Adenosine metabolic signature in circulating CD4+ T cells predicts remission in rheumatoid arthritis.

OBJECTIVES: Long-term outcomes in rheumatoid arthritis (RA) depend on early and effective disease control. Methotrexate (MTX) remains the first-line disease modifying therapy, however there are no biomarkers with which to identify those most likely to achieve remission. To address this unmet need we explored metabolic pathways involved in MTX mechanism of action within circulating CD4+T cells in a cohort of treatment naive patients with early RA. METHODS: Purified CD4+T cells were isolated from peripheral blood of 68 patients with early RA commencing MTX. The expression of a range of putative MTX metabolism and mechanism of action targets were explored by flow-cytometry and transcriptional analysis. From these data significant predictors of Disease Activity Score 28-C reactive protein (DAS28-CRP) remission (<2.4 at 6 months) were determined by logistic regression (clinical; flow-cytometry data) and linear modelling (gene expression data). RESULTS: Low baseline DAS28-CRP was associated with remission at 6 months (p=0.02). Expression of the ectonucleotidase CD39, involved in ATP-ADP conversion during adenosine synthesis, was higher on CD4+CD25 High regulatory T cells at baseline in those achieving remission (molecules of equivalent fluorescence 1264 vs 847; p=0.007). Expression of other adenosine signalling elements in CD4+T cells were also upregulated at baseline in patients achieving remission: AMPD1 (p<0.001), ADORA2b (p=0.039) and ADORA3 (p=0.047). When combined into a single predictive metric, a combination of these variables outperformed baseline DAS28-CRP in prediction of early remission (area under the curve 0.92 vs 0.67, p=0.001) CONCLUSIONS: Adenosine signalling is important in the achievement of early remission with MTX in RA and biomarkers of adenosine activity may hold utility for the stratification of therapy in early disease.

The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective.

Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.

Micro-RNA content of circulating extracellular vesicles in early rheumatoid arthritis as biomarkers and mediators of methotrexate efficacy.

OBJECTIVES: Extracellular vesicles (EVs) are abundant in body fluids, contributing to intercellular signalling by transferring cargo that includes microRNAs (miRs) - themselves implicated in pathobiology. For the first time we evaluated the potential of EV miRs to contribute diagnostic information in early RA, predict methotrexate (MTX) efficacy or shed light on the drug's mechanism of action. METHODS: 798 miRs isolated from serum-derived EVs of 46 patients with untreated RA, 23 with untreated polymyalgia rheumatica (PMR; inflammatory disease control group) and 12 in whom significant inflammatory disease had been excluded (non-inflammatory controls; NICs) were profiled (Nanostring); the same measurements were made for RA patients after 6 months' MTX treatment. Analyses took multiple testing into account. RESULTS: 28 EV miRs were robustly differentially expressed between early RA (but not PMR) patients and NICs after correction for age and sex, suggesting discriminatory value. Cross-validated partial least squared-discriminant analysis also indicated the predictive potential of a distinct baseline EV miR signature with respect to MTX-induced remission at 6 months. The change in expression of 13 miRs over the course of MTX treatment differed significantly between responders and non-responders, and four of those exhibiting increased relative abundance amongst responders have known roles in regulating the pathogenic potential of synovial fibroblasts, namely miR-212-3p, miR-338-5p, miR-410-3p, and miR-537. CONCLUSION: Our data highlight the potential of serum EV miRs as diagnostic and therapeutic biomarkers, highlighting a novel potential mechanism via which MTX may exert its therapeutic effect in early RA that warrants further investigation.

Characterization of age-associated B cells in early drug-naïve rheumatoid arthritis patients.

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.

Interferon-α-mediated therapeutic resistance in early rheumatoid arthritis implicates epigenetic reprogramming.

OBJECTIVES: An interferon (IFN) gene signature (IGS) is present in approximately 50% of early, treatment naive rheumatoid arthritis (eRA) patients where it has been shown to negatively impact initial response to treatment. We wished to validate this effect and explore potential mechanisms of action. METHODS: In a multicentre inception cohort of eRA patients (n=191), we examined the whole blood IGS (MxA, IFI44L, OAS1, IFI6, ISG15) with reference to circulating IFN proteins, clinical outcomes and epigenetic influences on circulating CD19+ B and CD4+ T lymphocytes. RESULTS: We reproduced our previous findings demonstrating a raised baseline IGS. We additionally showed, for the first time, that the IGS in eRA reflects circulating IFN-α protein. Paired longitudinal analysis demonstrated a significant reduction between baseline and 6-month IGS and IFN-α levels (p<0.0001 for both). Despite this fall, a raised baseline IGS predicted worse 6-month clinical outcomes such as increased disease activity score (DAS-28, p=0.025) and lower likelihood of a good EULAR clinical response (p=0.034), which was independent of other conventional predictors of disease activity and clinical response. Molecular analysis of CD4+ T cells and CD19+ B cells demonstrated differentially methylated CPG sites and dysregulated expression of disease relevant genes, including PARP9, STAT1, and EPSTI1, associated with baseline IGS/IFNα levels. Differentially methylated CPG sites implicated altered transcription factor binding in B cells (GATA3, ETSI, NFATC2, EZH2) and T cells (p300, HIF1α). CONCLUSIONS: Our data suggest that, in eRA, IFN-α can cause a sustained, epigenetically mediated, pathogenic increase in lymphocyte activation and proliferation, and that the IGS is, therefore, a robust prognostic biomarker. Its persistent harmful effects provide a rationale for the initial therapeutic targeting of IFN-α in selected patients with eRA.

A Bayesian network approach incorporating imputation of missing data enables exploratory analysis of complex causal biological relationships.

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.

Identification of Acute Myeloid Leukemia Bone Marrow Circulating MicroRNAs.

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.

Lymphocyte DNA methylation mediates genetic risk at shared immune-mediated disease loci.

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.

IL-6 Mediated Transcriptional Programming of Naïve CD4+ T Cells in Early Rheumatoid Arthritis Drives Dysregulated Effector Function.

Objective: We have previously shown that increased circulating interleukin-6 (IL-6) results in enhanced CD4+ T cell signaling via signal transduction and activator of transcription-3 (STAT3) in early rheumatoid arthritis (RA). We tested the hypothesis that transcriptional "imprinting" of T-cells by this mechanism skews downstream effector responses, reinforcing immune dysregulation at a critical, but targetable, disease phase. Methods: We modeled naïve CD4+ T cell exposure to pathophysiological concentrations of IL-6 in vitro, assessing the dynamic transcriptional and functional consequences for downstream effector cells utilizing microarray and flow cytometry. Fresh blood from treatment-naïve early arthritis patients was phenotyped in parallel for comparison. Results: T cell sensitivity to IL-6 was most marked in the naïve subset, and related to gp130 rather than IL-6R expression. Exposure of healthy naïve CD4+ T cells to IL-6 induced the same STAT3 target genes as previously seen to discriminate RA patients from disease controls. After TCR stimulation IL-6 pre-exposed cells exhibited enhanced proliferative capacity, activation, and a propensity toward Th1 differentiation, compared to non-exposed cells. An entirely analogous phenotype was observed in early RA compared to control CD4+ T cells. Conclusions: Sustained IL-6 exposure at a critical point in the natural history of RA "primes" the adaptive immune system to respond aberrantly to TCR stimulation, potentiating disease induction with implications for the optimal timing of targeted therapy.

Between a ROC and a hard place: Teaching prevalence plots to understand real world biomarker performance in the clinic.

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.

Death protein 5 and p53-upregulated modulator of apoptosis mediate the endoplasmic reticulum stress-mitochondrial dialog triggering lipotoxic rodent and human ß-cell apoptosis.

Environmental factors such as diets rich in saturated fats contribute to dysfunction and death of pancreatic ß-cells in diabetes. Endoplasmic reticulum (ER) stress is elicited in ß-cells by saturated fatty acids. Here we show that palmitate-induced ß-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 ß-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 ß-cell mass. This study elucidates the crosstalk between lipotoxic ER stress and the mitochondrial pathway of apoptosis that causes ß-cell death in diabetes.

DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients.

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 ß-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.

STAT1 is a master regulator of pancreatic {beta}-cell apoptosis and islet inflammation.

Cytokines produced by islet-infiltrating immune cells induce ß-cell apoptosis in type 1 diabetes. The IFN-γ-regulated transcription factors STAT1/IRF-1 have apparently divergent effects on ß-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 ß-cells. This was done by using siRNA approaches coupled to microarray analysis of insulin-producing cells exposed or not to IL-1ß and IFN-γ. Relevant microarray findings were further studied in INS-1E cells and primary rat ß-cells. STAT1, but not IRF-1, mediates the cytokine-induced loss of the differentiated ß-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 ß-cell function/differentiation, demise, and islet inflammation.

EuroDia: a beta-cell gene expression resource.

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.

Cytokines interleukin-1beta and tumor necrosis factor-alpha regulate different transcriptional and alternative splicing networks in primary beta-cells.

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.

In silico identification of NF-kappaB-regulated genes in pancreatic beta-cells.

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.

New approaches for in silico identification of cytokine-modified beta cell gene networks.

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.