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Autoantibodies are typically present many years before the diagnosis of SLE. Furthermore, the appearance of autoantibodies in patients with SLE tends to follow a predictable course, with a progressive accumulation of specific autoantibodies before the onset of SLE, while patients are still asymptomatic.

Identifying biological themes within lists of genes with EASEIdentifying biological themes within lists of genes with EASE EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'. AbstractEASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'. RationaleHigh-density microarray and proteomics technologies have enabled the discovery of global patterns of biological responses to experimental or natural perturbations [1]. Much work has addressed the issues of data normalization and statistical selection of the genes that are significantly modulated or clustered on the basis of expression profiles [2]. The net result of these efforts is one or more lists of genes. Unfortunately, little work has addressed the issue of rapidly identifying biological themes in such lists [3]. Most investigators currently annotate genes one-at-a-time using internet-based databases or manual literature searches. After this tedious process, it can still be a struggle to identify the most salient biological themes in order to make sense of the results and researchers have no systematic way to prioritize these themes for further analysis. A parallel issue in interpreting such data is how to exploit the ever-expanding flood of functional genomic data and tools. We developed the Expression Analysis Systematic Explorer (EASE) to automate the process of biological theme determination for lists of genes and to serve as a customizable gateway to online analysis tools. This is the first report to show that the highest-ranking themes derived by a computational method can recapitulate manually derived themes in previously published microarray, proteomics and SAGE results, and to provide evidence that these themes are stable to varying methods of gene selection.EASE performs three basic functions with any list of genes. The first is theme discovery, defined as the identification of terms or phrases that describe a statistically significant number of genes in the list with respect to the number of genes described by the term or phrase in the population of genes from which the list derived. The second is customiza...

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IntroductionRheumatoid arthritis (RA) is a complex and clinically heterogeneous autoimmune disease. Currently, the relationship between pathogenic molecular drivers of disease in RA and therapeutic response is poorly understood.MethodsWe analyzed synovial tissue samples from two RA cohorts of 49 and 20 patients using a combination of global gene expression, histologic and cellular analyses, and analysis of gene expression data from two further publicly available RA cohorts. To identify candidate serum biomarkers that correspond to differential synovial biology and clinical response to targeted therapies, we performed pre-treatment biomarker analysis compared with therapeutic outcome at week 24 in serum samples from 198 patients from the ADACTA (ADalimumab ACTemrA) phase 4 trial of tocilizumab (anti-IL-6R) monotherapy versus adalimumab (anti-TNFα) monotherapy.ResultsWe documented evidence for four major phenotypes of RA synovium – lymphoid, myeloid, low inflammatory, and fibroid - each with distinct underlying gene expression signatures. We observed that baseline synovial myeloid, but not lymphoid, gene signature expression was higher in patients with good compared with poor European league against rheumatism (EULAR) clinical response to anti-TNFα therapy at week 16 (P =0.011). We observed that high baseline serum soluble intercellular adhesion molecule 1 (sICAM1), associated with the myeloid phenotype, and high serum C-X-C motif chemokine 13 (CXCL13), associated with the lymphoid phenotype, had differential relationships with clinical response to anti-TNFα compared with anti-IL6R treatment. sICAM1-high/CXCL13-low patients showed the highest week 24 American College of Rheumatology (ACR) 50 response rate to anti-TNFα treatment as compared with sICAM1-low/CXCL13-high patients (42% versus 13%, respectively, P =0.05) while anti-IL-6R patients showed the opposite relationship with these biomarker subgroups (ACR50 20% versus 69%, P =0.004).ConclusionsThese data demonstrate that underlying molecular and cellular heterogeneity in RA impacts clinical outcome to therapies targeting different biological pathways, with patients with the myeloid phenotype exhibiting the most robust response to anti-TNFα. These data suggest a path to identify and validate serum biomarkers that predict response to targeted therapies in rheumatoid arthritis and possibly other autoimmune diseases.Trial registrationClinicalTrials.gov NCT01119859

The origins of autoimmunity in systemic lupus erythematosus (SLE) are thought to involve both genetic and environmental factors. To identify environmental agents that could potentially incite autoimmunity, we have traced the autoantibody response in human SLE back in time, prior to clinical disease onset, and identified the initial autoantigenic epitope for some lupus patients positive for antibodies to 60 kDa Ro. This initial epitope directly cross-reacts with a peptide from the latent viral protein Epstein-Barr virus nuclear antigen-1 (EBNA-1). Animals immunized with either the first epitope of 60 kDa Ro or the cross-reactive EBNA-1 epitope progressively develop autoantibodies binding multiple epitopes of Ro and spliceosomal autoantigens. They eventually acquire clinical symptoms of lupus such as leukopenia, thrombocytopenia and renal dysfunction. These data support the hypothesis that some humoral autoimmunity in human lupus arises through molecular mimicry between EBNA-1 and lupus autoantigens and provide further evidence to suspect an etiologic role for Epstein-Barr virus in SLE.

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HIV ͉ latency ͉ viremia ͉ DNA microarray