Dynamic tracking of functional gene modules in treated juvenile idiopathic arthritis

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches

Background

The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population.

Methods

Our dataset consisted of 50 samples, 23 from children in remission and 27 from children with an active disease on therapy. Nine of these samples were from children with mixed European/Native American ancestry. We used 4 different machine learning methods to create predictive models in 2 populations: the whole dataset and then the samples from children with exclusively European ancestry.

Results

In both populations, models were able to predict JIA status well, with training accuracies > 74% and testing accuracies > 78%. Performance was better in the whole dataset model. We note a high degree of overlap between genes identified in both populations. Using ingenuity pathway analysis, genes from the whole dataset associated with cell-to-cell signaling and interactions, cell morphology, organismal injury and abnormalities, and protein synthesis.

Conclusions

This study demonstrates it is feasible to use machine learning in conjunction with RNA sequencing of PBMCs to predict JIA stage. Thus, developing objective biomarkers from easy to obtain clinical samples remains an achievable goal.

Immunomics in Pediatric Rheumatic Diseases

The inherent complexity in the immune landscape of pediatric rheumatic disease necessitates a holistic system approach. Uncertainty in the mechanistic workings and etiological driving forces presents difficulty in personalized treatments. The development and progression of immunomics are well suited to deal with this complexity. Immunomics encompasses a spectrum of biological processes that entail genomics, transcriptomics, epigenomics, proteomics, and cytomics. In this review, we will discuss how various high dimensional technologies in immunomics have helped to grow a wealth of data that provide salient clues and biological insights into the pathogenesis of autoimmunity. Interfaced with critical unresolved clinical questions and unmet medical needs, these platforms have helped to identify candidate immune targets, refine patient stratification, and understand treatment response or resistance. Yet the unprecedented growth in data has presented both opportunities and challenges. Researchers are now facing huge heterogeneous data sets from different origins that need to be integrated and exploited for further data mining. We believe that the utilization and integration of these platforms will help unravel the complexities and expedite both discovery and validation of clinical targets.

Using Chromatin Architecture to Understand the Genetics and Transcriptomics of Juvenile Idiopathic Arthritis

The presence of abnormal gene expression signatures is a well-described feature of the oligoarticular and polyarticular forms of juvenile idiopathic arthritis. In this review, we discuss how new insights into genetic risk for JIA and the three dimensional architecture of the genome may be used to develop a better understanding of the mechanisms driving these gene expression patterns.

Disease-specific regulation of gene expression in a comparative analysis of juvenile idiopathic arthritis and inflammatory bowel disease

BACKGROUND

The genetic and immunological factors that contribute to differences in susceptibility and progression between sub-types of inflammatory and autoimmune diseases continue to be elucidated. Inflammatory bowel disease and juvenile idiopathic arthritis are both clinically heterogeneous and known to be due in part to abnormal regulation of gene activity in diverse immune cell types. Comparative genomic analysis of these conditions is expected to reveal differences in underlying genetic mechanisms of disease.

METHODS

We performed RNA-Seq on whole blood samples from 202 patients with oligoarticular, polyarticular, or systemic juvenile idiopathic arthritis, or with Crohn's disease or ulcerative colitis, as well as healthy controls, to characterize differences in gene expression. Gene ontology analysis combined with Blood Transcript Module and Blood Informative Transcript analysis was used to infer immunological differences. Comparative expression quantitative trait locus (eQTL) analysis was used to quantify disease-specific regulation of transcript abundance.

RESULTS

A pattern of differentially expressed genes and pathways reveals a gradient of disease spanning from healthy controls to oligoarticular, polyarticular, and systemic juvenile idiopathic arthritis (JIA); Crohn's disease; and ulcerative colitis. Transcriptional risk scores also provide good discrimination of controls, JIA, and IBD. Most eQTL are found to have similar effects across disease sub-types, but we also identify disease-specific eQTL at loci associated with disease by GWAS.

CONCLUSION

JIA and IBD are characterized by divergent peripheral blood transcriptomes, the genetic regulation of which displays limited disease specificity, implying that disease-specific genetic influences are largely independent of, or downstream of, eQTL effects.

Modeling Transcriptional Rewiring in Neutrophils Through the Course of Treated Juvenile Idiopathic Arthritis

Neutrophils in children with the polyarticular form of juvenile idiopathic arthritis (JIA) display abnormal transcriptional patterns linked to fundamental metabolic derangements. In this study, we sought to determine the effects of therapy on mRNA and miRNA expression networks in polyarticular JIA. Using exon and miRNA microarrays, we studied children with untreated active JIA (ADU, n = 35), children with active disease on therapy with methotrexate ± etanercept (ADT, n = 26), and children with inactive disease also on therapy (ID, n = 14). We compared the results to findings from healthy control children (HC, n = 35). We found substantial re-ordering of mRNA and miRNA expression networks after the initiation of therapy. Each disease state was associated with a distinct transcriptional profile, with the ADT state differing the most from HC, and ID more strongly resembling HC. Changes at the mRNA level were mirrored in changes in miRNA expression patterns. The analysis of the expression dynamics from differentially expressed genes across three disease states indicated that therapeutic response is a complex process. This process does not simply involve genes slowly correcting in a linear fashion over time. Computational modeling of miRNA and transcription factor (TF) co-regulatory networks demonstrated that combinational regulation of miRNA and TF might play an important role in dynamic transcriptome changes.

Effect of Scutellarin inhibits collagen‑induced arthritis through TLR4/NF‑κB‑mediated inflammation

Scutellarin is the major effective constituent of the commonly used Chinese medicine Erigeron breviscapus. It has been applied in the clinic to treat various diseases, and is characterized by high content, a stable source, controllable quality, high efficiency and low toxicity. In addition, its potential pharmacological effects have been increasingly identified and elucidated. The present study was performed to examine the role of scutellarin on collagen‑induced arthritis (CIA). Mice were injected subcutaneously with bovine collagen type II and administered scutellarin for 2 weeks by gavage 20 mg/kg/day. ELISA kits were used to measure the levels of interleukin (IL)‑1β, IL‑6, tumor necrosis factor‑α (TNF‑α), oxidative stress markers [superoxide dismutase (SOD) and malondialdehyde (MDA)] and caspase‑3/-9 activity. Bax, Bcl‑2, toll like receptor 4 (TLR4) and nuclear factor (NF)‑κB protein expression was analyzed using western immunoblot analyses. The present study demonstrated that scutellarin prevented CIA, and inhibited the expression of inflammation factors, IL‑1β, IL‑6 and TNF‑α. In addition, scutellarin reduced the levels of oxidative stress markers, SOD and MDA, as well as intercellular adhesion molecule‑1 and monocyte chemoattractant protein 1 in CIA mice. Caspase‑3/-9, Bax/Bcl‑2, TLR4 and NF‑κB protein expression were reduced in CIA mice following scutellarin treatment. The results of the current study suggest a novel effect of scutellarin involving the inhibition of TLR4/NF‑κB‑mediated inflammation.

Genetic insights into juvenile idiopathic arthritis derived from deep whole genome sequencing

Deep whole genome sequencing (WGS) allows for the comprehensive study of genetic landscapes at finer resolution than array based methods. We conducted deep WGS on children with the polyarticular form of juvenile idiopathic arthritis (JIA), using 2 independent cohorts to ascertain the sequencing fidelity. Genome wide SNP density analysis identified 18 SNP hotspots with comparison to the 1000 Genome Projects (1KGP) data. A subset of the genes adjacent to SNP hotspots showed statistically significant enrichment in immunological processes. Genes adjacent to indel hotspots were functionally related to G-protein coupled signaling pathways. Further analyses elucidated significantly more JIA SNPs with regulatory potential compared to 1KGP data. Furthermore, SNPs located within linkage disequibilium (LD) blocks containing previously identified JIA-associated SNPs demonstrated higher regulation potential compared to SNPs outside LD blocks. We also demonstrated enrichment of novel JIA variants in histone modification peaks and DNase hypersensitivity sites in B cells. This study greatly expands the number of genetic variants that may contribute to JIA and give us some clues into what may trigger this disease. To date, this study is the first deep WGS effort on children with JIA and provides useful genetic resources for research communities particularly in understanding JIA etiology.

Limits of Peripheral Blood Mononuclear Cells for Gene Expression-Based Biomarkers in Juvenile Idiopathic Arthritis

Juvenile Idiopathic Arthritis (JIA) is one of the most common chronic disease conditions affecting children in the USA. As with many rheumatic diseases, there is growing interest in using genomic technologies to develop biomarkers for either diagnosis or to guide treatment ("personalized medicine"). Here, we explore the use of gene expression patterns in peripheral blood mononuclear cells (PBMC) as a first step approach to developing such biomarkers. Although PBMC carry many theoretical advantages for translational research, we have found that sample heterogeneity makes RNASeq on PBMC unsuitable as a first-step method for screening biomarker candidates in JIA. RNASeq studies of homogeneous cell populations are more likely to be useful and informative.

Whole blood expression profiling from the TREAT trial: insights for the pathogenesis of polyarticular juvenile idiopathic arthritis

Background

The Trial of Early Aggressive Therapy in Juvenile Idiopathic Arthritis (TREAT trial) was accompanied by a once-in-a-generation sample collection for translational research. In this paper, we report the results of whole blood gene expression analyses and genomic data-mining designed to cast light on the immunopathogenesis of polyarticular juvenile idiopathic arthritis (JIA).

Methods

TREAT samples and samples from an independent cohort were analyzed on Affymetrix microarrays and compared to healthy controls. Data from the independent cohort were used to validate the TREAT data. Pathways analysis was used to characterize gene expression profiles. Furthermore, we correlated differential gene expression with new information about functional regulatory elements within the genome to develop models of aberrant gene expression in JIA.

Results

There was a strong concordance in gene expression between TREAT samples and the independent cohort. In addition, rheumatoid factor (RF)-positive and RF-negative patients showed only small differences on whole blood expression profiles. Analysis of the combined samples showed 158 genes represented by 176 probes that showed differential expression between TREAT subjects at baseline and healthy controls. None of the differentially expressed genes were encoded within linkage disequilibrium blocks containing single nucleotide polymorphisms known to be associated with risk for JIA. Functional analysis of these genes showed functional associations with multiple processes associated with innate and adaptive immunity, and appeared to reflect overall suppression of STAT1–3/interferon response factor-mediated pathways.

Conclusions

Despite their limitations, whole blood expression profiles clearly distinguish children with polyarticular JIA from healthy controls. Whole blood expression profiles identify several immunologic pathways of biologic relevance that will need to be pursued in homogeneous cell populations in order to clarify mechanisms of pathogenesis.

Trial registration

ClinicalTrials.gov registry #NCT00443430, originally registered 2 March 2007 and last updated 30 May 2013.

Network analysis and juvenile idiopathic arthritis (JIA): a new horizon for the understanding of disease pathogenesis and therapeutic target identification

Juvenile idiopathic arthritis (JIA) is a clinically diverse and genetically complex autoimmune disease. Currently, there is very limited understanding of the potential underlying mechanisms that result in the range of phenotypes which constitute JIA.The elucidation of the functional relevance of genetic associations with phenotypic traits is a fundamental problem that hampers the translation of genetic observations to plausible medical interventions. Genome wide association studies, and subsequent fine-mapping studies in JIA patients, have identified many genetic variants associated with disease. Such approaches rely on 'tag' single nucleotide polymorphisms (SNPs). The associated SNPs are rarely functional variants, so the extrapolation of genetic association data to the identification of biologically meaningful findings can be a protracted undertaking. Integrative genomics aims to bridge the gap between genotype and phenotype.Systems biology, principally through network analysis, is emerging as a valuable way to identify biological pathways of relevance to complex genetic diseases. This review aims to highlight recent findings in systems biology related to JIA in an attempt to assist in the understanding of JIA pathogenesis and therapeutic target identification.

Complexity and Specificity of the Neutrophil Transcriptomes in Juvenile Idiopathic Arthritis

NIH projects such as ENCODE and Roadmap Epigenomics have revealed surprising complexity in the transcriptomes of mammalian cells. In this study, we explored transcriptional complexity in human neutrophils, cells generally regarded as nonspecific in their functions and responses. We studied distinct human disease phenotypes and found that, at the gene, gene isoform, and miRNA level, neutrophils exhibit considerable specificity in their transcriptomes. Thus, even cells whose responses are considered non-specific show tailoring of their transcriptional repertoire toward specific physiologic or pathologic contexts. We also found that miRNAs had a global impact on neutrophil transcriptome and are associated with innate immunity in juvenile idiopathic arthritis (JIA). These findings have important implications for our understanding of the link between genes, non-coding transcripts and disease phenotypes.