Intrinsic gene expression subsets of diffuse cutaneous systemic sclerosis are stable in serial skin biopsies

Can we define difficult-to-treat systemic sclerosis?

INTRODUCTION

Systemic sclerosis (SSc) is a chronic autoimmune rheumatic disease characterized by microvascular alterations, immunopathology, and widespread fibrosis involving various organs. It is considered difficult to treat due to several reasons: complex pathogenesis, heterogeneity, late diagnosis, limited treatment options for certain organ manifestations, lack of personalized medicine.

AREAS COVERED

This review presents the heterogeneity, survival and organ manifestations with their risk factors of systemic sclerosis and their current treatment options, while drawing attention to difficult-to-treat forms of the disease, based on literature indexed in PubMed.

EXPERT OPINION

Despite recent advances in the management of SSc over the last decades, the disease presents significant morbidity and mortality. Although available treatment protocols brought significant advancements in terms of survival in SSc-associated interstitial lung disease and pulmonary arterial hypertension, less success has been achieved in the treatment of Raynaud's phenomenon and digital ulcers and the results are modest in case of heart, gastrointestinal, and renal manifestations. There are patients who do not respond to treatment and deteriorate even with adequate therapy. They can be considered difficult-to treat (D2T) cases. We have created a possible score system based on the individual organ manifestations and highlighted treatment options for the D2T SSc category.

Characteristics and impact of infiltration of B-cells from systemic sclerosis patients in a 3D healthy skin model

Introduction

In systemic sclerosis (SSc), B-cells are activated and present in the skin and lung of patients where they can interact with fibroblasts. The precise impact and mechanisms of the interaction of B-cells and fibroblasts at the tissular level are poorly studied.

Objective

We investigated the impact and mechanisms of B-cell/fibroblast interactions in cocultures between B-cells from patients with SSc and 3-dimensional reconstituted healthy skin model including fibroblasts, keratinocytes and extracellular matrix.

Methods

The quantification and description of the B-cell infiltration in 3D cocultures were performed using cells imagery strategy and cytometry. The effect of coculture on the transcriptome of B-cells and fibroblasts was studied with bulk and single-cell RNA sequencing approaches. The mechanisms of this interaction were studied by blocking key cytokines like IL-6 and TNF.

Results

We showed a significant infiltration of B-cells in the 3D healthy skin model. The amount but not the depth of infiltration was higher with B-cells from SSc patients and with activated B-cells. B-cell infiltrates were mainly composed of naïve and memory cells, whose frequencies differed depending on B-cells origin and activation state: infiltrated B-cells from patients with SSc showed an activated profile and an overexpression of immunoglobulin genes compared to circulating B-cells before infiltration. Our study has shown for the first time that activated B-cells modified the transcriptomic profile of both healthy and SSc fibroblasts, toward a pro-inflammatory (TNF and IL-17 signaling) and interferon profile, with a key role of the TNF pathway.

Conclusion

B-cells and 3D skin cocultures allowed the modelization of B-cells infiltration in tissues observed in SSc, uncovering an influence of the underlying disease and the activation state of B-cells. We showed a pro-inflammatory effect on skin fibroblasts and pro-activation effect on infiltrating B-cells during coculture. This reinforces the role of B-cells in SSc and provide potential targets for future therapeutic approach in this disease.

Fibroblast Subpopulations in Systemic Sclerosis: Functional Implications of Individual Subpopulations and Correlations with Clinical Features

Fibroblasts constitute a heterogeneous population of cells. In this study, we integrated single-cell RNA-sequencing and bulk RNA-sequencing data as well as clinical information to study the role of individual fibroblast populations in systemic sclerosis (SSc). SSc skin demonstrated an increased abundance of COMP+, COL11A1+, MYOC+, CCL19+, SFRP4/SFRP2+, and PRSS23/SFRP2+ fibroblasts signatures and decreased proportions of CXCL12+ and PI16+ fibroblast signatures in the Prospective Registry of Early Systemic Sclerosis and Genetics versus Environment in Scleroderma Outcome Study cohorts. Numerical differences were confirmed by multicolor immunofluorescence for selected fibroblast populations. COMP+, COL11A1+, SFRP4/SFRP2+, PRSS23/SFRP2+, and PI16+ fibroblasts were similarly altered between normal wound healing and patients with SSc. The proportions of profibrotic COMP+, COL11A1+, SFRP4/SFRP2+, and PRSS23/SFRP2+ and proinflammatory CCL19+ fibroblast signatures were positively correlated with clinical and histopathological parameters of skin fibrosis, whereas signatures of CXCL12+ and PI16+ fibroblasts were inversely correlated. Incorporating the proportions of COMP+, COL11A1+, SFRP4/SFRP2+, and PRSS23/SFRP2+ fibroblast signatures into machine learning models improved the classification of patients with SSc into those with progressive versus stable skin fibrosis. In summary, the profound imbalance of fibroblast subpopulations in SSc may drive the progression of skin fibrosis. Specific targeting of disease-relevant fibroblast populations may offer opportunities for the treatment of SSc and other fibrotic diseases.

Phenotypes of patients with systemic sclerosis in the Chinese Han population: a cluster analysis

OBJECTIVE

Systemic sclerosis (SSc) is a heterogeneous connective tissue disease that is commonly subdivided into limited cutaneous SSc (lcSSc) and diffuse cutaneous SSc (dcSSc) based on the extent of skin involvement. This subclassification may not reflect the full range of clinical phenotypic variation. This study aimed to investigate clinical features and aggregation of patients with SSc in Chinese based on SSc manifestations and organ involvements, in order to achieve precise treatment of SSc early prevention of complications.

METHODS

In total 287 SSc patients were included in this study. A cluster analysis was applied according to 13 clinical and serologic variables to determine subgroups of patients. Survival rates between obtained clusters and risk factors affecting prognosis were also compared.

RESULT

In this study, six clusters were observed: cluster 1 (n = 66) represented the skin type, with all patients showing skin thickening. In cluster 2 (n = 56), most patients had vascular and articular involvement. Cluster 3 (n = 14) individuals mostly had cardiac and pulmonary involvement. In cluster 4 (n = 52), the gastrointestinal type, 50 patients presented with stomach symptoms and 28 patients presented with esophageal symptoms. In cluster 5 (n = 50), patients barely had any major organ involvement. Cluster 6 (n = 49) included 46% of all patients presenting with renal crisis.

CONCLUSION

The results of our cluster analysis study implied that limiting SSc patient subgroups to those based only on skin involvement might not capture the full heterogeneity of the disease. Organ damage and antibody profiles should be considered when identifying homogeneous patient groups with a specific prognosis. Key Points • Provides a new method of categorizing SSc patients. • Can better explain disease progression and guide subsequent treatment.

Identification of Fibroinflammatory and Fibrotic Transcriptomic Subsets of Human Cutaneous Sclerotic Chronic Graft-Versus-Host Disease

Cutaneous sclerotic chronic graft-versus-host disease (cGVHD) is a common and highly morbid complication of allogeneic hematopoietic stem cell transplantation. Our goals were to identify signals active in the skin of patients with sclerotic cGVHD in an effort to better understand how to treat this manifestation and to explore the heterogeneity of the disease. We identified genes that are significantly upregulated in the skin of patients with sclerotic cGVHD (n = 17) compared with those in the skin of patients who underwent allogeneic hematopoietic stem cell transplantation without cutaneous cGVHD (n = 9) by bulk RNA sequencing. Sclerotic cGVHD was most associated with T helper 1, phagocytic, and fibrotic pathways. In addition, different transcriptomic groups of affected patients were discovered: those with fibrotic and inflammatory/T helper 1 gene expression (the fibroinflammatory group) and those with predominantly fibrotic/TGFβ-associated expression (the fibrotic group). Further study will help elucidate whether these gene expression findings can be used to tailor treatment decisions. Multiple proteins encoded by highly induced genes in the skin (SFRP4, SERPINE2, COMP) were also highly induced in the plasma of patients with sclerotic cGVHD (n = 16) compared with those in plasma of control patients who underwent allogeneic hematopoietic stem cell transplantation without sclerotic cGVHD (n = 17), suggesting these TGFβ and Wnt pathway mediators as candidate blood biomarkers of the disease.

Isoform-selective TGF-β3 inhibition for systemic sclerosis

BACKGROUND

Transforming growth factor β (TGF-β) is implicated as a key mediator of pathological fibrosis, but its pleiotropic activity in a range of homeostatic functions presents challenges to its safe and effective therapeutic targeting. There are three isoforms of TGF-β, TGF-β1, TGF-β2, and TGF-β3, which bind to a common receptor complex composed of TGF-βR1 and TGF-βR2 to induce similar intracellular signals in vitro. We have recently shown that the cellular expression patterns and activation thresholds of TGF-β2 and TGF-β3 are distinct from those of TGF-β1 and that selective short-term TGF-β2 and TGF-β3 inhibition can attenuate fibrosis in vivo without promoting excessive inflammation. Isoform-selective inhibition of TGF-β may therefore provide a therapeutic opportunity for patients with chronic fibrotic disorders.

METHODS

Transcriptomic profiling of skin biopsies from patients with systemic sclerosis (SSc) from multiple clinical trials was performed to evaluate the role of TGF-β3 in this disease. Antibody humanization, biochemical characterization, crystallization, and pre-clinical experiments were performed to further characterize an anti-TGF-β3 antibody.

FINDINGS

In the skin of patients with SSc, TGF-β3 expression is uniquely correlated with biomarkers of TGF-β signaling and disease severity. Crystallographic studies establish a structural basis for selective TGF-β3 inhibition with a potent and selective monoclonal antibody that attenuates fibrosis effectively in vivo at clinically translatable exposures. Toxicology studies suggest that, as opposed to pan-TGF-β inhibitors, this anti-TGF-β3 antibody has a favorable safety profile for chronic administration.

CONCLUSION

We establish a rationale for targeting TGF-β3 in SSc with a favorable therapeutic index.

FUNDING

This study was funded by Genentech, Inc.

Perspective to precision medicine in scleroderma

Systemic sclerosis (SSc) is a rare and heterogeneous disease with no relevant environmental trigger or significant responsible gene. It has been and will continue to be difficult to identify large enough patients to conduct classic population-based epidemiologic exposure/non-exposure studies with adequate power to ascertain environmental and genetic risk factors for these entities. The complexity of pathogenesis and heterogeneity are likely to require personalized/precision medicine for SSc. Since several potential drugs are currently available for specific patients if not whole SSc, classification of SSc seems to form the foundation for a better therapeutic strategy. To date, SSc has been classified based on the extent/severity of the affected area as well as some disease markers, including the autoantibody profile. However, such an analysis should also lead to improvements in the design of appropriately stratified clinical trials to determine the effects and prediction of targeted therapies. An approach based on drug response preclinically conducted using patients' own fibroblasts in vitro, can provide a precise disease marker/therapeutic selection for clinical practice. Because scleroderma dermal fibroblasts have a persistent hyper-productive phenotype occurring not only in person, but also in cell culture conditions. Thus, an accumulating approach based on disease markers ensures progression and de-escalation to re-establish a better life with a personally optimized drug environment after the onset of SSc.

An interleukin 6 responsive plasma cell signature is associated with disease progression in systemic sclerosis interstitial lung disease

Systemic sclerosis (SSc) interstitial lung disease (ILD) is among the leading causes of SSc-related morbidity and mortality. Tocilizumab (TCZ, anti-IL6RA) has demonstrated a reduced rate of pulmonary function decline in two phase 2/3 trials (faSScinate and focuSSced) in SSc-ILD patients. We performed transcriptome analysis of skin biopsy samples collected in the studies to decipher gene networks that were potentially associated with clinical responses to TCZ treatment. One module correlated with disease progression showed pharmacodynamic changes with TCZ treatment, and was characterized by plasma cell (PC) genes. PC signature gene expression levels were also significantly increased in both fibrotic SSc and IPF lungs compared to controls. scRNAseq analyses confirmed that PC signature genes were co-expressed in CD38 and CD138 expressing PC subsets in SSc lungs. These data provide insights into the potential role of PC in disease progression and mechanisms of action of TCZ in fibrotic interstitial lung diseases.

Cell Type-Specific Biomarkers of Systemic Sclerosis Disease Severity Capture Cell-Intrinsic and Cell-Extrinsic Circuits

OBJECTIVE

Systemic sclerosis (SSc) is a multifactorial autoimmune fibrotic disorder involving complex rewiring of cell-intrinsic and cell-extrinsic signaling coexpression networks involving a range of cell types. However, the rewired circuits as well as corresponding cell-cell interactions remain poorly understood. To address this, we used a predictive machine learning framework to analyze single-cell RNA-sequencing data from 24 SSc patients across the severity spectrum as quantified by the modified Rodnan skin score (MRSS).

METHODS

We used a least absolute shrinkage and selection operator (LASSO)-based predictive machine learning approach on the single-cell RNA-sequencing data set to identify predictive biomarkers of SSc severity, both across and within cell types. The use of L1 regularization helps prevent overfitting on high-dimensional data. Correlation network analyses were coupled to the LASSO model to identify cell-intrinsic and cell-extrinsic co-correlates of the identified biomarkers of SSc severity.

RESULTS

We found that the uncovered cell type-specific predictive biomarkers of MRSS included previously implicated genes in fibroblast and myeloid cell subsets (e.g., SFPR2+ fibroblasts and monocytes), as well as novel gene biomarkers of MRSS, especially in keratinocytes. Correlation network analyses revealed novel cross-talk between immune pathways and implicated keratinocytes in addition to fibroblast and myeloid cells as key cell types involved in SSc pathogenesis. We then validated the uncovered association of key gene expression and protein markers in keratinocytes, KRT6A and S100A8, with SSc skin disease severity.

CONCLUSION

Our global systems analyses reveal previously uncharacterized cell-intrinsic and cell-extrinsic signaling coexpression networks underlying SSc severity that involve keratinocytes, myeloid cells, and fibroblasts.

Skin Gene Expression Profiles in Systemic Sclerosis: From Clinical Stratification to Precision Medicine

Systemic sclerosis, also known as scleroderma or SSc, is a condition characterized by significant heterogeneity in clinical presentation, disease progression, and response to treatment. Consequently, the design of clinical trials to successfully identify effective therapeutic interventions poses a major challenge. Recent advancements in skin molecular profiling technologies and stratification techniques have enabled the identification of patient subgroups that may be relevant for personalized treatment approaches. This narrative review aims at providing an overview of the current status of skin gene expression analysis using computational biology approaches and highlights the benefits of stratifying patients upon their skin gene signatures. Such stratification has the potential to lead toward a precision medicine approach in the management of SSc.

Clinical Phenotypes of Patients With Systemic Sclerosis With Distinct Molecular Signatures in Skin

OBJECTIVE

Systemic sclerosis (SSc) patients are classified according to degree of skin fibrosis (limited and diffuse cutaneous [lc and dc]) and serum autoantibodies. We undertook the present multicenter study to determine whether intrinsic subset (IS) classification based upon skin gene expression yields additional valuable clinical information.

METHODS

SSc patients and healthy participants (HPs) were classified into Normal-like, Limited, Fibroproliferative, and Inflammatory ISs using a previously trained classifier. Clinical data were obtained (serum autoantibodies, pulmonary function testing, modified Rodnan skin thickness scores [mRSS], and high-resolution chest computed tomography [HRCT]). Statistical analyses were performed to compare patients classified by IS, traditional cutaneous classification, and serum autoantibodies.

RESULTS

A total of 223 participants (165 SSc [115 dcSSc and 50 lcSSc] and 58 HPs) were classified. Inflammatory IS patients had higher mRSS (22.1 ± 9.9; P < 0.001) than other ISs and dcSSc patients (19.4 ± 9.4; P = 0.05) despite similar disease duration (median [interquartile range] months 14.9 [19.9] vs. 18.4 [31.6]; P = 0.48). In multivariable modeling, no significant association between mRSS and RNA polymerase III (P = 0.07) or anti-topoisomerase I (Scl-70) (P = 0.09) was found. Radiographic interstitial lung disease (ILD) was more prevalent in Fibroproliferative IS compared with other ISs (91%; P = 0.04) with similar prevalence between lcSSc and dcSSc (67% vs. 76%; P = 0.73). Positive Scl-70 antibody was the strongest ILD predictor (P < 0.001). Interestingly, all lcSSc/Fibroproliferative patients demonstrated radiographic ILD.

CONCLUSIONS

Classification by IS identifies patients with distinct clinical phenotypes versus traditional cutaneous or autoantibody classification. IS classification identifies subgroups of SSc patients with more radiographic ILD (Fibroproliferative), higher mRSS (Inflammatory), and milder phenotype (Normal-like) and may provide additional clinically useful information to current SSc classification systems.

Therapeutic Approaches to Systemic Sclerosis: Recent Approvals and Future Candidate Therapies

Systemic sclerosis is the rheumatic disease with the highest individual mortality. The severity of the disease is determined by the extent of fibrotic changes to cutaneous and internal organ tissues, the most life-threatening visceral manifestations being interstitial lung disease, SSc-associated-pulmonary arterial hypertension and myocardial involvement. The heterogeneity of the disease has initially hindered the design of successful clinical trials, but considerations on classification criteria have improved patient selection in trials, allowing the identification of more homogeneous groups of patients based on progressive visceral manifestations or the extent of skin involvement with a focus of patients with early disease. Two major subsets of systemic sclerosis are classically described: limited cutaneous systemic sclerosis characterized by distal skin fibrosis and the diffuse subset with distal and proximal skin thickening. Beyond this dichotomic subgrouping of systemic sclerosis, new phenotypic considerations based on antibody subtypes have provided a better understanding of the heterogeneity of the disease, anti-Scl70 antibodies being associated with progressive interstitial lung disease regardless of cutaneous involvement. Two targeted therapies, tocilizumab (a monoclonal antibody targeting interleukin-6 receptors (IL-6R)) and nintedanib (a tyrosine kinase inhibitor), have recently been approved by the American Food & Drug Administration to limit the decline of lung function in patients with SSc-associated interstitial lung disease, demonstrating that such better understanding of the disease pathogenesis with the identification of key targets can lead to therapeutic advances in the management of some visceral manifestations of the disease. This review will provide a brief overview of the pathogenesis of SSc and will present a selection of therapies recently approved or evaluated in this context. Therapies evaluated and approved in SSc-ILD will be emphasized and a review of recent phase II trials in diffuse cutaneous systemic sclerosis will be proposed. We will also discuss selected therapeutic pathways currently under investigation in systemic sclerosis that still lack clinical data in this context but that may show promising results in the future based on preclinical data.

Three Distinct Transcriptional Profiles of Monocytes Associate with Disease Activity in Scleroderma Patients

OBJECTIVE

Patients with diffuse cutaneous systemic sclerosis (dcSSc) display a complex clinical phenotype. Transcriptional profiling of whole blood or tissue from patients are affected by changes in cellular composition that drive gene expression and an inability to detect minority cell populations. We undertook this study to focus on the 2 main subtypes of circulating monocytes, classical monocytes (CMs) and nonclassical monocytes (NCMs) as a biomarker of SSc disease severity.

METHODS

SSc patients were recruited from the Prospective Registry for Early Systemic Sclerosis. Clinical data were collected, as well as peripheral blood for isolation of CMs and NCMs. Age-, sex-, and race-matched healthy volunteers were recruited as controls. Bulk macrophages were isolated from the skin in a separate cohort. All samples were assayed by RNA sequencing (RNA-seq).

RESULTS

We used an unbiased approach to cluster patients into 3 groups (groups A-C) based on the transcriptional signatures of CMs relative to controls. Each group maintained their characteristic transcriptional signature in NCMs. Genes up-regulated in group C demonstrated the highest expression compared to the other groups in SSc skin macrophages, relative to controls. Patients from groups B and C exhibited worse lung function than group A, although there was no difference in SSc skin disease at baseline, relative to controls. We validated our approach by applying our group classifications to published bulk monocyte RNA-seq data from SSc patients, and we found that patients without skin disease were most likely to be classified as group A.

CONCLUSION

We are the first to show that transcriptional signatures of CMs and NCMs can be used to unbiasedly stratify SSc patients and correlate with disease activity outcome measures.

Differentially expressed genes in systemic sclerosis: Towards predictive medicine with new molecular tools for clinicians

Systemic sclerosis (SSc) is a rare and chronic autoimmune disease characterized by a pathogenic triad of immune dysregulation, vasculopathy, and progressive fibrosis. Clinical tools commonly used to assess patients, such as the modified Rodnan skin score, difference between limited or diffuse forms of skin involvement, presence of lung, heart or kidney involvement, or of various autoantibodies, are important prognostic factors, but still fail to reflect the large heterogeneity of the disease. SSc treatment options are diverse, ranging from conventional drugs to autologous hematopoietic stem cell transplantation, and predicting response is challenging. Genome-wide technologies, such as high throughput microarray analyses and RNA sequencing, allow accurate, unbiased, and broad assessment of alterations in expression levels of multiple genes. In recent years, many studies have shown robust changes in the gene expression profiles of SSc patients compared to healthy controls, mainly in skin tissues and peripheral blood cells. The objective analysis of molecular patterns in SSc is a powerful tool that can further classify SSc patients with similar clinical phenotypes and help predict response to therapy. In this review, we describe the journey from the first discovery of differentially expressed genes to the identification of enriched pathways and intrinsic subsets identified in SSc, using machine learning algorithms. Finally, we discuss the use of these new tools to predict the efficacy of various treatments, including stem cell transplantation. We suggest that the use of RNA gene expression-based classifications according to molecular subsets may bring us one step closer to precision medicine in Systemic Sclerosis.

Clinical and Molecular Findings After Autologous Stem Cell Transplantation or Cyclophosphamide for Scleroderma: Handling Missing Longitudinal Data

OBJECTIVE

Among individuals with systemic sclerosis (SSc) randomized to cyclophosphamide (CYC) (n = 34) or hematopoietic stem cell transplantation (HSCT) (n = 33), we examined longitudinal trends of clinical, pulmonary function, and quality of life measures while accounting for the influence of early failures on treatment comparisons.

METHODS

Assuming that data were missing at random, mixed-effects regression models were used to estimate longitudinal trends for clinical measures when comparing treatment groups. Results were compared to observed means and to longitudinal trends estimated from shared parameter models, assuming that data were missing not at random. Longitudinal trends for SSc intrinsic molecular subsets defined by baseline gene expression signatures (normal-like, inflammatory, and fibroproliferative signatures) were also studied.

RESULTS

Available observed means for pulmonary function tests appeared to improve over time in both arms. However, after accounting for participant loss, forced vital capacity in HSCT recipients increased by 0.77 percentage points/year but worsened by -3.70/year for CYC (P = 0.004). Similar results were found for diffusing capacity for carbon monoxide and quality of life indicators. Results for both analytic models were consistent. HSCT recipients in the inflammatory (n = 20) and fibroproliferative (n = 20) subsets had superior long-term trends compared to CYC for pulmonary and quality of life measures. HSCT was also superior for modified Rodnan skin thickness scores in the fibroproliferative subset. For the normal-like subset (n = 22), superiority of HSCT was less apparent.

CONCLUSION

Longitudinal trends estimated from 2 statistical models affirm the efficacy of HSCT over CYC in severe SSc. Failure to account for early loss of participants may distort estimated clinical trends over the long term.

Alterations of the Primary Cilia Gene SPAG17 and SOX9 Locus Noncoding RNAs Identified by RNA-Sequencing Analysis in Patients With Systemic Sclerosis

OBJECTIVE

Systemic sclerosis (SSc) is characterized by immune activation, vasculopathy, and unresolving fibrosis in the skin, lungs, and other organs. We performed RNA-sequencing analysis on skin biopsy samples and peripheral blood mononuclear cells (PBMCs) from SSc patients and unaffected controls to better understand the pathogenesis of SSc.

METHODS

We analyzed these data 1) to test for case/control differences and 2) to identify genes whose expression levels correlate with SSc severity as measured by local skin score, modified Rodnan skin thickness score (MRSS), forced vital capacity (FVC), or diffusing capacity for carbon monoxide (DLco).

RESULTS

We found that PBMCs from SSc patients showed a strong type I interferon signature. This signal was found to be replicated in the skin, with additional signals for increased extracellular matrix (ECM) genes, classical complement pathway activation, and the presence of B cells. Notably, we observed a marked decrease in the expression of SPAG17, a cilia component, in SSc skin. We identified genes that correlated with the MRSS, DLco, and FVC in SSc PBMCs and skin using weighted gene coexpression network analysis. These genes were largely distinct from the case/control differentially expressed genes. In PBMCs, type I interferon signatures negatively correlated with the DLco. In SSc skin, ECM gene expression positively correlated with the MRSS. Network analysis of SSc skin genes that correlated with clinical features identified the noncoding RNAs SOX9-AS1 and ROCR, both near the SOX9 locus, as highly connected, "hub-like" genes in the network.

CONCLUSION

These results identify noncoding RNAs and SPAG17 as novel factors potentially implicated in the pathogenesis of SSc.

Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets

Background

Systemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific expressed hub genes associated with SSc, determine potential competing endogenous RNAs (ceRNA) regulatory networks, and identify potential targeted drugs.

Methods

In this study, four datasets of SSc were acquired. To identify the genes specific to tissues or organs, the BioGPS web database was used. For differentially expressed genes (DEGs), functional and enrichment analyses were carried out, and hub genes were screened and shown in a network of protein-protein interactions (PPI). The potential lncRNA-miRNA-mRNA ceRNA network was constructed using the online databases. The specifically expressed hub genes and ceRNA network were validated in the SSc mouse and in normal mice. We also used the receiver operating characteristic (ROC) curve to determine the diagnostic values of effective biomarkers in SSc. Finally, the Drug-Gene Interaction Database (DGIdb) identified specific medicines linked to hub genes.

Results

The pooled datasets identified a total of 254 DEGs. The tissue/organ-specifically expressed genes involved in this analysis are commonly found in the hematologic/immune system and bone/muscle tissue. The enrichment analysis of DEGs revealed the significant terms such as regulation of actin cytoskeleton, immune-related processes, the VEGF signaling pathway, and metabolism. Cytoscape identified six gene cluster modules and 23 hub genes. And 4 hub genes were identified, including Serpine1, CCL2, IL6, and ISG15. Consistently, the expression of Serpine1, CCL2, IL6, and ISG15 was significantly higher in the SSc mouse model than in normal mice. Eventually, we found that MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1 may be promising RNA regulatory pathways in SSc. Besides, ten potential therapeutic drugs associated with the hub gene were identified.

Conclusions

This study revealed tissue-specific expressed genes, SERPINE1, CCL2, IL6, and ISG15, as effective biomarkers and provided new insight into the mechanisms of SSc. Potential RNA regulatory pathways, including MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1, contribute to our knowledge of SSc. Furthermore, the analysis of drug-hub gene interactions predicted TIPLASININ, CARLUMAB and BINDARIT as candidate drugs for SSc.

A genomic meta-analysis of clinical variables and their association with intrinsic molecular subsets in systemic sclerosis

OBJECTIVES

Four intrinsic molecular subsets (inflammatory, fibroproliferative, limited, normal-like) have previously been identified in SSc and are characterized by unique gene expression signatures and pathways. The intrinsic subsets have been linked to improvement with specific therapies. Here, we investigated associations between baseline demographics and intrinsic molecular subsets in a meta-analysis of published datasets.

METHODS

Publicly available gene expression data from skin biopsies of 311 SSc patients measured by DNA microarray were classified into the intrinsic molecular subsets. RNA-sequencing data from 84 participants from the ASSET trial were used as a validation cohort. Baseline clinical demographics and intrinsic molecular subsets were tested for statistically significant associations.

RESULTS

Males were more likely to be classified in the fibroproliferative subset (P = 0.0046). SSc patients who identified as African American/Black were 2.5 times more likely to be classified as fibroproliferative compared with White/Caucasian patients (P = 0.0378). ASSET participants sera positive for anti-RNA pol I and RNA pol III autoantibodies were enriched in the inflammatory subset (P = 5.8 × 10-5, P = 9.3 × 10-5, respectively), while anti-Scl-70 was enriched in the fibroproliferative subset. Mean modified Rodnan Skin Score (mRSS) was statistically higher in the inflammatory and fibroproliferative subsets compared with normal-like (P = 0.0027). The average disease duration for inflammatory subset was less than fibroproliferative and normal-like intrinsic subsets (P = 8.8 × 10-4).

CONCLUSIONS

We identified multiple statistically significant differences in baseline demographics between the intrinsic subsets that may represent underlying features of disease pathogenesis (e.g. chronological stages of fibrosis) and have implications for treatments that are more likely to work in certain SSc populations.

Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation

Here, the efficacy of abatacept in patients with early diffuse systemic sclerosis (dcSSc) was analyzed to test the hypothesis that patients in the inflammatory intrinsic subset would show the most significant clinical improvement. Eighty-four participants with dcSSc were randomized to receive abatacept or placebo for 12 months. RNA-Seq was performed on 233 skin paired biopsies at baseline and at 3 and 6 months. Improvement was defined as a 5-point or more than 20% change in modified Rodnan skin score (mRSS) between baseline and 12 months. Samples were assigned to intrinsic gene expression subsets (inflammatory, fibroproliferative, or normal-like subsets). In the abatacept arm, change in mRSS was most pronounced for the inflammatory and normal-like subsets relative to the placebo subset. Gene expression for participants on placebo remained in the original molecular subset, whereas inflammatory participants treated with abatacept had gene expression that moved toward the normal-like subset. The Costimulation of the CD28 Family Reactome Pathway decreased in patients who improved on abatacept and was specific to the inflammatory subset. Patients in the inflammatory subset had elevation of the Costimulation of the CD28 Family pathway at baseline relative to that of participants in the fibroproliferative and normal-like subsets. There was a correlation between improved ΔmRSS and baseline expression of the Costimulation of the CD28 Family pathway. This study provides an example of precision medicine in systemic sclerosis clinical trials.

The Microbiome in Systemic Sclerosis: Pathophysiology and Therapeutic Potential

Systemic sclerosis (SSc), also known as scleroderma, is an autoimmune disease with unknown etiology characterized by multi-organ fibrosis. Despite substantial investigation on SSc-related cellular and molecular mechanisms, effective therapies are still lacking. The skin, lungs, and gut are the most affected organs in SSc, which act as physical barriers and constantly communicate with colonized microbiota. Recent reports have documented a unique microbiome signature, which may be the pathogenic trigger or driver of SSc. Since gut microbiota influences the efficacy and toxicity of oral drugs, evaluating drug-microbiota interactions has become an area of interest in disease treatment. The existing evidence highlights the potential of the microbial challenge as a novel therapeutic option in SSc. In this review, we have summarized the current knowledge about molecular mechanisms of SSc and highlighted the underlying role of the microbiome in SSc pathogenesis. We have also discussed the latest therapeutic interventions using microbiomes in SSc, including drug-microbiota interactions and animal disease models. This review aims to elucidate the pathophysiological connection and therapeutic potential of the microbiome in SSc. Insights into the microbiome will significantly improve our understanding of etiopathogenesis and developing therapeutics for SSc.

Deficiency of inhibitory TLR4 homolog RP105 exacerbates fibrosis

Activation of TLR4 by its cognate damage-associated molecular patterns (DAMPs) elicits potent profibrotic effects and myofibroblast activation in systemic sclerosis (SSc), while genetic targeting of TLR4 or its DAMPs in mice accelerates fibrosis resolution. To prevent aberrant DAMP/TLR4 activity, a variety of negative regulators evolved to dampen the magnitude and duration of the signaling. These include radioprotective 105 kDa (RP105), a transmembrane TLR4 homolog that competitively inhibits DAMP recognition of TLR4, blocking TLR4 signaling in immune cells. The role of RP105 in TLR4-dependent fibrotic responses in SSc is unknown. Using unbiased transcriptome analysis of skin biopsies, we found that levels of both TLR4 and its adaptor protein MD2 were elevated in SSc skin and significantly correlated with each other. Expression of RP105 was negatively associated with myofibroblast differentiation in SSc. Importantly, RP105-TLR4 association was reduced, whereas TLR4-TLR4 showed strong association in fibroblasts from patients with SSc, as evidenced by PLA assays. Moreover, RP105 adaptor MD1 expression was significantly reduced in SSc skin biopsies and explanted SSc skin fibroblasts. Exogenous RP105-MD1 abrogated, while loss of RP105 exaggerated, fibrotic cellular responses. Importantly, ablation of RP105 in mice was associated with augmented TLR4 signaling and aggravated skin fibrosis in complementary disease models. Thus, we believe RP105-MD1 to be a novel cell-intrinsic negative regulator of TLR4-MD2-driven sustained fibroblast activation, representing a critical regulatory network governing the fibrotic process. Impaired RP105 function in SSc might contribute to persistence of progression of the disease.

The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review

BACKGROUND

Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes.

METHODS

Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context.

RESULTS

Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response.

CONCLUSION

Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.

Assessment of disease outcome measures in systemic sclerosis

The assessment of disease activity in systemic sclerosis (SSc) is challenging owing to its heterogeneous manifestations across multiple organ systems, the variable rate of disease progression and regression, and the relative paucity of patients in early-phase therapeutic trials. Despite some recent successes, most clinical trials have failed to show efficacy, underscoring the need for improved outcome measures linked directly to disease pathogenesis, particularly applicable for biomarker studies focused on skin disease. Current outcome measures in SSc-associated interstitial lung disease and SSc skin disease are largely adequate, although advancing imaging technology and the incorporation of skin mRNA biomarkers might provide opportunities for earlier detection of the therapeutic effect. Biomarkers can further inform pathogenesis, enabling early phase trials to act as reverse translational studies through the incorporation of routine high-throughput sequencing.

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.

Toward Molecular Stratification and Precision Medicine in Systemic Sclerosis

Systemic sclerosis (SSc), a complex multi-systemic disease characterized by immune dysregulation, vasculopathy and fibrosis, is associated with high mortality. Its pathogenesis is only partially understood. The heterogenous pathological processes that define SSc and its stages present a challenge to targeting appropriate treatment, with differing treatment outcomes of SSc patients despite similar initial clinical presentations. Timing of the appropriate treatments targeted at the underlying disease process is critical. For example, immunomodulatory treatments may be used for patients in a predominantly inflammatory phase, anti-fibrotic treatments for those in the fibrotic phase, or combination therapies for those in the fibro-inflammatory phase. In advancing personalized care through precision medicine, groups of patients with similar disease characteristics and shared pathological processes may be identified through molecular stratification. This would improve current clinical sub-setting systems and guide personalization of therapies. In this review, we will provide updates in SSc clinical and molecular stratification in relation to patient outcomes and treatment responses. Promises of molecular stratification through advances in high-dimensional tools, including omic-based stratification (transcriptomics, genomics, epigenomics, proteomics, cytomics, microbiomics) and machine learning will be discussed. Innovative and more granular stratification systems that integrate molecular characteristics to clinical phenotypes would potentially improve therapeutic approaches through personalized medicine and lead to better patient outcomes.

Expansion of Fcγ Receptor IIIa-Positive Macrophages, Ficolin 1-Positive Monocyte-Derived Dendritic Cells, and Plasmacytoid Dendritic Cells Associated With Severe Skin Disease in Systemic Sclerosis

OBJECTIVE

In this study, we sought a comprehensive understanding of myeloid cell types driving fibrosis in diffuse cutaneous systemic sclerosis (dcSSc) skin.

METHODS

We analyzed the transcriptomes of 2,465 myeloid cells from skin biopsy specimens from 12 dcSSc patients and 10 healthy control subjects using single-cell RNA sequencing. Monocyte-derived dendritic cells (mo-DCs) were assessed using immunohistochemical staining and immunofluorescence analyses targeting ficolin-1 (FCN-1).

RESULTS

A t-distributed stochastic neighbor embedding analysis of single-cell transcriptome data revealed 12 myeloid cell clusters, 9 of which paralleled previously described healthy control macrophage/DC clusters, and 3 of which were dcSSc-specific myeloid cell clusters. One SSc-associated macrophage cluster, highly expressing Fcγ receptor IIIA, was suggested on pseudotime analysis to be derived from normal CCR1+ and MARCO+ macrophages. A second SSc-associated myeloid population highly expressed monocyte markers FCN-1, epiregulin, S100A8, and S100A9, but was closely related to type 2 conventional DCs on pseudotime analysis and identified as mo-DCs. Mo-DCs were associated with more severe skin disease. Proliferating macrophages and plasmacytoid DCs were detected almost exclusively in dcSSc skin, the latter clustering with B cells and apparently derived from lymphoid progenitors.

CONCLUSION

Transcriptional signatures in these and other myeloid populations indicate innate immune system activation, possibly through Toll-like receptors and highly up-regulated chemokines. However, the appearance and activation of myeloid cells varies between patients, indicating potential differences in the underlying pathogenesis and/or temporal disease activity in dcSSc.

Neuropeptide Cortistatin Regulates Dermal and Pulmonary Fibrosis in an Experimental Model of Systemic Sclerosis

INTRODUCTION

Scleroderma, or systemic sclerosis, is a complex connective tissue disorder characterized by autoimmunity, vasculopathy, and progressive fibrosis of the skin and internal organs. Because its aetiology is unknown, the identification of genes/factors involved in disease severity, differential clinical forms, and associated complications is critical for understanding its pathogenesis and designing novel treatments. Neuroendocrine mediators in the skin emerge as potential candidates. We investigated the role played by the neuropeptide cortistatin in a preclinical model of scleroderma.

METHODS

Dermal fibrosis was induced by repetitive intradermal injections of bleomycin in wild-type and cortistatin-deficient mice. The histopathological signs and expression of fibrotic markers were evaluated in the skin and lungs.

RESULTS

An inverse correlation between cortistatin levels and fibrogenic activation exists in the damaged skin and dermal fibroblasts. Bleomycin-challenged skin lesions of mice that are partially and totally deficient in cortistatin showed exacerbated histopathological signs of scleroderma, characterized by thicker and more fibrotic dermal layer, enlarged epidermis, and increased inflammatory infiltration in comparison to those of wild-type mice. Cortistatin deficiency enhanced dermal collagen deposits, connective tissue growth factor expression, loss of microvessels, and predisposition to suffer severe complications that co-occur with dermal exposition to bleomycin, including pulmonary fibrotic disease and increased mortality. Treatment with cortistatin mitigated these pathological processes.

DISCUSSION/CONCLUSION

We identify cortistatin as an endogenous break of skin inflammation and fibrosis. Deficiency in cortistatin could be a marker of poor prognosis of scleroderma and associated complications. Cortistatin-based therapies emerge as attractive candidates to treat severe forms of systemic sclerosis and to manage fibrosis-related side effects of bleomycin chemotherapy in oncologic patients.

Large-scale analysis of longitudinal skin gene expression in systemic sclerosis reveals relationships of immune cell and fibroblast activity with skin thickness and a trend towards normalisation over time

OBJECTIVES

Determine relationships between skin gene expression and systemic sclerosis (SSc) clinical disease features, and changes in skin gene expression over time.

METHODS

A total of 339 forearm skin biopsies were obtained from 113 SSc patients and 44 matched healthy controls. 105 SSc patients had a second biopsy, and 76 had a third biopsy. Global gene expression profiling was performed, and differentially expressed genes and cell type-specific signatures in SSc were evaluated for relationships to modified Rodnan Skin Score (mRSS) and other clinical variables. Changes in skin gene expression over time were analysed by mixed effects models and principal component analysis. Immunohistochemical staining was performed to validate conclusions.

RESULTS

Gene expression dysregulation was greater in SSc patients with affected skin than in those with unaffected skin. Immune cell and fibroblast signatures positively correlated with mRSS. High baseline immune cell and fibroblast signatures predicted higher mRSS over time, but were not independently predictive of longitudinal mRSS after adjustment for baseline mRSS. In early diffuse cutaneous SSc, immune cell and fibroblast signatures declined over time, and overall skin gene expression trended towards normalisation. On immunohistochemical staining, most early diffuse cutaneous SSc patients with high baseline T cell and macrophage numbers had declines in these numbers at follow-up.

CONCLUSIONS

Skin thickness in SSc is related to dysregulated immune cell and fibroblast gene expression. Skin gene expression changes over time in early diffuse SSc, with a tendency towards normalisation. These observations are relevant for understanding SSc pathogenesis and could inform treatment strategies and clinical trial design.

Systematic Analysis of the Literature in Search of Defining Systemic Sclerosis Subsets

OBJECTIVE

Systemic sclerosis (SSc) is a multisystem disease with heterogeneity in presentation and prognosis.An international collaboration to develop new SSc subset criteria is underway. Our objectives were to identify systems of SSc subset classification and synthesize novel concepts to inform development of new criteria.

METHODS

Medline, Cochrane MEDLINE, the Cumulative Index to Nursing and Allied Health Literature, EMBASE, and Web of Science were searched from their inceptions to December 2019 for studies related to SSc subclassification, limited to humans and without language or sample size restrictions.

RESULTS

Of 5686 citations, 102 studies reported original data on SSc subsets. Subset classification systems relied on extent of skin involvement and/or SSc-specific autoantibodies (n = 61), nailfold capillary patterns (n = 29), and molecular, genomic, and cellular patterns (n = 12). While some systems of subset classification confer prognostic value for clinical phenotype, severity, and mortality, only subsetting by gene expression signatures in tissue samples has been associated with response to therapy.

CONCLUSION

Subsetting on extent of skin involvement remains important. Novel disease attributes including SSc-specific autoantibodies, nailfold capillary patterns, and tissue gene expression signatures have been proposed as innovative means of SSc subsetting.

Current Concepts on the Pathogenesis of Systemic Sclerosis

From the clinical standpoint, systemic sclerosis (SSc) is characterized by skin and internal organ fibrosis, diffuse fibroproliferative vascular modifications, and autoimmunity. Clinical presentation and course are highly heterogenous and life expectancy variably affected mostly dependent on lung and heart involvement. SSc touches more women than men with differences in disease severity and environmental exposure. Pathogenetic events originate from altered homeostasis favored by genetic predisposition, environmental cues and a variety of endogenous and exogenous triggers. Epigenetic modifications modulate SSc pathogenesis which strikingly associate profound immune-inflammatory dysregulation, abnormal endothelial cell behavior, and cell trans-differentiation into myofibroblasts. SSc myofibroblasts show enhanced survival and enhanced extracellular matrix deposition presenting altered structure and altered physicochemical properties. Additional cell types of likely pathogenic importance are pericytes, platelets, and keratinocytes in conjunction with their relationship with vessel wall cells and fibroblasts. In SSc, the profibrotic milieu is favored by cell signaling initiated in the one hand by transforming growth factor-beta and related cytokines and in the other hand by innate and adaptive type 2 immune responses. Radical oxygen species and invariant receptors sensing danger participate to altered cell behavior. Conventional and SSc-specific T cell subsets modulate both fibroblasts as well as endothelial cell dysfunction. Beside autoantibodies directed against ubiquitous antigens important for enhanced clinical classification, antigen-specific agonistic autoantibodies may have a pathogenic role. Recent studies based on single-cell RNAseq and multi-omics approaches are revealing unforeseen heterogeneity in SSc cell differentiation and functional states. Advances in system biology applied to the wealth of data generated by unbiased screening are allowing to subgroup patients based on distinct pathogenic mechanisms. Deciphering heterogeneity in pathogenic mechanisms will pave the way to highly needed personalized therapeutic approaches.

Non-Coding RNA in Systemic Sclerosis: A Valuable Tool for Translational and Personalized Medicine

Epigenetic factors are heritable and ultimately play a role in modulating gene expression and, thus, in regulating cell functions. Non-coding RNAs have growing recognition as novel biomarkers and crucial regulators of pathological conditions in humans. Their characteristic feature is being transcribed in a tissue-specific pattern. Now, there is emerging evidence that lncRNAs have been identified to be involved in the differentiation of human skin, wound healing, fibrosis, inflammation, and immunological response. Systemic sclerosis (SSc) is a heterogeneous autoimmune disease characterized by fibrosis, vascular abnormalities, and immune system activation. The pathogenesis remains elusive, but clinical manifestations reveal autoimmunity with the presence of specific autoantibodies, activation of innate and adaptive immunity, vascular changes, and active deposition of extracellular matrix components leading to fibrosis. The use of multi-omics studies, including NGS, RNA-seq, or GWAS, has proposed that the non-coding genome may be a significant player in its pathogenesis. Moreover, it may unravel new therapeutic targets in the future. The aim of this review is to show the pathogenic role of long non-coding RNAs in systemic sclerosis. Investigation of these transcripts' functions has the potential to elucidate the molecular pathology of SSc and provide new opportunities for drug-targeted therapy for this disorder.

Myofibroblast transcriptome indicates SFRP2hi fibroblast progenitors in systemic sclerosis skin

Skin and lung fibrosis in systemic sclerosis (SSc) is driven by myofibroblasts, alpha-smooth muscle actin expressing cells. The number of myofibroblasts in SSc skin correlates with the modified Rodnan skin score, the most widely used clinical measure of skin disease severity. Murine fibrosis models indicate that myofibroblasts can arise from a variety of different cell types, but their origin in SSc skin has remained uncertain. Utilizing single cell RNA-sequencing, we define different dermal fibroblast populations and transcriptome changes, comparing SSc to healthy dermal fibroblasts. Here, we show that SSc dermal myofibroblasts arise in two steps from an SFRP2^hi/DPP4-expressing progenitor fibroblast population. In the first step, SSc fibroblasts show globally upregulated expression of transcriptome markers, such as PRSS23 and THBS1. A subset of these cells shows markers indicating that they are proliferating. Only a fraction of SFRP2^hi SSc fibroblasts differentiate into myofibroblasts, as shown by expression of additional markers, SFRP4 and FNDC1. Bioinformatics analysis of the SSc fibroblast transcriptomes implicated upstream transcription factors, including FOSL2, RUNX1, STAT1, FOXP1, IRF7 and CREB3L1, as well as SMAD3, driving SSc myofibroblast differentiation.

Myofibroblasts drive fibrosis in systemic sclerosis (SSc), but the cellular progenitors are unknown. Utilizing single cell RNA-sequencing, the authors show that SSc dermal myofibroblasts arise in a two-step process from SFRP2/DPP4-expressing progenitors and implicate upstream transcription factors.

Novel classifications for systemic sclerosis: challenging historical subsets to unlock new doors

PURPOSE OF REVIEW

Systemic sclerosis (SSc) is a severe rheumatic disease characterized by a considerable heterogeneity in clinical presentations and pathophysiological mechanisms. This variability has a substantial impact on morbidity and mortality and limits the generalizability of clinical trial results. This review aims to highlight recent studies that have proposed new innovative approaches to decipher this heterogeneity, in particular, by attempting to optimize disease classification.

RECENT FINDINGS

The historical dichotomy limited/diffuse subsets based on cutaneous involvement has been challenged by studies highlighting an underestimated heterogeneity between these two subtypes and showing that presence of organ damage and autoantibody profiles markedly influenced survival beyond skin extension. Advanced computational methods using unsupervised machine learning analyses of clinical variables and/or high-throughput omics technologies, clinical variables trajectories modelling overtime or radiomics have provided significant insights on key pathogenic processes that could help defining new subgroups beyond the diffuse/limited subsets.

SUMMARY

We can anticipate that a future classification of SSc patients will integrate innovative approaches encompassing clinical phenotypes, variables trajectories, serological features and innovative omics molecular signatures. It nevertheless seems crucial to also pursue the implementation and standardization of readily available and easy to use tools that can be used in clinical practice.

Association of differentially expressed genes and autoantibody type in patients with systemic sclerosis

OBJECTIVES

The aims of this study were to investigate the relationship between the type of autoantibody and gene expression profile in skin lesions from patients with SSc, and to identify specific dysregulated pathways in SSc patients compared with healthy controls.

METHODS

Sixty-one patients with SSc from the Genetics vs Environment in Scleroderma Outcome Study cohort and 36 healthy controls were included in this study. Differentially expressed genes were extracted and functional enrichment and pathway analysis were conducted.

RESULTS

Compared with healthy controls, lists containing 2, 71, 10, 144 and 78 differentially expressed genes were created for patients without specific autoantibody, ACA, anti-U1 RNP antibody (RNP), anti-RNA polymerase III antibody (RNAP) and anti-topoisomerase I antibody (ATA), respectively. While part of the enriched pathways overlapped, distinct pathways were identified except in those patients lacking specific autoantibody. The distinct enriched pathways included 'keratinocyte differentiation' for ACA, 'nuclear factor κB signalling' and 'cellular response to TGF-β stimulus' for RNAP, 'interferon α/β signalling' for RNP, and 'cellular response to stress' for ATA. Cell type signature score analysis revealed that macrophages/monocytes, endothelial cells and fibroblasts were associated with ACA, RNAP, ATA and the severity of the SSc skin lesions.

CONCLUSION

Pathogenic pathways were identified according to the type of autoantibody by leveraging gene expression data of patients and controls from a multicentre cohort. The current study may promote the search for new therapeutic targets for SSc.

Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement

OBJECTIVE

We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma).

METHODS

Fifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis).

RESULTS

aSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts.

CONCLUSION

CD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.

Differential DNA Methylation Landscape in Skin Fibroblasts from African Americans with Systemic Sclerosis

The etiology and reasons underlying the ethnic disparities in systemic sclerosis (SSc) remain unknown. African Americans are disproportionally affected by SSc and yet are underrepresented in research. The aim of this study was to comprehensively investigate the association of DNA methylation levels with SSc in dermal fibroblasts from patients of African ancestry. Reduced representation bisulfite sequencing (RRBS) was performed on primary dermal fibroblasts from 15 SSc patients and 15 controls of African ancestry, and over 3.8 million CpG sites were tested for differential methylation patterns between cases and controls. The dermal fibroblasts from African American patients exhibited widespread reduced DNA methylation. Differentially methylated CpG sites were most enriched in introns and intergenic regions while depleted in 5' UTR, promoters, and CpG islands. Seventeen genes and eleven promoters showed significant differential methylation, mostly in non-coding RNA genes and pseudogenes. Gene set enrichment analysis (GSEA) and gene ontology (GO) analyses revealed an enrichment of pathways related to interferon signaling and mesenchymal differentiation. The hypomethylation of DLX5 and TMEM140 was accompanied by these genes' overexpression in patients but underexpression for lncRNA MGC12916. These data show that differential methylation occurs in dermal fibroblasts from African American patients with SSc and identifies novel coding and non-coding genes.

Transcriptional insights into pathogenesis of cutaneous systemic sclerosis using pathway driven meta-analysis assisted by machine learning methods

Pathophysiology of systemic sclerosis (SSc, Scleroderma), an autoimmune rheumatic disease, comprises of mechanisms that drive vasculopathy, inflammation and fibrosis. Understanding of the disease and associated clinical heterogeneity has advanced considerably in the past decade, highlighting the necessity of more specific targeted therapy. While many of the recent trials in SSc failed to meet the primary end points that predominantly relied on changes in modified Rodnan skin scores (MRSS), sub-group analysis, especially those focused on the basal skin transcriptomic data have provided insights into patient subsets that respond to therapies. These findings suggest that deeper understanding of the molecular changes in pathways is very important to define disease drivers in various patient subgroups. In view of these challenges, we performed meta-analysis on 9 public available SSc microarray studies using a novel pathway pivoted approach combining consensus clustering and machine learning assisted feature selection. Selected pathway modules were further explored through cluster specific topological network analysis in search of novel therapeutic concepts. In addition, we went beyond previously described SSc class divisions of 3 clusters (e.g. inflammation, fibro-proliferative, normal-like) and expanded into a much finer stratification in order to profile SSc patients more accurately. Our analysis unveiled an important 80 pathway signatures that differentiated SSc patients into 8 unique subtypes. The 5 pathway modules derived from such signature successfully defined the 8 SSc subsets and were validated by in-silico cellular deconvolution analysis. Myeloid cells and fibroblasts involvement in different clusters were confirmed and linked to corresponding pathway activities. Collectively, our findings revealed more complex disease subtypes in SSc; Key gene mediators such as IL6, FGFR1, TLR7, PLCG2, IRK2 identified by network analysis underscored the scientific rationale for exploring additional targets in treatment of SSc.

Genomics of Human Fibrotic Diseases: Disordered Wound Healing Response

Fibrotic disease, which is implicated in almost half of all deaths worldwide, is the result of an uncontrolled wound healing response to injury in which tissue is replaced by deposition of excess extracellular matrix, leading to fibrosis and loss of organ function. A plethora of genome-wide association studies, microarrays, exome sequencing studies, DNA methylation arrays, next-generation sequencing, and profiling of noncoding RNAs have been performed in patient-derived fibrotic tissue, with the shared goal of utilizing genomics to identify the transcriptional networks and biological pathways underlying the development of fibrotic diseases. In this review, we discuss fibrosing disorders of the skin, liver, kidney, lung, and heart, systematically (1) characterizing the initial acute injury that drives unresolved inflammation, (2) identifying genomic studies that have defined the pathologic gene changes leading to excess matrix deposition and fibrogenesis, and (3) summarizing therapies targeting pro-fibrotic genes and networks identified in the genomic studies. Ultimately, successful bench-to-bedside translation of observations from genomic studies will result in the development of novel anti-fibrotic therapeutics that improve functional quality of life for patients and decrease mortality from fibrotic diseases.

Molecular "omic" signatures in systemic sclerosis

Systemic sclerosis (SSc) is a connective tissue disorder characterized by immunologic, vascular, and extracellular matrix abnormalities. Variation in the proportion and/or timing of activation in the deregulated molecular pathways that underlie SSc may explain the observed clinical heterogeneity in terms of disease phenotype and treatment response. In recent years, SSc research has generated massive amounts of "omics" level data. In this review, we discuss the body of "omics" level work in SSc and how each layer provides unique insight to our understanding of SSc. We posit that effective integration of genomic, transcriptomic, metagenomic, and epigenomic data is an important step toward precision medicine and is vital to the identification of effective therapeutic options for patients with SSc.

Machine learning predicts stem cell transplant response in severe scleroderma

OBJECTIVE

The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT clinical trial to scleroderma intrinsic subsets and tested the hypothesis that they predict long-term response to HSCT.

METHODS

We analysed gene expression from PBCs of SCOT participants to identify differential treatment response. PBC gene expression data were generated from 63 SCOT participants at baseline and follow-up timepoints. Participants who completed treatment protocol were stratified by intrinsic gene expression subsets at baseline, evaluated for event-free survival (EFS) and analysed for differentially expressed genes (DEGs).

RESULTS

Participants from the fibroproliferative subset on HSCT experienced significant improvement in EFS compared with fibroproliferative participants on CYC (p=0.0091). In contrast, EFS did not significantly differ between CYC and HSCT arms for the participants from the normal-like subset (p=0.77) or the inflammatory subset (p=0.1). At each timepoint, we observed considerably more DEGs in HSCT arm compared with CYC arm with HSCT arm showing significant changes in immune response pathways.

CONCLUSIONS

Participants from the fibroproliferative subset showed the most significant long-term benefit from HSCT compared with CYC. This study suggests that intrinsic subset stratification of patients may be used to identify patients with SSc who receive significant benefit from HSCT.

Regulator combinations identify systemic sclerosis patients with more severe disease

Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, autoantibody production, and internal organ dysfunction. We previously identified 4 “intrinsic” subsets of SSc based upon skin gene expression that are found across organ systems. Gene expression regulators that underlie the SSc-intrinsic subsets, or are associated with clinical covariates, have not been systematically characterized. Here, we present a computational framework to calculate the activity scores of gene expression regulators and identify their associations with SSc clinical outcomes. We found that regulator activity scores can reproduce the intrinsic molecular subsets, with distinct sets of regulators identified for inflammatory, fibroproliferative, limited, and normal-like samples. Regulators most highly correlated with modified Rodnan skin score (MRSS) also varied by intrinsic subset. We identified subgroups of patients with fibroproliferative and inflammatory SSc with more severe pathophenotypes, such as higher MRSS and increased likelihood of interstitial lung disease (ILD). Using an independent cohort, we show that the group with more severe ILD was more likely to show forced vital capacity decline over a period of 36–54 months. Our results demonstrate an association among the activation of regulators, gene expression subsets, and clinical variables that can identify patients with SSc with more severe disease.

An association between the activation of regulators, gene expression subsets, and clinical variables identifies systemic sclerosis patients with more severe disease.

Macrophages and cadherins in fibrosis and systemic sclerosis

PURPOSE OF REVIEW

Macrophages are key players in systemic sclerosis (SSc) and fibrosis. The mechanism by which macrophages regulate fibrogenesis is unclear and understanding the origin and function of macrophages is critical to developing effective therapeutics. Novel targets on macrophages are under investigation and recently, cadherins have emerged as a potential therapeutic target on macrophages. The current review will discuss the importance of macrophages in SSc and fibrosis and summarize recent studies on the role of cadherin-11 (Cdh11) on macrophages and fibrosis.

RECENT FINDINGS

Genome-wide expression studies demonstrate the importance of macrophages in SSc and fibrosis. Although M2 macrophages are associated with fibrosis, the presence of a mixed M1/M2 phenotype in fibrosis has recently been reported. Several studies aiming to identify macrophage subsets involved in fibrogenesis suggest that monocyte-derived alveolar macrophages are key players in the development of murine lung fibrosis. Recent functional studies show that Cdh11 regulates macrophages, fibroblast invasion, and adhesion of macrophages to myofibroblasts.

SUMMARY

Macrophages play an important role in SSc and fibrosis. New insights into the mechanisms by which macrophages regulate fibrogenesis have been discovered on the basis of Cdh11 studies and suggest that targeting Cdh11 may be an effective target to treat fibrosis.

Enhancing reproducibility of gene expression analysis with known protein functional relationships: The concept of well-associated protein

Identification of differentially expressed genes (DEGs) is well recognized to be variable across independent replications of genome-wide transcriptional studies. These are often employed to characterize disease state early in the process of discovery and prioritize novel targets aimed at addressing unmet medical need. Increasing reproducibility of biological findings from these studies could potentially positively impact the success rate of new clinical interventions. This work demonstrates that statistically sound combination of gene expression data with prior knowledge about biology in the form of large protein interaction networks can yield quantitatively more reproducible observations from studies characterizing human disease. The novel concept of Well-Associated Proteins (WAPs) introduced herein-gene products significantly associated on protein interaction networks with the differences in transcript levels between control and disease-does not require choosing a differential expression threshold and can be computed efficiently enough to enable false discovery rate estimation via permutation. Reproducibility of WAPs is shown to be on average superior to that of DEGs under easily-quantifiable conditions suggesting that they can yield a significantly more robust description of disease. Enhanced reproducibility of WAPs versus DEGs is first demonstrated with four independent data sets focused on systemic sclerosis. This finding is then validated over thousands of pairs of data sets obtained by random partitions of large studies in several other diseases. Conditions that individual data sets must satisfy to yield robust WAP scores are examined. Reproducible identification of WAPs can potentially benefit drug target selection and precision medicine studies.

Overexpression of ubiquitin-specific peptidase 15 in systemic sclerosis fibroblasts increases response to transforming growth factor β

Objective

Ubiquitination of proteins leads to their degradation by the proteasome, and is regulated by ubiquitin ligases and substrate-specific ubiquitin-specific peptidases (USPs). The ubiquitination process also plays important roles in the regulation of cell metabolism and cell cycle. Here, we found that the expression of several USPs is increased in SSc tenosynovial and skin biopsies, and we demonstrated that USP inhibition decreases TGF-β signalling in primary fibroblast cell lines.

Methods

High-density transcriptomic studies were performed using total RNA obtained from SSc tenosynovial samples. Confirmatory immunostaining experiments were performed on tenosynovial and skin samples. In vitro experiments were conducted in order to study the influence of USP modulation on responses to TGF-β stimulation.

Results

Tenosynovial biopsies from SSc patients overexpressed known disease-associated gene pathways: fibrosis, cytokines and chemokines, and Wnt/TGF-β signalling, but also several USPs. Immunohistochemistry experiments confirmed the detection of USPs in the same samples, and in SSc skin biopsies. Exposure of primary fibroblast cell lines to TGF-β induced USP gene expression. The use of a pan-USP inhibitor decreased SMAD3 phosphorylation, and expression of COL1A1, COL3A1 and fibronectin gene expression in TGF-β-stimulated fibroblasts. The effect of the USP inhibitor resulted in increased SMAD3 ubiquitination, and was blocked by a proteasome inhibitor, thereby confirming the specificity of its action.

Conclusion

Overexpression of several USPs, including USP15, amplifies fibrotic responses induced by TGF-β, and is a potential therapeutic target in SSc.

Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis

Background

Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients.

Methods

An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein–protein interaction (PPI) networks. Then, functional clusters in PPI networks were determined. Enrichr, a gene list enrichment analysis tool, was utilized for the functional enrichment of clusters.

Results

A total of 12, 2, and 4 functional clusters from 619, 52, and 119 DEGs were determined in the lung, peripheral blood mononuclear cell (PBMC), and skin tissues, respectively. Analysis revealed that the tumor necrosis factor (TNF) signaling pathway was enriched significantly in the three investigated tissues as a common pathway. In addition, clusters associated with inflammation and immunity were common in the three investigated tissues. However, clusters related to the fibrosis process were common in lung and skin tissues.

Conclusions

Analysis indicated that there were common pathological clusters that contributed to the pathogenesis of SSc in different tissues. Moreover, it seems that the common pathways in distinct tissues stem from a diverse set of genes.

Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile

OBJECTIVES

Determine global skin transcriptome patterns of early diffuse systemic sclerosis (SSc) and how they differ from later disease.

METHODS

Skin biopsy RNA from 48 patients in the Prospective Registry for Early Systemic Sclerosis (PRESS) cohort (mean disease duration 1.3 years) and 33 matched healthy controls was examined by next-generation RNA sequencing. Data were analysed for cell type-specific signatures and compared with similarly obtained data from 55 previously biopsied patients in Genetics versus Environment in Scleroderma Outcomes Study cohort with longer disease duration (mean 7.4 years) and their matched controls. Correlations with histological features and clinical course were also evaluated.

RESULTS

SSc patients in PRESS had a high prevalence of M2 (96%) and M1 (94%) macrophage and CD8 T cell (65%), CD4 T cell (60%) and B cell (69%) signatures. Immunohistochemical staining of immune cell markers correlated with the gene expression-based immune cell signatures. The prevalence of immune cell signatures in early diffuse SSc patients was higher than in patients with longer disease duration. In the multivariable model, adaptive immune cell signatures were significantly associated with shorter disease duration, while fibroblast and macrophage cell type signatures were associated with higher modified Rodnan Skin Score (mRSS). Immune cell signatures also correlated with skin thickness progression rate prior to biopsy, but did not predict subsequent mRSS progression.

CONCLUSIONS

Skin in early diffuse SSc has prominent innate and adaptive immune cell signatures. As a prominently affected end organ, these signatures reflect the preceding rate of disease progression. These findings could have implications in understanding SSc pathogenesis and clinical trial design.