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 TGFβ reduced adropin/ ENHO expression in a JNK-dependent manner. Restoration of adropin signaling by therapeutic application of bioactive adropin ^34–76 peptides in turn inhibited TGFβ-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 adropin ^34–76 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 adropin ^34–76 -induced changes in TGFβ/GLI1 signaling. Our study characterizes the TGFβ-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.

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.

Purpose of the study: Tumor microenvironment (TME)-induced plasticity of fibroblasts and immune cells are known to contribute to cancer pathogenesis. In contrast, only little is known about the existence, stability, functions and prognostic potential of different plastic phenotypes of tumor vessel endothelial cells (TECs) in cancer. Recently, transcriptional memory effects have been observed in different cell lines after cytokine stimulation in vitro. From this we developed the hypothesis that isolated TEC may exhibit TME-dependent transcriptional memories, which may provide novel insights into the intratumoral effects on TEC plasticity in cancer. To investigate this, we isolated ultrapure TECs from colorectal cancer (CRC) with prognostically favorable (Th-1-like) or worse (non-Th1-like) immune-micro-environments and analyzed the prescence of TME-associated transcriptional memory patterns. Methods: TECs and as controls normal colon endothelial cells (NECs), PBMCs and tumor cells (isolated by laser-microdissection) from the same CRC patients (n=75) were isolated by FACS-sorting. Ultra high purity (98%) of TECs and NECs was validated by qPCR and immunocytochemistry. Quality validated complete sample sets from 12 patients (Th-1-like, n=6, non-Th1-like, n=6) were compared using exome sequencing, Clariome S chips and EPICmethylation. Integrative bioinformatics was used to identify differential gene sets and to predict their association with prognosis. Results: Differential transcriptional memory patterns were detected in TECs derived from CRC with different prognostic TMEs. Of note, the aggressiveness of the originating tumors was reflected by the functions of the vascular transcriptional memory genes. The transcriptional memory was imprinted by epigenetic DNA methylation and not due to acquired somatic variants in the genome. These results could be successfully confirmed by alternative methods. Most importantly, integrative bioinformatics analyses retrieved seven vascular memory genes (VMG), which were expressed in the TECs in a TME-dependent manner and predicted the outcome of patients in an independent cohort of CRC patients. Conclusion: Our findings demonstrate for the first time a TME-dependent transcriptional memory of TECs, providing a clinically relevant indicator of intra-tumoral TME-dependent vascular plasticity with perspectives for patient stratification in vessel-directed tumor therapies. Citation Format: Michael Stürzl, Maximilian Fuchs, Meik Kunz, Nicholas Dickel, Bernt Popp, Steffen Uebe, Arif B. Ekici, Carol I. Geppert, Susanne Merkel, Vera S. Schellerer, Elisabeth Naschberger. Micromilieu-dependent transcriptional memory of tumor vessel endothelial cells in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3802.