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Background/Aims Early diagnosis and prompt and effective intervention for people with immune-mediated inflammatory diseases (IMIDs) such as rheumatoid arthritis (RA) improves outcomes. Non-invasive biomarkers that reflect unique aspects of underlying biology could facilitate this. Extracellular vesicles (EVs) released by many cell types are abundantly present in body fluids where they contribute to intercellular signalling by transferring cargo including microRNAs (miRs) - themselves implicated in disease pathogenesis. We investigated whether serum EV miR expression (i) discriminates patients with newly presenting, untreated RA from those with an alternative IMID or none, (ii) predicts therapeutic response to first-line methotrexate (MTX) treatment amongst RA patients, or, in the same individuals (iii) exhibit dynamically altered expression in MTX-responders versus non-responders during treatment. Methods Using a validated protocol, serum EVs were isolated from 43 early RA patients. Identically isolated EV were available from sera of 23 newly presenting, untreated polymyalgia rheumatica (PMR, “IMID control”) patients and 12 “non-inflammatory control” patients (NICs) in whom significant inflammatory disease had been excluded. miRs were isolated and subject to NanoString sequencing (798 miRs). Following quality control, DESeq2 was first used to identify miRs uniquely deregulated in RA EVs at baseline. Within the early RA cohort, a baseline comparison between individuals who subsequently achieved clinical remission after 6 months treatment with MTX (DAS28-CRP Results A signature of 28 miRs uniquely differentially expressed between early RA patients and NICs (i.e. not also between PMR and NICs) was identified, which was robust to MTC after correction for age and sex. Amongst those upregulated in RA were miR-337-3p, whose predicted target proteins include Jak2 and STAT3, and miR-144-3p, recently shown to mediate the expression of pro-inflammatory cytokines and chemokines by macrophages. Whilst EV miRs differentially expressed at baseline between RA patients who subsequently achieved early MTX-induced remission and those who did not were not robust to MTC, PLS-DA indicated their predictive potential. Finally, several miRs whose change in expression over the course of treatment differed significantly between responders and non-responders; namely miR-548ar-3p, miR-212-3p, miR-188-5p, miR-410-3p, and miR-338-5p amongst others, may offer insight into the mechanism of MTX’s therapeutic action. Conclusion Serum EV miRs are a possible source of clinically valuable biomarkers that may shed light on pathophysiology and/or mechanisms of MTX efficacy. Our findings highlight their potential as diagnostic and prognostic tools in early RA. Larger studies and validation in independent cohorts are warranted to pursue this potential, alongside mechanistic evaluation. Disclosure D. Maunder: None. P.M. Brown: None. B. Barron-Millar: None. D. Lendrem: None. N. Naamane: None. J. Macdonald: None. X. Wang: None. A.E. Anderson: None. A.W. Morgan: None. S.L. Mackie: Consultancies; Roche/Chugai, Sanofi, AbbVie and AstraZeneca. Honoraria; Pfizer, Vifor and UCB. Grants/research support; Vifor. R. Crossland: None. A.G. Pratt: Consultancies; Inflection Biosciences.

Age-associated B cells (ABCs) are an immune cell subset linked to autoimmunity, infection and ageing, and whose pathophysiological importance was recently highlighted using single cell synovial tissue profiling. To elucidate their pathophysiological relevance, peripheral blood (PB) ABCs from early rheumatoid arthritis (eRA) patients naïve to disease-modifying anti-rheumatic drugs (DMARDs) were compared with their synovial fluid (SF) counterparts, and to PB ABCs from psoriatic arthritis patients and healthy controls. PB and SF B-cell subsets were phenotyped by multi-parameter flow cytometry, sorted and subjected to gene expression profiling (NanoString nCounter® Immunology V2 Panel) and functional characterization (stimulated cytokine measurements by immunoassay). PB ABCs of eRA patients, which are transcriptionally distinct from those of control cohorts, express chemokine receptors and adhesion molecules, such as CXCR3, that favour homing to inflammatory sites over lymphoid tissue. These cells are an activated, class-switched B-cell subset expressing high levels of HLA-DR, co-stimulatory molecules and T-bet. Their secretion profile includes IL-12p70 and IL-23 but low levels of IL-10. High surface expression of FcRL family members, including FcRL3, furthermore suggests a role for these cells in autoimmunity. Finally, and unlike in the periphery where they are rare, ABCs are the predominant B-cell subsets in SF. These observations indicate the predilection of ABCs for inflammatory tissue in RA, where their propensity for antigen presentation and pro-inflammatory phenotype may support autoimmune pathology. Their potential as a therapeutic target therefore warrants further study.

Bayesian networks can be used to identify possible causal relationships between variables based on their conditional dependencies and independencies, which can be particularly useful in complex biological scenarios with many measured variables. Here we propose two improvements to an existing method for Bayesian network analysis, designed to increase the power to detect potential causal relationships between variables (including potentially a mixture of both discrete and continuous variables). Our first improvement relates to the treatment of missing data. When there is missing data, the standard approach is to remove every individual with any missing data before performing analysis. This can be wasteful and undesirable when there are many individuals with missing data, perhaps with only one or a few variables missing. This motivates the use of imputation. We present a new imputation method that uses a version of nearest neighbour imputation, whereby missing data from one individual is replaced with data from another individual, their nearest neighbour. For each individual with missing data, the subsets of variables to be used to select the nearest neighbour are chosen by sampling without replacement the complete data and estimating a best fit Bayesian network. We show that this approach leads to marked improvements in the recall and precision of directed edges in the final network identified, and we illustrate the approach through application to data from a recent study investigating the causal relationship between methylation and gene expression in early inflammatory arthritis patients. We also describe a second improvement in the form of a pseudo-Bayesian approach for upweighting certain network edges, which can be useful when there is prior evidence concerning their directions., Author summary Data analysis using Bayesian networks can help identify possible causal relationships between measured biological variables. Here we propose two improvements to an existing method for Bayesian network analysis. Our first improvement relates to the treatment of missing data. When there is missing data, the standard approach is to remove every individual with any missing data before performing analysis, even if only one or a few variables are missing. This is undesirable as it can reduce the ability of the approach to infer correct relationships. We propose a new method to instead fill in (impute) the missing data prior to analysis. We show through computer simulations that our method improves the reliability of the results obtained, and we illustrate the proposed approach by applying it to data from a recent study in early inflammatory arthritis. We also describe a second improvement involving the upweighting of certain network edges, which can be useful when there is prior evidence concerning their directions.

Background/Aims Early diagnosis and intervention improves outcomes of immune mediated rheumatic and musculoskeletal diseases (RMDs) but may be hampered by diagnostic uncertainty. The extent to which rationally selected molecular parameters add value to clinical characteristics for diagnostic prediction in undifferentiated disease states warrants investigation. B lymphocytes play an increasingly recognised role in rheumatoid arthritis (RA) pathogenesis, and cell-specific methylation patterns link environmental exposures to genetic risk. We derived and tested the practical utility of a B lymphocyte-derived DNA methylation signature for predicting RA in an early arthritis clinic cohort. Methods CD19+ B cell and peripheral blood mononuclear cell (PBMC) whole genome DNA methylation array data were available, respectively, from 109 inflammatory arthritis patients naïve to immunomodulatory drugs (Newcastle, UK; 38% confirmed to have a diagnosis of RA within 1 year) and 50 untreated undifferentiated arthritis (UA) patients (Leiden, The Netherlands; 68% classifiable RA within 1 year by 1987 ACR criteria versus alternate diagnoses). A bespoke machine learning pipeline employed a sequential model-based optimisation (SMBO) procedure for selecting, tuning and applying methods amongst ten feature-selection, six data-sampling and two classification algorithms in the Newcastle “training cohort.” The predictive performance of the resultant optimised molecular classifier was assessed in the independent Leiden “test cohort” alongside a previously described clinical prediction rule, using comparative area under receiver operating characteristic (AUROC) curves. A modification to the clinical prediction rule that incorporated a single parameter to reflect molecular classification was also assessed. The pipeline was implemented using the R machine learning package mlr. Results Using the SMBO approach, 27 CpGs maximally discriminatory for RA were selected from B lymphocyte DNA methylome training data, and a molecular classifier was derived using the random forest algorithm. Applied to the independent PBMC methylome in UA patients, the classifier and the validated Leiden prediction rule performed similarly in predicting RA (AUROC [95% CI] = 0.8 [0.66-0.94] versus 0.78 [0.64-0.92]). Interestingly, incorporating a molecular risk score based on the 27-CpG signature into the validated Leiden clinical prediction rule significantly improved its performance (AUROC [95% CI] = 0.89 [0.79-0.98] versus 0.78 [0.64-0.92]; p = 0.048). When applied to the sub-cohort of 25 patients in the Leiden cohort who were negative for anti-citrullinated peptide autoantibodies (ACPA), enhanced performance of the modified over the un-modified clinical prediction rule was maintained (AUROC [95% CI] = 0.82 [0.65-1] versus 0.70 [0.45-0.95], respectively), although the difference did not reach statistical significance in this smaller cohort. Conclusion We provide a proof of principle for the application of a B lymphocyte-derived epigenetic signature to enhance prediction of RA in UA patients using stored PBMCs. Further refinement of our pipeline represents a plausible means to expedite the diagnosis in undifferentiated RMDs and could offer pathophysiological insight. Disclosure N. Naamane: None. E. Niemantsverdriet: None. N. Thalayasingam: None. N. Nair: None. A.D. Clark: None. K. Murray: None. B. Hargreaves: None. L.N. Reynard: None. S. Eyre: None. A. Barton: None. A.H.M. van der Helm-van Mil: None. A.G. Pratt: None.

BACKGROUND: In addition to their roles in different biological processes, microRNAs in the tumor microenvironment appear to be potential diagnostic and prognostic biomarkers for various malignant diseases, including acute myeloid leukemia (AML). To date, no screening of circulating miRNAs has been carried out in the bone marrow compartment of AML. Accordingly, we investigated the circulating miRNA profile in AML bone marrow at diagnosis (AMLD) and first complete remission post treatment (AMLPT) in comparison to healthy donors (HD). METHODS: Circulating miRNAs were isolated from AML bone marrow aspirations, and a low-density TaqMan miRNA array was performed to identify deregulated miRNAs followed by quantitative RT-PCR to validate the results. Bioinformatic analysis was conducted to evaluate the diagnostic and prognostic accuracy of the highly and significantly identified deregulated miRNA(s) as potential candidate biomarker(s). RESULTS: We found several deregulated miRNAs between the AMLD vs. HD vs. AMLPT groups, which were involved in tumor progression and immune suppression pathways. We also identified significant diagnostic and prognostic signatures with the ability to predict AML patient treatment response. CONCLUSIONS: This study provides a possible role of enriched circulating bone marrow miRNAs in the initiation and progression of AML and highlights new markers for prognosis and treatment monitoring., SCOPUS: ar.j, info:eu-repo/semantics/published

Background The aetiology of rheumatoid arthritis (RA) is complex. In particular, the vast majority of disease-associated variants implicated by genome-wide association studies are non-coding, leaving genetic mechanisms of adaptive immune dysregulation unresolved. The contribution to this process of epigenetic factors, including the addition of methyl groups to DNA, also remains uncertain. To address these issues and prioritise causal genes for downstream study, genome-wide data incorporating DNA methylation and gene expression measurements from lymphocyte subsets in an early arthritis inception cohort, were available. Methods Whole genome methylation and transcription data from isolated CD4+ T cells and B cells of > 100 well-characterised inflammatory arthritis patients, all of whom were naïve to immunomodulatory treatments and of Northern European ancestry, were obtained (Illumina HumanHT-12 v4 Expression BeadChip and Infinium MethylationEPIC BeadChip arrays, respectively). Genotyping was undertaken using the Illumina Human CoreExome-24 version 1-0 array. After independent pre-processing, normalisation and quality control of paired CD4+ and B lymphocyte data, methylation quantitative trait loci (meQTLs) were first modelled using the MatrixEQTL package in each cell type. Next, at RA risk-associated cis-CpGs, correlations between site-specific methylation and the expression of genes within ±500Kb identified quantitative trait methylations (eQTMs). To infer directionality of SNP-CpG-transcript associations a causal inference test (CIT) was applied. Multiple testing was accounted for, and in vitro assays were used to validate meQTLs at loci of interest and confirm regulatory mechanisms. Further analysis integrated GWAS data from other immune mediated diseases (IMDs) and additional publically available resources. Results We found strong evidence that disease-associated DNA variants regulate cis-CpG methylation of DNA in CD4+ T and/or B cells at 37% RA loci. In general we observed these variants to preferentially modify methylation at sites mapping to lymphocyte enhancers and regions flanking transcription start sites, and at positions bound by the NFκB transcription factor. Using paired, cell-specific transcriptomic data and a statistical approach to infer causality, we then identified examples where site-specific DNA methylation in turn mediates gene expression, including ORMDL3/GSDMB, IL6ST/ANKRD55, FCRL3 and JAZF1 in CD4+ lymphocytes. Leveraging GWAS data we noted that a number of genes regulated in this way highlight mechanisms common to RA, multiple sclerosis and asthma, distinguishing these IMDs from osteoarthritis which is considered a primarily degenerative disease. To validate our findings, cis-meQTL effects at sentinel loci were replicated by pyrosequencing in an independent cohort of genotyped early arthritis patients, and methylation-mediated regulation of FCRL3 expression downstream of the regulatory SNP was confirmed experimentally using a luciferase reporter assay in Jurkat T-cells. Conclusion Our observations highlight important mechanisms of genetic risk in RA and the wider context of autoimmunity. They confirm the utility of DNA methylation profiling as a tool for causal gene prioritisation and, potentially, therapeutic targeting in complex IMD. Disclosures A.D. Clark None. N. Nair None. A.E. Anderson None. N. Thalayasingam None. N. Naamane None. A.J. Skelton None. J. Diboll None. A. Barton None. S. Eyre None. J.D. Isaacs None. L.N. Reynard None. A.G. Pratt Grants/research support; I am a recipient of an unrestricted, investigator initiated research grant from Pfizer, paid to Newcastle University.

The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) of the ROC curve are widely used in discovery to compare the performance of diagnostic and prognostic assays. The ROC curve has the advantage that it is independent of disease prevalence. However, in this note, we remind scientists and clinicians that the performance of an assay upon translation to the clinic is critically dependent upon that very same prevalence. Without an understanding of prevalence in the test population, even robust bioassays with excellent ROC characteristics may perform poorly in the clinic. While the exact prevalence in the target population is not always known, simple plots of candidate assay performance as a function of prevalence rate give a better understanding of the likely real-world performance and a greater understanding of the likely impact of variation in that prevalence on translation to the clinic.

Background: Defining optimal strategies for translating “big datasets” of transcriptome and methylome data into clinically valuable tools in RA will benefit from comparisons where potential confounders – including therapeutic background, disease phase and cell substrate heterogeneity – are controlled as far as possible. Objectives: We have obtained paired B- and CD4+ T-lymphocyte whole genome expression and methylation data from drug-naive early arthritis clinic patients at a single centre. Focussing on B-lymphocyte data we here ask which of these datasets has most value in discriminating early RA – and the additive value of combining them. Methods: CD19+ B-lymphocytes were isolated by positive selection from fresh peripheral blood of 90 drug-naive patients attending the Newcastle Early Arthritis Clinic (NEAC), comprising 36 RA patients and 54 disease controls matched, so far as possible, for age, sex and acute phase response. Paired RNA and DNA extracted. Gene expression was profiled using the Human HT12 v4 BeadChip, and DNA methylation at >850,000 CpG sites quantified with the MethylationEPIC array (both Illumina). Gene expression and/or DNA methylation classifiers for RA prediction were developed based on a combined approach of classification algorithm selection and hyperparameter optimisation. The Sequential Model-Based Optimization procedure was used to automatically select the best model configuration amongst 10 feature selection filters, 2 class imbalance correction techniques, 3 data cleaning methods and 4 classification algorithms along with their respective hyperparameters. Model optimisation was performed using a 10-fold Cross Validation (CV) while a 10 times repeated 10-fold CV was used for the predictive performance evaluation. Receiver operating characteristic (ROC) and precision recall (PR) curves were used to directly compare the classification potential of molecular approaches. Results: 12,035 gene expression features and 714,486 methylation features were available for classifier building after quality control in our early arthritis B-lymphocyte dataset. Performance estimation showed the expression profile of B-lymphocytes alone to have no significant discriminatory utility for RA in this setting (average area under [AU] ROC 0.48). Methylation profiles were significantly superior for discriminating RA (average AU ROC 0.71). Interestingly, when a combined trancriptome/methylome model was generated, further enhancement of discriminatory function was observed, although this was modest (AU ROC 0.74). Conclusion: Methylation profiling of B-lymphocytes affords better discrimination for RA than does transcriptional profiling. This may differ in other cell types. Combining data from molecular platforms may be an optimal approach. Functional analyses of the resultant B-lymphocyte signatures may yield insight into molecular pathobiology. Acknowledgement: Versus Arthritis Centres of Excellence for Rheumatoid Arthritis Pathogeneisis and Genetics Disclosure of Interests: Najib Naamane: None declared, Nishanthi Thalayasingam: None declared, Nisha Nair: None declared, Alexander Clar: None declared, Amy Anderson: None declared, Dennis Lendrem: None declared, Louise Reynard: None declared, Stephen Eyre: None declared, Anne Barton: None declared, John Isaacs Grant/research support from: Pfizer, Grant/research support from: Pfizer, Consultant for: Abbvie, Pfizer, Roche, Galvani, Merck, Gilead, Eli Lilly, Amgen, Janssen, Celltrion, NAPP, Consultant for: Abbvie, Pfizer, Roche, Galvani, Merck, Gilead, Eli Lilly, Amgen, Janssen, Celltrion, NAPP, Speakers bureau: Abbvie, Pfizer, Eli Lilly, Speakers bureau: Abbvie, Pfizer, Eli Lilly, Arthur Pratt Grant/research support from: Dr. Pratt is in receipt of an externally peer-reviewed Investigator Initiated Research grant from Pfizer (£66,000)., Grant/research support from: Dr. Pratt is in receipt of an externally peer-reviewed Investigator Initiated Research grant from Pfizer (£66,000)., Speakers bureau: Dr Pratt has received honoraria from Eli Lilly and Janssen-Cilag Ltd. for his time in preparing presentations for non-promotional meetings that have been paid directly to Newcastle University., Speakers bureau: Dr Pratt has received honoraria from Eli Lilly and Janssen-Cilag Ltd. for his time in preparing presentations for non-promotional meetings that have been paid directly to Newcastle University.

Background Endogenous retroelements of which LINE1, Alu and LTR are the main subsets, constitute approximately 50% of the human genome. They are mobile genetic elements derived from the historical genomic integration of retroviruses. Whilst most are inactive, some expression of viral mRNA and subsequent protein is retained. This theoretically could promote immune activation, for example by triggering type 1 interferon (IFN-I) production and notably Alu has been identified as a target for Ro60 autoantibody. Retroelements may therefore provide a mechanism whereby tolerance is breached in autoimmunity. Objectives To examine the potential clinical impact of retroelement activity in early drug naive rheumatoid arthritis (eRA). This stage of disease was chosen to minimise therapeutic confounding. Methods Drug-naive eRA and healthy controls (HC) were recruited and clinical characteristics including disease activity, immunoglobulins (IgA, IgG, IgM), ESR, CRP, rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) autoantibody titres documented. Serum cytokines (IFN-γ, IL-6, IL-12 p70, TNF-α, IL-1β, IL-2, IL-13, IL-4, IL-10) were also measured. Whole blood (WB) RNA was extracted and expression of an IFN-I gene signature (IGS: MxA, IFI6, OAS1, ISG15, IFI44L) and LINE1 5’UTR activity quantified using RT-PCR. In addition anti-CCP and RF positive (double seropositive) eRA peripheral blood plasmacytoid dendritic cells (pDCs), myeloid DCs, CD19+ B cells, monocytes, CD4+ and CD8+ T cells were flow cytometrically sorted. CD19+ B cell subsets were further sorted from double seropositive eRA and disease controls (early, drug naive, psoriatic arthritis, PsA) into CD5+ B cells, age associated B cells (CD19hiCD11c+CD21-), naive (IgD+CD27-) and memory (IgD-CD27+) B cells. Cell specific expression of LINE1, Alu and LTR was quantified by NanoString nCounter and analysed using R Core Team (2013). WB RNA analysis for continuous and categorical variables included linear regression and Mann-Whitney U tests respectively (GraphPad Prism, significance p Results WB LINE1 expression for 56 eRA patients (median age 58 [range 30-87], male:female 3:4, 77% seropositive for either RF or anti-CCP) and 23 HC (median age 39 [range 23-62], male:female 1:1) was obtained. WB LINE1 activity was not associated with age or gender and was comparable between cohorts. However in eRA a unique, robust positive association between WB LINE1 and anti-CCP titres was identified (see table and figure). Additionally, when comparing eRA circulating cell subsets, B cells had the highest expression of Alu, LINE1 and LTR (n=8, median age 56 [range 49-64], male:female 1:1). Furthermore, there was significantly increased expression of all retroelement classes in eRA CD5+ and naive B cells compared with PsA (n=4, male:female 1:1 and median age 62 for both with age range [63-78] and [60-80] respectively). Conclusion This is the first time peripheral blood retroelement activity has been examined in both HC and RA. It highlights intriguing, IFN-I independent, associations between anti-CCP titres, autoantibody producing B cells (CD5+) and retroelements. We suggest retroelements may play a more extensive role in autoantibody generation and may ultimately provide a novel target in the treatment or prevention of RA. Disclosure of Interests Faye Cooles: None declared, Gemma Vidal Pedrola: None declared, Amy Anderson: None declared, Najib Naamane: None declared, Andrew Skelton: None declared, Arthur Pratt Grant/research support from: Dr. Pratt is in receipt of an externally peer-reviewed Investigator Initiated Research grant from Pfizer (£66,000)., Grant/research support from: Dr. Pratt is in receipt of an externally peer-reviewed Investigator Initiated Research grant from Pfizer (£66,000)., Speakers bureau: Dr Pratt has received honoraria from Eli Lilly and Janssen-Cilag Ltd. for his time in preparing presentations for non-promotional meetings that have been paid directly to Newcastle University., Speakers bureau: Dr Pratt has received honoraria from Eli Lilly and Janssen-Cilag Ltd. for his time in preparing presentations for non-promotional meetings that have been paid directly to Newcastle University., Catharien Hilkens: None declared, Ruchi Shukla: None declared, John Isaacs Grant/research support from: Pfizer, Grant/research support from: Pfizer, Consultant for: Abbvie, Pfizer, Roche, Galvani, Merck, Gilead, Eli Lilly, Amgen, Janssen, Celltrion, NAPP, Consultant for: Abbvie, Pfizer, Roche, Galvani, Merck, Gilead, Eli Lilly, Amgen, Janssen, Celltrion, NAPP, Speakers bureau: Abbvie, Pfizer, Eli Lilly, Speakers bureau: Abbvie, Pfizer, Eli Lilly

Background Defining regulatory mechanisms through which noncoding risk variants influence the cell-mediated pathogenesis of immune-mediated disease (IMD) has emerged as a priority in the post–genome-wide association study era. Objectives With a focus on rheumatoid arthritis, we sought new insight into genetic mechanisms of adaptive immune dysregulation to help prioritize molecular pathways for targeting in this and related immune pathologies. Methods Whole-genome methylation and transcriptional data from isolated CD4+ T cells and B cells of more than 100 genotyped and phenotyped patients with inflammatory arthritis, all of whom were naive to immunomodulatory treatments, were obtained. Analysis integrated these comprehensive data with genome-wide association study findings across IMDs and other publicly available resources. Results We provide strong evidence that disease-associated DNA variants regulate cis-CpG methylation in CD4+ T and/or B cells at 37% RA loci. Using paired, cell-specific transcriptomic data and causal inference testing, we identify examples where site-specific DNA methylation in turn mediates gene expression, including FCRL3 in both cell types and ORMDL3/GSDMB, IL6ST/ANKRD55, and JAZF1 in CD4+ T cells. A number of genes regulated in this way highlight mechanisms common to RA and other IMDs including multiple sclerosis and asthma, in turn distinguishing them from osteoarthritis, a primarily degenerative disease. Finally, we corroborate the observed effects experimentally. Conclusions Our observations highlight important mechanisms of genetic risk in RA and the wider context of immune dysregulation. They confirm the utility of DNA methylation profiling as a tool for causal gene prioritization and, potentially, therapeutic targeting in complex IMD., Graphical abstract

Background:Rheumatoid arthritis (RA) is a chronic inflammatory disease of immune dysregulation affecting the joints. While T cells play a recognised role in disease pathogenesis, the presence of autoantibodies years before the clinical onset of disease, and the efficacy of the B cell-depleting therapy rituximab, highlight a pathogenic role for these functionally diverse lymphocytes. A novel subset, termed age-associated B cells (ABCs), are described as CD19high CD21- CD11c+, with a high proportion expressing the transcription factor T-bet; they are elevated in murine models of autoimmunity and produce autoantibodies characteristic of autoimmune disease. A detailed characterisation of ABCs in early, drug naïve RA has not yet been conducted.Objectives:We aimed to characterise peripheral blood (PB) and synovial fluid (SF) ABCs in patients suffering from early drug naïve RA. As a secondary objective we sought to determine whether this population differs between RA patients and age-matched early psoriatic arthritis (PsA; disease controls) and healthy controls.Methods:Newly presenting early RA and other inflammatory arthritis patients, naïve to immunomodulatory treatment, were recruited from the Newcastle Early Arthritis Clinic. Patients with established RA (≥1 year duration on treatment and age-matched healthy controls were recruited in parallel. B cell subsets (naïve (CD19+IgD+CD27-), switched memory (CD19+IgD-CD27+) and ABC (CD19+CD21-CD11c+)) in PB and SF were characterised by flow cytometry. FACS sorted PB B cell subset gene expression profiles were assessed using a customised NanoString nCounter Human Immunology v2 Panel.Results:Transcriptionally, ABC differed from both naïve and switched memory B cells. In keeping with published studies ABCs had an activated memory phenotype, exemplified by elevated expression of CD69, CD80, and CD86, as well as Ki67, HLA-DR, IgG and T-bet. Interestingly, ABC had high expression of the Fc Receptor Like (FcRL) family (FcRL1-5), and an inflammatory homing profile (high levels of CXCR3 and low levels of CXCR4 and CXCR5). We found no difference in the proportion of PB ABC between RA patients and control groups, and no association of ABC frequencies with age. Focussing on RA ABCs specifically, we observed elevated ABC frequencies in females, but no association with disease activity. In keeping with their chemokine receptor profile, cross-sectional analysis also demonstrated ABC enrichment in SF compared to PB, with SF frequencies higher in established than early disease. The transcriptome of ABCs from early RA patients differed from both age-matched disease controls and healthy donors (Figure 1 next page).Conclusion:These data demonstrate that ABCs have a unique, activated, class-switched proliferative phenotype that is transcriptionally distinct from switched memory and naïve B cells. Interestingly, at the transcriptome level early RA ABCs differ to their counterparts in health and other inflammatory arthritides, suggesting they may differ functionally and contribute to pathogenesis. Of note, their high expression of MHC class II (HLA-DR) and co-stimulatory (CD80 and CD86) molecules suggests an important antigen-presentation function of ABC, which together with their unique FcRL family expression pattern, warrants further functional characterisation.Figure 1.Differential gene expression in ABCs from early RA patients compared to early PsA patients (A) and healthy controls (B). A NanoString nCounter Technologies chip was used to assess gene expression. Raw counts were normalised to the housekeeping genes. Sample quality was then assessed using the arrayQualityMetrics package. Gene expression profiles between different donor groups was assessed using the DESeq2 R package. In both hierarchical clustering heatmaps gene expression intensities were log2 transformed and their z-scores are displayed as colours ranging from yellow (low expression) to red (high expression) as shown in the key.Disclosure of Interests:Gemma Vidal-Pedrola: None declared, Najib Naamane: None declared, Dagmar Scheel-Toellner: None declared, Arthur Pratt Grant/research support from: GSK, Pfizer and Janssen, Andrew Mellor: None declared, John D Isaacs Speakers bureau: Abbvie, Gilead, Roche, UC, Consultant of: Abbvie, Gilead, Roche, UC, Grant/research support from: Pfizer, GSK and Janssen, Amy E. Anderson Grant/research support from: Pfizer, GSK and Janssen

Background:DNA methylation patterns differ between leukocyte subsets and mediate the impact of environmental exposures on the molecular and functional phenotype of immune cells. Besides differences in mean methylation of CpG positions amongst patients with immune mediated diseases, recent evidence indicates variability of site-specific DNA methylation also contributes to pathogenesis1,2.Objectives:To seek evidence of altered DNA methylation patterns in RA, controlling for systemic inflammation and immunotherapy use.Methods:Patients with confirmed clinical diagnoses were enrolled from the Northeast Early Arthritis Cohort (NEAC). CD4+and CD19+lymphocytes were isolated from fresh blood by positive selection prior to therapeutic immune modulation. Methylation was quantified in cell subset-specific DNA (Infinium MethylationEPIC BeadChip, Illumina)3. Differentially methylated positions and regions (DMPs, DMRs) between RA and non-RA patients were identified (linear modelling, filtering on 5% pairwise difference in mean DNA methylation, and DMRcate package). Next, to identify instances where methylation variance differed between comparator groups, Bartlett’s test was performed using the iEVORA package, which accounts for outlier values4. Findings were controlled for technical confounders and subject to multiple test correction (FDR). A validated hypergeometric test was used to annotate enriched pathways.Results:After sample- and probe-level quality control, CD4+ and B lymphocyte specific data were respectively available for 45 and 49 RA patients, and 64 and 81 disease controls matched for systemic inflammation (CRP, ESR). No DMPs were identified in either cell type at FDR < 0.05 and Δβ ≥0.05. Only following relaxation of multiple test correction was it possible to identify DMRs in either cell type, most notably encapsulating 10 CpGs relatively hypomethylated at the promoter of the endosome protein-encodingRUFY1gene in CD4+ lymphocytes of RA patients (Δβ = 0.076). By contrast, striking evidence for differential variation in DNA methylation was observed at 291 and 601 CpGs of CD4+ and B lymphocytes, respectively (exemplars depicted in Figure 1). Only 15 of these differentially variable positions (DVPs) were common to both cell types. Pathway analysis highlighted potential functional consequences of DVP associations; for example, RA-specific hypervariability implicates prostaglandinsignalling in CD4+ lymphocytes.Conclusion:We highlight a role for altered variability in DNA methylation during the molecular pathogenesis of RA, and emphasise the importance of its study in relevant cell subsets.References:[1]Paul DSet al. Nature Communications 7, 13555 doi: 10.1038/ncomms13555 (2016).[2]Webster AP et al. Genome Medicine 10, 64 (2018)doi:10.1186/s13073-018-0575-9.[3]Clark AD et al. Journal of Allergy and Clinical Immunology 2019; doi: 10.1016/j.jaci.2019.12.910[4]Teschendorff AE et al. Nature Communications 2016; 7:12.Disclosure of Interests:Alexander Clark: None declared, Najib Naamane: None declared, Nisha Nair: None declared, Amy Anderson: None declared, Nishanthi Thalayasingam: None declared, Julie Diboll: None declared, Anne Barton Consultant of: AbbVie, Stephen Eyre: None declared, John D Isaacs Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Gilead, Janssen, Merck, Pfizer, Roche, Louise Reynard: None declared, Arthur Pratt Grant/research support from: Pfizer, GlaxoSmithKlein

Diets rich in saturated fats may contribute to the loss of pancreatic β-cells in type 2 diabetes. JunB, a member of the activating protein 1 (AP-1) transcription factor family, promotes β-cell survival and mediates part of the beneficial effects of GLP-1 agonists. In this study we interrogated the molecular mechanisms involved in JunB-mediated β-cell protection from lipotoxicity. The saturated fatty acid palmitate decreased JunB expression, and this loss may contribute to β-cell apoptosis, as overexpression of JunB protected cells from lipotoxicity. Array analysis of JunB-deficient β-cells identified a gene expression signature of a downregulated endoplasmic reticulum (ER) stress response and inhibited AKT signaling. JunB stimulates XBP1 expression via the transcription factor c/EBPδ during ER stress, and forced expression of XBP1s rescued the viability of JunB-deficient cells, constituting an important antiapoptotic mechanism. JunB silencing inhibited AKT activation and activated the proapoptotic Bcl-2 protein BAD via its dephosphorylation. BAD knockdown reversed lipotoxic β-cell death potentiated by JunB siRNA. Interestingly, XBP1s links JunB and AKT signaling as XBP1 knockdown also reduced AKT phosphorylation. GLP-1 agonists induced cAMP-dependent AKT phosphorylation leading to β-cell protection against palmitate-induced apoptosis. JunB and XBP1 knockdown or IRE1 inhibition decreased AKT activation by cAMP, leading to β-cell apoptosis. In conclusion, JunB modulates the β-cell ER stress response and AKT signaling via the induction of XBP1s. The activation of the JunB gene network and the crosstalk between the ER stress and AKT pathway constitute a crucial defense mechanism by which GLP-1 agonists protect against lipotoxic β-cell death. These findings elucidate novel β-cell-protective signal transduction in type 2 diabetes.

In addition to genetic predisposition, environmental and lifestyle factors contribute to the pathogenesis of type 2 diabetes (T2D). Epigenetic changes may provide the link for translating environmental exposures into pathological mechanisms. In this study, we performed the first comprehensive DNA methylation profiling in pancreatic islets from T2D and non-diabetic donors. We uncovered 276 CpG loci affiliated to promoters of 254 genes displaying significant differential DNA methylation in diabetic islets. These methylation changes were not present in blood cells from T2D individuals nor were they experimentally induced in non-diabetic islets by exposure to high glucose. For a subgroup of the differentially methylated genes, concordant transcriptional changes were present. Functional annotation of the aberrantly methylated genes and RNAi experiments highlighted pathways implicated in β-cell survival and function; some are implicated in cellular dysfunction while others facilitate adaptation to stressors. Together, our findings offer new insights into the intricate mechanisms of T2D pathogenesis, underscore the important involvement of epigenetic dysregulation in diabetic islets and may advance our understanding of T2D aetiology.

Beta cell dysfunction and death in type 1 diabetes mellitus (T1DM) is caused by direct contact with activated macrophages and T lymphocytes and by exposure to soluble mediators secreted by these cells, such as cytokines and nitric oxide. Cytokine-induced apoptosis depends on the expression of pro- and anti-apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and "effector" gene networks regulated by interleukin-1beta and/or interferon-gamma in beta cells. This suggests that beta cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF-kappaB (previously shown to be pivotal for beta cell apoptosis) in 15 temporally related gene clusters, identified by time-course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer-based findings must be validated by use of positive and negative controls, and by "ChIP on chip" analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a "proof of principle" for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine-regulated beta cell gene networks leading to beta cell apoptosis in T1DM.

Introduction: Vitamin D deficiency has been linked to type 1 and 2 diabetes, whereas supplementation may prevent both diseases. However, the extent of the effects of vitamin D or its metabolites directly on pancreatic islets is still largely unknown. The aim of the present study was to investigate how active vitamin D, 1,25(OH) 2D 3, affects beta cells directly by establishing its effects on global gene expression in healthy murine islets. Materials and methods: Pancreatic islets were isolated from 2 to 3 week old C57BL/6 mice and cultured in vitro with 1,25(OH) 2D 3 or vehicle for 6 and 24 h. Total RNA was extracted from the islets and the effects on global gene expression were analyzed using Affymetrix microarrays. Results and discussion: Exposure to 1,25(OH) 2D 3 compared to vehicle resulted in 306 and 151 differentially expressed genes after 6 and 24 h, respectively (n = 4,1.3-fold, p 0.02). Of these 220 were up-regulated, whereas 86 displayed a decreased expression after 6 h. Furthermore, expression levels were increased for 124 and decreased for 27 genes following 24 h of exposure. Formation of intercellular junctions, cytoskeletal organization, and intracellular trafficking as well as lipid metabolism and ion transport were among the most affected gene classes. Effects on several genes already identified as being part of vitamin D signaling in other cell types were observed along with genes known to affect insulin release, although with our assay we were not able to detect any effects of 1,25(OH) 2D 3 on glucose-stimulated insulin release from healthy pancreatic islets. Conclusion: The effects of 1,25(OH) 2D 3 on the expression of cytoskeletal and intracellular trafficking genes along with genes involved in ion transport may influence insulin exocytosis. However, an effect of 1,25(OH) 2D 3 on insulin release could not be detected for healthy islets in contrast to islets subjected to pathological conditions such as cytokine exposure and vitamin D deficiency as suggested by other studies. Thus, in addition to previously identified tolerogenic effects on the immune system, 1,25(OH) 2D 3 may affect basic functions of pancreatic beta cells, with the potential to render them more resistant to the detrimental conditions encountered during type 1 and 2 diabetes.

Environmental factors such as diets rich in saturated fats contribute to dysfunction and death of pancreatic β-cells in diabetes. Endoplasmic reticulum (ER) stress is elicited in β-cells by saturated fatty acids. Here we show that palmitate-induced β-cell apoptosis is mediated by the intrinsic mitochondrial pathway. By microarray analysis, we identified a palmitate-triggered ER stress gene expression signature and the induction of the BH3-only proteins death protein 5 (DP5) and p53-upregulated modulator of apoptosis (PUMA). Knockdown of either protein reduced cytochrome c release, caspase-3 activation, and apoptosis in rat and human β-cells. DP5 induction depends on inositol-requiring enzyme 1 (IRE1)-dependent c-Jun NH₂-terminal kinase and PKR-like ER kinase (PERK)-induced activating transcription factor (ATF3) binding to its promoter. PUMA expression is also PERK/ATF3-dependent, through tribbles 3 (TRB3)-regulated AKT inhibition and FoxO3a activation. DP5(-/-) mice are protected from high fat diet-induced loss of glucose tolerance and have twofold greater pancreatic β-cell mass. This study elucidates the crosstalk between lipotoxic ER stress and the mitochondrial pathway of apoptosis that causes β-cell death in diabetes., info:eu-repo/semantics/published

Destruction of insulin-producing pancreatic β-cells by local autoimmune inflammation is a hallmark of type 1 diabetes. Histochemical analysis of pancreases from non-obese diabetic mice indicated activation of the transcription factor JunB/AP-1 (activator protein-1) after autoimmune infiltration of the islets. In vitro studies demonstrated that the cytokines tumor necrosis factor (TNF)-α and interferon (IFN)-γ induce JunB expression as a protective mechanism against apoptosis in both human and rodent β-cells. The gene network affected was studied by microarray analysis showing that JunB regulates nearly 20% of the cytokine-modified β-cell genes, including the transcription factor ATF3. Direct transcriptional induction of ATF3 by JunB is a key event for β-cell survival after TNF-α+IFN-γ treatment. Moreover, pharmacological upregulation of JunB/ATF3 via increased cAMP protected rodent primary β-cells and human islet cells against pro-inflammatory mediators. These results were confirmed in genetically modified islets derived from Ubi-JunB transgenic mice. Our findings identify ATF3 as a novel downstream target of JunB in the survival mechanism of β-cells under inflammatory stress. Destruction of insulin-producing pancreatic β-cells by local autoimmune inflammation is a hallmark of type 1 diabetes. Histochemical analysis of pancreases from non-obese diabetic mice indicated activation of the transcription factor JunB/AP-1 (activator protein-1) after autoimmune infiltration of the islets. In vitro studies demonstrated that the cytokines tumor necrosis factor (TNF)-α and interferon (IFN)-γ induce JunB expression as a protective mechanism against apoptosis in both human and rodent β-cells. The gene network affected was studied by microarray analysis showing that JunB regulates nearly 20% of the cytokine-modified β-cell genes, including the transcription factor ATF3. Direct transcriptional induction of ATF3 by JunB is a key event for β-cell survival after TNF-α+IFN-γ treatment. Moreover, pharmacological upregulation of JunB/ATF3 via increased cAMP protected rodent primary β-cells and human islet cells against pro-inflammatory mediators. These results were confirmed in genetically modified islets derived from Ubi-JunB transgenic mice. Our findings identify ATF3 as a novel downstream target of JunB in the survival mechanism of β-cells under inflammatory stress.Oncogene advance online publication, 15 August 2011; doi:10.1038/onc.2011.353.

Cytokines produced by islet-infiltrating immune cells induce β-cell apoptosis in type 1 diabetes. The IFN-γ-regulated transcription factors STAT1/IRF-1 have apparently divergent effects on β-cells. Thus, STAT1 promotes apoptosis and inflammation, whereas IRF-1 down-regulates inflammatory mediators. To understand the molecular basis for these differential outcomes within a single signal transduction pathway, we presently characterized the gene networks regulated by STAT1 and IRF-1 in β-cells. This was done by using siRNA approaches coupled to microarray analysis of insulin-producing cells exposed or not to IL-1β and IFN-γ. Relevant microarray findings were further studied in INS-1E cells and primary rat β-cells. STAT1, but not IRF-1, mediates the cytokine-induced loss of the differentiated β-cell phenotype, as indicated by decreased insulin, Pdx1, MafA, and Glut2. Furthermore, STAT1 regulates cytokine-induced apoptosis via up-regulation of the proapoptotic protein DP5. STAT1 and IRF-1 have opposite effects on cytokine-induced chemokine production, with IRF-1 exerting negative feedback inhibition on STAT1 and downstream chemokine expression. The present study elucidates the transcriptional networks through which the IFN-γ/STAT1/IRF-1 axis controls β-cell function/differentiation, demise, and islet inflammation.

OBJECTIVE: Cytokines contribute to pancreatic beta-cell death in type 1 diabetes. This effect is mediated by complex gene networks that remain to be characterized. We presently utilized array analysis to define the global expression pattern of genes, including spliced variants, modified by the cytokines interleukin (IL)-1beta + interferon (IFN)-gamma and tumor necrosis factor (TNF)-alpha + IFN-gamma in primary rat beta-cells. RESEARCH DESIGN AND METHODS: Fluorescence-activated cell sorter-purified rat beta-cells were exposed to IL-1beta + IFN-gamma or TNF-alpha + IFN-gamma for 6 or 24 h, and global gene expression was analyzed by microarray. Key results were confirmed by RT-PCR, and small-interfering RNAs were used to investigate the mechanistic role of novel and relevant transcription factors identified by pathway analysis. RESULTS Nearly 16,000 transcripts were detected as present in beta-cells, with temporal differences in the number of genes modulated by IL-1beta + IFNgamma or TNF-alpha + IFN-gamma. These cytokine combinations induced differential expression of inflammatory response genes, which is related to differential induction of IFN regulatory factor-7. Both treatments decreased the expression of genes involved in the maintenance of beta-cell phenotype and growth/regeneration. Cytokines induced hypoxia-inducible factor-alpha, which in this context has a proapoptotic role. Cytokines also modified the expression of >20 genes involved in RNA splicing, and exon array analysis showed cytokine-induced changes in alternative splicing of >50% of the cytokine-modified genes. CONCLUSIONS: The present study doubles the number of known genes expressed in primary beta-cells, modified or not by cytokines, and indicates the biological role for several novel cytokine-modified pathways in beta-cells. It also shows that cytokines modify alternative splicing in beta-cells, opening a new avenue of research for the field., Journal Article, Research Support, Non-U.S. Gov't, info:eu-repo/semantics/published

Udgivelsesdato: July AIMS/HYPOTHESIS: IL-1beta and TNF-alpha contribute to pancreatic beta cell death in type 1 diabetes. Both cytokines activate the transcription factor nuclear factor-kappaB (NF-kappaB), but recent observations suggest that NF-kappaB blockade prevents IL-1beta + IFN-gamma- but not TNF-alpha + IFN-gamma-induced beta cell apoptosis. The aim of the present study was to compare the effects of IL-1beta and TNF-alpha on cell death and the pattern of NF-kappaB activation and global gene expression in beta cells. METHODS: Cell viability was measured after exposure to IL-1beta or to TNF-alpha alone or in combination with IFN-gamma, and blockade of NF-kappaB activation or protein synthesis. INS-1E cells exposed to IL-1beta or TNF-alpha in time course experiments were used for IkappaB kinase (IKK) activation assay, detection of p65 NF-kappaB by immunocytochemistry, real-time RT-PCR and microarray analysis. RESULTS: Blocking NF-kappaB activation protected beta cells against IL-1beta + IFNgamma- or TNFalpha + IFNgamma-induced apoptosis. Blocking de novo protein synthesis did not increase TNF-alpha- or IL-1beta-induced beta cell death, in line with the observations that cytokines induced the expression of the anti-apoptotic genes A20, Iap-2 and Xiap to a similar extent. Microarray analysis of INS-1E cells treated with IL-1beta or TNF-alpha showed similar patterns of gene expression. IL-1beta, however, induced a higher rate of expression of NF-kappaB target genes putatively involved in beta cell dysfunction and death and a stronger activation of the IKK complex, leading to an earlier translocation of NF-kappaB to the nucleus. CONCLUSIONS/INTERPRETATION: NF-kappaB activation in beta cells has a pro-apoptotic role following exposure not only to IL-1beta but also to TNF-alpha. The more marked beta cell death induced by IL-1beta is explained at least in part by higher intensity NF-kappaB activation, leading to increased transcription of key target genes.

BACKGROUND: Pancreatic beta-cells are the target of an autoimmune attack in type 1 diabetes mellitus (T1DM). This is mediated in part by cytokines, such as interleukin (IL)-1beta and interferon (IFN)-gamma. These cytokines modify the expression of hundreds of genes, leading to beta-cell dysfunction and death by apoptosis. Several of these cytokine-induced genes are potentially regulated by the IL-1beta-activated transcription factor (TF) nuclear factor (NF)-kappaB, and previous studies by our group have shown that cytokine-induced NF-kappaB activation is pro-apoptotic in beta-cells. To identify NF-kappaB-regulated gene networks in beta-cells we presently used a discriminant analysis-based approach to predict NF-kappaB responding genes on the basis of putative regulatory elements. RESULTS: The performance of linear and quadratic discriminant analysis (LDA, QDA) in identifying NF-kappaB-responding genes was examined on a dataset of 240 positive and negative examples of NF-kappaB regulation, using stratified cross-validation with an internal leave-one-out cross-validation (LOOCV) loop for automated feature selection and noise reduction. LDA performed slightly better than QDA, achieving 61% sensitivity, 91% specificity and 87% positive predictive value, and allowing the identification of 231, 251 and 580 NF-kappaB putative target genes in insulin-producing INS-1E cells, primary rat beta-cells and human pancreatic islets, respectively. Predicted NF-kappaB targets had a significant enrichment in genes regulated by cytokines (IL-1beta or IL-1beta + IFN-gamma) and double stranded RNA (dsRNA), as compared to genes not regulated by these NF-kappaB-dependent stimuli. We increased the confidence of the predictions by selecting only evolutionary stable genes, i.e. genes with homologs predicted as NF-kappaB targets in rat, mouse, human and chimpanzee. CONCLUSION: The present in silico analysis allowed us to identify novel regulatory targets of NF-kappaB using a supervised classification method based on putative binding motifs. This provides new insights into the gene networks regulating cytokine-induced beta-cell dysfunction and death., Journal Article, Research Support, Non-U.S. Gov't, info:eu-repo/semantics/published

Pancreatic beta cells respond to endoplasmic reticulum (ER) stress by activating the unfolded protein response. If the stress is prolonged, or the adaptive response fails, apoptosis is triggered. We used a 'homemade' microarray specifically designed for the study of beta cell apoptosis (the APOCHIP) to uncover mechanisms regulating beta cell responses to ER stress.A time course viability and microarray analysis was performed in insulin-producing INS-1E cells exposed to the reversible ER stress inducer cyclopiazonic acid (CPA). Modification of selected genes was confirmed by real-time RT-PCR, and the observed inhibition of expression of the insulin-1 (Ins1) and insulin-2 (Ins2) genes was further characterised in primary beta cells exposed to a diverse range of agents that induce ER stress.CPA-induced ER stress modified the expression of 183 genes at one or more of the time points studied. The expression of most of these genes returned to control levels after a 3 h recovery period following CPA removal, with all cells surviving. Two groups of genes were particularly affected by CPA, namely, those related to cellular responses to ER stress, which were mostly upregulated, and those related to differentiated beta cell functions, which were downregulated. Levels of Ins1 and Ins2 mRNAs were severely decreased in response to CPA treatment as a result of degradation, and there was a concomitant increase in the level of IRE1 activation.In this study we provide the first global analysis of beta cell molecular responses to a severe ER stress, and identify the early degradation of mRNA transcripts of the insulin genes as an important component of this response.

Type 2 diabetes mellitus (T2DM) is a major disease affecting nearly 280 million people worldwide. Whilst the pathophysiological mechanisms leading to disease are poorly understood, dysfunction of the insulin-producing pancreatic beta-cells is key event for disease development. Monitoring the gene expression profiles of pancreatic beta-cells under several genetic or chemical perturbations has shed light on genes and pathways involved in T2DM. The EuroDia database has been established to build a unique collection of gene expression measurements performed on beta-cells of three organisms, namely human, mouse and rat. The Gene Expression Data Analysis Interface (GEDAI) has been developed to support this database. The quality of each dataset is assessed by a series of quality control procedures to detect putative hybridization outliers. The system integrates a web interface to several standard analysis functions from R/Bioconductor to identify differentially expressed genes and pathways. It also allows the combination of multiple experiments performed on different array platforms of the same technology. The design of this system enables each user to rapidly design a custom analysis pipeline and thus produce their own list of genes and pathways. Raw and normalized data can be downloaded for each experiment. The flexible engine of this database (GEDAI) is currently used to handle gene expression data from several laboratory-run projects dealing with different organisms and platforms. Database URL: http://eurodia.vital-it.ch., Journal Article, Research Support, Non-U.S. Gov't, info:eu-repo/semantics/published