Immunoglobulin G-dependent inhibition of inflammatory bone remodeling requires pattern recognition receptor Dectin-1.

Immunoglobulin G (IgG) antibodies are major drivers of inflammation during infectious and autoimmune diseases. In pooled serum IgG (IVIg), however, antibodies have a potent immunomodulatory and anti-inflammatory activity, but how this is mediated is unclear. We studied IgG-dependent initiation of resolution of inflammation in cytokine- and autoantibody-driven models of rheumatoid arthritis and found IVIg sialylation inhibited joint inflammation, whereas inhibition of osteoclastogenesis was sialic acid independent. Instead, IVIg-dependent inhibition of osteoclastogenesis was abrogated in mice lacking receptors Dectin-1 or FcγRIIb. Atomistic molecular dynamics simulations and super-resolution microscopy revealed that Dectin-1 promoted FcγRIIb membrane conformations that allowed productive IgG binding and enhanced interactions with mouse and human IgG subclasses. IVIg reprogrammed monocytes via FcγRIIb-dependent signaling that required Dectin-1. Our data identify a pathogen-independent function of Dectin-1 as a co-inhibitory checkpoint for IgG-dependent inhibition of mouse and human osteoclastogenesis. These findings may have implications for therapeutic targeting of autoantibody and cytokine-driven inflammation.

IL-33-induced metabolic reprogramming controls the differentiation of alternatively activated macrophages and the resolution of inflammation.

Alternatively activated macrophages (AAMs) contribute to the resolution of inflammation and tissue repair. However, molecular pathways that govern their differentiation have remained incompletely understood. Here, we show that uncoupling protein-2-mediated mitochondrial reprogramming and the transcription factor GATA3 specifically controlled the differentiation of pro-resolving AAMs in response to the alarmin IL-33. In macrophages, IL-33 sequentially triggered early expression of pro-inflammatory genes and subsequent differentiation into AAMs. Global analysis of underlying signaling events revealed that IL-33 induced a rapid metabolic rewiring of macrophages that involved uncoupling of the respiratory chain and increased production of the metabolite itaconate, which subsequently triggered a GATA3-mediated AAM polarization. Conditional deletion of GATA3 in mononuclear phagocytes accordingly abrogated IL-33-induced differentiation of AAMs and tissue repair upon muscle injury. Our data thus identify an IL-4-independent and GATA3-dependent pathway in mononuclear phagocytes that results from mitochondrial rewiring and controls macrophage plasticity and the resolution of inflammation.

XCR1 expression distinguishes human conventional dendritic cell type 1 with full effector functions from their immediate precursors.

Dendritic cells (DCs) are major regulators of innate and adaptive immune responses. DCs can be classified into plasmacytoid DCs and conventional DCs (cDCs) type 1 and 2. Murine and human cDC1 share the mRNA expression of XCR1. Murine studies indicated a specific role of the XCR1-XCL1 axis in the induction of immune responses. Here, we describe that human cDC1 can be distinguished into XCR1- and XCR1+ cDC1 in lymphoid as well as nonlymphoid tissues. Steady-state XCR1+ cDC1 display a preactivated phenotype compared to XCR1- cDC1. Upon stimulation, XCR1+ cDC1, but not XCR1- cDC1, secreted high levels of inflammatory cytokines as well as chemokines. This was associated with enhanced activation of NK cells mediated by XCR1+ cDC1. Moreover, XCR1+ cDC1 excelled in inhibiting replication of Influenza A virus. Further, under DC differentiation conditions, XCR1- cDC1 developed into XCR1+ cDC1. After acquisition of XCR1 expression, XCR1- cDC1 secreted comparable level of inflammatory cytokines. Thus, XCR1 is a marker of terminally differentiated cDC1 that licenses the antiviral effector functions of human cDC1, while XCR1- cDC1 seem to represent a late immediate precursor of cDC1.

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.

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.

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.

Transcription factor combinations that define human astrocyte identity encode significant variation of maturity and function.

Increasing evidence indicates that cellular identity can be reduced to the distinct gene regulatory networks controlled by transcription factors (TFs). However, redundancy exists in these states as different combinations of TFs can induce broadly similar cell types. We previously demonstrated that by overcoming gene silencing, it is possible to deterministically reprogram human pluripotent stem cells directly into cell types of various lineages. In the present study we leverage the consistency and precision of our approach to explore four different TF combinations encoding astrocyte identity, based on previously published reports. Analysis of the resulting induced astrocytes (iAs) demonstrated that all four cassettes generate cells with the typical morphology of in vitro astrocytes, which expressed astrocyte-specific markers. The transcriptional profiles of all four iAs clustered tightly together and displayed similarities with mature human astrocytes, although maturity levels differed between cells. Importantly, we found that the TF cassettes induced iAs with distinct differences with regards to their cytokine response and calcium signaling. In vivo transplantation of selected iAs into immunocompromised rat brains demonstrated long term stability and integration. In conclusion, all four TF combinations were able to induce stable astrocyte-like cells that were morphologically similar but showed subtle differences with respect to their transcriptome. These subtle differences translated into distinct differences with regards to cell function, that could be related to maturation state and/or regional identity of the resulting cells. This insight opens an opportunity to precision-engineer cells to meet functional requirements, for example, in the context of therapeutic cell transplantation.

In silico signaling modeling to understand cancer pathways and treatment responses.

Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.

A comprehensive method protocol for annotation and integrated functional understanding of lncRNAs.

Long non-coding RNAs (lncRNAs) are of fundamental biological importance; however, their functional role is often unclear or loosely defined as experimental characterization is challenging and bioinformatic methods are limited. We developed a novel integrated method protocol for the annotation and detailed functional characterization of lncRNAs within the genome. It combines annotation, normalization and gene expression with sequence-structure conservation, functional interactome and promoter analysis. Our protocol allows an analysis based on the tissue and biological context, and is powerful in functional characterization of experimental and clinical RNA-Seq datasets including existing lncRNAs. This is demonstrated on the uncharacterized lncRNA GATA6-AS1 in dilated cardiomyopathy.

Protein kinase C-ß-dependent changes in the glucose metabolism of bone marrow stromal cells of chronic lymphocytic leukemia.

Survival of chronic lymphocytic leukemia (CLL) cells critically depends on the support of an adapted and therefore appropriate tumor microenvironment. Increasing evidence suggests that B-cell receptor-associated kinases such as protein kinase C-ß (PKCß) or Lyn kinase are essential for the formation of a microenvironment supporting leukemic growth. Here, we describe the impact of PKCß on the glucose metabolism in bone marrow stromal cells (BMSC) upon CLL contact. BMSC get activated by CLL contact expressing stromal PKCß that diminishes mitochondrial stress and apoptosis in CLL cells by stimulating glucose uptake. In BMSC, the upregulation of PKCß results in increased mitochondrial depolarization and leads to a metabolic switch toward oxidative phosphorylation. In addition, PKCß-deficient BMSC regulates the expression of Hnf1 promoting stromal insulin signaling after CLL contact. Our data suggest that targeting PKCß and the glucose metabolism of the leukemic niche could be a potential therapeutic strategy to overcome stroma-mediated drug resistance.

Comprehensive Bioinformatics Identifies Key microRNA Players in ATG7-Deficient Lung Fibroblasts.

BACKGROUND: Deficient autophagy has been recently implicated as a driver of pulmonary fibrosis, yet bioinformatics approaches to study this cellular process are lacking. Autophagy-related 5 and 7 (ATG5/ATG7) are critical elements of macro-autophagy. However, an alternative ATG5/ATG7-independent macro-autophagy pathway was recently discovered, its regulation being unknown. Using a bioinformatics proteome profiling analysis of ATG7-deficient human fibroblasts, we aimed to identify key microRNA (miR) regulators in autophagy. METHOD: We have generated ATG7-knockout MRC-5 fibroblasts and performed mass spectrometry to generate a large-scale proteomics dataset. We further quantified the interactions between various proteins combining bioinformatics molecular network reconstruction and functional enrichment analysis. The predicted key regulatory miRs were validated via quantitative polymerase chain reaction. RESULTS: The functional enrichment analysis of the 26 deregulated proteins showed decreased cellular trafficking, increased mitophagy and senescence as the major overarching processes in ATG7-deficient lung fibroblasts. The 26 proteins reconstitute a protein interactome of 46 nodes and miR-regulated interactome of 834 nodes. The miR network shows three functional cluster modules around miR-16-5p, miR-17-5p and let-7a related to multiple deregulated proteins. Confirming these results in a biological setting, serially passaged wild-type and autophagy-deficient fibroblasts displayed senescence-dependent expression profiles of miR-16-5p and miR-17-5p. CONCLUSIONS: We have developed a bioinformatics proteome profiling approach that successfully identifies biologically relevant miR regulators from a proteomics dataset of the ATG-7-deficient milieu in lung fibroblasts, and thus may be used to elucidate key molecular players in complex fibrotic pathological processes. The approach is not limited to a specific cell-type and disease, thus highlighting its high relevance in proteome and non-coding RNA research.

Integrative Bioinformatic Analyses of Global Transcriptome Data Decipher Novel Molecular Insights into Cardiac Anti-Fibrotic Therapies.

Integrative bioinformatics is an emerging field in the big data era, offering a steadily increasing number of algorithms and analysis tools. However, for researchers in experimental life sciences it is often difficult to follow and properly apply the bioinformatical methods in order to unravel the complexity and systemic effects of omics data. Here, we present an integrative bioinformatics pipeline to decipher crucial biological insights from global transcriptome profiling data to validate innovative therapeutics. It is available as a web application for an interactive and simplified analysis without the need for programming skills or deep bioinformatics background. The approach was applied to an ex vivo cardiac model treated with natural anti-fibrotic compounds and we obtained new mechanistic insights into their anti-fibrotic action and molecular interplay with miRNAs in cardiac fibrosis. Several gene pathways associated with proliferation, extracellular matrix processes and wound healing were altered, and we could identify micro (mi) RNA-21-5p and miRNA-223-3p as key molecular components related to the anti-fibrotic treatment. Importantly, our pipeline is not restricted to a specific cell type or disease and can be broadly applied to better understand the unprecedented level of complexity in big data research.

MicroRNA-21 versus microRNA-34: Lung cancer promoting and inhibitory microRNAs analysed in silico and in vitro and their clinical impact.

MicroRNAs are well-known strong RNA regulators modulating whole functional units in complex signaling networks. Regarding clinical application, they have potential as biomarkers for prognosis, diagnosis, and therapy. In this review, we focus on two microRNAs centrally involved in lung cancer progression. MicroRNA-21 promotes and microRNA-34 inhibits cancer progression. We elucidate here involved pathways and imbed these antagonistic microRNAs in a network of interactions, stressing their cancer microRNA biology, followed by experimental and bioinformatics analysis of such microRNAs and their targets. This background is then illuminated from a clinical perspective on microRNA-21 and microRNA-34 as general examples for the complex microRNA biology in lung cancer and its diagnostic value. Moreover, we discuss the immense potential that microRNAs such as microRNA-21 and microRNA-34 imply by their broad regulatory effects. These should be explored for novel therapeutic strategies in the clinic.

Bioinformatics of cardiovascular miRNA biology.

MicroRNAs (miRNAs) are small ~22 nucleotide non-coding RNAs and are highly conserved among species. Moreover, miRNAs regulate gene expression of a large number of genes associated with important biological functions and signaling pathways. Recently, several miRNAs have been found to be associated with cardiovascular diseases. Thus, investigating the complex regulatory effect of miRNAs may lead to a better understanding of their functional role in the heart. To achieve this, bioinformatics approaches have to be coupled with validation and screening experiments to understand the complex interactions of miRNAs with the genome. This will boost the subsequent development of diagnostic markers and our understanding of the physiological and therapeutic role of miRNAs in cardiac remodeling. In this review, we focus on and explain different bioinformatics strategies and algorithms for the identification and analysis of miRNAs and their regulatory elements to better understand cardiac miRNA biology. Starting with the biogenesis of miRNAs, we present approaches such as LocARNA and miRBase for combining sequence and structure analysis including phylogenetic comparisons as well as detailed analysis of RNA folding patterns, functional target prediction, signaling pathway as well as functional analysis. We also show how far bioinformatics helps to tackle the unprecedented level of complexity and systemic effects by miRNA, underlining the strong therapeutic potential of miRNA and miRNA target structures in cardiovascular disease. In addition, we discuss drawbacks and limitations of bioinformatics algorithms and the necessity of experimental approaches for miRNA target identification. This article is part of a Special Issue entitled 'Non-coding RNAs'.

Diagnostic value of preoperative CT scan to stratify colon cancer for neoadjuvant therapy.

BACKGROUND AND OBJECTIVE: Neoadjuvant therapy could improve oncological outcome of patients suffering from colon cancer. An accurate staging method is needed to define suitable patients. The aim of this retrospective study was to validate the value of CT for identifying patients with local advanced (T3/4) or nodal-positive colon cancer. METHODS AND MATERIAL: Preoperative abdominal CT scans of 210 patients with colon cancer were evaluated by two radiologists independently for the T stage and N stage. Results were compared to pathology. Patients were stratified according to the guidelines for rectal cancer into patients with low risk (T0/1/2 and N0) or high risk (T3/4 or N+). RESULTS: Inter-observer correlation was high with over 90 %. Overall sensitivity T stage was 93.0 % and for N stage 76.9 %. Using CT scan to identify local advanced (T3/4 or N+) tumors, the consensus sensitivity was 94.9 %, the specificity 53.6 %, the positive predictive value (PPV) 92.8 %, and the negative predictive value (NPV) 62.5 %. CONCLUSION: Computer tomography represents an effective tool for identifying patients with colon cancer suitable for neoadjuvant therapy according to the guidelines for rectal cancer.

Individualized neoantigen peptide immunization of a metastatic pancreatic cancer patient: a case report of combined tumor and liquid biopsy.

This report details a case of pancreatic cancer with liver metastasis that exhibited a positive immune response to personalized immunization therapy. Our study involved the identification of neoantigens and their corresponding immunogenic peptides using an in-house bioinformatic pipeline. This process included the identification of somatic mutations through DNA/RNA sequencing of solid tumor tissue and blood liquid biopsy. Computational prediction techniques were then employed to identify novel epitopes, followed by the design and manufacture of patient-specific immunization peptides. In combination with standard-of-care chemotherapy, the patient received a sequence of 5 biweekly prime injections followed by 2 boost injections 2 and 5 months later. The peptides were emulsified in Montanide and the injection-site was conditioned with nivolumab and imiquimod. The combined regimen of peptide immunization and chemotherapy resulted in a notable decline in CA19-9 tumor marker levels following both prime and boost applications. Subsequent MRI assessments revealed a reduction in the size of liver metastases several months post-immunization initiation. Importantly, the patient showed and improved overall survival and reported an improved quality of life without experiencing significant treatment-related adverse effects. This case underscores the potential benefits of personalized peptide-based immunization as an adjunctive therapy in the treatment of advanced pancreatic cancer, showcasing promising outcomes in tumor marker reduction, tumor shrinkage, and enhanced patient well-being.

A PLURIPOTENT STEM CELL PLATFORM FOR IN VITRO SYSTEMS GENETICS STUDIES OF MOUSE DEVELOPMENT.

The directed differentiation of pluripotent stem cells (PSCs) from panels of genetically diverse individuals is emerging as a powerful experimental system for characterizing the impact of natural genetic variation on developing cell types and tissues. Here, we establish new PSC lines and experimental approaches for modeling embryonic development in a genetically diverse, outbred mouse stock (Diversity Outbred mice). We show that a range of inbred and outbred PSC lines can be stably maintained in the primed pluripotent state (epiblast stem cells -- EpiSCs) and establish the contribution of genetic variation to phenotypic differences in gene regulation and directed differentiation. Using pooled in vitro fertilization, we generate and characterize a genetic reference panel of Diversity Outbred PSCs (n = 230). Finally, we demonstrate the feasibility of pooled culture of Diversity Outbred EpiSCs as "cell villages", which can facilitate the differentiation of large numbers of EpiSC lines for forward genetic screens. These data can complement and inform similar efforts within the stem cell biology and human genetics communities to model the impact of natural genetic variation on phenotypic variation and disease-risk.

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.

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.

Improved Survival of a HER2-Positive Metastatic Breast Cancer Patient Following a Personalized Peptide Immunization.

Cancer neoantigens that arise from somatic mutations have emerged as important targets for personalized immunization. Here, we report an improved overall survival of a HER2-positive metastatic breast cancer patient using a bioinformatic-based personalized peptide immunization called BITAP (BioInformatic Tumor Address Peptides). The epitopes were predicted using our in-house bioinformatic pipeline, and the immunogenicity was tested by IFN-γ ELISPOT and intracellular cytokine staining assays. In total, a significant peptide-specific T-cell response was detected against 18 out of the 76 (≈24%) tested peptides. The patient's follow-up by measuring serologic markers showed a significant reduction in the tumor marker levels following BITAP immunization. Along with standard treatment, the patient treated with the BITAP showed stable disease with a remarkably improved overall survival, and no serious treatment-related adverse effects. In conclusion, our findings suggest that BITAP immunization is feasible, and safe, and may induce tumor regressions in patients with HER2-positive subsets of breast cancer.