Systemic sclerosis (SSc) is a rare autoimmune disease with the highest case-fatality rate of all connective tissue diseases. Current efforts to determine patient response to a given treatment using the modified Rodnan skin score (mRSS) are complicated by interclinician variability, confounding, and the time required between sequential mRSS measurements to observe meaningful change. There is an unmet critical need for an objective metric of SSc disease severity. Here, we performed an integrated, multicohort analysis of SSc transcriptome data across 7 datasets from 6 centers composed of 515 samples. Using 158 skin samples from SSc patients and healthy controls recruited at 2 centers as a discovery cohort, we identified a 415-gene expression signature specific for SSc, and validated its ability to distinguish SSc patients from healthy controls in an additional 357 skin samples from 5 independent cohorts. Next, we defined the SSc skin severity score (4S). In every SSc cohort of skin biopsy samples analyzed in our study, 4S correlated significantly with mRSS, allowing objective quantification of SSc disease severity. Using transcriptome data from the largest longitudinal trial of SSc patients to date, we showed that 4S allowed us to objectively monitor individual SSc patients over time, as (a) the change in 4S of a patient is significantly correlated with change in the mRSS, and (b) the change in 4S at 12 months of treatment could predict the change in mRSS at 24 months. Our results suggest that 4S could be used to distinguish treatment responders from nonresponders prior to mRSS change. Our results demonstrate the potential clinical utility of a novel robust molecular signature and a computational approach to SSc disease severity quantification.

T helper 17 (T _H 17) cells are found in the periphery and synovium of patients with rheumatoid arthritis (RA); however, IL-17–targeted interventions have limited efficacy in established RA. Inflammation can induce T _H 17 cell transdifferentiation into IL-17–negative exT _H 17 cells, but the role of exT _H 17 cells in arthritis is unknown. We performed T _H 17 cell lineage tracing in the SKG mouse model of RA. In arthritic mice, synovial T _H 17 cells transdifferentiate into CD44 ⁺ exT _H 17 cells, which are more arthritogenic and sustain inflammation that is IL-17 independent. The exT _H 17 cell gene signature includes up-regulation of CD44 and sphingosine-1-phosphate receptor 4 (S1PR4) and correlates with the profile of human RA synovial CD4 ⁺ T cells. We demonstrate that cross-talk between T _H 17 cells and fibroblast-like synoviocytes (FLSs) via S1P promotes T _H 17-exT _H 17 cell conversion. CD44 is necessary for exT _H 17 cell–mediated arthritis. Our study suggests that FLS expansion during RA progression promotes T _H 17-exT _H 17 cell conversion. These results could potentially enable RA precision therapy.

Interstitial lung disease (ILD) is present in over 60% of patients with systemic sclerosis (SSc) and is the leading cause of SSc-related deaths. Profibrotic monocyte-derived alveolar macrophages (MoAM) play a causal role in the pathogenesis of pulmonary fibrosis in animal models where their persistence in the niche requires signaling through Colony Stimulating Factor 1 Receptor (CSF1R). We hypothesized that the presence and proportion of MoAM in bronchoalveolar lavage (BAL) fluid from patients with SSc-ILD may be a biomarker of ILD severity. To test this hypothesis, we analyzed BAL fluid from 9 prospectively enrolled patients with SSc-ILD and 13 healthy controls using flow cytometry and single-cell RNA sequencing. Patients with SSc-ILD had more MoAM and interstitial macrophages in BAL fluid than healthy controls, and their abundance was associated with lung fibrosis severity. We identified changes in the MoAM transcriptome as a function of treatment with mycophenolate, an effective therapy for SSc-ILD. In SSc-ILD lung explants, spatial transcriptomics identified an expanded population of interstitial macrophages spilling into the alveolar space. Our findings suggest that the proportion of profibrotic MoAM and interstitial macrophages in BAL fluid may serve as a biomarker of SSc-ILD and credential them as possible targets for therapy.

Background/Objectives: Alterations in fecal microbial communities in patients with systemic sclerosis (SSc) are common, but the clinical significance of this observation is poorly understood. Gut microbial production of trimethylamine (TMA), and its conversion by the host to trimethylamine N-oxide (TMAO), has clinical and mechanistic links to cardiovascular and renal diseases. Direct provision of TMAO has been shown to promote fibrosis and vascular injury, hallmarks of SSc. We sought to determine levels of TMAO and related metabolites in SSc patients and investigate associations between the metabolite levels with disease features. Methods: This is an observational case:control study. Adults with SSc (n = 200) and non-SSc controls (n = 400) were matched for age, sex, indices of renal function, diabetes mellitus, and cardiovascular disease. Serum TMAO, choline, betaine, carnitine, γ-butyrobetaine, and crotonobetaine were measured using stable isotope dilution liquid chromatography tandem mass spectrometry. Results: Median TMAO concentration was higher (p = 0.020) in SSc patients (3.31 [interquartile range 2.18, 5.23] µM) relative to controls (2.85 [IQR 1.88, 4.54] µM). TMAO was highest among obese and male SSc participants compared to all other groups. Following adjustment for sex, BMI, age, race, and eGFR in a quantile regression model, elevated TMAO levels remained associated with SSc at each quantile of TMAO. Conclusions: Patients with SSc have increased circulating levels of TMAO independent of comorbidities including age, sex, renal function, diabetes mellitus, and cardiovascular disease. As a potentially modifiable factor, further studies examining the link between TMAO and SSc disease severity and course are warranted.

Background: Cardiac involvement is a leading cause of death in systemic sclerosis (SSc/scleroderma). The complexity of SSc cardiac manifestations is not fully captured by the current clinical SSc classification, which is based on extent of skin involvement and specific autoantibodies. Therefore, we sought to develop a clinically relevant SSc cardiac disease classification to improve clinical care and increase understanding of SSc cardiac disease pathobiology. We hypothesized that machine learning could identify novel SSc cardiac disease subgroups, and that gene expression assessment of skin could provide insights into molecular pathogenesis of these SSc pheno-groups. Methods: We used unsupervised model-based clustering (phenomapping) of SSc patient echocardiographic and clinical data to identify clinically relevant SSc pheno-groups in a discovery cohort (n=316), and validated these findings in an external SSc validation cohort (n=67). Cox regression was used to evaluate survival differences among groups. Gene expression profiles from skin biopsies from a subset of SSc patients (n=68) and controls (n=18) were analyzed with weighted gene co-expression network analyses to identify gene modules that were associated with cardiac pheno-groups and echocardiographic parameters. Results: Four SSc cardiac pheno-groups were identified with distinct profiles. Pheno-group #1 displayed a predominant cutaneous phenotype without cardiac involvement; pheno-group #2 had long-standing SSc with limited skin and cardiac involvement; pheno-group #3 had diffuse skin involvement, a high frequency of interstitial lung disease (88%), and significant right heart remodeling/dysfunction; and pheno-group #4 had prolonged SSc disease duration, limited skin involvement, and marked biventricular cardiac involvement. After multivariable adjustment, pheno-group #3 (hazard ratio [HR] 7.8, 95% confidence interval [CI] 1.5–33.0) and pheno-group #4 (HR 10.5, 95% CI 2.1–52.7) remained associated with mortality (P<0.05). The addition of pheno-group classification was additive to conventional survival models (P<0.05 by likelihood ratio test for all models), a finding that was replicated in the validation cohort. Skin gene expression analysis identified 2 gene modules (representing fibrosis and skin integrity, respectively) that differed among the cardiac pheno-groups and were associated with specific echocardiographic parameters. Conclusions: Machine learning of echocardiographic and skin gene expression data in SSc identifies clinically relevant subgroups with distinct cardiac phenotypes, survival, and associated molecular pathways in skin.