSTRACT FROM AUTHOR]

Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of muscle disorders including adult and juvenile dermatomyositis, polymyositis, immune-mediated necrotising myopathy and sporadic inclusion body myositis, all of which present with variable symptoms and disease progression. The identification of effective biomarkers for IIMs has been challenging due to the heterogeneity between IIMs and within IIM subgroups, but recent advances in machine learning (ML) techniques have shown promises in identifying novel biomarkers. This paper reviews recent studies on potential biomarkers for IIM and evaluates their clinical utility. We also explore how data analytic tools and ML algorithms have been used to identify biomarkers, highlighting their potential to advance our understanding and diagnosis of IIM and improve patient outcomes. Overall, ML techniques have great potential to revolutionize biomarker discovery in IIMs and lead to more effective diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This article discusses the potential of machine learning (ML) models to improve the phenotypic stratification of idiopathic inflammatory myopathies (IIMs), with a focus on anti-MDA5-positive dermatomyositis (MDA5-DM). MDA5-DM is a subgroup of dermatomyositis that is commonly associated with comorbidities, particularly rapidly progressing interstitial lung disease (RP-ILD). However, the variability in patient cohort sizes, clinical characteristics, and lack of standardized diagnostic criteria for MDA5-DM pose challenges for ML models. The article emphasizes the need for unbiased and consistent data across studies to achieve rigorous replication of ML models and highlights the importance of integrating scientific insights with the development of practical and easily applicable clinical variables. [Extracted from the article]

Introduction: Inclusion body myositis (IBM) is a progressive inflammatory myopathy characterised by skeletal muscle infiltration and myofibre invasion by CD8+ T lymphocytes. In some cases, IBM has been reported to be associated with a systemic lymphoproliferative disorder of CD8+ T cells exhibiting a highly differentiated effector phenotype known as T cell Large Granular Lymphocytic Leukemia (T-LGLL). Methods: We investigated the incidence of a CD8+ T-LGL lymphoproliferative disorder in 85 IBM patients and an aged-matched group of 56 Healthy Controls (HC). Further, we analysed the phenotypical characteristics of the expanded TLGLs and investigated whether their occurrence was associated with any particular HLA alleles or clinical characteristics. Results: Blood cell analysis by flow cytometry revealed expansion of T-LGLs in 34 of the 85 (40%) IBM patients. The T cell immunophenotype of T-LGLHIGH patients was characterised by increased expression of surface molecules including CD57 and KLRG1, and to a lesser extent of CD94 and CD56 predominantly in CD8+ T cells, although we also observed modest changes in CD4+ T cells and γδ T cells. Analysis of Ki67 in CD57+ KLRG1+ T cells revealed that only a small proportion of these cells was proliferating. Comparative analysis of CD8+ and CD4+ T cells isolated from matched blood and muscle samples donated by three patients indicated a consistent pattern of more pronounced alterations in muscles, although not significant due to small sample size. In the TLGLHIGH patient group, we found increased frequencies of perforin-producing CD8+ and CD4+ T cells that were moderately correlated to combined CD57 and KLRG1 expression. Investigation of the HLA haplotypes of 75 IBM patients identified that carriage of the HLA-C*14:02:01 allele was significantly higher in T-LGLHIGH compared to T-LGLLOW individuals. Expansion of T-LGL was not significantly associated with seropositivity patient status for anti-cytosolic 5'-nucleotidase 1A autoantibodies. Clinically, the age at disease onset and disease duration were similar in the T-LGLHIGH and T-LGLLOW patient groups. However, metadata analysis of functional alterations indicated that patients with expanded T-LGL more frequently relied on mobility aids than T-LGLLOW patients indicating greater disease severity. Conclusion: Altogether, these results suggest that T-LGL expansion occurring in IBM patients is correlated with exacerbated immune dysregulation and increased disease burden. [ABSTRACT FROM AUTHOR]

The activating immune receptor natural killer group member D (NKG2D) and its cognate ligands represent a fundamental surveillance system of cellular distress, damage or transformation. Signaling through the NKG2D receptor-ligand axis is critical for early detection of viral infection or oncogenic transformation and the presence of functional NKG2D ligands (NKG2D-L) is associated with tumor rejection and viral clearance. Many viruses and tumors have developed mechanisms to evade NKG2D recognition via transcriptional, post-transcriptional or post-translational interference with NKG2D-L, supporting the concept that circumventing immune evasion of the NKG2D receptor-ligand axis may be an attractive therapeutic avenue for antiviral therapy or cancer immunotherapy. To date, the complexity of the NKG2D receptor-ligand axis and the lack of specificity of current NKG2D-targeting therapies has not allowed for the precise manipulation required to optimally harness NKG2D-mediated immunity. However, with the discovery of clustered regularly interspaced short palindromic repeats (CRISPRs) and CRISPR-associated (Cas) proteins, novel opportunities have arisen in the realm of locus-specific gene editing and regulation. Here, we give a brief overview of the NKG2D receptor-ligand axis in humans and discuss the levels at which NKG2D-L are regulated and dysregulated during viral infection and oncogenesis. Moreover, we explore the potential for CRISPR-based technologies to provide novel therapeutic avenues to improve and maximize NKG2D-mediated immunity. [ABSTRACT FROM AUTHOR]

In inflammatory myopathies, the self-reactive immune cells involved in muscle aggression have been studied mostly using histological assessment of muscle biopsy sections; this methodology provides the advantage of visualizing and identifying cells within the tissue, but it does not allow further investigation. To gain access to live and isolated cells, many studies utilized blood samples; however, in the absence of biological tools to discriminate the leukocytes associated with the autoimmune process from those that emerged from responses against pathogens, the information observed on circulating immune cells often lacks in specificity, and thus result interpretation may prove difficult. In order to selectively retrieve self-reactive immune cells, we developed a protocol to isolate live leukocytes from human muscle biopsies, which allows for further analysis using a large range of methodologies. The protocol uses enzymatic digestion to release live leukocytes from freshly collected skeletal muscle samples, followed by filtration and separation of the leukocytes from the myocytes by density gradient centrifugation. The isolated cells can be submitted immediately to various analysis strategies to characterize ex vivo the specific cellular and molecular mechanisms responsible for self-directed immune muscle aggression or may be placed in culture for expansion. [ABSTRACT FROM AUTHOR]