Combined inhibition of IL-1, IL-33 and IL-36 signalling by targeting IL1RAP ameliorates skin and lung fibrosis in preclinical models of systemic sclerosis.

BACKGROUND: The interleukin (IL)-1 receptor accessory protein (IL1RAP) is an essential coreceptor required for signalling through the IL-1, IL-33 and IL-36 receptors. Here, we investigate the antifibrotic potential of the combined inhibition of these cytokines by an anti-IL1RAP antibody to provide a scientific background for clinical development in systemic sclerosis (SSc). METHODS: The expression of IL1RAP-associated signalling molecules was determined by data mining of publicly available RNA sequencing (RNAseq) data as well as by imaging mass cytometry. The efficacy of therapeutic dosing of anti-IL1RAP antibodies was determined in three complementary mouse models: sclerodermatous chronic graft-versus-host disease (cGvHD), bleomycin-induced dermal fibrosis model and topoisomerase-I (topo)-induced fibrosis. RESULTS: SSc skin showed upregulation of IL1RAP and IL1RAP-related signalling molecules on mRNA and protein level compared with normal skin. IL-1, IL-33 and IL-36 all regulate distinct gene sets related to different pathophysiological processes in SSc. The responses of human fibroblasts and endothelial cells to IL-1, IL-33 and IL-36 were completely blocked by treatment with an anti-IL1RAP antibody in vitro. Moreover, anti-IL1RAP antibody treatment reduced dermal and pulmonary fibrosis in cGvHD-induced, bleomycin-induced and topoisomerase-induced fibrosis. Importantly, RNAseq analyses revealed effects of IL1RAP inhibition on multiple processes related to inflammation and fibrosis that are also deregulated in human SSc skin. CONCLUSION: This study provides the first evidence for the therapeutic benefits of targeting IL1RAP in SSc. Our findings have high translational potential as the anti-IL1RAP antibody CAN10 has recently entered a phase one clinical trial.

Attenuation of fibroblast activation and fibrosis by adropin in systemic sclerosis.

Fibrotic diseases impose a major socioeconomic challenge on modern societies and have limited treatment options. Adropin, a peptide hormone encoded by the energy homeostasis-associated (ENHO) gene, is implicated in metabolism and vascular homeostasis, but its role in the pathogenesis of fibrosis remains enigmatic. Here, we used machine learning approaches in combination with functional in vitro and in vivo experiments to characterize adropin as a potential regulator involved in fibroblast activation and tissue fibrosis in systemic sclerosis (SSc). We demonstrated consistent down-regulation of adropin/ENHO in skin across multiple cohorts of patients with SSc. The prototypical profibrotic cytokine TGFß reduced adropin/ENHO expression in a JNK-dependent manner. Restoration of adropin signaling by therapeutic application of bioactive adropin34-76 peptides in turn inhibited TGFß-induced fibroblast activation and fibrotic tissue remodeling in primary human dermal fibroblasts, three-dimensional full-thickness skin equivalents, mouse models of bleomycin-induced pulmonary fibrosis and sclerodermatous chronic graft-versus-host-disease (sclGvHD), and precision-cut human skin slices. Knockdown of GPR19, an adropin receptor, abrogated the antifibrotic effects of adropin in fibroblasts. RNA-seq demonstrated that the antifibrotic effects of adropin34-76 were functionally linked to deactivation of GLI1-dependent profibrotic transcriptional networks, which was experimentally confirmed in vitro, in vivo, and ex vivo using cultured human dermal fibroblasts, a sclGvHD mouse model, and precision-cut human skin slices. ChIP-seq confirmed adropin34-76-induced changes in TGFß/GLI1 signaling. Our study characterizes the TGFß-induced down-regulation of adropin/ENHO expression as a potential pathomechanism of SSc as a prototypical systemic fibrotic disease that unleashes uncontrolled activation of profibrotic GLI1 signaling.

Performance of serum biomarkers reflective of different pathogenic processes in systemic sclerosis-associated interstitial lung disease.

OBJECTIVE: Interstitial lung disease (ILD) is the leading cause of mortality in SSc. Novel biomarkers are crucial to improve outcomes in SSc-ILD. We aimed to compare the performance of potential serum biomarkers of SSc-ILD that reflect different pathogenic processes: KL-6 and SP-D (epithelial injury), CCL18 (type 2 immune response), YKL-40 (endothelial injury and matrix remodelling) and MMP-7 (ECM remodelling). METHODS: Baseline and follow-up serum samples from 225 SSc patients were analysed by ELISA. Progressive ILD was defined according to the 2022-ATS/ERS/JRS/ALAT guidelines. Linear mixed models and random forest models were used for statistical analyses. RESULTS: Serum levels of KL-6 [MD 35.67 (95% CI 22.44-48.89, P < 0.01)], SP-D [81.13 (28.46-133.79, P < 0.01)], CCL18 [17.07 (6.36-27.77, P < 0.01)], YKL-40 [22.81 (7.19-38.44, P < 0.01)] and MMP-7 [2.84 (0.88-4.80, P < 0.01)] were independently associated with the presence of SSc-ILD. A machine-learning model including all candidates classified patients with or without ILD with an accuracy of 85%. The combination of KL-6 and SP-D was associated with the presence [0.77 (0.53-1.00, P' <0.01)] and previous progression of SSc-ILD [OR 1.28 (1.01-1.61, P' =0.047)]. Higher baseline levels of KL-6 [OR 3.70 (1.52-9.03, P < 0.01)] or SP-D [OR 2.00 (1.06-3.78, P = 0.03)] increased the odds of future SSc-ILD progression, independent of other conventional risk factors, and the combination of KL-6 and SP-D [1.109 (0.665-1.554, P < 0.01)] showed improved performance compared with KL-6 and SP-D alone. CONCLUSION: All candidates performed well as diagnostic biomarkers for SSc-ILD. The combination of KL-6 and SP-D might serve as biomarker for the identification of SSc patients at risk of ILD progression.

Effect of Anti-S100A4 Monoclonal Antibody Treatment on Experimental Skin Fibrosis and Systemic Sclerosis-Specific Transcriptional Signatures in Human Skin.

OBJECTIVE: S100A4 is a DAMP protein. S100A4 is overexpressed in patients with systemic sclerosis (SSc), and levels correlate with organ involvement and disease activity. S100A4-/- mice are protected from fibrosis. The aim of this study was to assess the antifibrotic effects of anti-S100A4 monoclonal antibody (mAb) in murine models of SSc and in precision cut skin slices of patients with SSc. METHODS: The effects of anti-S100A4 mAbs were evaluated in a bleomycin-induced skin fibrosis model and in Tsk-1 mice with a therapeutic dosing regimen. In addition, the effects of anti-S100A4 mAbs on precision cut SSc skin slices were analyzed by RNA sequencing. RESULTS: Inhibition of S100A4 was effective in the treatment of pre-established bleomycin-induced skin fibrosis and in regression of pre-established fibrosis with reduced dermal thickening, myofibroblast counts, and collagen accumulation. Transcriptional profiling demonstrated targeting of multiple profibrotic and proinflammatory processes relevant to the pathogenesis of SSc on targeted S100A4 inhibition in a bleomycin-induced skin fibrosis model. Moreover, targeted S100A4 inhibition also modulated inflammation- and fibrosis-relevant gene sets in precision cut SSc skin slices in an ex vivo trial approach. Selected downstream targets of S100A4, such as AMP-activated protein kinase, calsequestrin-1, and phosphorylated STAT3, were validated on the protein level, and STAT3 inhibition was shown to prevent the profibrotic effects of S100A4 on fibroblasts in human skin. CONCLUSION: Inhibition of S100A4 confers dual targeting of inflammatory and fibrotic pathways in complementary mouse models of fibrosis and in SSc skin. These effects support the further development of anti-S100A4 mAbs as disease-modifying targeted therapies for SSc.

Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters.

BACKGROUND: Peripheral artery disease (PAD) is a significant burden, particularly among patients with severe disease requiring invasive treatment. We applied a general Machine Learning (ML) workflow and investigated if a multi-dimensional marker set of standard clinical parameters can identify patients in need of vascular intervention without specialized intra-hospital diagnostics. METHODS: This is a retrospective study involving patients with stable PAD (sPAD, Fontaine Class I and II, n = 38) and unstable PAD (unPAD, Fontaine Class III and IV, n = 18) in need of invasive therapeutic measures. ML algorithms such as Random Forest were utilized to evaluate a matrix consisting of multiple routinely clinically available parameters (age, complete blood count, inflammation, lipid, iron metabolism). RESULTS: ML has enabled a generation of an Artificial Intelligence (AI) PAD score (AI-PAD) that successfully divided sPAD from unPAD patients (high AI-PAD in sPAD, low AI-PAD in unPAD, cutoff at 50 AI-PAD units). Furthermore, the probability score positively coincided with gold-standard intra-hospital mean ankle-brachial index (ABI). CONCLUSION: AI-based tools may be promising to enable the correct identification of patients with unstable PAD by using existing clinical information, thus supplementing clinical decision making. Additional studies in larger prospective cohorts are necessary to determine the usefulness of this approach in comparison to standard diagnostic measures.