Motivation: 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 (multiomics differentially expressed gene-gene pairs), a CRAN R package that enables differential network analysis in single or multiomic scenarios. Methods: multiDEGGs uses a multiomic graph framework where, for each data type, differential networks are generated and the statistical significance of each link is evaluated. These networks are then integrated into a comprehensive visualization that allows interactive exploration of cross-omic patterns and interactions. The package facilitates seamless integration into cross-validation machine learning pipelines for feature selection and identification of biologically relevant interactions for feature engineering. Results: We validated multiDEGGs using two cohorts of patients with rheumatoid arthritis. For each treatment group, multiomic differential interactions were identified, and eight machine learning models were trained to predict treatment resistance using synovial RNA sequencing data. We systematically compared multiDEGGs against seven feature selection methods. On average, area under the receiver operating characteristic curve values obtained with multiDEGGs showed an improvement of 0.10 compared to conventional filters. Availability and Implementation: multiDEGGs is freely available on CRAN, and the source code is available on GitHub at https://github.com/elisabettasciacca/multiDEGGs under GPL-3.0 license. Source code used to generate figures and analyses conducted in this paper is available at https://github.com/EMR-bioinformatics/multiDEGGs_supplementary.

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 (p53K382me1), resulting in the inhibition of its pro-apoptotic and growth-arresting functions. We analyzed SETD8 and p53K382me1 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. By integrating bulk RNAseq and scRNAseq approaches in CRC patients, SETD8-mediated p53 regulation resulted the most significantly enriched pathway. p53K382me1 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. These findings suggest that p53K382me1 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.

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. KEY POINTS ### Competing Interest Statement The authors have declared no competing interest. Fondazione Ceschina, HFR083 European Commission Innovative Medicines Initiative, 831434

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 learningmodels 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.

OBJECTIVES:This study aims to identify salivary gland (SG) transcriptomic signatures associated with peripheral and histological biomarkers of disease activity and lymphoma risk factors to inform on Sjögren's disease (SjD) stratification. METHODS:Bulk RNA-sequencing of labial SG from exploratory Queen Mary University of London (QMUL) cohort (SjD [n = 55] and non specific-chronic-sialadenitis [sicca, n = 44]) and trial for anti-B-cell therapy in Sjogren's syndrome (TRACTISS)validation cohort (SjD, n = 29) analysed integrating transcriptomic, clinical, serological, and histological data. RESULTS:Unsupervised gene clustering confirmed clear transcriptome segregation between sicca and SjD. The most differentially expressed genes were either common to all SjD's versus sicca or specific to SjD's glands with lymphocytic infiltrates with features of ectopic lymphoid structures (ELS). In SjD, principal component analysis identified a significant proportion of variability associated with rheumatoid factor (RF)-seropositivity, exceeding that associated with SG-ELS and anti-Ro/Sjögren's syndrome antigen-A (SSA) seropositivity. Transcriptomes of SjD-SG with ELS from patients with positivity for either RF, anti-Ro/SSA, and anti-La/SSB showed mainly genes associated with germinal centre formation. Conversely, SG without ELS from patients with either RF or double anti-Ro (SSA)/anti-La (SSB) seropositivity (unlike patients with seronegative or single anti-Ro/SSA seropositivity) displayed a unique extrafollicular B-cell gene set associated with type-I interferon (IFN), through the retinoic acid-inducible gene-I (RIG-1) endogenous RNAs (including viral) sensing pathway and E3-ubiquitin ligases. The identified IFN genes showed strong positive correlations with serum RF levels in 2 independent cohorts (QMUL and TRACTISS). CONCLUSIONS:Comprehensive SG bulk-RNA sequencing provided the first transcriptomic evidence of distinct RF and anti-La/SSB-driven SG transcriptomic signatures in patients with SjD with and without ELS. These findings suggest that both classical follicular and extrafollicular viral-associated responses contribute to the selection of autoreactive B-cells in the glands of patients with SjD.

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.

Background: Despite the availability of effective treatments, up to 10-20% of patients with Rheumatoid Arthritis (RA) do not respond to multiple targeted medications and can be classified as multi-drug resistant (MDR). MDR-RA has been linked to both extrinsic factors, such as comorbidities and non-compliance and, intrinsic factors related to the molecular pathology of the diseased tissue. For example, recently, a stromal synovial signature has been associated to multi-drug resistance. However, the role of immune cells and pathways in mediating multi-drug resistance is unknown. Objectives: To investigate the contribution of specific synovial immune cells in the pathogenesis of MDR-RA. Methods: In The R4RA trial, TNF-inhibitors inadequate responders RA patients were randomised to Tocilizumab or Rituximab following a synovial biopsy. At week 16, CDAI50 non-responders were switched to the alternative medication. At the end of the trial, we identified patients who did not respond to at least 3x bDMARDs, defined as MDR-RA (n=39), and compared with first-line (16 weeks) responders to either Rituximab or Tocilizumab (n=65), for baseline demographics, clinical characteristics and synovitis assessed by immunohistochemistry. In silico deconvolution (xCell) and differentially expressed gene (DEG) analyses were used to identify cell types and molecular signatures associated with treatment response/refractoriness specific to the lympho-myeloid population, i.e. with synovial infiltration of B and T cells. Results: The synovial pathotype distribution was similar in MDR-RA patients and first-line responders, with a comparable prevalence of the lympho-myeloid pathotype, characterised by an abundance of B and T cells—specifically 16 (41%) in MDR-RA and 34 (52.3%) in first-line responders. However, within this highly-inflamed lympho-myeloid population, MDR-RA patients showed significantly higher disease activity (CDAI 37.0 [33.9-41] vs 25.5 [17.6-35.3]; p=0.02), tender joint count (16.0 [12.3-22.3] vs 9.5 [4.0-15.5]; p=0.02), circulating white blood cells (10.3 [7.8-12.3] vs 8.2 [6.7-9.1]; p=0.02) and neutrophils (7.2 [5.6-9.3] vs 5.2 [4.4-6.5]; p=0.004) (Table 1). Molecular analysis showed 39 Differentially Expressed Genes in the MDR-RA lympho-myeloid population, particularly genes related to the innate immunity, such as CXCR1, CXCR2, S100A12, MS4A3, and TNFRSF10C. Accordingly, pathway analysis highlighted pathways related to granulocyte chemotaxis and migration upregulated in MDRA-RA lympho-myeloid patients. In silico deconvolution using xCell showed significantly higher levels of neutrophils, eosinophils, and mast cells in MDR-RA lympho-myeloid patients (Figure 1). Conclusion: Our study highlights a considerable heterogeneity in synovial inflammation among MDR-RA patients, pointing to diverse mechanisms underlying treatment resistance. Particularly within the lympho-myeloid population, our findings unveil an innate immune signature linked to MDR-RA, suggesting a potential role for innate immune cells in driving treatment resistance through potential crosstalk with adaptive immunity. REFERENCES: [1] Rivellese et al, Nat Med 28, 1256–1268 (2022). Acknowledgements: We thank all patients participating in the trial and the Precision Medicine Patient Advisory Group (PM-PAG) for their continuous support. The R4RA trial was funded by the Efficacy and Mechanism Evaluation (EME) Programme, a partnership between the Medical Research Council (MRC) and the National Institute for Health and Care Research (NIHR) (grant no. 11/100/76). This study was further supported by NIHR (grant 131575) and MRC (MR/V012509/1) and acknowledges the support of the National Institute for Health Research Barts Biomedical Research Centre (NIHR203330). Disclosure of Interests: None declared.Figure 1 Table 1MDR-RA vs First line responders (lympho-myeloid patients)MDR-RA(n=16)1st line Responders(n=34)p-valueAge56.2 (50.7-68.1)58.1 (52.3-65.4)0.99Gender (F)13 (81.3)24 (70.6)0.64BMI26.1 (20.4-30.0)26.4 (22.6-32.1)0.43WBC10.3 (7.8-12.3)8.2 (6.7-9.1)0.02Platelets335 (254-405)290 (241-263)0.08Neutrophils7.2 (5.6-9.3)5.2 (4.4-6.5)0.004Lymphocytes1.6 (1.4-2.3)1.7 (1.4-2.4)0.97RA duration9.0 (3.8-25.3)10.0 (4.5-16.8)0.93TJC 2816.0 (12.3-22.3)9.5 (4.0-15.5)0.02SJC 287.5 (6.0-9.5)4.5 (2.0-9.0)0.08ESR42.0 (29.0-59.6)28.5 (18.0-43.5)0.15CRP19.5 (6.7-47.0)10.5 (5.6-28.8)0.32VAS Pain66.0 (54.3-81.8)73.5 (54.0-88.8)0.45Patient Global Assessment75.5 (55.5-81.3)71 (54.3-85.5)0.84CDAI37.0 (33.9-41)25.5 (17.6-35.3)0.02DAS28 CRP5.91 (5.26-6.45)5.25 (4.16-6.02)0.04DAS28 ESR6.6 (5.9-6.8)5.4 (4.4-6.5)0.04Data shown as median (IQR) or n (%)

Cellular senescence is a hallmark of advanced age and a major instigator of numerous inflammatory pathologies. While endothelial cell (EC) senescence is aligned with defective vascular functionality, its impact on fundamental inflammatory responses in vivo at single-cell level remain unclear. To directly investigate the role of EC senescence on dynamics of neutrophil-venular wall interactions, we applied high resolution confocal intravital microscopy to inflamed tissues of an EC-specific progeroid mouse model, characterized by profound indicators of EC senescence. Progerin-expressing ECs supported prolonged neutrophil adhesion and crawling in a cell autonomous manner that additionally mediated neutrophil-dependent microvascular leakage. Transcriptomic and immunofluorescence analysis of inflamed tissues identified elevated levels of EC CXCL1 on progerin-expressing ECs and functional blockade of CXCL1 suppressed the dysregulated neutrophil responses elicited by senescent ECs. Similarly, cultured progerin-expressing human ECs exhibited a senescent phenotype, were pro-inflammatory and prompted increased neutrophil attachment and activation. Collectively, our findings support the concept that senescent ECs drive excessive inflammation and provide new insights into the mode, dynamics, and mechanisms of this response at single-cell level.

Background: RNA sequencing (RNA-seq) has emerged as a widely embraced technique for comprehensive gene expression profiling on a large scale. Nonetheless, there is a current absence of accessible and versatile tools that facilitate efficient exploration of RNA-seq datasets from individuals with Sjogren’s disease (SD) for researchers. Objectives: Our goal is to create user-friendly websites based on R Shiny, designed for gene expression analyses within two distinct SD cohorts: i) an observational disease-control cohort (sicca/Sjogren’s disease), and ii) the TRACTISS randomized clinical trial, comparing Rituximab versus placebo with longitudinal data pre/post-treatment1. Methods: The searchable web interfaces was developed using R Shiny (v.1.7.2) to investigate the associations between individual gene transcript levels and histological as well as clinical parameters, including clinical response. These interfaces incorporate salivary gland (SG) biopsy and peripheral blood (PB) RNAseq data (Table 1) from both a disease-control (Sjogren’s/sicca) cohort and the TRACTISS randomized clinical trial, encompassing longitudinal data from Sjogren’s patients exclusively. Results: The websites enable data exploration and visualization, facilitating the comparison of gene expression levels among defined groups (Sicca/Sjogren or Sjogren: Placebo/Rituximab) based on user-selected clinical variables. Whether the variable is continuous or categorical, users can visualize box plots or correlation plots. Additionally, correlations between individual clinical variables and all genes can be visualized, providing the list of genes significantly correlated with the variable of interest.Differential expression analysis of RNA-seq data is available, offering interactive volcano plots that easily depict the user’s genes of interest. Users can also generate heatmaps using a custom list of genes.For the TRACTISS randomized clinical trial, a time-series analysis of gene expression over time (with three time points for each patient) can be visualized for both SG biopsy and PB RNAseq data. Moreover, gene expression can be stratified based on response criteria, including ESSDAI improvement, CRESS, and STAR composite scores. Conclusion: We have developed two user-friendly websites for Sjogren’s RNA-seq differential expression and time-series analysis. These intuitive platforms offer a diverse selection of interactive plots, enabling researchers to efficiently explore hypotheses, identify expression patterns, and expedite their research with prompt results. All generated results can be downloaded in a high-quality, publication-ready format. REFERENCES: [1] Pontarini E, Sciacca E, Chowdhury F, et al. Serum and tissue biomarkers associated with CRESS and STAR response to B-cell targeted therapy in TRACTISS trial of Sjogren’s syndrome. Arthritis Rheumatol Published Online First: 10 December 2023. doi:10.1002/art.42772 Acknowledgements: NIL. Disclosure of Interests: Elena Pontarini: None declared, Elisabetta Sciacca: None declared, Giulia Cavallaro: None declared, David Galbraith Janssen Pharmaceuticals, Alfredo Pulvirenti: None declared, Ling-Yang Hao Janssen Pharmaceuticals, Kathy Sivils Janssen Pharmaceuticals, Myles Lewis: None declared, Costantino Pitzalis: None declared, Michele Bombardieri Janssen Pharmaceuticals.Figure 1

Objective This study aimed to identify peripheral and salivary gland (SG) biomarkers of response/resistance to B cell depletion based on the novel concise Composite of Relevant Endpoints for Sjögren Syndrome (cCRESS) and candidate Sjögren Tool for Assessing Response (STAR) composite endpoints. Methods Longitudinal analysis of peripheral blood and SG biopsies was performed pre‐ and post‐treatment from the Trial of Anti–B Cell Therapy in Patients With Primary Sjögren Syndrome (TRACTISS) combining flow cytometry immunophenotyping, serum cytokines, and SG bulk RNA sequencing. Results Rituximab treatment prevented the worsening of SG inflammation observed in the placebo arm, by inhibiting the accumulation of class‐switched memory B cells within the SG. Furthermore, rituximab significantly down‐regulated genes involved in immune‐cell recruitment, lymphoid organization alongside antigen presentation, and T cell co‐stimulatory pathways. In the peripheral compartment, rituximab down‐regulated immunoglobulins and auto‐antibodies together with pro‐inflammatory cytokines and chemokines. Interestingly, patients classified as responders according to STAR displayed significantly higher baseline levels of C‐X‐C motif chemokine ligand‐13 (CXCL13), interleukin (IL)‐22, IL‐17A, IL‐17F, and tumor necrosis factor‐α (TNF‐α), whereas a longitudinal analysis of serum T cell–related cytokines showed a selective reduction in both STAR and cCRESS responder patients. Conversely, cCRESS response was better associated with biomarkers of SG immunopathology, with cCRESS‐responders showing a significant decrease in SG B cell infiltration and reduced expression of transcriptional gene modules related to T cell costimulation, complement activation, and Fcγ‐receptor engagement. Finally, cCRESS and STAR response were associated with a significant improvement in SG exocrine function linked to transcriptional evidence of SG epithelial and metabolic restoration. Conclusion Rituximab modulates both peripheral and SG inflammation, preventing the deterioration of exocrine function with functional and metabolic restoration of the glandular epithelium. Response assessed by newly developed cCRESS and STAR criteria was associated with differential modulation of peripheral and SG biomarkers, emerging as novel tools for patient stratification. image

Background: In the R4RA biopsy-driven randomized clinical trial[1] rheumatoid arthritis (RA) patients were randomised to rituximab or tocilizumab based on their synovial tissue B cell rich/poor signature. Response, defined as 50% improvement of clinical disease activity index (CDAI50), was assessed at 16 weeks. Samples from this cohort were utilised to improve understanding of pathogenic mechanisms determining response to rituximab. This is still a major issue with up to 5-20% of RA patients not responding to all current medication. While rituximab was designed to target CD20 on B cells, effects on CD4 T cells[2] and a highly inflammatory CD20dim T cell population[3] has been described. Several mechanisms of non-response have been proposed, including incomplete B cell depletion in synovium after rituximab. Objectives: Investigate mechanisms of response and non-response to rituximab through deep molecular and cellular phenotyping of blood and synovial biopsies pre and post treatment. Methods: B cell receptor (BCR) repertoires from synovial biopsies (n=9) and matched peripheral blood (n=7) RNA from RA patients at baseline and 16 weeks post rituximab were amplified and sequenced. Blood T and B cells (total n=39 baseline, n=33 post-rituximab) were phenotyped and quantified with a 27-marker panel on a spectral flow cytometer and matched with serum protein profiling (proteomics platform) and bulk RNA-Seq from blood pre- and post-rituximab. Results: The baseline BCR repertoire in blood showed no differences in isotype usage between responders and non-responders. In contrast high levels of IgG2 in synovial tissue was associated with non-response at baseline, and IgA2 was higher at week 16 in the response group compared to non-response (p<0.05). Incomplete depletion of synovial B cells was observed in both responders and non-responders. Highly expanded clones in the joint post-rituximab were found to originate from the baseline synovium. However, there was no association with response with any specific individual clones. This suggests that while failure of depletion of specific B cell clones is linked to treatment failure, these individual clonotypes vary between patients, but tend to originate from within synovium. At baseline, non-responder blood samples showed a more inflammatory serum profile with higher levels of proteins such as IL18RAP, IL1R1, associated with higher levels of inflammatory cells such as Th17 cells with increased rates of activation markers ICOS and PD1 on NKT cells and CD95 on B cells. Furthermore, the ratio of CD20 expressing B cells compared to CD19 only expressing B cells was significantly 3.8 times higher in response patients compared to non-response patients (p=0.02). Additionally, response patients had 2.04 times higher levels of CD3+CD20dim T cells compared to non-responders (p=0.02). In post-rituximab patients higher NK cell levels were associated with greater response to treatment (p=0.03). Conclusion: Our results show that there may be multiple mechanisms explaining rituximab response including i) failure to deplete B cell clonotypes in the synovium, ii) higher NK-cell levels that may help clear rituximab-coated B cells and higher levels of inflammatory, CD20 targetable T cells, iii) higher activation profile in T cells preventing response and iv) a B cell profile skewed towards CD19 in non-responders and towards CD20 in responders in the blood. These findings enrich our understanding of mechanisms of response and non-response to rituximab, and could be used as biomarkers for patient stratification at baseline. We observed B cell repopulation originating from within synovium in all patients, which may also explain future relapse. REFERENCES: [1] Humby, et al. Lancet (2021). [2] Lavielle, et al. Arthritis Research and Therapy (2016). [3] Palanichamy and Jahn, et al. Journal of Immunology (2014). Acknowledgements: We thank all patients participating in the trial and the Patient Advisory Group. The R4RA trial was funded by the Efficacy and Mechanism Evaluation (EME) Programme, a partnership between the Medical Research Council (MRC) and the National Institute for Health and Care Research (NIHR) (grant no. 11/100/76), Versus Arthritis (Experimental Arthritis Treatment Centre, grant number 20022) and Barts Charity (grant number 523/819), MRC and Arthritis Research UK (ARUK) by joint funding of Maximizing Therapeutic Utility in Rheumatoid Arthritis (MATURA) (grant numbers MR/K015346/1 and 20670 respectively), NIHR (grant 131575) and MRC TRACT-RA (MR/V012509/1). This work acknowledges the support of the National Institute for Health Research Barts Biomedical Research Centre (NIHR 203330) as well as the Wellcome Trust for providing funding for the stipend (Wellcome Trust GMS stipend: BST00080.H508.01) for Lauren Overend. Disclosure of Interests: Anna Surace: None declared, Lauren Overend: None declared, Elisabetta Sciacca: None declared, Liliane Fossati-Jimack: None declared, Edyta Jaworska: None declared, Elena Pontarini: None declared, Rachael J.M. Bashford-Rogers Co-founder of Alchemab Therapeutics Ltd, Alchemab Therapeutics Ltd and GSK, Costantino Pitzalis: None declared, Myles Lewis: None declared.

Both TLR7 and NF-κB hyperactivity are known to contribute to pathogenesis in Systemic Lupus Erythematosus (SLE), driving a pro-interferon response, autoreactive B cell expansion and autoantibody production. UBE2L3 is an SLE susceptibility gene which drives plasmablast/plasma cell expansion in SLE, but its role in TLR7 signalling has not been elucidated. We aimed to investigate the role of UBE2L3 in TLR7-mediated NF-κB activation, and the effect of UBE2L3 inhibition by Dimethyl Fumarate (DMF) on SLE B cell differentiation in vitro. Our data demonstrate that UBE2L3 is critical for activation of NF-κB downstream of TLR7 stimulation, via interaction with LUBAC. DMF, which directly inhibits UBE2L3, significantly inhibited TLR7-induced NF-κB activation, differentiation of memory B cells and plasmablasts, and autoantibody secretion in SLE. DMF also downregulated interferon signature genes and plasma cell transcriptional programmes. These results demonstrate that UBE2L3 inhibition could potentially be used as a therapy in SLE through repurposing of DMF, thus preventing TLR7-driven autoreactive B cell maturation.

Abstract Summary The discovery of differential gene–gene correlations across phenotypical groups can help identify the activation/deactivation of critical biological processes underlying specific conditions. The presented R package, provided with a count and design matrix, extract networks of group-specific interactions that can be interactively explored through a shiny user-friendly interface. For each gene–gene link, differential statistical significance is provided through robust linear regression with an interaction term. Availability and implementation DEGGs is implemented in R and available on GitHub at https://github.com/elisabettasciacca/DEGGs. The package is also under submission on Bioconductor.

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 BRAFV600E or NRASQ61R mutations generate papillary or follicular TC, respectively, whereas addition of TP53R248Q 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.

Background The R4RA trial, the first biopsy-based randomised trial in TNF-i inadequate responder patients with Rheumatoid Arthritis, showed that molecular stratification of RA synovial tissue was associated with clinical response, demonstrating that, in patients with low/absent B-cell lineage signature in synovial-tissue, tocilizumab is superior to rituximab 1 . Objectives Here, we aimed to perform cell-transcript deconvolution of pre-and post-treatment synovial biopsies from the R4RA trial. Methods A total of 164 patients underwent pre-treatment synovial biopsy (US-guided or arthroscopic) prior to randomization 1:1 to rituximab (83) or tocilizumab (81). 65 patients had a repeat biopsy at 16 weeks when clinical response was assessed using Clinical Disease Activity Index (CDAI) 50% improvement. RNA extracted from a minimum of 6 synovial samples/patient underwent RNA-sequencing and the abundance of tissue-infiltrating immune and stromal cell populations was estimated using the Microenvironment Cell Populations-counter (MCP-counter) method (Figure 1a). Results At baseline, while synovial semiquantitative immunohistochemistry scores did not differ between CDAI50% responders and non-responders, both for rituximab and tocilizumab, MCP-counter analysis showed significantly higher CD8 T-cells in responders to rituximab and higher macrophage-monocytes and myeloid dendritic cells (mDC) in responders to tocilizumab (Figure 1b). Moreover, when patients were classified according to MCP-counter scores, B-cell poor patients (MCP-counter B cell score <median value) showed significantly higher response rates to tocilizumab, while no difference was found in B-cell rich patients (Figure 1c). In contrast, macrophage and myeloid dendritic cell (mDC) rich individuals showed higher responses to tocilizumab (Figure 1d). Combined scores for lymphoid and myeloid cells demonstrated that patients poor in B-cells but rich in macrophages/mDC had a significantly higher response to tocilizumab (77% responders to tocilizumab vs 14% responders to rituximab, p=0.017, OR 16.48, 95%CI 1.29-1000.5) (Figure 1e). By analysing disease activity over time from baseline to week 16, we found a statistically significant interaction effect between treatments and time in B-cell poor (p=0.003), T-cell poor (p=0.022), mDC rich (p=0.029) and B-cell poor/Macrophages-mDC rich patients (p=0.006) (Figure 1f-g-h). Finally, by applying MCP-counter on matched pre-and post-treatment biopsies, rituximab-treated patients showed a significant reduction of B-cells, T-cells and monocyte/macrophages, while tocilizumab-treated patients showed a significant reduction of monocyte/macrophages, T-cells, but also neutrophils, myeloid dendritic cells and, interestingly, an increase in fibroblast signature (Figure 1i). Conclusion In silico deconvolution of the synovial tissue identify pre-treatment lymphoid cell lineages associated with response to rituximab and myeloid cells for tocilizumab. The longitudinal analysis of matched pre- and post-treatment synovial biopsies indicated that both medications have an effect on synovial immune cells, but tocilizumab can also affect stromal cells. References [1]Humby et al. Rituximab versus tocilizumab in anti-TNF inadequate responder patients with rheumatoid arthritis (R4RA): 16-week outcomes of a stratified, biopsy-driven, multicentre, open-label, phase 4 randomised controlled trial Lancet. 2021 Jan 23;397(10271):305-317. doi: 10.1016/S0140-6736(20)32341-2. Acknowledgements We would like to thank all patients and the R4RA recruiting centres and principal investigators http://www.r4ra-nihr.whri.qmul.ac.uk/recruiting_centres.php We would also like to acknowledge the UK National Institute of Health Research for funding the R4RA trial (grant reference: 11/100/76) and Versus Arthritis for providing infrastructure support through the Experimental Arthritis Treatment Centre (grant number: 20022). Disclosure of Interests None declared.

Background The TRial for Anti-B-Cell Therapy In patients with pSS (TRACTISS) is the largest multi-centre, placebo-controlled, phase-III trial with the administration of 2 cycles of Rituximab (RTX) or placebo at week 0 and 24, with trial clinical endpoints at week 48. Despite the primary endpoints (30% reduction in fatigue or oral dryness) were not met, RTX treated patients showed an improvement in secondary endpoints, such as unstimulated whole salivary flow (UWSF), and salivary gland (SG) total ultrasound score 1,2 . Additionally, recent post-hoc analysis of TRACTISS using novel CRESS composite endpoints 3 , highlighted a significantly increased response rate in the RTX vs placebo arm. Objectives To perform the first longitudinal analysis of matched transcriptomic and histological data of SG biopsies of pSS patients treated with RTX vs placebo at 3 time points, over 48 weeks, from the TRACTISS cohort, in order to identify mechanisms of response/resistance to B cell depletion. Methods 29 pSS patients randomised to RTX or placebo arm consented for labial SG biopsies at week 0, 16 and 48. Patients received two 1000mg cycles of RTX or placebo at week 0 and 24. SG focus score, inflammatory aggregate area fraction, B-cells (CD20+), T-cells (CD3+), follicular dendritic cells (FDCs) (CD21+) and plasma cells (CD138+) density were assessed using quantitative digital image analysis. RNA sequencing with deconvolution and pathway analysis was performed to identify genes signatures and consensus gene modules as biomarkers of disease evolution and response/resistance to therapy. Results Placebo-treated SGs showed worsening of SG inflammation highlighted by the increment of aggregate size, B-cell density, development of new FDC networks, and a higher ectopic GC prevalence over 48 weeks, compared to RTX-treated patients. No difference in focus score, total T-cell and plasma cell infiltration was observed. RTX downregulated genes involved in immune cell recruitment and inflammatory aggregate organisation (e.g. CXCL13, CCR7 and PDCD1). Gene signature-based analysis of 35 immune cell types using XCell highlighted how RTX blocked class-switched and memory-B-cells accumulation in SGs over 48 weeks. Pathway analyses confirmed the downregulation of leukocyte migration, MHC-II antigen presentation, and T-cell co-stimulation immunological pathways, such as the CD40 receptor complex pathway. Among RTX-treated patients, only CRESS-responders demonstrated prevention of worsening B cell-driven molecular pathology signatures over time and a significant improvement in UWSF, in parallel with the upregulation of molecular pathways associated to SG restoration of the glandular epithelium. None of the above effects were observed at week 16 after the first RTX cycle. Conclusion Two RTX infusions repeated at week 24 exerted beneficial effects on labial SG inflammatory infiltration in pSS by downregulating genes involved in immune cell recruitment, activation and organisation in ectopic GCs. Conversely, all the above parameters showed significant evolution in placebo treated patients over 48 weeks demonstrating progression of SG immunopathology. Clinical responders to RTX based on CRESS response criteria were characterised by preservation of exocrine function which appear driven by SG epithelial restoration. References [1]Fisher, B. A. et al. Effect of rituximab on a salivary gland ultrasound score in primary Sjögren’s syndrome: results of the TRACTISS randomised double-blind multicentre substudy. Ann. Rheum. Dis. 77 , 412–416 (2018). [2]Bowman, S. J. et al. Randomized Controlled Trial of Rituximab and Cost-Effectiveness Analysis in Treating Fatigue and Oral Dryness in Primary Sjögren’s Syndrome. Arthritis Rheumatol. 69 , 1440–1450 (2017). [3]Arends, S. et al. Composite of Relevant Endpoints for Sjögren’s Syndrome (CRESS): development and validation of a novel outcome measure. Lancet Rheumatol. 3 , e553–e562 (2021). Disclosure of Interests None declared

BackgroundTyrosine kinases receptors MerTK and Axl have been implicated in the pathogenesis of several autoimmune diseases. Despite sharing significant structural homology and having common ligands, Axl and MerTK have distinct features and biological functions [1]. A growing body of evidence suggests that both Axl and MerTK play a crucial role in Rheumatoid Arthritis (RA) pathogenesis and progression and may be exploited as novel therapeutic targets [2]. However, numerous unanswered questions remain to be addressed.Objectives:i.To define common and distinct gene-partners of Axl/MerTK and quantify their expression in RA synovial tissue.ii.To assess the co-expression of Axl/MerTK by synovial cells.iii.To outline the longitudinal variation in Axl/MerTK expression upon treatment intervention.MethodsSynovial tissue samples were collected by US-guided synovial biopsy from: i. Patients with early (<12 months) RA DMARDs/steroid-naïve [n=87]; and ii. RA patients who failed the first-line biologic with TNF-inhibitors (TNFi) before and 16 weeks after receiving either Rituximab (RTX) or Tocilizumab (TOC) [n=164] [3]. Gene expression was obtained by bulk RNAseq performed on an Illumina HiSeq2500 platform. Axl-/MerTK-modules were defined using STRING networks and the module expression determined by the mean z-score of regularized log transformed expression for all genes in the set. Axl, MerTK, CD55, CD90, CD68 protein expression was analysed by multiplex immunofluorescence staining.ResultsUsing STRING network analysis, we defined an Axl- and a MerTK-module composed of 31 predicted gene-partners of either Axl or MerTK. Thirteen genes were common to both modules and included the ligands Gas6 and ProteinS, and EGFR. Conversely, eighteen genes were uniquely present in the Axl-module (e.g., PIK3-family, IGF1R, IFNAR1 and STAT3) or the MerTK-module (e.g., Galectin3 and TULP, recently discovered MerTK ligands, FCGR1A/CD64, PTPN1and MEGF10). Axl/MerTK-modules quantified in the early-arthritis treatment-naïve RNAseq dataset showed a significant negative correlation with the synovitis score (Axl r=−0.33, p=0.0032; MerTK r=-0.33, p=0.003). At protein level, CD68+macrophages of the Lining showed notable heterogeneity between patients: they could express either Axl or MerTK alone, or co-express both. Axl was also present in most CD55+ Lining Fibroblast-Like-Cells (FLS) but not by CD90+ Sublining FLS while MerTK, as expected, was restricted to macrophages, including intra-aggregate tingible-body-macrophages.To define how Axl and MerTK vary depending on disease stage and treatment exposure, we quantified their gene expression in active RA patients inadequately responding to TNFi, prior and 16 weeks after starting second-line biologic (RTX or TOC) [3]. Differently from the early-arthritis cohort, MerTK was significantly up-regulated in synovia characterised by higher degree of tissue inflammation (lympho-myeloid > diffuse-myeloid > pauci-immune, p<0.0001) and significantly positively correlated with several cytokines’ genes such as TNF, IL-6, CCL8 and IL-10. MerTK expression was dependent on clinical response to RTX but not TOC as assessed by EULAR response (DAS28CRP, good vs none/mod, FDRresp 0.048). Conversely, Axl expression significantly increased upon IL-6 blockade by TOC independently of the clinical response (FDRtime 0.016).ConclusionOur data further corroborate that Axl and MerTK constitute a dynamic axis influenced by the synovial tissue inflammatory features, the disease stage, the exposure and the response to targeted treatment and the blockade of critical inflammatory pathways over time. A better understanding of the individual features of these tyrosine kinases as well as their interaction would be beneficial to define novel treatment approaches.References[1]Zagórska A, et al. Nat Immunol. 2014 Oct;15(10):920-8[2]Kemble S, Croft AP. Front Immunol. 2021 Sep 3;12:715894[3]Humby F et al. Lancet. 2021 Jan 23;397(10271):305-317AcknowledgementsVersus Arthritis.Disclosure of InterestsNone declared.

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.