Whole blood gene expression profiling predicts therapeutic response at six months in patients with polyarticular juvenile idiopathic arthritis

CD4+ T cells from children with active juvenile idiopathic arthritis show altered chromatin features associated with transcriptional abnormalities

Juvenile idiopathic arthritis (JIA) is one of the most common chronic diseases in children. While clinical outcomes for patients with juvenile JIA have improved, the underlying biology of the disease and mechanisms underlying therapeutic response/non-response are poorly understood. We have shown that active JIA is associated with distinct transcriptional abnormalities, and that the attainment of remission is associated with reorganization of transcriptional networks. In this study, we used a multi-omics approach to identify mechanisms driving the transcriptional abnormalities in peripheral blood CD4+ T cells of children with active JIA. We demonstrate that active JIA is associated with alterations in CD4+ T cell chromatin, as assessed by ATACseq studies. However, 3D chromatin architecture, assessed by HiChIP and simultaneous mapping of CTCF anchors of chromatin loops, reveals that normal 3D chromatin architecture is largely preserved. Overlapping CTCF binding, ATACseq, and RNAseq data with known JIA genetic risk loci demonstrated the presence of genetic influences on the observed transcriptional abnormalities and identified candidate target genes. These studies demonstrate the utility of multi-omics approaches for unraveling important questions regarding the pathobiology of autoimmune diseases.

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.

Application of machine learning in the diagnosis of axial spondyloarthritis

PURPOSE OF REVIEW

In this review article, we describe the development and application of machine-learning models in the field of rheumatology to improve the detection and diagnosis rates of underdiagnosed rheumatologic conditions, such as ankylosing spondylitis and axial spondyloarthritis (axSpA).

RECENT FINDINGS

In an attempt to aid in the earlier diagnosis of axSpA, we developed machine-learning models to predict a diagnosis of ankylosing spondylitis and axSpA using administrative claims and electronic medical record data. Machine-learning algorithms based on medical claims data predicted the diagnosis of ankylosing spondylitis better than a model developed based on clinical characteristics of ankylosing spondylitis. With additional clinical data, machine-learning algorithms developed using electronic medical records identified patients with axSpA with 82.6-91.8% accuracy. These two algorithms have helped us understand potential opportunities and challenges associated with each data set and with different analytic approaches. Efforts to refine and validate these machine-learning models are ongoing.

SUMMARY

We discuss the challenges and benefits of machine-learning models in healthcare, along with potential opportunities for its application in the field of rheumatology, particularly in the early diagnosis of axSpA and ankylosing spondylitis.

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.

Identification of enhanced IFN-γ signaling in polyarticular juvenile idiopathic arthritis with mass cytometry

Polyarticular juvenile idiopathic arthritis (JIA) is among the most challenging of the JIA subtypes to treat. Even with current biologic therapies, the disease remains difficult to control in a substantial subset of patients, highlighting the need for new therapies. The aim of this study was to use the high dimensionality afforded by mass cytometry with phospho-specific antibodies to delineate signaling abnormalities in immune cells from treatment-naive polyarticular JIA patients. Peripheral blood mononuclear cells were isolated from 17 treatment-naive polyarticular JIA patients, 10 of the patients after achieving clinical remission, and 19 healthy controls. Samples were stimulated for 15 minutes with IL-6 or IFN-γ and analyzed by mass cytometry. Following IFN-γ stimulation, increased STAT1 and/or STAT3 phosphorylation was observed in subsets of CD4 T cells and classical monocytes from treatment-naive patients. The enhanced IFN-γ signaling was associated with increased expression of JAK1 and SOCS1 in CD4 T cells. Furthermore, substantial heterogeneity in surface marker expression was observed among the subsets of CD4 T cells and classical monocytes with increased IFN-γ responsiveness. The identification of enhanced IFN-γ signaling in CD4 T cells and classical monocytes from treatment-naive polyarticular JIA patients provides mechanistic support for investigations into therapies that attenuate IFN-γ signaling in this disease.

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.

RNA sequencing data from neutrophils of patients with cystic fibrosis reveals potential for developing biomarkers for pulmonary exacerbations

BACKGROUND

There is no effective way to predict cystic fibrosis (CF) pulmonary exacerbations (CFPE) before they become symptomatic or to assess satisfactory treatment responses.

METHODS

RNA sequencing of peripheral blood neutrophils from CF patients before and after therapy for CFPE was used to create transcriptome profiles. Transcripts with an average transcripts per million (TPM) level > 1.0 and a false discovery rate (FDR) < 0.05 were used in a cosine K-nearest neighbor (KNN) model. Real time PCR was used to corroborate RNA sequencing expression differences in both neutrophils and whole blood samples from an independent cohort of CF patients. Furthermore, sandwich ELISA was conducted to assess plasma levels of MRP8/14 complexes in CF patients before and after therapy.

RESULTS

We found differential expression of 136 transcripts and 83 isoforms when we compared neutrophils from CF patients before and after therapy (>1.5 fold change, FDR-adjusted P < 0.05). The model was able to successfully separate CF flare samples from those taken from the same patients in convalescence with an accuracy of 0.75 in both the training and testing cohorts. Six differently expressed genes were confirmed by real time PCR using both isolated neutrophils and whole blood from an independent cohort of CF patients before and after therapy, even though levels of myeloid related protein MRP8/14 dimers in plasma of CF patients were essentially unchanged by therapy.

CONCLUSIONS

Our findings demonstrate the potential of machine learning approaches for classifying disease states and thus developing sensitive biomarkers that can be used to monitor pulmonary disease activity in CF.

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.

Predicting unfavorable long-term outcome in juvenile idiopathic arthritis: results from the Nordic cohort study

Background

The aim was to develop prediction rules that may guide early treatment decisions based on baseline clinical predictors of long-term unfavorable outcome in juvenile idiopathic arthritis (JIA).

Methods

In the Nordic JIA cohort, we assessed baseline disease characteristics as predictors of the following outcomes 8 years after disease onset. Non-achievement of remission off medication according to the preliminary Wallace criteria, functional disability assessed by Childhood Health Assessment Questionnaire (CHAQ) and Physical Summary Score (PhS) of the Child Health Questionnaire, and articular damage assessed by the Juvenile Arthritis Damage Index-Articular (JADI-A). Multivariable models were constructed, and cross-validations were performed by repeated partitioning of the cohort into training sets for developing prediction models and validation sets to test predictive ability.

Results

The total cohort constituted 423 children. Remission status was available in 410 children: 244 (59.5%) of these did not achieve remission off medication at the final study visit. Functional disability was present in 111/340 (32.7%) children assessed by CHAQ and 40/199 (20.1%) by PhS, and joint damage was found in 29/216 (13.4%). Model performance was acceptable for making predictions of long-term outcome. In validation sets, the area under the curves (AUCs) in the receiver operating characteristic (ROC) curves were 0.78 (IQR 0.72–0.82) for non-achievement of remission off medication, 0.73 (IQR 0.67–0.76) for functional disability assessed by CHAQ, 0.74 (IQR 0.65–0.80) for functional disability assessed by PhS, and 0.73 (IQR 0.63–0.76) for joint damage using JADI-A.

Conclusion

The feasibility of making long-term predictions of JIA outcome based on early clinical assessment is demonstrated. The prediction models have acceptable precision and require only readily available baseline variables. Further testing in other cohorts is warranted.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1571-6) contains supplementary material, which is available to authorized users.

Transcriptional profiles of JIA patient blood with subsequent poor response to methotrexate

Objective

The mechanisms that determine the efficacy or inefficacy of MTX in JIA are ill-defined. The objective of this study was to identify a gene expression transcriptional signature associated with poor response to MTX in patients with JIA.

Methods

RNA sequencing was used to measure gene expression in peripheral blood mononuclear cells collected from 47 patients with JIA prior to MTX treatment and 14 age-matched controls. Differentially expressed baseline genes between responders and non-responders were evaluated. Biological differences between all JIA patients and controls were explored by constructing a signature of differentially expressed genes. Unsupervised clustering and pathway analysis was performed.

Results

A signature of 99 differentially expressed genes (Bonferroni-corrected P < 0.05) capturing the biological differences between all JIA patients and controls was identified. Unsupervised clustering of samples based on this list of 99 genes produced subgroups enriched for MTX response status. Comparing this gene signature with reference signatures from sorted cell populations revealed high concordance between the expression signatures of monocytes and of MTX non-responders. CXCL8 (IL-8) was the most significantly differentially expressed gene transcript comparing all JIA patients with controls (Bonferroni-corrected P = 4.12 × 10-10).

Conclusion

Variability in clinical response to MTX in JIA patients is associated with differences in gene transcripts modulated in monocytes. These gene expression profiles may provide a basis for biomarkers predictive of treatment response.

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.

Dynamic tracking of functional gene modules in treated juvenile idiopathic arthritis

BACKGROUND

We have previously shown that childhood-onset rheumatic diseases show aberrant patterns of gene expression that reflect pathology-associated co-expression networks. In this study, we used novel computational approaches to examine how disease-associated networks are altered in one of the most common rheumatic diseases of childhood, juvenile idiopathic arthritis (JIA).

METHODS

Using whole blood gene expression profiles derived from children in a pediatric rheumatology clinical trial, we used a network approach to understanding the impact of therapy and the underlying biology of response/non-response to therapy.

RESULTS

We demonstrate that therapy for JIA is associated with extensive re-ordering of gene expression networks, even in children who respond inadequately to therapy. Furthermore, we observe distinct differences in the evolution of specific network properties when we compare children who have been treated successfully with those who have inadequate treatment response.

CONCLUSIONS

Despite the inherent noisiness of whole blood gene expression data, our findings demonstrate how therapeutic response might be mapped and understood in pathologically informative cells in a broad range of human inflammatory diseases.

Genetic determinants for methotrexate response in juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIAs) is the most common chronic rheumatic disease of childhood and is an important cause of disability. The folic acid analog methotrexate is the first choice disease-modifying anti-rheumatic drug in this disease, however, 35–45% of patients fail to respond. Molecular elements, such as variants in genes of pharmacological relevance, influencing response to methotrexate in JIA, would be important to individualize treatment strategies. Several studies have evaluated the effects of candidate genetic variants in the complex pathway of genes involved in methotrexate pharmacodynamics and pharmacokinetics, however, results are still contrasting and no definitive genetic marker of methotrexate response useful for the clinician to tailor therapy of children with JIA has been identified. Recently, genome-wide approaches have been applied, identifying new potential biological processes involved in methotrexate response in JIA such as TGF-beta signaling and calcium channels. If these genomic results are properly validated and integrated with innovative analyses comprising deep sequencing, epigenetics, and pharmacokinetics, they will greatly contribute to personalize therapy with methotrexate in children with JIA.

Predictors of juvenile idiopathic arthritis course

Introduction

Juvenile idiopathic arthritis (JIA) is a heterogeneous group of inflammatory diseases of joints in children with various and often unfavourable prognosis. It is possible to improve the outcome of the disease in patients with JIA by a correct therapeutic choice made at disease onset – one that enables fast achievement of an inactive disease state and remission.

The aim of the investigation was to develop a model/application for automatic calculation of risk of treatment-refractory JIA taking into account the combined action of clinical and cytokine factors.

Material and methods

Disease subtype was determined in 105 patients with JIA, as well as the number of poor prognostic factors and disease activity level. Blood serum cytokine levels (IL-1β, IL-4, IL-6, IL-8, IL-10, IL-17, TNF-α, IFN-γ) and their soluble receptors and agonists – interleukin 1 receptor agonist (IL-1Ra), soluble interleukin 2 receptor (sCD25), interleukin 6 (sIL6R), soluble tumour necrosis factor receptor 1 (sTNFR1) – were determined in these patients using immunoenzymatic laboratory methods.

Results

The following prognostic factors were taken into account in the study: JIA subtype, disease activity, presence of clinically unfavourable factors and cytokine characteristics. We determined that systemic subtype of JIA, moderate and high disease activity, presence of factors of poor disease course and sCD25 and IL-6 levels are statistically significant factors of treatment-refractory disease course. A Microsoft Excel application was developed for automatic calculation of risk of treatment-refractory JIA in a specific patient based on 10 factors.

Conclusions

Use of an application for automatic calculation of risk of treatment-refractory JIA enables prediction of JIA disease course in patients at disease onset and personalization of the treatment protocol.