Cell type specific long non-coding RNA targets identified by integrative analysis of single-cell and bulk colorectal cancer transcriptomes.

Long non-coding RNAs (lncRNAs) represent an emerging class of genes which play significant and diverse roles in human cancers. Nevertheless, the functional repertoires of lncRNAs in cancer cell subtypes remains unknown since most studies are focused on protein coding genes. Here, we explored the contribution of lncRNAs in Colorectal Cancer (CRC) heterogeneity. We analyzed 49'436 single-cells from 29 CRC patients and showed that lncRNAs are significantly more cell type specific compared to protein-coding genes. We identified 996 lncRNAs strongly enriched in epithelial cells. Among these, 98 were found to be differentially expressed in tumor samples compared to normal controls, when integrating 270 bulk CRC profiles. We validated the upregulation of two of them (CASC19 and LINC00460) in CRC cell lines and showed their involvement in CRC proliferation by CRISPR-Cas9 knock down experiments. This study highlights a list of novel RNA targets for potential CRC therapeutics, substantiated through experimental validation.

Overcoming limitations in current measures of drug response may enable AI-driven precision oncology.

Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of personalized prediction models - they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests that using z-scored drug response measures mitigates these limitations and leads to meaningful predictions, opening the door for sophisticated ML precision oncology models.

ScLinear predicts protein abundance at single-cell resolution.

Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches.

Functional identification of cis-regulatory long noncoding RNAs at controlled false discovery rates.

A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such 'cis-lncRNAs' are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistor's simple and conservative statistical models are compatible with functionally defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. We detect cis-activity in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target genes and are weakly enriched with enhancer elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance.

Assessment of the Cardiac Noncoding Transcriptome by Single-Cell RNA Sequencing Identifies FIXER, a Conserved Profibrogenic Long Noncoding RNA.

BACKGROUND: Cardiac fibroblasts have crucial roles in the heart. In particular, fibroblasts differentiate into myofibroblasts in the damaged myocardium, contributing to scar formation and interstitial fibrosis. Fibrosis is associated with heart dysfunction and failure. Myofibroblasts therefore represent attractive therapeutic targets. However, the lack of myofibroblast-specific markers has precluded the development of targeted therapies. In this context, most of the noncoding genome is transcribed into long noncoding RNAs (lncRNAs). A number of lncRNAs have pivotal functions in the cardiovascular system. lncRNAs are globally more cell-specific than protein-coding genes, supporting their importance as key determinants of cell identity. METHODS: In this study, we evaluated the value of the lncRNA transcriptome in very deep single-cell RNA sequencing. We profiled the lncRNA transcriptome in cardiac nonmyocyte cells after infarction and probed heterogeneity in the fibroblast and myofibroblast populations. In addition, we searched for subpopulation-specific markers that can constitute novel targets in therapy for heart disease. RESULTS: We demonstrated that cardiac cell identity can be defined by the sole expression of lncRNAs in single-cell experiments. In this analysis, we identified lncRNAs enriched in relevant myofibroblast subpopulations. Selecting 1 candidate we named FIXER (fibrogenic LOX-locus enhancer RNA), we showed that its silencing limits fibrosis and improves heart function after infarction. Mechanitically, FIXER interacts with CBX4, an E3 SUMO protein ligase and transcription factor, guiding CBX4 to the promoter of the transcription factor RUNX1 to control its expression and, consequently, the expression of a fibrogenic gene program.. FIXER is conserved in humans, supporting its translational value. CONCLUSIONS: Our results demonstrated that lncRNA expression is sufficient to identify the various cell types composing the mammalian heart. Focusing on cardiac fibroblasts and their derivatives, we identified lncRNAs uniquely expressed in myofibroblasts. In particular, the lncRNA FIXER represents a novel therapeutic target for cardiac fibrosis.

Molecular profiling of osteoprogenitor cells reveals FOS as a master regulator of bone non-union.

Despite the advances in bone fracture treatment, a significant fraction of fracture patients will develop non-union. Most non-unions are treated with surgery since identifying the molecular causes of these defects is exceptionally challenging. In this study, compared with marrow bone, we generated a transcriptional atlas of human osteoprogenitor cells derived from healing callus and non-union fractures. Detailed comparison among the three conditions revealed a substantial similarity of callus and nonunion at the gene expression level. Nevertheless, when assayed functionally, they showed different osteogenic potential. Utilizing longitudinal transcriptional profiling of the osteoprogenitor cells, we identified FOS as a putative master regulator of non-union fractures. We validated FOS activity by profiling a validation cohort of 31 tissue samples. Our work identified new molecular targets for non-union classification and treatment while providing a valuable resource to better understand human bone healing biology.

Bladder cancer organoids as a functional system to model different disease stages and therapy response.

Bladder Cancer (BLCa) inter-patient heterogeneity is the primary cause of treatment failure, suggesting that patients could benefit from a more personalized treatment approach. Patient-derived organoids (PDOs) have been successfully used as a functional model for predicting drug response in different cancers. In our study, we establish PDO cultures from different BLCa stages and grades. PDOs preserve the histological and molecular heterogeneity of the parental tumors, including their multiclonal genetic landscapes, and consistently share key genetic alterations, mirroring tumor evolution in longitudinal sampling. Our drug screening pipeline is implemented using PDOs, testing standard-of-care and FDA-approved compounds for other tumors. Integrative analysis of drug response profiles with matched PDO genomic analysis is used to determine enrichment thresholds for candidate markers of therapy response and resistance. Finally, by assessing the clinical history of longitudinally sampled cases, we can determine whether the disease clonal evolution matched with drug response.

Transcriptomic data meta-analysis reveals common and injury model specific gene expression changes in the regenerating zebrafish heart.

Zebrafish have the capacity to fully regenerate the heart after an injury, which lies in sharp contrast to the irreversible loss of cardiomyocytes after a myocardial infarction in humans. Transcriptomics analysis has contributed to dissect underlying signaling pathways and gene regulatory networks in the zebrafish heart regeneration process. This process has been studied in response to different types of injuries namely: ventricular resection, ventricular cryoinjury, and genetic ablation of cardiomyocytes. However, there exists no database to compare injury specific and core cardiac regeneration responses. Here, we present a meta-analysis of transcriptomic data of regenerating zebrafish hearts in response to these three injury models at 7 days post injury (7dpi). We reanalyzed 36 samples and analyzed the differentially expressed genes (DEG) followed by downstream Gene Ontology Biological Processes (GO:BP) analysis. We found that the three injury models share a common core of DEG encompassing genes involved in cell proliferation, the Wnt signaling pathway and genes that are enriched in fibroblasts. We also found injury-specific gene signatures for resection and genetic ablation, and to a lower extent the cryoinjury model. Finally, we present our data in a user-friendly web interface that displays gene expression signatures across different injury types and highlights the importance to consider injury-specific gene regulatory networks when interpreting the results related to cardiac regeneration in the zebrafish. The analysis is freely available at: https://mybinder.org/v2/gh/MercaderLabAnatomy/PUB_Botos_et_al_2022_shinyapp_binder/HEAD?urlpath=shiny/bus-dashboard/ .

Repurposing the antipsychotic drug amisulpride for targeting synovial fibroblast activation in arthritis.

Synovial fibroblasts (SFs) are key pathogenic drivers in rheumatoid arthritis (RA). Their in vivo activation by TNF is sufficient to orchestrate full arthritic pathogenesis in animal models, and TNF blockade proved efficacious for a high percentage of patients with RA albeit coinducing rare but serious side effects. Aiming to find new potent therapeutics, we applied the L1000CDS2 search engine, to repurpose drugs that could reverse the pathogenic expression signature of arthritogenic human TNF-transgenic (hTNFtg) SFs. We identified a neuroleptic drug, namely amisulpride, which reduced SFs' inflammatory potential while decreasing the clinical score of hTNFtg polyarthritis. Notably, we found that amisulpride function was neither through its known targets dopamine receptors D2 and D3 and serotonin receptor 7 nor through TNF-TNF receptor I binding inhibition. Through a click chemistry approach, potentially novel targets of amisulpride were identified, which were further validated to repress hTNFtg SFs' inflammatory potential ex vivo (Ascc3 and Sec62), while phosphoproteomics analysis revealed that treatment altered important fibroblast activation pathways, such as adhesion. Thus, amisulpride could prove beneficial to patients experiencing RA and the often-accompanying comorbid dysthymia, reducing SF pathogenicity along with its antidepressive activity, serving further as a "lead" compound for the development of novel therapeutics against fibroblast activation.

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.

A transposable element into the human long noncoding RNA CARMEN is a switch for cardiac precursor cell specification.

AIMS: The major cardiac cell types composing the adult heart arise from common multipotent precursor cells. Cardiac lineage decisions are guided by extrinsic and cell-autonomous factors, including recently discovered long noncoding RNAs (lncRNAs). The human lncRNA CARMEN, which is known to dictate specification toward the cardiomyocyte (CM) and the smooth muscle cell (SMC) fates, generates a diversity of alternatively spliced isoforms. METHODS AND RESULTS: The CARMEN locus can be manipulated to direct human primary cardiac precursor cells (CPCs) into specific cardiovascular fates. Investigating CARMEN isoform usage in differentiating CPCs represents therefore a unique opportunity to uncover isoform-specific functions in lncRNAs. Here, we identify one CARMEN isoform, CARMEN-201, to be crucial for SMC commitment. CARMEN-201 activity is encoded within an alternatively spliced exon containing a MIRc short interspersed nuclear element. This element binds the transcriptional repressor REST (RE1 Silencing Transcription Factor), targets it to cardiogenic loci, including ISL1, IRX1, IRX5, and SFRP1, and thereby blocks the CM gene program. In turn, genes regulating SMC differentiation are induced. CONCLUSIONS: These data show how a critical physiological switch is wired by alternative splicing and functional transposable elements in a long noncoding RNA. They further demonstrated the crucial importance of the lncRNA isoform CARMEN-201 in SMC specification during heart development.

The conserved long non-coding RNA CARMA regulates cardiomyocyte differentiation.

AIMS: Production of functional cardiomyocytes from pluripotent stem cells requires tight control of the differentiation process. Long non-coding RNAs (lncRNAs) exert critical regulatory functions in cell specification during development. In this study, we designed an integrated approach to identify lncRNAs implicated in cardiogenesis in differentiating human embryonic stem cells (ESCs). METHODS AND RESULTS: We identified CARMA (CARdiomyocyte Maturation-Associated lncRNA), a conserved lncRNA controlling cardiomyocyte differentiation and maturation in human ESCs. CARMA is located adjacent to MIR-1-1HG, the host gene for two cardiogenic miRNAs: MIR1-1 and MIR-133a2, and transcribed in an antisense orientation. The expression of CARMA and the miRNAs are negatively correlated, and CARMA knockdown increases MIR1-1 and MIR-133a2 expression. In addition, CARMA possesses MIR-133a2 binding sites, suggesting the lncRNA could be also a target of miRNA action. Upon CARMA down-regulation, MIR-133a2 target protein-coding genes are coordinately down-regulated. Among those, we found RBPJ, the gene encoding the effector of the NOTCH pathway. NOTCH has been shown to control a binary cell fate decision between the mesoderm and the neuroectoderm lineages, and NOTCH inhibition leads to enhanced cardiomyocyte differentiation at the expense of neuroectodermal derivatives. Interestingly, two lncRNAs, linc1230 and linc1335, which are known repressors of neuroectodermal specification, were found up-regulated upon Notch1 silencing in ESCs. Forced expression of either linc1230 or linc1335 improved ESC-derived cardiomyocyte production. These two lncRNAs were also found up-regulated following CARMA knockdown in ESCs. CONCLUSIONS: Altogether, these data suggest the existence of a network, implicating three newly identified lncRNAs, the two myomirs MIR1-1 and MIR-133a2 and the NOTCH signalling pathway, for the coordinated regulation of cardiogenic differentiation in ESCs.

Multi-hallmark long noncoding RNA maps reveal non-small cell lung cancer vulnerabilities.

Long noncoding RNAs (lncRNAs) are widely dysregulated in cancer, yet their functional roles in cancer hallmarks remain unclear. We employ pooled CRISPR deletion to perturb 831 lncRNAs detected in KRAS-mutant non-small cell lung cancer (NSCLC) and measure their contribution to proliferation, chemoresistance, and migration across two cell backgrounds. Integrative analysis of these data outperforms conventional "dropout" screens in identifying cancer genes while prioritizing disease-relevant lncRNAs with pleiotropic and background-independent roles. Altogether, 80 high-confidence oncogenic lncRNAs are active in NSCLC, which tend to be amplified and overexpressed in tumors. A follow-up antisense oligonucleotide (ASO) screen shortlisted two candidates, Cancer Hallmarks in Lung LncRNA 1 (CHiLL1) and GCAWKR, whose knockdown consistently suppressed cancer hallmarks in two- and three-dimension tumor models. Molecular phenotyping reveals that CHiLL1 and GCAWKR control cellular-level phenotypes via distinct transcriptional networks. This work reveals a multi-dimensional functional lncRNA landscape underlying NSCLC that contains potential therapeutic vulnerabilities.

Col6a1+/CD201+ mesenchymal cells regulate intestinal morphogenesis and homeostasis.

Intestinal mesenchymal cells encompass multiple subsets, whose origins, functions, and pathophysiological importance are still not clear. Here, we used the Col6a1Cre mouse, which targets distinct fibroblast subsets and perivascular cells that can be further distinguished by the combination of the CD201, PDGFRα and αSMA markers. Developmental studies revealed that the Col6a1Cre mouse also targets mesenchymal aggregates that are crucial for intestinal morphogenesis and patterning, suggesting an ontogenic relationship between them and homeostatic PDGFRαhi telocytes. Cell depletion experiments in adulthood showed that Col6a1+/CD201+ mesenchymal cells regulate homeostatic enteroendocrine cell differentiation and epithelial proliferation. During acute colitis, they expressed an inflammatory and extracellular matrix remodelling gene signature, but they also retained their properties and topology. Notably, both in homeostasis and tissue regeneration, they were dispensable for normal organ architecture, while CD34+ mesenchymal cells expanded, localised at the top of the crypts, and showed increased expression of villous-associated morphogenetic factors, providing thus evidence for the plasticity potential of intestinal mesenchymal cells. Our results provide a comprehensive analysis of the identities, origin, and functional significance of distinct mesenchymal populations in the intestine.

Combination of subtherapeutic anti-TNF dose with dasatinib restores clinical and molecular arthritogenic profiles better than standard anti-TNF treatment.

BACKGROUND: New medications for Rheumatoid Arthritis (RA) have emerged in the last decades, including Disease Modifying Antirheumatic Drugs (DMARDs) and biologics. However, there is no known cure, since a significant proportion of patients remain or become non-responders to current therapies. The development of new mode-of-action treatment schemes involving combination therapies could prove successful for the treatment of a greater number of RA patients. METHODS: We investigated the effect of the Tyrosine Kinase inhibitors (TKIs) dasatinib and bosutinib, on the human TNF-dependent Tg197 arthritis mouse model. The inhibitors were administered either as a monotherapy or in combination with a subtherapeutic dose of anti-hTNF biologics and their therapeutic effect was assessed clinically, histopathologically as well as via gene expression analysis and was compared to that of an efficient TNF monotherapy. RESULTS: Dasatinib and, to a lesser extent, bosutinib inhibited the production of TNF and proinflammatory chemokines from arthritogenic synovial fibroblasts. Dasatinib, but not bosutinib, also ameliorated significantly and in a dose-dependent manner both the clinical and histopathological signs of Tg197 arthritis. Combination of dasatinib with a subtherapeutic dose of anti-hTNF biologic agents, resulted in a synergistic inhibitory effect abolishing all arthritis symptoms. Gene expression analysis of whole joint tissue of Tg197 mice revealed that the combination of dasatinib with a low subtherapeutic dose of Infliximab most efficiently restores the pathogenic gene expression profile to that of the healthy state compared to either treatment administered as a monotherapy. CONCLUSION: Our findings show that dasatinib exhibits a therapeutic effect in TNF-driven arthritis and can act in synergy with a subtherapeutic anti-hTNF dose to effectively treat the clinical and histopathological signs of the pathology. The combination of dasatinib and anti-hTNF exhibits a distinct mode of action in restoring the arthritogenic gene signature to that of a healthy profile. Potential clinical applications of combination therapies with kinase inhibitors and anti-TNF agents may provide an interesting alternative to high-dose anti-hTNF monotherapy and increase the number of patients responding to treatment.

LnCompare: gene set feature analysis for human long non-coding RNAs.

Interest in the biological roles of long noncoding RNAs (lncRNAs) has resulted in growing numbers of studies that produce large sets of candidate genes, for example, differentially expressed between two conditions. For sets of protein-coding genes, ontology and pathway analyses are powerful tools for generating new insights from statistical enrichment of gene features. Here we present the LnCompare web server, an equivalent resource for studying the properties of lncRNA gene sets. The Gene Set Feature Comparison mode tests for enrichment amongst a panel of quantitative and categorical features, spanning gene structure, evolutionary conservation, expression, subcellular localization, repetitive sequences and disease association. Moreover, in Similar Gene Identification mode, users may identify other lncRNAs by similarity across a defined range of features. Comprehensive results may be downloaded in tabular and graphical formats, in addition to the entire feature resource. LnCompare will empower researchers to extract useful hypotheses and candidates from lncRNA gene sets.

An integrative transcriptome analysis framework for drug efficacy and similarity reveals drug-specific signatures of anti-TNF treatment in a mouse model of inflammatory polyarthritis.

Anti-TNF agents have been in the first line of treatment of various inflammatory diseases such as Rheumatoid Arthritis and Crohn's Disease, with a number of different biologics being currently in use. A detailed analysis of their effect at transcriptome level has nevertheless been lacking. We herein present a concise analysis of an extended transcriptomics profiling of four different anti-TNF biologics upon treatment of the established hTNFTg (Tg197) mouse model of spontaneous inflammatory polyarthritis. We implement a series of computational analyses that include clustering of differentially expressed genes, functional analysis and random forest classification. Taking advantage of our detailed sample structure, we devise metrics of treatment efficiency that take into account changes in gene expression compared to both the healthy and the diseased state. Our results suggest considerable variability in the capacity of different biologics to modulate gene expression that can be attributed to treatment-specific functional pathways and differential preferences to restore over- or under-expressed genes. Early intervention appears to manage inflammation in a more efficient way but is accompanied by increased effects on a number of genes that are seemingly unrelated to the disease. Administration at an early stage is also lacking in capacity to restore healthy expression levels of under-expressed genes. We record quantifiable differences among anti-TNF biologics in their efficiency to modulate over-expressed genes related to immune and inflammatory pathways. More importantly, we find a subset of the tested substances to have quantitative advantages in addressing deregulation of under-expressed genes involved in pathways related to known RA comorbidities. Our study shows the potential of transcriptomic analyses to identify comprehensive and distinct treatment-specific gene signatures combining disease-related and unrelated genes and proposes a generalized framework for the assessment of drug efficacy, the search of biosimilars and the evaluation of the efficacy of TNF small molecule inhibitors.

Wnt1 silences chemokine genes in dendritic cells and induces adaptive immune resistance in lung adenocarcinoma.

Lung adenocarcinoma (LUAD)-derived Wnts increase cancer cell proliferative/stemness potential, but whether they impact the immune microenvironment is unknown. Here we show that LUAD cells use paracrine Wnt1 signaling to induce immune resistance. In TCGA, Wnt1 correlates strongly with tolerogenic genes. In another LUAD cohort, Wnt1 inversely associates with T cell abundance. Altering Wnt1 expression profoundly affects growth of murine lung adenocarcinomas and this is dependent on conventional dendritic cells (cDCs) and T cells. Mechanistically, Wnt1 leads to transcriptional silencing of CC/CXC chemokines in cDCs, T cell exclusion and cross-tolerance. Wnt-target genes are up-regulated in human intratumoral cDCs and decrease upon silencing Wnt1, accompanied by enhanced T cell cytotoxicity. siWnt1-nanoparticles given as single therapy or part of combinatorial immunotherapies act at both arms of the cancer-immune ecosystem to halt tumor growth. Collectively, our studies show that Wnt1 induces immunologically cold tumors through cDCs and highlight its immunotherapeutic targeting.

Cigarette Smoke-Induced Emphysema Exhausts Early Cytotoxic CD8+ T Cell Responses against Nascent Lung Cancer Cells.

Chronic obstructive pulmonary disease is a chronic inflammatory disorder with an increased incidence of lung cancer. The emphysema component of chronic obstructive pulmonary disease confers the greatest proportion to lung cancer risk. Although tumors create inflammatory conditions to escape immunity, the immunological responses that control growth of nascent cancer cells in pre-established inflammatory microenvironments are unknown. In this study, we addressed this issue by implanting OVA-expressing cancer cells in the lungs of mice with cigarette smoke-induced emphysema. Emphysema augmented the growth of cancer cells, an effect that was dependent on T cytotoxic cells. OVA-specific OTI T cells showed early signs of exhaustion upon transfer in emphysema tumor hosts that was largely irreversible because sorting, expansion, and adoptive transfer failed to restore their antitumor activity. Increased numbers of PD-L1- and IDO-positive CD11c+ myeloid dendritic cells (DCs) infiltrated emphysema tumors, whereas sorted emphysema tumor DCs poorly stimulated OTI T cells. Upon adoptive transfer in immunocompetent hosts, T cells primed by emphysema tumor DCs were unable to halt tumor growth. DCs exposed to the emphysema tumor microenvironment downregulated MHC class II and costimulatory molecules, whereas they upregulated PD-L1/IDO via oxidative stress-dependent mechanisms. T cell activation increased upon PD-L1 blockade in emphysema DC-T cell cocultures and in emphysema tumor hosts in vivo. Analysis of the transcriptome of primary human lung tumors showed a strong association between computed tomography-based emphysema scoring and downregulation of immunogenic processes. Thus, suppression of adaptive immunity against lung cancer cells links a chronic inflammatory disorder, emphysema, to cancer, with clinical implications for emphysema patients to be considered optimal candidates for cancer immunotherapies.

Mesenchymal TNFR2 promotes the development of polyarthritis and comorbid heart valve stenosis.

Mesenchymal TNF signaling is etiopathogenic for inflammatory diseases such as rheumatoid arthritis and spondyloarthritis (SpA). The role of Tnfr1 in arthritis has been documented; however, Tnfr2 functions are unknown. Here, we investigate the mesenchymal-specific role of Tnfr2 in the TnfΔARE mouse model of SpA in arthritis and heart valve stenosis comorbidity by cell-specific, Col6a1-cre-driven gene targeting. We find that TNF/Tnfr2 signaling in resident synovial fibroblasts (SFs) and valvular interstitial cells (VICs) is detrimental for both pathologies, pointing to common cellular mechanisms. In contrast, systemic Tnfr2 provides protective signaling, since its complete deletion leads to severe deterioration of both pathologies. SFs and VICs lacking Tnfr2 fail to acquire pathogenic activated phenotypes and display increased expression of antiinflammatory cytokines associated with decreased Akt signaling. Comparative RNA sequencing experiments showed that the majority of the deregulated pathways in TnfΔARE mesenchymal-origin SFs and VICs, including proliferation, inflammation, migration, and disease-specific genes, are regulated by Tnfr2; thus, in its absence, they are maintained in a quiescent nonpathogenic state. Our data indicate a pleiotropy of Tnfr2 functions, with mesenchymal Tnfr2 driving cell activation and arthritis/valve stenosis pathogenesis only in the presence of systemic Tnfr2, whereas nonmesenchymal Tnfr2 overcomes this function, providing protective signals and, thus, containing both pathologies.