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The TAM tyrosine kinases, Axl and MerTK, play an important role in rheumatoid arthritis (RA). Here, using a unique synovial tissue bioresource of patients with RA matched for disease stage and treatment exposure, we assessed how Axl and MerTK relate to synovial histopathology and disease activity, and their topographical expression and longitudinal modulation by targeted treatments. We show that in treatment-naive patients, high AXL levels are associated with pauci-immune histology and low disease activity and inversely correlate with the expression levels of pro-inflammatory genes. We define the location of Axl/MerTK in rheumatoid synovium using immunohistochemistry/fluorescence and digital spatial profiling and show that Axl is preferentially expressed in the lining layer. Moreover, its ectodomain, released in the synovial fluid, is associated with synovial histopathology. We also show that Toll-like-receptor 4-stimulated synovial fibroblasts from patients with RA modulate MerTK shedding by macrophages. Lastly, Axl/MerTK synovial expression is influenced by disease stage and therapeutic intervention, notably by IL-6 inhibition. These findings suggest that Axl/MerTK are a dynamic axis modulated by synovial cellular features, disease stage and treatment. The TAM tyrosine kinases, Axl and MerTK, have been implicated in rheumatoid arthritis (RA). Here, using a synovial tissue bioresource of patients with RA, the authors describe how Axl and MerTK expression and function are linked to synovial histopathology, disease activity, and therapeutic intervention with IL-6 inhibitors.

Thyroid carcinoma (TC) is the most common malignancy of endocrine organs. The cell subpopulation in the lineage hierarchy that serves as cell of origin for the different TC histotypes is unknown. Human embryonic stem cells (hESCs) with appropriate in vitro stimulation undergo sequential differentiation into thyroid progenitor cells (TPCs-day 22), which maturate into thyrocytes (day 30). Here, we create follicular cell-derived TCs of all the different histotypes based on specific genomic alterations delivered by CRISPR-Cas9 in hESC-derived TPCs. Specifically, TPCs harboring BRAF V600E or NRAS Q61R mutations generate papillary or follicular TC, respectively, whereas addition of TP53 R248Q generate undifferentiated TCs. Of note, TCs arise by engineering TPCs, whereas mature thyrocytes have a very limited tumorigenic capacity. The same mutations result in teratocarcinomas when delivered in early differentiating hESCs. Tissue Inhibitor of Metalloproteinase 1 (TIMP1)/Matrix metallopeptidase 9 (MMP9)/Cluster of differentiation 44 (CD44) ternary complex, in cooperation with Kisspeptin receptor (KISS1R), is involved in TC initiation and progression. Increasing radioiodine uptake, KISS1R and TIMP1 targeting may represent a therapeutic adjuvant option for undifferentiated TCs. Thyroid carcinoma (TC) is the most common malignancy of endocrine organs. Here, the authors show the ability of human embryonic stem cells (hESCs) to recapitulate the different TC histotypes upon specific genomic alterations delivered by CRISPR-Cas9 and identify KISS1R and TIMP1 targeting as a therapeutic adjuvant option for undifferentiated TCs.

Background To determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models. Methods We utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis. Results Analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73. Conclusions We demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

Background In many tumors, the tumor suppressor TP53 is not mutated, but functionally inactivated. However, mechanisms underlying p53 functional inactivation remain poorly understood. SETD8 is the sole enzyme known to mono-methylate p53 on lysine 382 (p53 K382me1 ), resulting in the inhibition of its pro-apoptotic and growth-arresting functions. Methods We analyzed SETD8 and p53 K382me1 expression in clinical colorectal cancer (CRC) and inflammatory bowel disease (IBD) samples. Histopathological examinations, RNA sequencing, ChIP assay and preclinical in vivo CRC models, were used to assess the functional role of p53 inactivation in tumor cells and immune cell infiltration. Results By integrating bulk RNAseq and scRNAseq approaches in CRC patients, SETD8-mediated p53 regulation resulted the most significantly enriched pathway. p53 K382me1 expression was confined to colorectal cancer stem cells (CR-CSCs) and C1Q + TPP1 + tumor-associated macrophages (TAMs) in CRC patient tissues, with high levels predicting decreased survival probability. TAMs promote p53 functional inactivation in CR-CSCs through IL-6 and MCP-1 secretion and increased levels of CEBPD, which directly binds SETD8 promoter thus enhancing its transcription. The direct binding of C1Q present on macrophages and C1Q receptor (C1QR) present on cancer stem cells mediates the cross-talk between the two cell compartments. As monotherapy, SETD8 genetic and pharmacological (UNC0379) inhibition affects the tumor growth and metastasis formation in CRC mouse avatars, with enhanced effects observed when combined with IL-6 receptor targeting. Conclusions These findings suggest that p53 K382me1 may be an early step in tumor initiation, especially in inflammation-induced CRC, and could serve as a functional biomarker and therapeutic target in adjuvant setting for advanced CRCs. Graphical Abstract

Primary Sjögren’s syndrome (pSS) is a chronic autoimmune disease resulting from the inflammatory infiltration of exocrine glands, mainly salivary and lacrimal glands, leading to secretory dysfunction and serious complications including debilitating fatigue, systemic autoimmunity, and lymphoma. Like other autoimmune disorders, a strong interferon (IFN) signature is present among subsets of pSS patients, suggesting the involvement of innate immunity in pSS pathogenesis. NCR3 /NKp30 is a natural killer (NK) cell-specific activating receptor regulating the cross talk between NK and dendritic cells including type II IFN secretion upon NK-cell activation. A genetic association between single-nucleotide polymorphisms (SNPs) in the NCR3 /NKp30 promoter gene and a higher susceptibility for pSS has been previously described, with pSS patients most frequently carrying the major allele variant associated with a higher NKp30 transcript and IFN-γ release as a consequence of the receptor engagement. In the present study, we combined RNA-sequencing and histology from pSS salivary gland biopsies to better characterize NKp30 ( NCR3 ) and its ligand B7/H6 ( NCR3LG1 ) in pSS salivary gland tissues. Levels of NCR3 /NKp30 were significantly increased both in salivary glands and in circulating NK cells of pSS patients compared with sicca controls, especially in salivary glands with organized ectopic lymphoid structures. In line with this observation, a strong correlation between NCR3 /NKp30 levels and salivary gland infiltrating immune cells (CD3, CD20) was found. Furthermore, NCR3 /NKp30 levels also correlated with higher IFN-γ, Perforin, and Granzyme-B expression in pSS SGs with organized ectopic lymphoid structures, suggesting an activation state of NK cells infiltrating SG tissue. Of note, NKp30+ NK cells accumulated at the border of the inflammatory foci , while the NKp30 ligand, B7/H6, is shown to be expressed mainly by ductal epithelial cells in pSS salivary glands. Finally, immunomodulatory treatment, such as the B-cell depleting agent rituximab, known to reduce the infiltration of immune cells in pSS SGs, prevented the upregulation of NCR3 /NKp30 within the glands.

Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5–20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n  = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment–response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients. Biomarker analysis of the phase 4 R4RA trial identifies pretreatment synovial biopsy features selectively associated with response to rituximab or tocilizumab, and leads to the development of models that might predict treatment benefit in patients with rheumatoid arthritis

Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment response are lacking. Here we analyse RNA-sequencing (RNA-Seq) of pre-treatment synovial tissue from the biopsy-based, precision-medicine STRAP trial ( n  = 208), to identify gene response signatures to the randomised therapies: etanercept (TNF-inhibitor), tocilizumab (interleukin-6 receptor inhibitor) and rituximab (anti-CD20 B-cell depleting antibody). Machine learning models applied to RNA-Seq predict clinical response to etanercept, tocilizumab and rituximab at the 16-week primary endpoint with area under receiver operating characteristic curve (AUC) values of 0.763, 0.748 and 0.754 respectively ( n  = 67-72) as determined by repeated nested cross-validation. Prediction models for tocilizumab and rituximab are validated in an independent cohort (R4RA): AUC 0.713 and 0.786 respectively ( n  = 65-68). Predictive signatures are converted for use with a custom synovium-specific 524-gene nCounter panel and retested on synovial biopsy RNA from STRAP patients, demonstrating accurate prediction of treatment response (AUC 0.82-0.87). The converted models are combined into a unified clinical decision algorithm that has the potential to transform future clinical practice by assisting the selection of biologic therapies. The heterogenous nature of rheumatoid arthritis renders the prediction of responsiveness to biological treatments difficult. Here the authors analyze bulk RNA-seq data from the STRAP trial ( n  = 208) to build a machine-learning model for predicting responses to etanercept, tocilizumab and rituximab with AUCs around 0.75 to potentially assist in therapy planning.

Modern clinical trials increasingly leverage high-throughput omic data for patient stratification and biomarker discovery. While traditional differential gene expression analysis disregards the networked nature of molecular entities and produces extensive gene lists with limited interpretability, differential network analysis has emerged as a crucial complementary analysis for comparative studies. Here we present multiDEGGs, a CRAN R package that enables differential network analysis in multi-omic scenarios. multiDEGGs uses a multi-layer graph framework to model omic data by leveraging an internal network of over 10 000 literature-validated biological interactions. For each data type, differential networks are generated, and the statistical significance of each link (p-values or adjusted p-values) is evaluated through robust linear regression with interaction terms. These networks are then integrated into a comprehensive visualisation that allows interactive exploration of cross-omic patterns. Beyond network visualization and exploration, multiDEGGs extends its utility to predictive modelling applications. The package facilitates seamless integration into cross-validation machine learning pipelines, serving as feature selection and augmentation tool. We validated multiDEGGs using two cohorts of rheumatoid arthritis patients who underwent tocilizumab and rituximab therapy, respectively. For each treatment group, multi-layer differential interactions were identified, and seven machine learning models were trained to predict treatment resistance using synovial RNA-seq data. We systematically compared multiDEGGs against five traditional feature selection methods. On average, AUC values obtained with multiDEGGs showed an improvement of 0.10 compared to conventional filters. Traditional gene expression analysis leaves researchers with hundreds of ‘significant’ genes but no clear biological story. The multiDEGGs CRAN package shifts the focus: instead of asking which genes change, it asks which gene relationships change. It can be used with single or multi-omic data: differential networks are calculated separately for each data type, with results integrated into a comprehensive, interactive view. multiDEGGs can be combined with the nestedcv CRAN package (nested cross-validation) to serve as feature selection and augmentation tool. In comparative evaluations, machine learning models trained with multiDEGGs-selected features showed AUC improvements of 0.10 compared to other feature selection methods.

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response. Competing Interest Statement The authors have declared no competing interest.

Kernel size and shape are important parameters determining the wheat profitability, being main determinants of yield and its technological quality. In this study, a segregating population of 118 recombinant inbred lines, derived from a cross between the Iranian durum landrace accession \"Iran_249\" and the Iranian durum cultivar \"Zardak\", was used to investigate durum wheat kernel morphology factors and their relationships with kernel weight, and to map the corresponding QTLs. A high density genetic map, based on wheat 90k iSelect Infinium SNP assay, comprising 6,195 markers, was developed and used to perform the QTL analysis for kernel length and width, traits related to kernel shape and weight, and heading date, using phenotypic data from three environments. Overall, a total of 31 different QTLs and 9 QTL interactions for kernel size, and 21 different QTLs and 5 QTL interactions for kernel shape were identified. The landrace Iran_249 contributed the allele with positive effect for most of the QTLs related to kernel length and kernel weight suggesting that the landrace might have considerable potential toward enhancing the existing gene pool for grain shape and size traits and for further yield improvement in wheat. The correlation among traits and co-localization of corresponding QTLs permitted to define 11 clusters suggesting causal relationships between simplest kernel size trait, like kernel length and width, and more complex secondary trait, like kernel shape and weight related traits. Lastly, the recent release of the reference genome sequence allowed to define the physical interval of our QTL/clusters and to hypothesize novel candidate genes inspecting the gene content of the genomic regions associated to target traits.