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.