L-arginine metabolism inhibits arthritis and inflammatory bone loss.

OBJECTIVES: To investigate the effect of the L-arginine metabolism on arthritis and inflammation-mediated bone loss. METHODS: L-arginine was applied to three arthritis models (collagen-induced arthritis, serum-induced arthritis and human TNF transgenic mice). Inflammation was assessed clinically and histologically, while bone changes were quantified by µCT and histomorphometry. In vitro, effects of L-arginine on osteoclast differentiation were analysed by RNA-seq and mass spectrometry (MS). Seahorse, Single Cell ENergetIc metabolism by profilIng Translation inHibition and transmission electron microscopy were used for detecting metabolic changes in osteoclasts. Moreover, arginine-associated metabolites were measured in the serum of rheumatoid arthritis (RA) and pre-RA patients. RESULTS: L-arginine inhibited arthritis and bone loss in all three models and directly blocked TNFα-induced murine and human osteoclastogenesis. RNA-seq and MS analyses indicated that L-arginine switched glycolysis to oxidative phosphorylation in inflammatory osteoclasts leading to increased ATP production, purine metabolism and elevated inosine and hypoxanthine levels. Adenosine deaminase inhibitors blocking inosine and hypoxanthine production abolished the inhibition of L-arginine on osteoclastogenesis in vitro and in vivo. Altered arginine levels were also found in RA and pre-RA patients. CONCLUSION: Our study demonstrated that L-arginine ameliorates arthritis and bone erosion through metabolic reprogramming and perturbation of purine metabolism in osteoclasts.

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.

The soluble CD83 protein prevents bone destruction by inhibiting the formation of osteoclasts and inducing resolution of inflammation in arthritis.

Here we show that soluble CD83 induces the resolution of inflammation in an antigen-induced arthritis (AIA) model. Joint swelling and the arthritis-related expression levels of IL-1ß, IL-6, RANKL, MMP9, and OC-Stamp were strongly reduced, while Foxp3 was induced. In addition, we observed a significant inhibition of TRAP+ osteoclast formation, correlating with the reduced arthritic disease score. In contrast, cell-specific deletion of CD83 in human and murine precursor cells resulted in an enhanced formation of mature osteoclasts. RNA sequencing analyses, comparing sCD83- with mock treated cells, revealed a strong downregulation of osteoclastogenic factors, such as Oc-Stamp, Mmp9 and Nfatc1, Ctsk, and Trap. Concomitantly, transcripts typical for pro-resolving macrophages, e.g., Mrc1/2, Marco, Klf4, and Mertk, were upregulated. Interestingly, members of the metallothionein (MT) family, which have been associated with a reduced arthritic disease severity, were also highly induced by sCD83 in samples derived from RA patients. Finally, we elucidated the sCD83-induced signaling cascade downstream to its binding to the Toll-like receptor 4/(TLR4/MD2) receptor complex using CRISPR/Cas9-induced knockdowns of TLR4/MyD88/TRIF and MTs, revealing that sCD83 acts via the TRIF-signaling cascade. In conclusion, sCD83 represents a promising therapeutic approach to induce the resolution of inflammation and to prevent bone erosion in autoimmune arthritis.

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.

A combined tissue-engineered/in silico signature tool patient stratification in lung cancer.

Patient-tailored therapy based on tumor drivers is promising for lung cancer treatment. For this, we combined in vitro tissue models with in silico analyses. Using individual cell lines with specific mutations, we demonstrate a generic and rapid stratification pipeline for targeted tumor therapy. We improve in vitro models of tissue conditions by a biological matrix-based three-dimensional (3D) tissue culture that allows in vitro drug testing: It correctly shows a strong drug response upon gefitinib (Gef) treatment in a cell line harboring an EGFR-activating mutation (HCC827), but no clear drug response upon treatment with the HSP90 inhibitor 17AAG in two cell lines with KRAS mutations (H441, A549). In contrast, 2D testing implies wrongly KRAS as a biomarker for HSP90 inhibitor treatment, although this fails in clinical studies. Signaling analysis by phospho-arrays showed similar effects of EGFR inhibition by Gef in HCC827 cells, under both 2D and 3D conditions. Western blot analysis confirmed that for 3D conditions, HSP90 inhibitor treatment implies different p53 regulation and decreased MET inhibition in HCC827 and H441 cells. Using in vitro data (western, phospho-kinase array, proliferation, and apoptosis), we generated cell line-specific in silico topologies and condition-specific (2D, 3D) simulations of signaling correctly mirroring in vitro treatment responses. Networks predict drug targets considering key interactions and individual cell line mutations using the Human Protein Reference Database and the COSMIC database. A signature of potential biomarkers and matching drugs improve stratification and treatment in KRAS-mutated tumors. In silico screening and dynamic simulation of drug actions resulted in individual therapeutic suggestions, that is, targeting HIF1A in H441 and LKB1 in A549 cells. In conclusion, our in vitro tumor tissue model combined with an in silico tool improves drug effect prediction and patient stratification. Our tool is used in our comprehensive cancer center and is made now publicly available for targeted therapy decisions.