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.

Systemic Sclerosis (Scleroderma, SSc) is an autoimmune disease characterized by vasculopathy, inflammation, and fibrosis that can lead to loss of organ function. Type I interferons (IFNs) are family of cytokines that mitigate the deleterious effects of viral and bacterial infections in the innate immunity system. Past several years, research efforts have been focused on the role of type I IFN and IFN-inducible genes in the pathogenesis of SSc. Polymorphisms in the Interferon regulatory factor (IRF)-5, IRF7 and IRF8 are associated with SSc, Similarly, polymorphism of Signal Transducer and Activator of Transcription (STAT)-4, has been established as a genetic risk factor of SSc. IRFs and STAT4 proteins are key activators of type I IFN signaling pathways. An IFN signature (increased expression and activation of IFN-regulated genes) has been observed in the peripheral blood and skin biopsy samples of patients with SSc. Furthermore, a plasma IFN-inducible chemokine score correlated with markers of disease severity and autoantibody subtypes in SSc. In this review, we summarize our current knowledge of the role of type I IFNs and IFN-inducible genes in the pathogenesis of SSc and their potential role as biomarkers and therapeutic targets.

Goal: Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found to be greatly useful in biology, medicine, healthcare, and biomedical applications, in the areas of medical image processing and speech recognition. However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). Methods: In this paper, we propose a novel mobile deep learning network for the characterization of SSc skin. The proposed network architecture consists of the UNet, a dense connectivity convolutional neural network (CNN) with added classifier layers that when combined with limited training data, yields better image segmentation and more accurate classification, and a mobile training module. In addition, to improve the computational efficiency and diagnostic accuracy, the highly efficient training model called “MobileNetV2,” which is designed for mobile and embedded applications, was used to train the network. Results: The proposed network was implemented using a standard laptop (2.5 GHz Intel Core i7). After fine tuning, our results showed the proposed network reached 100% accuracy on the training image set, 96.8% accuracy on the validation image set, and 95.2% on the testing image set. The training time was less than 5 hours. We also analyzed the same normal vs SSc skin image sets using the CNN using the same laptop. The CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. Additionally, it took more than 14 hours to train the CNN architecture. We also utilized the MobileNetV2 model to analyze an additional dataset of images and classified them as normal, early (mid and moderate) SSc or late (severe) SSc skin images. The network reached 100% accuracy on the training image set, 97.2% on the validation set, and 94.8% on the testing image set. Using the same normal, early and late phase SSc skin images, the CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. These results indicated that the MobileNetV2 architecture is more accurate and efficient compared to the CNN to classify normal, early and late phase SSc skin images. Conclusions: Our preliminary study, intended to show the efficacy of the proposed network architecture, holds promise in the characterization of SSc. We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive, and accurate screening tool for SSc.

Objectives There is considerable evidence that implicates dysregulation of type I interferon signalling (or type I IFN signature) in the pathogenesis of systemic sclerosis (SSc). Interferon regulatory factor 7 (IRF7) has been recognised as a master regulator of type I IFN signalling. The objective of this study was to elucidate the role of IRF7 in dermal fibrosis and SSc pathogenesis. Methods SSc and healthy control skin biopsies were investigated to determine IRF7 expression and activation. The role of IRF7 in fibrosis was investigated using IRF7 knockout (KO) mice in the bleomycin-induced and TSK/+mouse models. In vitro experiments with dermal fibroblasts from patients with SSc and healthy controls were performed. Results IRF7 expression was significantly upregulated and activated in SSc skin tissue and explanted SSc dermal fibroblasts compared with unaffected, matched controls. Moreover, IRF7 expression was stimulated by IFN-α in dermal fibroblasts. Importantly, IRF7 co-immunoprecipitated with Smad3, a key mediator of transforming growth factor (TGF)-β signalling, and IRF7 knockdown reduced profibrotic factors in SSc fibroblasts. IRF7 KO mice demonstrated attenuated dermal fibrosis and inflammation compared with wild-type mice in response to bleomycin. Specifically, hydroxyproline content, dermal thickness as well as Col1a2, ACTA2 and interleukin-6 mRNA levels were significantly attenuated in IRF7 KO mice skin tissue. Furthermore, IRF7 KO in TSK/+mice attenuated hydroxyproline content, subcutaneous hypodermal thickness, Col1a2 mRNA as well as α-smooth muscle actin and fibronectin expression. Conclusions IRF7 is upregulated in SSc skin, interacts with Smad3 and potentiates TGF-β-mediated fibrosis, and therefore may represent a promising therapeutic target in SSc.

Introduction: Increased levels of genes in the type I interferon (IFN) pathway have been observed in patients with systemic sclerosis (SSc), or scleroderma. How type I IFN regulates the dermal fibroblast and its participation in the development of dermal fibrosis is not known. We hypothesized that one mechanism by which type I IFN may contribute to dermal fibrosis is through upregulation of specific Toll-like receptors (TLRs) on dermal fibroblasts. Therefore, we investigated the regulation of TLR expression on dermal fibroblasts by IFN. Methods: The expression of TLRs was assessed in cultured dermal fibroblasts from control and SSc patients stimulated with IFNα2. The ability of IFNα2 to regulate TLR-induced interleukin (IL)-6 and CC chemokine ligand 2 production was also assessed. Immunohistochemical analyses were performed to determine whether TLR3 was expressed in skin biopsies in the bleomycin-induced skin fibrosis model and in patients with SSc. Results: IFNα2 increased TLR3 expression on human dermal fibroblasts, which resulted in enhanced TLR3-induced IL-6 production. SSc fibroblasts have an augmented TLR3 response to IFNα2 relative to control fibroblasts. Pretreatment of fibroblasts with transforming growth factor (TGF)-β increased TLR3 induction by IFNα2, but coincubation of TGF-β did not alter TLR3 induction by IFN. Furthermore, IFNα2 inhibits but does not completely block the induction of connective tissue growth factor and collagen expression by TGF-βin fibroblasts. TLR3 expression was observed in dermal fibroblasts and inflammatory cells from skin biopsies from patients with SSc as well as in the bleomycin-induced skin fibrosis model. Conclusions: Type I IFNs can increase the inflammatory potential of dermal fibroblasts through the upregulation of TLR3.