Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

Synovial fibroblast gene expression is associated with sensory nerve growth and pain in rheumatoid arthritis

It has been presumed that rheumatoid arthritis (RA) joint pain is related to inflammation in the synovium; however, recent studies reveal that pain scores in patients do not correlate with synovial inflammation. We developed a machine-learning approach (graph-based gene expression module identification or GbGMI) to identify an 815-gene expression module associated with pain in synovial biopsy samples from patients with established RA who had limited synovial inflammation at arthroplasty. We then validated this finding in an independent cohort of synovial biopsy samples from patients who had early untreated RA with little inflammation. Single-cell RNA sequencing analyses indicated that most of these 815 genes were most robustly expressed by lining layer synovial fibroblasts. Receptor-ligand interaction analysis predicted cross-talk between human lining layer fibroblasts and human dorsal root ganglion neurons expressing calcitonin gene-related peptide (CGRP+). Both RA synovial fibroblast culture supernatant and netrin-4, which is abundantly expressed by lining fibroblasts and was within the GbGMI-identified pain-associated gene module, increased the branching of pain-sensitive murine CGRP+ dorsal root ganglion neurons in vitro. Imaging of solvent-cleared synovial tissue with little inflammation from humans with RA revealed CGRP+ pain-sensing neurons encasing blood vessels growing into synovial hypertrophic papilla. Together, these findings support a model whereby synovial lining fibroblasts express genes associated with pain that enhance the growth of pain-sensing neurons into regions of synovial hypertrophy in RA.

Axl and MerTK regulate synovial inflammation and are modulated by IL-6 inhibition in rheumatoid arthritis

The TAM tyrosine kinases, Axl and MerTK, play an important role in rheumatoid arthritis (RA). Here, using a unique synovial tissue bioresource of patients with RA matched for disease stage and treatment exposure, we assessed how Axl and MerTK relate to synovial histopathology and disease activity, and their topographical expression and longitudinal modulation by targeted treatments. We show that in treatment-naive patients, high AXL levels are associated with pauci-immune histology and low disease activity and inversely correlate with the expression levels of pro-inflammatory genes. We define the location of Axl/MerTK in rheumatoid synovium using immunohistochemistry/fluorescence and digital spatial profiling and show that Axl is preferentially expressed in the lining layer. Moreover, its ectodomain, released in the synovial fluid, is associated with synovial histopathology. We also show that Toll-like-receptor 4-stimulated synovial fibroblasts from patients with RA modulate MerTK shedding by macrophages. Lastly, Axl/MerTK synovial expression is influenced by disease stage and therapeutic intervention, notably by IL-6 inhibition. These findings suggest that Axl/MerTK are a dynamic axis modulated by synovial cellular features, disease stage and treatment.

Having More Tender Than Swollen Joints is Associated With Worse Function and Work Impairment in Patients With Early Rheumatoid Arthritis

OBJECTIVE

Patients with early rheumatoid arthritis (RA) may present with more tender than swollen joints, which can persist. Elevated tender-swollen joint difference (TSJD) is often challenging, because there may be multiple causes and it may contribute to overestimating disease activity. Little is known about the phenotype and impact of TSJDs on patient function. Our objective was to evaluate the impact of TSJD on functional outcomes in early RA and to see whether associations vary by joint size.

METHODS

Data were from patients with active, early RA (≤12 months) enrolled in the Canadian Early Arthritis Cohort, who completed assessments of general function (Multidimensional Health Assessment Questionnaire [MDHAQ]), upper extremity (UE) function (Quality of Life in Neurological Disorders [Neuro-QoL] UE scale), and work/activity impairment (Work Productivity and Activity Impairment RA) over their first year of follow-up. A total of 28 joint counts were performed. TSJDs were calculated. Adjusted associations between TSJDs and functional outcomes were estimated in separate multivariable linear mixed effects models. Separate analyses were performed for large- versus small-joint TSJD.

RESULTS

Patients (N = 547) were 70% female, mean age 56 (SD 15) years, mean disease duration 5.3 (SD 2.9) months. At baseline, 287 (52%) had TSJD >0 (43% involved large joints and 34% small joints), decreasing to 32% at 12 months. A one-point increase in TSJD was significantly associated with worse function (MDHAQ: adjusted mean change 0.10, 95% confidence interval [CI] 0.08-0.13; Neuro-QoL UE function T score: adjusted mean change -0.59, 95% CI -0.76 to -0.43; and greater work impairment: adjusted mean change 1.95%, 95% CI 0.85%-3.05%). Higher large-joint TSJDs were associated with the worst functional outcomes.

CONCLUSION

Having more tender than swollen joints is common in early RA and is associated with worse function, most notably when involving large joints. Early identification and targeted intervention strategies may be needed.

Associations Between Rheumatoid Arthritis Clinical Factors and Synovial Cell Types and States

OBJECTIVE

Recent studies have uncovered diverse cell types and states in the rheumatoid arthritis (RA) synovium; however, limited data exist correlating these findings with patient-level clinical information. Using the largest cohort to date with clinical and multicell data, we determined associations between RA clinical factors with cell types and states in the RA synovium.

METHODS

The Accelerated Medicines Partnership Rheumatoid Arthritis study recruited patients with active RA who were not receiving disease-modifying antirheumatic drugs (DMARDs) or who had an inadequate response to methotrexate (MTX) or tumor necrosis factor inhibitors. RA clinical factors were systematically collected. Biopsies were performed on an inflamed joint, and tissue were disaggregated and processed with a cellular indexing of transcriptomes and epitopes sequencing pipeline from which the following cell type percentages and cell type abundance phenotypes (CTAPs) were derived: endothelial, fibroblast, and myeloid (EFM); fibroblasts; myeloid; T and B cells; T cells and fibroblasts (TF); and T and myeloid cells. Correlations were measured between RA clinical factors, cell type percentage, and CTAPs.

RESULTS

We studied 72 patients (mean age 57 years, 75% women, 83% seropositive, mean RA duration 6.6 years, mean Disease Activity Score-28 C-reactive Protein 3 [DAS28-CRP3] score 4.8). Higher DAS28-CRP3 correlated with a higher T cell percentage (P < 0.01). Those receiving MTX and not a biologic DMARD (bDMARD) had a higher percentage of B cells versus those receiving no DMARDs (P < 0.01). Most of those receiving bDMARDs were categorized as EFM (57%), whereas none were TF. No significant difference was observed across CTAPs for age, sex, RA disease duration, or DAS28-CRP3.

CONCLUSION

In this comprehensive screen of clinical factors, we observed differential associations between DMARDs and cell phenotypes, suggesting that RA therapies, more than other clinical factors, may impact cell type/state in the synovium and ultimately influence response to subsequent therapies.

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

The long non-coding RNA HOTAIR contributes to joint-specific gene expression in rheumatoid arthritis

Although patients with rheumatoid arthritis (RA) typically exhibit symmetrical joint involvement, some patients develop alternative disease patterns in response to treatment, suggesting that different molecular mechanism may underlie disease progression depending on joint location. Here, we identify joint-specific changes in RA synovium and synovial fibroblasts (SF) between knee and hand joints. We show that the long non-coding RNA HOTAIR, which is only expressed in knee SF, regulates more than 50% of this site-specific gene expression in SF. HOTAIR is downregulated after stimulation with pro-inflammatory cytokines and is expressed at lower levels in knee samples from patients with RA, compared with osteoarthritis. Knockdown of HOTAIR in knee SF increases PI-Akt signalling and IL-6 production, but reduces Wnt signalling. Silencing HOTAIR inhibits the migratory function of SF, decreases SF-mediated osteoclastogenesis, and increases the recruitment of B cells by SF. We propose that HOTAIR is an important epigenetic factor in joint-specific gene expression in RA.

Classification of distinct osteoarthritis subtypes with different knee joint tissues by gene expression profiles

Aims

Knee osteoarthritis (OA) involves a variety of tissues in the joint. Gene expression profiles in different tissues are of great importance in order to understand OA.

Methods

First, we obtained gene expression profiles of cartilage, synovium, subchondral bone, and meniscus from the Gene Expression Omnibus (GEO). Several datasets were standardized by merging and removing batch effects. Then, we used unsupervised clustering to divide OA into three subtypes. The gene ontology and pathway enrichment of three subtypes were analyzed. CIBERSORT was used to evaluate the infiltration of immune cells in different subtypes. Finally, OA-related genes were obtained from the Molecular Signatures Database for validation, and diagnostic markers were screened according to clinical characteristics. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to verify the effectiveness of markers.

Results

C1 subtype is mainly concentrated in the development of skeletal muscle organs, C2 lies in metabolic process and immune response, and C3 in pyroptosis and cell death process. Therefore, we divided OA into three subtypes: bone remodelling subtype (C1), immune metabolism subtype (C2), and cartilage degradation subtype (C3). The number of macrophage M0 and activated mast cells of C2 subtype was significantly higher than those of the other two subtypes. COL2A1 has significant differences in different subtypes. The expression of COL2A1 is related to age, and trafficking protein particle complex subunit 2 is related to the sex of OA patients.

Conclusion

This study linked different tissues with gene expression profiles, revealing different molecular subtypes of patients with knee OA. The relationship between clinical characteristics and OA-related genes was also studied, which provides a new concept for the diagnosis and treatment of OA.

Computer Vision Analysis of Rheumatoid Arthritis Synovium Reveals Lymphocytic Inflammation Is Associated With Immunoglobulin Skewing in Blood

OBJECTIVE

We sought to develop computer vision methods to quantify aggregates of cells in synovial tissue and compare these with clinical and gene expression parameters.

METHODS

We assembled a computer vision pipeline to quantify five features encompassing synovial cell density and aggregates and compared these with pathologist scores, disease classification, autoantibody status, and RNA expression in a cohort of 156 patients with rheumatoid arthritis (RA) and 149 patients with osteoarthritis (OA).

RESULTS

All five features were associated with pathologist scores of synovial lymphocytic inflammation (P < 0.0001). Three features that related to the cells per unit of tissue were significantly increased in patients with both seronegative and seropositive RA compared with those with OA; on the other hand, aggregate features (number and diameter) were significantly increased in seropositive, but not seronegative, RA compared with OA. Aggregate diameter was associated with the gene expression of immunoglobulin heavy-chain genes in the synovial tissue. Compared with blood, synovial immunoglobulin isotypes were skewed from IGHM and IGHD to IGHG3 and IGHG1. Further, patients with RA with high levels of lymphocytic infiltrates in the synovium demonstrated parallel skewing in their blood with a relative decrease in IGHGM (P < 0.002) and IGHD (P < 0.03) and an increase in class-switched immunoglobulin genes IGHG3 (P < 0.03) and IGHG1 (P < 0.002).

CONCLUSION

High-resolution automated identification and quantification of synovial immune cell aggregates uncovered skewing in the synovium from naïve IGHD and IGHM to memory IGHG3 and IGHG1 and revealed that this process is reflected in the blood of patients with high inflammatory synovium.

Disease activity drives transcriptomic heterogeneity in early untreated rheumatoid synovitis

OBJECTIVES

Transcriptomic profiling of synovial tissue from patients with early, untreated rheumatoid arthritis (RA) was used to explore the ability of unbiased, data-driven approaches to define clinically relevant subgroups.

METHODS

RNASeq was performed on 74 samples, with disease activity data collected at inclusion. Principal components analysis (PCA) and unsupervised clustering were used to define patient clusters based on expression of the most variable genes, followed by pathway analysis and inference of relative abundance of immune cell subsets. Histological assessment and multiplex immunofluorescence (for CD45, CD68, CD206) were performed on paraffin sections.

RESULTS

PCA on expression of the (n=894) most variable genes across this series did not divide samples into distinct groups, instead yielding a continuum correlated with baseline disease activity. Two patient clusters (PtC1, n=52; PtC2, n=22) were defined based on expression of these genes. PtC1, with significantly higher disease activity and probability of response to methotrexate therapy, showed upregulation of immune system genes; PtC2 showed upregulation of lipid metabolism genes, described to characterise tissue resident or M2-like macrophages. In keeping with these data, M2-like:M1-like macrophage ratios were inversely correlated with disease activity scores and were associated with lower synovial immune infiltration and the presence of thinner, M2-like macrophage-rich synovial lining layers.

CONCLUSION

In this large series of early, untreated RA, we show that the synovial transcriptome closely mirrors clinical disease activity and correlates with synovial inflammation. Intriguingly, lower inflammation and disease activity are associated with higher ratios of M2:M1 macrophages, particularly striking in the synovial lining layer. This may point to a protective role for tissue resident macrophages in RA.

Artificial intelligence in rheumatoid arthritis: potential applications and future implications

The widespread adoption of digital health records, coupled with the rise of advanced diagnostic testing, has resulted in an explosion of patient data, comparable in scope to genomic datasets. This vast information repository offers significant potential for improving patient outcomes and decision-making, provided one can extract meaningful insights from it. This is where artificial intelligence (AI) tools like machine learning (ML) and deep learning come into play, helping us leverage these enormous datasets to predict outcomes and make informed decisions. AI models can be trained to analyze and interpret patient data, including physician notes, laboratory testing, and imaging, to aid in the management of patients with rheumatic diseases. As one of the most common autoimmune diseases, rheumatoid arthritis (RA) has attracted considerable attention, particularly concerning the evolution of diagnostic techniques and therapeutic interventions. Our aim is to underscore those areas where AI, according to recent research, demonstrates promising potential to enhance the management of patients with RA.

The preferred technique for knee synovium biopsy and synovial fluid arthrocentesis

For knee osteoarthritis and related conditions, analysis of biomarkers hold promise to improve early diagnosis and/or offer patient-specific treatment. To compare biomarker analyses, reliable, high-quality biopsies are needed. The aim of this work is to summarize the literature on the current best practices of biopsy of the synovium and synovial fluid arthrocentesis. Therefore, PubMed, Embase and Web of Science were systematically searched for articles that applied, demonstrated, or evaluated synovial biopsies or arthrocentesis. Expert recommendations and applications were summarized, and evidence for superiority of techniques was evaluated. Thirty-one studies were identified for inclusion. For arthrocentesis, the superolateral approach in a supine position, with a 0°-30° knee flexion was generally recommended. 18-gage needles, mechanical compression and ultrasound-guidance were found to give superior results. For blind and image-guided synovial biopsy techniques, superolateral and infrapatellar approaches were recommended. Single-handed tools were preconized, including Parker-Pearson needles and forceps. Sample quantity ranged approximately from 2 to 20. Suggestions were compiled for arthrocentesis regarding approach portal and patient position. Further evidence regarding needle size, ultrasound-guidance and mechanical compression were found. More comparative studies are needed before evidence-based protocols can be developed.

CD64 as novel molecular imaging marker for the characterization of synovitis in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is one of the most prevalent and debilitating joint diseases worldwide. RA is characterized by synovial inflammation (synovitis), which is linked to the development of joint destruction. Magnetic resonance imaging and ultrasonography are widely being used to detect the presence and extent of synovitis. However, these techniques do not reveal the activation status of inflammatory cells such as macrophages that play a crucial role in synovitis and express CD64 (Fc gamma receptor (FcγR)I) which is considered as macrophage activation marker.

OBJECTIVES

We aimed to investigate CD64 expression and its correlation with pro-inflammatory cytokines and pro-damaging factors in human-derived RA synovium. Furthermore, we aimed to set up a molecular imaging modality using a radiolabeled CD64-specific antibody as a novel imaging tracer that could be used to determine the extent and phenotype of synovitis using optical and nuclear imaging.

METHODS

First, we investigated CD64 expression in synovium of early- and late-stage RA patients and studied its correlation with the expression of pro-inflammatory and tissue-damaging factors. Next, we conjugated an anti-CD64 antibody with IRDye 800CW and diethylenetriamine penta-acetic acid (DTPA; used for 111In labeling) and tested its binding on cultured THP1 cells, ex vivo RA synovium explants and its imaging potential in SCID mice implanted with human RA synovium explants obtained from RA patients who underwent total joint replacement.

RESULTS

We showed that CD64 is expressed in synovium of early and late-stage RA patients and that FCGR1A/CD64 expression is strongly correlated with factors known to be involved in RA progression. Combined, this makes CD64 a useful marker for imaging the extent and phenotype of synovitis. We reported higher binding of the [111In]In-DTPA-IRDye 800CW anti-CD64 antibody to in vitro cultured THP1 monocytes and ex vivo RA synovium compared to isotype control. In human RA synovial explants implanted in SCID mice, the ratio of uptake of the antibody in synovium over blood was significantly higher when injected with anti-CD64 compared to isotype and injecting an excess of unlabeled antibody significantly reduced the antibody-binding associated signal, both indicating specific receptor binding.

CONCLUSION

Taken together, we successfully developed an optical and nuclear imaging modality to detect CD64 in human RA synovium in vivo.

A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients

Rheumatoid arthritis (RA) is an autoimmune disease that exhibits a high degree of heterogeneity, marked by unpredictable disease flares and significant variations in the response to available treatments. The lack of optimal stratification for RA patients may be a contributing factor to the poor efficacy of current treatment options. The objective of this study is to elucidate the molecular characteristics of RA through the utilization of mitochondrial genes and subsequently construct and authenticate a diagnostic framework for RA. Mitochondrial proteins were obtained from the MitoCarta database, and the R package limma was employed to filter for differentially expressed mitochondrial genes (MDEGs). Metascape was utilized to perform enrichment analysis, followed by an unsupervised clustering algorithm using the ConsensuClusterPlus package to identify distinct subtypes based on MDEGs. The immune microenvironment, biological pathways, and drug response were further explored in these subtypes. Finally, a multi-biomarker-based diagnostic model was constructed using machine learning algorithms. Utilizing 88 MDEGs present in transcript profiles, it was possible to classify RA patients into three distinct subtypes, each characterized by unique molecular and cellular signatures. Subtype A exhibited a marked activation of inflammatory cells and pathways, while subtype C was characterized by the presence of specific innate lymphocytes. Inflammatory and immune cells in subtype B displayed a more modest level of activation (Wilcoxon test P < 0.05). Notably, subtype C demonstrated a stronger correlation with a superior response to biologics such as infliximab, anti-TNF, rituximab, and methotrexate/abatacept (P = 0.001) using the fisher test. Furthermore, the mitochondrial diagnosis SVM model demonstrated a high degree of discriminatory ability in distinguishing RA in both training (AUC = 100%) and validation sets (AUC = 80.1%). This study presents a pioneering analysis of mitochondrial modifications in RA, offering a novel framework for patient stratification and potentially enhancing therapeutic decision-making.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

Clonally expanded CD38hi cytotoxic CD8 T cells define the T cell infiltrate in checkpoint inhibitor-associated arthritis

Immune checkpoint inhibitor (ICI) therapies used to treat cancer, such as anti-PD-1 antibodies, can induce autoimmune conditions in some individuals. The T cell mechanisms mediating such iatrogenic autoimmunity and their overlap with spontaneous autoimmune diseases remain unclear. Here, we compared T cells from the joints of 20 patients with an inflammatory arthritis induced by ICI therapy (ICI-arthritis) with two archetypal autoimmune arthritides, rheumatoid arthritis (RA) and psoriatic arthritis (PsA). Single-cell transcriptomic and antigen receptor repertoire analyses highlighted clonal expansion of an activated effector CD8 T cell population in the joints and blood of patients with ICI-arthritis. These cells were identified as CD38hiCD127- CD8 T cells and were uniquely enriched in ICI-arthritis joints compared with RA and PsA and also displayed an elevated interferon signature. In vitro, type I interferon induced CD8 T cells to acquire the ICI-associated CD38hi phenotype and enhanced cytotoxic function. In a cohort of patients with advanced melanoma, ICI therapy markedly expanded circulating CD38hiCD127- T cells, which were frequently bound by the therapeutic anti-PD-1 drug. In patients with ICI-arthritis, drug-bound CD8 T cells in circulation showed marked clonal overlap with drug-bound CD8 T cells from synovial fluid. These results suggest that ICI therapy directly targets CD8 T cells in patients who develop ICI-arthritis and induces an autoimmune pathology that is distinct from prototypical spontaneous autoimmune arthritides.

Development and characterization of an intra-articular fracture mediated model of post-traumatic osteoarthritis

PURPOSE

This study aimed to develop and characterize a closed intra-articular fracture (IAF) mediated post-traumatic osteoarthritis (PTOA) model in rats to serve as a testbed for putative disease modifying interventions.

METHODS

Male rats were subject to a 0 Joule (J), 1 J, 3 J, or 5 J blunt-force impact to the lateral aspect of the knee and allowed to heal for 14 and 56 days. Micro-CT was performed at time of injury and at the specified endpoints to assess bone morphometry and bone mineral density measurements. Cytokines and osteochondral degradation markers were assayed from serum and synovial fluid via immunoassays. Histopathological analyses were performed on decalcified tissues and assessed for evidence of osteochondral degradation.

RESULTS

High-energy (5 J) blunt impacts consistently induced IAF to the proximal tibia, distal femur, or both while lower energy (1 J and 3 J) impacts did not. CCL2 was found to be elevated in the synovial fluid of rats with IAF at both 14- and 56-days post-injury while COMP and NTX-1 were upregulated chronically relative to sham controls. Histological analysis showed increased immune cell infiltration, increased osteoclasts and osteochondral degradation with IAF relative to sham.

CONCLUSION

Based on results from the current study, our data indicates that a 5 J blunt-forced impact adequately and consistently induces hallmark osteoarthritic changes to the articular surface and subchondral bone at 56 days after IAF. Marked development of PTOA pathobiology suggest this model will provide a robust testbed for screening putative disease modifying interventions that might be translated to the clinic for militarily relevant, high-energy joint injuries.

MSdb: An integrated expression atlas of human musculoskeletal system

The global prevalence and burden of musculoskeletal (MSK) disorders are immense. Advancements in next-generation sequencing (NGS) have generated vast amounts of data, accelerating the research of pathological mechanisms and the development of therapeutic approaches for MSK disorders. However, scattered datasets across various repositories complicate uniform analysis and comparison. Here, we introduce MSdb, a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis. Overall, systematic categorizing, standardized processing, and freely accessible knowledge features MSdb a valuable resource for MSK research community.

Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs

Cycling of biologic or targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA) patients due to non-response is a problem preventing and delaying disease control. We aimed to assess and validate treatment response of b/tsDMARDs among clusters of RA patients identified by deep learning. We clustered RA patients clusters at first-time b/tsDMARD (cohort entry) in the Swiss Clinical Quality Management in Rheumatic Diseases registry (SCQM) [1999-2018]. We performed comparative effectiveness analyses of b/tsDMARDs (ref. adalimumab) using Cox proportional hazard regression. Within 15 months, we assessed b/tsDMARD stop due to non-response, and separately a ≥20% reduction in DAS28-esr as a response proxy. We validated results through stratified analyses according to most distinctive patient characteristics of clusters. Clusters comprised between 362 and 1481 patients (3516 unique patients). Stratified (validation) analyses confirmed comparative effectiveness results among clusters: Patients with ≥2 conventional synthetic DMARDs and prednisone at b/tsDMARD initiation, male patients, as well as patients with a lower disease burden responded better to tocilizumab than to adalimumab (hazard ratio [HR] 5.46, 95% confidence interval [CI] [1.76-16.94], and HR 8.44 [3.43-20.74], and HR 3.64 [2.04-6.49], respectively). Furthermore, seronegative women without use of prednisone at b/tsDMARD initiation as well as seropositive women with a higher disease burden and longer disease duration had a higher risk of non-response with golimumab (HR 2.36 [1.03-5.40] and HR 5.27 [2.10-13.21], respectively) than with adalimumab. Our results suggest that RA patient clusters identified by deep learning may have different responses to first-line b/tsDMARD. Thus, it may suggest optimal first-line b/tsDMARD for certain RA patients, which is a step forward towards personalizing treatment. However, further research in other cohorts is needed to verify our results.

Artificial Intelligence and laboratory data in rheumatic diseases

Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.

Integrative Proteomics and N-Glycoproteomics Analyses of Rheumatoid Arthritis Synovium Reveal Immune-Associated Glycopeptides

Rheumatoid arthritis (RA) is a typical autoimmune disease characterized by synovial inflammation, synovial tissue hyperplasia, and destruction of bone and cartilage. Protein glycosylation plays key roles in the pathogenesis of RA but in-depth glycoproteomics analysis of synovial tissues is still lacking. Here, by using a strategy to quantify intact N-glycopeptides, we identified 1260 intact N-glycopeptides from 481 N-glycosites on 334 glycoproteins in RA synovium. Bioinformatics analysis revealed that the hyper-glycosylated proteins in RA were closely linked to immune responses. By using DNASTAR software, we identified 20 N-glycopeptides whose prototype peptides were highly immunogenic. We next calculated the enrichment scores of nine types of immune cells using specific gene sets from public single-cell transcriptomics data of RA and revealed that the N-glycosylation levels at some sites, such as IGSF10_N2147, MOXD2P_N404, and PTCH2_N812, were significantly correlated with the enrichment scores of certain immune cell types. Furthermore, we showed that aberrant N-glycosylation in the RA synovium was related to increased expression of glycosylation enzymes. Collectively, this work presents, for the first time, the N-glycoproteome of RA synovium and describes immune-associated glycosylation, providing novel insights into RA pathogenesis.

Predictive factors for degenerative lumbar spinal stenosis: a model obtained from a machine learning algorithm technique

BACKGROUND

Degenerative lumbar spinal stenosis (DLSS) is the most common spine disease in the elderly population. It is usually associated with lumbar spine joints/or ligaments degeneration. Machine learning technique is an exclusive method for handling big data analysis; however, the development of this method for spine pathology is rare. This study aims to detect the essential variables that predict the development of symptomatic DLSS using the random forest of machine learning (ML) algorithms technique.

METHODS

A retrospective study with two groups of individuals. The first included 165 with symptomatic DLSS (sex ratio 80 M/85F), and the second included 180 individuals from the general population (sex ratio: 90 M/90F) without lumbar spinal stenosis symptoms. Lumbar spine measurements such as vertebral or spinal canal diameters from L1 to S1 were conducted on computerized tomography (CT) images. Demographic and health data of all the participants (e.g., body mass index and diabetes mellitus) were also recorded.

RESULTS

The decision tree model of ML demonstrate that the anteroposterior diameter of the bony canal at L5 (males) and L4 (females) levels have the greatest stimulus for symptomatic DLSS (scores of 1 and 0.938). In addition, combination of these variables with other lumbar spine features is mandatory for developing the DLSS.

CONCLUSIONS

Our results indicate that combination of lumbar spine characteristics such as bony canal and vertebral body dimensions rather than the presence of a sole variable is highly associated with symptomatic DLSS onset.

Machine learning identification of thresholds to discriminate osteoarthritis and rheumatoid arthritis synovial inflammation

BACKGROUND

We sought to identify features that distinguish osteoarthritis (OA) and rheumatoid arthritis (RA) hematoxylin and eosin (H&E)-stained synovial tissue samples.

METHODS

We compared fourteen pathologist-scored histology features and computer vision-quantified cell density (147 OA and 60 RA patients) in H&E-stained synovial tissue samples from total knee replacement (TKR) explants. A random forest model was trained using disease state (OA vs RA) as a classifier and histology features and/or computer vision-quantified cell density as inputs.

RESULTS

Synovium from OA patients had increased mast cells and fibrosis (p < 0.001), while synovium from RA patients exhibited increased lymphocytic inflammation, lining hyperplasia, neutrophils, detritus, plasma cells, binucleate plasma cells, sub-lining giant cells, fibrin (all p < 0.001), Russell bodies (p = 0.019), and synovial lining giant cells (p = 0.003). Fourteen pathologist-scored features allowed for discrimination between OA and RA, producing a micro-averaged area under the receiver operating curve (micro-AUC) of 0.85±0.06. This discriminatory ability was comparable to that of computer vision cell density alone (micro-AUC = 0.87±0.04). Combining the pathologist scores with the cell density metric improved the discriminatory power of the model (micro-AUC = 0.92±0.06). The optimal cell density threshold to distinguish OA from RA synovium was 3400 cells/mm2, which yielded a sensitivity of 0.82 and specificity of 0.82.

CONCLUSIONS

H&E-stained images of TKR explant synovium can be correctly classified as OA or RA in 82% of samples. Cell density greater than 3400 cells/mm2 and the presence of mast cells and fibrosis are the most important features for making this distinction.

Best practices for ultrasound-guided synovial biopsy in the United States

The target organ in many forms of inflammatory arthritis is the synovium. However, synovial tissue has historically been perceived as either difficult to obtain or of little practical value. Ultrasound-guided synovial biopsy [UGSB] is a safe and well-tolerated bedside procedure that is established in Europe and rapidly growing in popularity in the United States. The technique can be mastered by rheumatologists who are already experienced in ultrasound-guided procedures such as joint aspirations. The USGB procedure allows the proceduralist to access small, medium, and large joints and is inexpensive and less invasive compared to surgical alternatives. The relative ease of obtaining this tissue, along with recent research suggesting that synovium may have more clinical and investigational utility than previously thought, has led clinicians and researchers to a new appreciation of the role of synovial biopsy in both the clinical and research setting. In this manuscript, the authors present recommendations on best practices for ultrasound-guided synovial biopsy in the United States, based on our initial training with well-established experts overseas and our own subsequent collective experience in performing numerous synovial biopsies in the United States over the past 7 years for both clinical and research indications. We envision a future where UGSB is more frequently incorporated in the standard diagnostic workup of arthritis and drives novel research initiatives.

Unmet need in rheumatology: reports from the Advances in Targeted Therapies meeting, 2022

To detail the unmet clinical and scientific needs in the field of rheumatology. After a 2-year hiatus due to the SARS-CoV-2 pandemic, the 22nd annual international Advances in Targeted Therapies meeting brought together more than 100 leading basic scientists and clinical researchers in rheumatology, immunology, epidemiology, molecular biology and other specialties. Breakout sessions were convened with experts in five rheumatological disease-specific groups including: rheumatoid arthritis (RA), psoriatic arthritis, axial spondyloarthritis, systemic lupus erythematosus and connective tissue diseases (CTDs). In each group, experts were asked to identify and prioritise current unmet needs in clinical and translational research, as well as highlight recent progress in meeting formerly identified unmet needs. Clinical trial design innovation was emphasised across all disease states. Within RA, developing therapies and trials for refractory disease patients remained among the most important identified unmet needs and within lupus and spondyloarthritis the need to account for disease endotypes was highlighted. The RA group also identified the need to better understand the natural history of RA, pre-RA states and the need ultimately for precision medicine. In CTD generally, experts focused on the need to better identify molecular, cellular and clinical signals of early and undifferentiated disease in order to identify novel drug targets. There remains a strong need to develop therapies and therapeutic strategies for those with treatment-refractory disease. Increasingly it is clear that we need to better understand the natural history of these diseases, including their 'predisease' states, and identify molecular signatures, including at a tissue level, which can facilitate disease diagnosis and treatment. As these unmet needs in the field of rheumatic diseases have been identified based on consensus of expert clinicians and scientists in the field, this document may serve individual researchers, institutions and industry to help prioritise their scientific activities.

Allergy Testing Has No Correlation with Intraoperative Histopathology from Revision Total Knee Arthroplasty for Implant-Related Metal Allergy

Lymphocyte transformation testing (LTT) is often used in the workup for possible metal allergy after total knee arthroplasty (TKA) but the correlation of this test with other diagnostic metal-allergy findings in patients undergoing revision TKA for suspected metal allergy has not been established. A single-center, single-surgeon cohort of 19 TKAs in which both components were revised for presumed implant-related metal allergy based on history, physical, and LTT testing, to nonnickel-containing implants were retrospectively identified. Histopathologic samples obtained intraoperatively were semiquantitatively analyzed using both the Hospital for Special Surgery (HSS) synovial pathology score and the Campbell aseptic lymphocyte-dominant vasculitis-associated lesion (ALVAL) score. As histopathology control group, we included in the study an additional cohort of 17 patients who received aseptic revision TKA and had no history of reported or tested metal sensitivity. All preoperative LTT results were highly reactive to nickel. However, this did not correlate with local periarticular tissue response in 18 of 19 cases which demonstrated a low HSS synovial score (mean: 3.8 ± 2.8, of a maximum score of 28) and the low Campbell ALVAL scores (mean: 2.5/10 ± 1.3, of a maximum score of 10). There were not any significant differences between the study group (suspected implant-related metal allergy) and the control group (nonsuspected implant-related metal allergy) in regard to (1) the Campbell score and (2) the HSS synovial inflammatory score. Knee Society Clinical Rating System (KSCRS) function score improved significantly after revision (mean postoperative increase: 34.0 ± 17. 2; p < 0.001), as well as mean visual analog scale (VAS) pain (mean postoperative decrease: 33.3 ± 26.4; p < 0.01) score. The short-term survival rate (at mean follow-up of 26.1 months) of this patient cohort was 100%. In this cohort of revised TKA patients with suspected nickel allergy based on clinical presentation and LTT positive results, intraoperative histopathology was essentially normal. However, all patients with suspected nickel allergy showed a significant clinical and functional improvement with excellent short-term survival rates. The clinical significance of a positive LTT needs further study.

Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study

BACKGROUND

Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models.

METHODS

RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted.

RESULTS

Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups.

CONCLUSION

Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

Assessment of type I interferon signatures in undifferentiated inflammatory diseases: A Japanese multicenter experience

Purpose

Upregulation of type I interferon (IFN) signaling has been increasingly detected in inflammatory diseases. Recently, upregulation of the IFN signature has been suggested as a potential biomarker of IFN-driven inflammatory diseases. Yet, it remains unclear to what extent type I IFN is involved in the pathogenesis of undifferentiated inflammatory diseases. This study aimed to quantify the type I IFN signature in clinically undiagnosed patients and assess clinical characteristics in those with a high IFN signature.

Methods

The type I IFN signature was measured in patients’ whole blood cells. Clinical and biological data were collected retrospectively, and an intensive genetic analysis was performed in undiagnosed patients with a high IFN signature.

Results

A total of 117 samples from 94 patients with inflammatory diseases, including 37 undiagnosed cases, were analyzed. Increased IFN signaling was observed in 19 undiagnosed patients, with 10 exhibiting clinical features commonly found in type I interferonopathies. Skin manifestations, observed in eight patients, were macroscopically and histologically similar to those found in proteasome-associated autoinflammatory syndrome. Genetic analysis identified novel mutations in the PSMB8 gene of one patient, and rare variants of unknown significance in genes linked to type I IFN signaling in four patients. A JAK inhibitor effectively treated the patient with the PSMB8 mutations. Patients with clinically quiescent idiopathic pulmonary hemosiderosis and A20 haploinsufficiency showed enhanced IFN signaling.

Conclusions

Half of the patients examined in this study, with undifferentiated inflammatory diseases, clinically quiescent A20 haploinsufficiency, or idiopathic pulmonary hemosiderosis, had an elevated type I IFN signature.

Improving Transcriptome Fidelity Following Synovial Tissue Disaggregation

Objective

To improve the fidelity of the cellular transcriptome of disaggregated synovial tissue for applications such as single-cell RNA sequencing (scRNAseq) by modifying the disaggregation technique.

Methods

Osteoarthritis (OA) and rheumatoid arthritis (RA) synovia were collected at arthroplasty. RNA was extracted from intact or disaggregated replicate pools of tissue fragments. Disaggregation was performed with either a proprietary protease, Liberase TL (Lib) as a reference method, Liberase TL with an RNA polymerase inhibitor flavopyridol (Flavo), or a cold digestion with subtilisin A (SubA). qPCR on selected markers and RNAseq were used to compare disaggregation methods using the original intact tissue as reference.

Results

Disaggregated cell yield and viability were similar for all three methods with some viability improved (SubA). Candidate gene analysis showed that Lib alone dramatically increased expression of several genes involved in inflammation and immunity compared with intact tissue and was unable to differentiate RA from OA. Both alternative methods reduced the disaggregation induced changes. Unbiased analysis using bulk RNAseq and the 3 protocols confirmed the candidate gene studies and showed that disaggregation-induced changes were largely prevented. The resultant data improved the ability to distinguish RA from OA synovial transcriptomes.

Conclusions

Disaggregation of connective tissues such as synovia has complex and selective effects on the transcriptome. We found that disaggregation with an RNA polymerase inhibitor or using a cold enzyme tended to limit induction of some relevant transcripts during tissue processing. The resultant data in the disaggregated transcriptome better represented the in situ transcriptome. The specific method chosen can be tailored to the genes of interest and the hypotheses being tested in order to optimize the fidelity of technique for applications based on cell suspensions such as sorted populations or scRNAseq.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs)

Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.

The promise of precision medicine in rheumatology

Systemic autoimmune rheumatic diseases (SARDs) exhibit extensive heterogeneity in clinical presentation, disease course, and treatment response. Therefore, precision medicine - whereby treatment is tailored according to the underlying pathogenic mechanisms of an individual patient at a specific time - represents the 'holy grail' in SARD clinical care. Current strategies include treat-to-target therapies and autoantibody testing for patient stratification; however, these are far from optimal. Recent innovations in high-throughput 'omic' technologies are now enabling comprehensive profiling at multiple levels, helping to identify subgroups of patients who may taper off potentially toxic medications or better respond to current molecular targeted therapies. Such advances may help to optimize outcomes and identify new pathways for treatment, but there are many challenges along the path towards clinical translation. In this Review, we discuss recent efforts to dissect cellular and molecular heterogeneity across multiple SARDs and future directions for implementing stratification approaches for SARD treatment in the clinic.

Angiotensin II type 2 receptor pharmacological agonist, C21, reduces the inflammation and pain hypersensitivity in mice with joint inflammatory pain

Primary and secondary hyperalgesia develop in response to chronic joint inflammation due to peripheral and central mechanisms. Synovial macrophage and spinal microglia are involved in pain sensitization in arthritis. The level of angiotensin II type 2 receptor (AT₂R) is related to the severity of arthritis. This study aimed to determine the role of AT₂R in primary and secondary hyperalgesia in joint inflammatory pain in mice. After intra-articular CFA injection, primary hyperalgesia in the ipsilateral knee joint was measured by pressure application meter and gait analysis, secondary hypersensitivity in ipsilateral hind-paw was measured by von-Frey and Hargreaves tests following a combination of global AT₂R-deficient (Agtr2^-/-) mice and AT₂R pharmacological agonist C21. Synovial macrophage and spinal microglia were collected for flow cytometry. Morphological reconstruction of microglia was detected by immunostaining. AT₂R expression was investigated by quantitative polymerase chain reaction and western blot. Neuronal hyperactivity was evaluated by c-Fos and CGRP immunostaining. We found that pain hypersensitivity and synovial inflammation in Agtr2^-/- mice were significantly exacerbated compared with wild-type mice; conversely, systemically administrated C21 attenuated both of the symptoms. Additionally, spinal microglia were activated, and an abundant increase of spinal AT₂R was expressed on activated microglia in response to peripheral joint inflammation. Intrathecally-administrated C21 reversed the secondary hypersensitivity, accompanied by alleviation of spinal microglial activation, spinal neuronal hyperactivity, and calcitonin gene-related peptide content. These findings revealed a beneficial role of AT₂R activating stimulation against pain hypersensitivity in joint inflammatory pain via direct modulation of synovial macrophage and spinal microglial activity.

The transcription factor RFX5 coordinates antigen-presenting function and resistance to nutrient stress in synovial macrophages

Tissue macrophages (Mϕ) are essential effector cells in rheumatoid arthritis (RA), contributing to autoimmune tissue inflammation through diverse effector functions. Their arthritogenic potential depends on their proficiency to survive in the glucose-depleted environment of the inflamed joint. Here, we identify a mechanism that links metabolic adaptation to nutrient stress with the efficacy of tissue Mϕ to activate adaptive immunity by presenting antigen to tissue-invading T cells. Specifically, Mϕ populating the rheumatoid joint produce and respond to the small cytokine CCL18, which protects against cell death induced by glucose withdrawal. Mechanistically, CCL18 induces the transcription factor RFX5 that selectively upregulates glutamate dehydrogenase 1 (GLUD1), thus enabling glutamate utilization to support energy production. In parallel, RFX5 enhances surface expression of HLA-DR molecules, promoting Mϕ-dependent expansion of antigen-specific T cells. These data place CCL18 at the top of a RFX5-GLUD1 survival pathway and couple adaptability to nutrient conditions in the tissue environment to antigen-presenting function in autoimmune tissue inflammation.

A multicentre validation study of a smartphone application to screen hand arthritis

BACKGROUND

Arthritis is a common condition, and the prompt and accurate assessment of hand arthritis in primary care is an area of unmet clinical need. We have previously developed and tested a screening tool combining machine-learning algorithms, to help primary care physicians assess patients presenting with arthritis affecting the hands. The aim of this study was to assess the validity of the screening tool among a number of different Rheumatologists.

METHODS

Two hundred and forty-eight consecutive new patients presenting to 7 private Rheumatology practices across Australia were enrolled. Using a smartphone application, each patient had photographs taken of their hands, completed a brief 9-part questionnaire, and had a single examination result (wrist irritability) recorded. The Rheumatologist diagnosis was entered following a 45-minute consultation. Multiple machine learning models were applied to both the photographic and survey/examination results, to generate a screening outcome for the primary diagnoses of osteoarthritis, rheumatoid and psoriatic arthritis.

RESULTS

The combined algorithms in the application performed well in identifying and discriminating between different forms of hand arthritis. The algorithms were able to predict rheumatoid arthritis with accuracy, precision, recall and specificity of 85.1, 80.0, 88.1 and 82.7% respectively. The corresponding results for psoriatic arthritis were 95.2, 76.9, 90.9 and 95.8%, and for osteoarthritis were 77.4, 78.3, 80.6 and 73.7%. The results were maintained when each contributor was excluded from the analysis. The median time to capture all data across the group was 2 minutes and 59 seconds.

CONCLUSIONS

This multicentre study confirms the results of the pilot study, and indicates that the performance of the screening tool is maintained across a group of different Rheumatologists. The smartphone application can provide a screening result from a combination of machine-learning algorithms applied to hand images and patient symptom responses. This could be used to assist primary care physicians in the assessment of patients presenting with hand arthritis, and has the potential to improve the clinical assessment and management of such patients.

Standardized quantification of biofilm in a novel rabbit model of periprosthetic joint infection

Periprosthetic joint infection (PJI) is one of the most devastating complications of total joint arthroplasty. The underlying pathogenesis involves the formation of bacterial biofilm that protects the pathogen from the host immune response and antibiotics, making eradication difficult. The aim of this study was to develop a rabbit model of knee PJI that would allow reliable biofilm quantification and permit the study of treatments for PJI. In this work, New Zealand white rabbits (n=19) underwent knee joint arthrotomy, titanium tibial implant insertion, and inoculation with Xen36 (bioluminescent Staphylococcus aureus) or a saline control after capsule closure. Biofilm was quantified via scanning electron microscopy (SEM) of the tibial explant 14 d after inoculation (n=3 noninfected, n=2 infected). Rabbits underwent debridement, antibiotics, and implant retention (DAIR) (n=6) or sham surgery (n=2 noninfected, n=6 infected) 14 d after inoculation, and they were sacrificed 14 d post-treatment. Tibial explant and periprosthetic tissues were examined for infection. Laboratory assays supported bacterial infection in infected animals. No differences in weight or C-reactive protein (CRP) were detected after DAIR compared to sham treatment. Biofilm coverage was significantly decreased with DAIR treatment when compared with sham treatment (61.4 % vs. 90.1 %, p<0.0011) and was absent in noninfected control explants. In summary, we have developed an experimental rabbit hemiarthroplasty knee PJI model with bacterial infection that reliably produces quantifiable biofilm and provides an opportunity to introduce treatments at 14 d. This model may be used to better understand the pathogenesis of this condition and to measure treatment strategies for PJI.

Rheumatoid Arthritis Synovial Inflammation Quantification Using Computer Vision

OBJECTIVE

We quantified inflammatory burden in rheumatoid arthritis (RA) synovial tissue by using computer vision to automate the process of counting individual nuclei in hematoxylin and eosin images.

METHODS

We adapted and applied computer vision algorithms to quantify nuclei density (count of nuclei per unit area of tissue) on synovial tissue from arthroplasty samples. A pathologist validated algorithm results by labeling nuclei in synovial images that were mislabeled or missed by the algorithm. Nuclei density was compared with other measures of RA inflammation such as semiquantitative histology scores, gene-expression data, and clinical measures of disease activity.

RESULTS

The algorithm detected a median of 112,657 (range 8,160-821,717) nuclei per synovial sample. Based on pathologist-validated results, the sensitivity and specificity of the algorithm was 97% and 100%, respectively. The mean nuclei density calculated by the algorithm was significantly higher (P < 0.05) in synovium with increased histology scores for lymphocytic inflammation, plasma cells, and lining hyperplasia. Analysis of RNA sequencing identified 915 significantly differentially expressed genes in correlation with nuclei density (false discovery rate is less than 0.05). Mean nuclei density was significantly higher (P < 0.05) in patients with elevated levels of C-reactive protein, erythrocyte sedimentation rate, rheumatoid factor, and cyclized citrullinated protein antibody.

CONCLUSION

Nuclei density is a robust measurement of inflammatory burden in RA and correlates with multiple orthogonal measurements of inflammation.

Use of machine learning in osteoarthritis research: a systematic literature review

Objective

The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA).

Methods

A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected.

Results

From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles.

Conclusion

This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.

Mechanisms underlying DMARD inefficacy in difficult-to-treat rheumatoid arthritis: a narrative review with systematic literature search

Management of RA patients has significantly improved over the past decades. However, a substantial proportion of patients is difficult-to-treat (D2T), remaining symptomatic after failing biological and/or targeted synthetic DMARDs. Multiple factors can contribute to D2T RA, including treatment non-adherence, comorbidities and co-existing mimicking diseases (e.g. fibromyalgia). Additionally, currently available biological and/or targeted synthetic DMARDs may be truly ineffective (‘true’ refractory RA) and/or lead to unacceptable side effects. In this narrative review based on a systematic literature search, an overview of underlying (immune) mechanisms is presented. Potential scenarios are discussed including the influence of different levels of gene expression and clinical characteristics. Although the exact underlying mechanisms remain largely unknown, the heterogeneity between individual patients supports the assumption that D2T RA is a syndrome involving different pathogenic mechanisms.

Pre-RA: Can early diagnosis lead to prevention?

Rheumatoid arthritis (RA) is currently diagnosed and treated once an individual displays the clinical findings of inflammatory arthritis (IA). However, growing evidence supports that there is a 'pre-RA' stage that can be identified through factors such as autoantibodies in absence of clinically apparent IA. In particular, biomarkers, including antibodies to citrullinated protein antigens (ACPA), demonstrate a high risk for future IA/RA, and multiple clinical trials have been developed to intervene in individuals in pre-RA to prevent or delay clinically apparent disease. Herein, we will discuss in more depth what is currently known about the natural history of RA, and the emerging possibility that early 'diagnosis' of RA-related autoimmunity followed by an intervention can lead to the delay or prevention of the first onset of clinically apparent RA.

Effect of JAK Inhibition on the Induction of Proinflammatory HLA-DR+CD90+ Rheumatoid Arthritis Synovial Fibroblasts by Interferon-γ

OBJECTIVE

Findings from recent transcriptome analyses of the synovium of patients with rheumatoid arthritis (RA) have revealed that 15-fold expanded HLA-DR+CD90+ synovial fibroblasts potentially act as key mediators of inflammation. The reasons for the expansion of HLA-DR+CD90+ synovial fibroblasts are unclear, but genetic signatures indicate that interferon-γ (IFNγ) plays a central role in the generation of this fibroblast subset. The present study was undertaken to investigate the generation, function and therapeutically intended blockage of HLA-DR+CD90+ synovial fibroblasts.

METHODS

We combined functional assays using primary human materials and focused bioinformatic analyses of mass cytometry and transcriptomics patient data sets.

RESULTS

We detected enriched and activated Fcγ receptor type IIIa-positive (CD16+) NK cells in the synovial tissue from patients with active RA. Soluble immune complexes were recognized by CD16 in a newly described reporter cell model, a mechanism that could be contributing to the activation of natural killer (NK) cells in RA. In vitro, NK cell-derived IFNγ induced HLA-DR on CD90+ synovial fibroblasts, leading to an inflammatory, cytokine-secreting HLA-DR+CD90+ phenotype. HLA-DR+CD90+ synovial fibroblasts consecutively activated CD4+ T cells upon receptor crosslinking via superantigens. HLA-DR+CD90+ synovial fibroblasts also activated CD4+ T cells in the absence of superantigens, an effect that was initiated by NK cell-derived IFNγ and that was 4 times stronger in patients with RA compared to patients with osteoarthritis. Finally, JAK inhibition in synovial fibroblasts prevented HLA-DR induction and blocked proinflammatory signals to T cells.

CONCLUSION

The HLA-DR+CD90+ phenotype represents an activation state of synovial fibroblasts during the process of inflammation in RA that can be induced by IFNγ, likely generated from infiltrating leukocytes such as activated NK cells. The induction of these proinflammatory, interleukin-6-producing, and likely antigen-presenting synovial fibroblasts can be targeted by JAK inhibition.

Plasma interleukin-23 and circulating IL-17A+IFNγ+ ex-Th17 cells predict opposing outcomes of anti-TNF therapy in rheumatoid arthritis

OBJECTIVES

TNF-α inhibitors are widely used in rheumatoid arthritis (RA) with varying success. Response to TNF-α inhibition may reflect the evolution of rheumatoid inflammation through fluctuating stages of TNF-α dependence. Our aim was to assess plasma concentrations of Th-17-related cytokines and the presence of circulating effector T-cells to identify predictors of response to TNF-α inhibitors.

METHODS

Ninety-three people with RA were seen prior to and 4-6 months after commencing etanercept or adalimumab. Plasma concentrations of Th17-related cytokines, circulating effector T-cells, their production of relevant transcription factors and intracellular cytokines were measured at baseline. EULAR response criteria were used to define poor (ΔDAS28 ≤ 1.2 and/or DAS28 > 3.2) and good (ΔDAS28 > 1.2 and DAS28 ≤ 3.2) responders. Multivariate logistic regression was used to identify predictors of response.

RESULTS

Participants with plasma IL-23 present at baseline were more likely to be poor responders [15/20 (75%) of IL-23⁺ versus 36/73 (49.3%) of IL-23^-; p = 0.041]. While frequencies of Th1, Th17, ex-Th17 and T_reg cell populations were similar between good and poor responders to anti-TNF therapy, IL-17A⁺IFNγ⁺ ex-Th17 cells were more prevalent in good responders (0.83% of ex-T_H17 cells) compared to poor responders (0.24% of ex-Th17 cells), p = 0.023. Both plasma IL-23 cytokine status (OR = 0.17 (95% CI 0.04-0.73)) and IL-17A⁺IFNγ⁺ ex-Th17 cell frequency (OR = 1.64 (95% CI 1.06 to 2.54)) were independently associated with a good response to anti-TNF therapy. Receiver operator characteristic (ROC) analysis, including both parameters, demonstrated an area under the ROC curve (AUC) of 0.70 (95% CI 0.60-0.82; p = 0.001).

CONCLUSIONS

Plasma IL-23 and circulating IL-17A⁺IFNγ⁺ ex-Th17 cells are independently associated with response to anti-TNF therapy. In combination, plasma IL-23 and circulating IL-17A⁺IFNγ⁺ ex-Th17 cells provide additive value to the prediction of response to anti-TNF therapy in RA.

New Druggable Targets for Rheumatoid Arthritis Based on Insights From Synovial Biology

Rheumatoid arthritis (RA) is a multifactorial autoimmune disease characterized by chronic inflammation and destruction of multiple small joints which may lead to systemic complications. Altered immunity via pathogenic autoantibodies pre-date clinical symptom development by several years. Incompletely understood range of mechanisms trigger joint-homing, leading to clinically evident articular disease. Advances in therapeutic approaches and understanding pathogenesis have improved prognosis and likely remission. However, partial/non-response to conventional and biologic therapies witnessed in a subset of patients highlights the need for new therapeutics. It is now evident that joint disease chronicity stems from recalcitrant inflammatory synovial environment, majorly maintained by epigenetically and metabolically reprogrammed synoviocytes. Therefore, interference with effector functions of activated cell types seems a rational strategy to reinstate synovial homeostasis and complement existing anti-inflammatory interventions to mitigate chronic RA. Presenting this newer aspect of fibroblast-like synoviocytes and myeloid cells underlying the altered synovial biology in RA and its potential for identification of new druggable targets is attempted in this review. Major leads from i) molecular insights of pathogenic cell types from hypothesis free OMICS approaches; ii) hierarchy of their dysregulated signaling pathways; and iii) knowledge of druggability of molecular nodes in these pathways are highlighted. Development of such synovial biology-directed therapeutics hold promise for an enriched drug repertoire for RA.

proBDNF/p75NTR promotes rheumatoid arthritis and inflammatory response by activating proinflammatory cytokines

P75 pan-neurotrophin receptor (p75NTR) is an important receptor for the role of neurotrophins in survival and death of neurons during development and after nerve injury. Our previous research found that the precursor of brain-derived neurotrophic factor (proBDNF) regulates pain as an inflammatory mediator. The current understanding of the role of proBDNF/p75NTR signaling pathway in inflammatory arthritis pain and rheumatoid arthritis (RA) is unclear. We recruited 20 RA patients, 20 healthy donors (HDs), and 10 osteoarthritis (OA) patients. Hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC) of proBDNF and p75NTR in synovial membrane were performed and evaluated. We next examined the mRNA and protein expression of proBDNF/p75NTR signaling pathway in peripheral blood mononuclear cells (PBMCs) and synovial tissue. ELISA and flow cytometry were assessed between the blood of RA patients and HD. To induce RA, collagen-induced arthritis (CIA) were induced in mice. We found over-synovitis of RA synovial membrane compared to OA controls in histologic sections. P75NTR and sortilin mRNA, and proBDNF protein level were significantly increased in PBMCs of RA patients compared with the HD. Consistently, ELISA showed that p75NTR, sortilin, tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-10 (IL-10) levels in the serum of RA patients were increased compared with HD and p75NTR, sortilin were positively correlated with Disease Activity Score in 28 joints (DAS28). In addition, using flow cytometry we showed that the increased levels of proBDNF and p75NTR characterized in CD4⁺ and CD8⁺ T cells of RA patients were subsequently reversed with methotrexate (MTX) treatment. Furthermore, we found pathological changes, inflammatory pain, upregulation of the mRNA and protein expression of proBDNF/p75NTR signaling pathway, and upregulation of inflammatory cytokines in spinal cord using a well-established CIA mouse model. We showed intravenous treatment of recombinant p75ECD-Fc that biologically blocked all inflammatory responses and relieved inflammatory pain of animals with CIA. Our findings showed the involvement of proBDNF/p75NTR pathway in the RA inflammatory response and how blocking it with p75ECD-Fc may be a promising therapeutic treatment for RA.

Addition of Fibroblast-Stromal Cell Markers to Immune Synovium Pathotypes Better Predicts Radiographic Progression at 1 Year in Active Rheumatoid Arthritis

Objectives

This study aims to investigate if addition of fibroblast-stromal cell markers to a classification of synovial pathotypes improves their predictive value on clinical outcomes in rheumatoid arthritis (RA).

Methods

Active RA patients with a knee needle synovial biopsy at baseline and finished 1-year follow-up were recruited from a real-world prospective cohort. Positive staining for CD20, CD38, CD3, CD68, CD31, and CD90 were scored semiquantitatively (0-4). The primary outcome was radiographic progression defined as a minimum increase of 0.5 units of the modified total Sharp score from baseline to 1 year.

Results

Among 150 recruited RA patients, 123 (82%) had qualified synovial tissue. Higher scores of CD20+ B cells, sublining CD68+ macrophages, CD31+ endothelial cells, and CD90+ fibroblasts were associated with less decrease in disease activity and greater increase in radiographic progression. A new fibroblast-based classification of synovial pathotypes giving more priority to myeloid and stromal cells classified samples as myeloid-stromal (57.7%, 71/123), lymphoid (31.7%, 39/123), and paucicellular pathotypes (10.6%, 13/123). RA patients with myeloid-stromal pathotype showed the highest rate of radiographic progression (43.7% vs. 23.1% vs. 7.7%, p = 0.011), together with the lowest rate of Boolean remission at 3, 6, and 12 months. Baseline synovial myeloid-stromal pathotype independently predicted radiographic progression at 1 year (adjusted OR: 3.199, 95% confidence interval (95% CI): 1.278, 8.010). Similar results were obtained in a subgroup analysis of treatment-naive RA.

Conclusions

This novel fibroblast-based myeloid-stromal pathotype could predict radiographic progression at 1 year in active RA patients which may contribute to the shift of therapeutic decision in RA.