Background Juvenile idiopathic arthritis (JIA) is considered a complex trait in which the environment interacts with inherited genes to produce a phenotype that shows broad inter-individual variance. A recently completed genome-wide association study (GWAS) identified 24 regions of genetic risk for JIA, for example. However, as is typical for GWAS, most of the regions of genetic risk for JIA (22 of 24) were in non-coding regions of the genome. The studies reported here were undertaken to identify functional elements (other than genes) that might be located within the regions of genetic risk. Methods We used paired end RNA sequencing to identify non-coding RNAs located within 5 kb of the disease-associated SNPs. In addition, we used chromatin immunoprecipitation-sequencing (ChIP-Seq) to identify epigenetic marks associated with enhancer function (H3K4me1 and H3K27ac) in human neutrophils to determine whether there was enrichment of enhancer-associated histone marks in linkage disequilibrium (LD) blocks that encompassed the 22 GWAS SNPs from the non-coding genome. Results In human neutrophils, we identified H3K4me1 and/or H3K27ac marks in 15 of the 22 regions previously as identified as risk loci for JIA. In CD4+ T cells, 18 regions demonstrate H3K4me1 and/or H3K27ac marks. In addition, we identified non-coding RNA transcripts at the rs4705862 and rs6894249 loci in human neutrophils. Conclusion Much of the genetic risk for JIA lies within or adjacent to regions of neutrophil and CD4+ T cell genomes that carry epigenetic marks associated with enhancer function and/or ncRNA transcripts. These findings are consistent with the hypothesis that JIA is fundamentally a disorder of gene regulation that includes both the innate and adaptive immune system. Elucidating the specific roles of these non-coding elements within leukocyte genomes in JIA pathogenesis will be critical to our understanding disease pathogenesis.

BackgroundThe transcriptional complexity of mammalian cells suggests that they have broad abilities to respond to specific environmental stimuli and physiologic contexts. These abilities were not apparent a priori from the structure of mammalian genomes, but have been identified through detailed transcriptome analyses. In this study, we examined the transcriptomes of cells of the innate immune system, human neutrophils, using RNA sequencing (RNAseq).MethodsWe sequenced poly-A RNA from nine individual samples corresponding to specific phenotypes: three children with active, untreated juvenile idiopathic arthritis (JIA)(AD), three children with the same disease whose disease was inactive on medication (CRM), and three children with cystic fibrosis (CF).ResultsWe demonstrate that transcriptomes of neutrophils, typically considered non-specific in their responses and functions, display considerable specificity in their transcriptional repertoires dependent on the pathologic context, and included genes, gene isoforms, and long non-coding RNA transcripts. Furthermore, despite the small sample numbers, these findings demonstrate the potential of RNAseq approaches to biomarker development in rheumatic diseases.ConclusionsThese data demonstrate the capacity of cells previously considered non-specific in function to adapt their transcriptomes to specific biologic contexts. These data also provide insight into previously unrecognized pathological pathways and show considerable promise for elucidating disease and disease-state specific regulatory networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-015-0128-7) contains supplementary material, which is available to authorized users.

BackgroundUnruptured intracranial aneurysms (IAs) are typically asymptomatic and undetected except for incidental discovery on imaging. Blood-based diagnostic biomarkers could lead to improvements in IA management. This exploratory study examined circulating neutrophils to determine whether they carry RNA expression signatures of IAs.MethodsBlood samples were collected from patients receiving cerebral angiography. Eleven samples were collected from patients with IAs and 11 from patients without IAs as controls. Samples from the two groups were paired based on demographics and comorbidities. RNA was extracted from isolated neutrophils and subjected to next-generation RNA sequencing to obtain differential expressions for identification of an IA-associated signature. Bioinformatics analyses, including gene set enrichment analysis and Ingenuity Pathway Analysis, were used to investigate the biological function of all differentially expressed transcripts.ResultsTranscriptome profiling identified 258 differentially expressed transcripts in patients with and without IAs. Expression differences were consistent with peripheral neutrophil activation. An IA-associated RNA expression signature was identified in 82 transcripts (p<0.05, fold-change ≥2). This signature was able to separate patients with and without IAs on hierarchical clustering. Furthermore, in an independent, unpaired, replication cohort of patients with IAs (n = 5) and controls (n = 5), the 82 transcripts separated 9 of 10 patients into their respective groups.ConclusionPreliminary findings show that RNA expression from circulating neutrophils carries an IA-associated signature. These findings highlight a potential to use predictive biomarkers from peripheral blood samples to identify patients with IAs.

Objective. We have previously reported a defect in neutrophil activation in children with polyarticular juvenile idiopathic arthritis (JIA). The current study was undertaken to determine whether gene expression abnormalities persist in JIA in remission and to use systems biology analysis to elucidate pathologic pathways in polyarticular JIA.Methods. We performed gene expression profiling on neutrophils from children with polyarticular JIA. Children were grouped according to disease status. We studied 14 children with active disease who were taking medication, 8 children with clinical remission of disease who were taking medication (CRM status), and 6 children with clinical remission of disease who were not taking medication (CR status). We also studied 13 healthy children whose age ranges overlapped those of the patients.Results. Neutrophil abnormalities persisted in children with polyarticular JIA even after disease remission was achieved. Children with active disease and those with CRM status showed no differences in expression of specific genes, although they could be separated on cluster analysis. A comparison of children with CR status and healthy control children revealed networks of pro-and antiinflammatory genes that suggested that remission is a state of homeostasis and balance rather than a return to normal immune function. Furthermore, gene overexpression in patients with CR status supports the hypothesis that neutrophils play a role in regulating adaptive immunity in this disease.Conclusion. Neutrophil gene profiling in polyarticular JIA suggests important roles for neutrophils in disease pathogenesis. These findings suggest the presence of complex interactions between innate and adaptive immunity, that are not easily modeled in conventional, linear, reductionist systems.Juvenile idiopathic arthritis (JIA) is a term used to denote a family of diseases of unknown etiology characterized by chronic inflammation of synovial membranes (1). Distinct phenotypes are recognized clinically, with specific immunogenetic markers associated with each of the phenotypes (2,3).While the JIA subtypes have commonly been assumed to have an "autoimmune" origin, our growing understanding of biologic complexity makes any such simple, linear hypothesis of disease pathogenesis unlikely (4). We have hypothesized that the pathogenesis

Although strong epidemiologic evidence suggests an important role for adaptive immunity in the pathogenesis of polyarticular juvenile rheumatoid arthritis (JRA), there remain many aspects of the disease that suggest equally important contributions of the innate immune system. We used gene expression arrays and computer modeling to examine the function in neutrophils of 25 children with polyarticular JRA. Computer analysis identified 712 genes that were differentially expressed between patients and healthy controls. Computer-assisted analysis of the differentially expressed genes demonstrated functional connections linked to both interleukin (IL)-8-and interferon-γ (IFN-γ)-regulated processes. Of special note is that the gene expression fingerprint of children with active JRA remained essentially unchanged even after they had responded to therapy. This result differed markedly from our previously reported work, in which gene expression profiles in buffy coats of children with polyarticular JRA reverted to normal after disease control was achieved pharmacologically. These findings suggest that JRA neutrophils remain in an activated state even during disease quiescence. Computer modeling of array data further demonstrated disruption of gene regulatory networks in clusters of genes modulated by IFN-γ and IL-8. These cytokines have previously been shown to independently regulate the frequency (IFN-γ) and amplitude (IL-8) of the oscillations of key metabolites in neutrophils, including nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and superoxide ion. Using real-time, high-speed, single-cell photoimaging, we observed that 6/6 JRA patients displayed a characteristic defect in 12% to 23% of the neutrophils tested. Reagents known to induce only frequency fluctuations of NAD(P)H and superoxide ion induced both frequency and amplitude fluctuations in JRA neutrophils. This is a novel finding that was observed in children with both active (n = 4) and inactive (n = 2) JRA. A subpopulation of polyarticular JRA neutrophils are in a chronic, activated state, a state that persists when the disease is well controlled pharmacologically. Furthermore, polyarticular JRA neutrophils exhibit an intrinsic defect in the regulation of metabolic oscillations and superoxide ion production. Our data are consistent with the hypothesis that neutrophils play an essential role in the pathogenesis of polyarticular JRA.

BackgroundIntracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA.MethodsNeutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate < 0.05, fold change ≥ 1.5) were used to construct prediction models for IA using four well-known supervised machine-learning approaches (diagonal linear discriminant analysis, cosine nearest neighbors, nearest shrunken centroids, and support vector machines). These models were tested in a testing cohort of the remaining 10 neutrophil samples from the 40 patients (5 with IA, 5 controls), and model performance was assessed by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (PCR) was used to corroborate expression differences of a subset of model transcripts in neutrophil samples from a new, separate validation cohort of 10 patients (5 with IA, 5 controls).ResultsThe training cohort yielded 26 highly significantly differentially expressed neutrophil transcripts. Models using these transcripts identified IA patients in the testing cohort with accuracy ranging from 0.60 to 0.90. The best performing model was the diagonal linear discriminant analysis classifier (area under the ROC curve = 0.80 and accuracy = 0.90). Six of seven differentially expressed genes we tested were confirmed by quantitative PCR using isolated neutrophils from the separate validation cohort.ConclusionsOur findings demonstrate the potential of machine-learning methods to classify IA cases and create predictive models for unruptured IAs using circulating neutrophil transcriptome data. Future studies are needed to replicate these findings in larger cohorts.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1749-3) contains supplementary material, which is available to authorized users.

IntroductionThe attainment of remission has become an important end point for clinical trials in juvenile idiopathic arthritis (JIA), although we do not yet have a full understanding of what remission is at the cell and molecular level.MethodsTwo independent cohorts of patients with JIA and healthy child controls were studied. RNA was prepared separately from peripheral blood mononuclear cells (PBMC) and granulocytes to identify differentially expressed genes using whole genome microarrays. Expression profiling results for selected genes were confirmed by quantitative, real-time polymerase chain reaction (RT-PCR).ResultsWe found that remission in JIA induced by either methotrexate (MTX) or MTX plus a TNF inhibitor (etanercept, Et) (MTX + Et) is characterized by numerous differences in gene expression in peripheral blood mononuclear cells and in granulocytes compared with healthy control children; that is, remission is not a restoration of immunologic normalcy. Network analysis of the differentially expressed genes demonstrated that the steroid hormone receptor superfamily member hepatocyte nuclear factor 4 alpha (HNF4α) is a hub in several of the gene networks that distinguished children with arthritis from controls. Confocal microscopy revealed that HNF4a is present in both T lymphocytes and granulocytes, suggesting a previously unsuspected role for this transcription factor in regulating leukocyte function and therapeutic response in JIA.ConclusionsThese findings provide a framework from which to understand therapeutic response in JIA and, furthermore, may be used to develop strategies to increase the frequency with which remission is achieved in adult forms of rheumatoid arthritis.

Objective. The development of biomarkers to predict response to therapy in polyarticular juvenile idiopathic arthritis (JIA) is an important issue in pediatric rheumatology. A critical step in this process is determining whether there is biologic meaning to clinically derived terms such as "active disease" and "remission." The aim of this study was to use a systems biology approach to address this question. Methods.We performed gene transcriptional profiling on children who fulfilled the criteria for specific disease states as defined by the consensus criteria developed by Wallace and colleagues. The study group comprised children with active disease (n ‫؍‬ 14), children with clinical remission on medication (CRM; n ‫؍‬ 9), children with clinical remission off medication (CR; n ‫؍‬ 6), and healthy control children (n ‫؍‬ 13). Transcriptional profiles in peripheral blood mononuclear cells (PBMCs) were obtained using Affymetrix U133 Plus 2.0 arrays.Results. Hierarchical cluster analysis and predictive modeling demonstrated that the clinically derived criteria represent biologically distinct states. Minimal differences were seen between children with active disease and those with disease in CRM. Thus, underlying immune/inflammatory abnormalities persist despite a response to therapy. The PBMC transcriptional profiles of children whose disease was in remission did not return to normal but revealed networks of proinflammatory and antiinflammatory genes, suggesting that remission is a state of homeostasis, not a return to a normal state.Conclusion. Gene transcriptional profiling of PBMCs revealed that clinically derived criteria for JIA disease states reflect underlying biology. We also demonstrated that neither CRM nor CR status results in resolution of the underlying inflammatory process, but that these conditions are more likely to be states of balanced homeostasis between proinflammatory and antiinflammatory mechanisms.