Gene expression signatures in polyarticular juvenile idiopathic arthritis demonstrate disease heterogeneity and offer a molecular classification of disease subsets

Overlap of International League of Associations for Rheumatology and Preliminary Pediatric Rheumatology International Trials Organization Classification Criteria for Nonsystemic Juvenile Idiopathic Arthritis in an Established UK Multicentre Inception Cohort

OBJECTIVE

The goal was to assess the degree of overlap between existing International League of Associations for Rheumatology (ILAR) and preliminary Paediatric Rheumatology International Trials Organisation (PRINTO) classification criteria for juvenile idiopathic arthritis (JIA).

METHODS

Participants from the Childhood Arthritis Prospective Study, a multicenter UK JIA inception cohort, were classified using the PRINTO and ILAR classification criteria into distinct categories. Systemic JIA was excluded because several classification items were not collected in this cohort. Adaptations to PRINTO criteria were required to apply to a UK health care setting, including limiting the number of blood biomarker tests required. The overlap between categories under the two systems was determined, and any differences in characteristics between groups were described.

RESULTS

A total of 1,223 children and young people with a physician's diagnosis of JIA were included. Using PRINTO criteria, the majority of the patients had "other JIA" (69.5%). There was a high degree of overlap (91%) between the PRINTO enthesitis/spondylitis- and ILAR enthesitis-related JIA categories. The PRINTO rheumatoid factor (RF)-positive category was composed of 48% ILAR RF-positive polyarthritis and 52% undifferentiated JIA. The early-onset antinuclear antibodies-positive PRINTO category was largely composed of ILAR oligoarthritis (50%), RF-negative polyarthritis (24%), and undifferentiated JIA (23%). A few patients were unclassified under PRINTO (n = 3) and would previously have been classified as enthesitis-related JIA (n = 1) and undifferentiated JIA (n = 2) under ILAR.

CONCLUSION

Under the preliminary PRINTO classification criteria for childhood arthritis, most children are not yet classified into a named category. These data can help support further delineation of the PRINTO criteria to ensure homogenous groups of children can be identified.

Profiling the peripheral immune response to ex vivo TNF stimulation in untreated juvenile idiopathic arthritis using single cell RNA sequencing

BACKGROUND

Juvenile Idiopathic Arthritis (JIA) is an autoimmune disease with a heterogenous clinical presentation and unpredictable response to available therapies. This personalized transcriptomics study sought proof-of-concept for single-cell RNA sequencing to characterize patient-specific immune profiles.

METHODS

Whole blood samples from six untreated children, newly diagnosed with JIA, and two healthy controls were cultured for 24 h with or without ex vivo TNF stimulation and subjected to scRNAseq to examine cellular populations and transcript expression in PBMCs. A novel analytical pipeline, scPool, was developed wherein cells are first pooled into pseudocells prior to expression analysis, facilitating variance partitioning of the effects of TNF stimulus, JIA disease status, and individual donor.

RESULTS

Seventeen robust immune cell-types were identified, the abundance of which was significantly affected by TNF stimulus, which resulted in notable elevation of memory CD8 + T-cells and NK56 cells, but down-regulation of naïve B-cell proportions. Memory CD8 + and CD4 + T-cells were also both reduced in the JIA cases relative to two controls. Significant differential expression responses to TNF stimulus were also characterized, with monocytes showing more transcriptional shifts than T-lymphocyte subsets, while the B-cell response was more limited. We also show that donor variability exceeds the small degree of possible intrinsic differentiation between JIA and control profiles. An incidental finding of interest was association of HLA-DQA2 and HLA-DRB5 expression with JIA status.

CONCLUSIONS

These results support the development of personalized immune-profiling combined with ex-vivo immune stimulation for evaluation of patient-specific modes of immune cell activity in autoimmune rheumatic disease.

Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study

BACKGROUND

Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models.

METHODS

RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted.

RESULTS

Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups.

CONCLUSION

Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.

Signals and Mechanisms Regulating Monocyte and Macrophage Activation in the Pathogenesis of Juvenile Idiopathic Arthritis

Monocytes (Mos) and macrophages (Mφs) are key players in the innate immune system and are critical in coordinating the initiation, expansion, and regression of many autoimmune diseases. In addition, they display immunoregulatory effects that impact inflammation and are essential in tissue repair and regeneration. Juvenile idiopathic arthritis (JIA) is an umbrella term describing inflammatory joint diseases in children. Accumulated evidence suggests a link between Mo and Mφ activation and JIA pathogenesis. Accordingly, topics regarding the signals and mechanisms regulating Mo and Mφ activation leading to pathologies in patients with JIA are of great interest. In this review, we critically summarize recent advances in the understanding of how Mo and Mφ activation is involved in JIA pathogenesis and focus on the signaling pathways and mechanisms participating in the related cell activation processes.

Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis

There is an urgent need to identify biomarkers for diagnosis and disease activity monitoring in rheumatoid arthritis (RA). We leveraged publicly available microarray gene expression data in the NCBI GEO database for whole blood (N=1,885) and synovial (N=284) tissues from RA patients and healthy controls. We developed a robust machine learning feature selection pipeline with validation on five independent datasets culminating in 13 genes: TNFAIP6, S100A8, TNFSF10, DRAM1, LY96, QPCT, KYNU, ENTPD1, CLIC1, ATP6V0E1, HSP90AB1, NCL and CIRBP which define the RA score and demonstrate its clinical utility: the score tracks the disease activity DAS28 (p = 7e-9), distinguishes osteoarthritis (OA) from RA (OR 0.57, p = 8e-10) and polyJIA from healthy controls (OR 1.15, p = 2e-4) and monitors treatment effect in RA (p = 2e-4). Finally, the immunoblotting analysis of six proteins on an independent cohort confirmed two proteins, TNFAIP6/TSG6 and HSP90AB1/HSP90.

Building a Population Representative Pediatric Biobank: Lessons Learned From the Greater Cincinnati Childhood Cohort

Background: Biobanks can accelerate research by providing researchers with samples and data. However, hospital-based recruitment as a source for controls may create bias as who comes to the hospital may be different from the broader population. Methods: In an effort to broadly improve the quality of research studies and reduce costs and challenges associated with recruitment and sample collection, a group of diverse researchers at Cincinnati Children's Hospital Medical Center led an institution-supported initiative to create a population representative pediatric "Greater Cincinnati Childhood Cohort (GCC)." Participants completed a detailed survey, underwent a brief physician-led physical exam, and provided blood, urine, and hair samples. DNA underwent high-throughput genotyping. Results: In total, 1,020 children ages 3-18 years living in the 7 county Greater Cincinnati Metropolitan region were recruited. Racial composition of the cohort was 84% non-Hispanic white, 15% non-Hispanic black, and 2% other race or Hispanic. Participants exhibited marked demographic and disease burden differences by race. Overall, the cohort was broadly used resulting in publications, grants and patents; yet, it did not meet the needs of all potential researchers. Conclusions: Learning from both the strengths and weaknesses, we propose leveraging a community-based participatory research framework for future broad use biobanking efforts.

The Multi-Omics Architecture of Juvenile Idiopathic Arthritis

Juvenile idiopathic arthritis (JIA) is highly heterogeneous in terms of etiology and clinical presentation with ambiguity in JIA classification. The advance of high-throughput omics technologies in recent years has gained us significant knowledge about the molecular mechanisms of JIA. Besides a minor proportion of JIA cases as monogenic, most JIA cases are polygenic disease caused by autoimmune mechanisms. A number of HLA alleles (including both HLA class I and class II genes), and 23 non-HLA genetic loci have been identified of association with different JIA subtypes. Omics technologies, i.e., transcriptome profiling and epigenomic analysis, contributed significant knowledge on the molecular mechanisms of JIA in addition to the genetic approach. New molecular knowledge on different JIA subtypes enables us to reconsider the JIA classification, but also highlights novel therapeutic targets to develop a cure for the devastating JIA.

Novel biomarkers of a peripheral blood interferon signature associated with drug-naïve early arthritis patients distinguish persistent from self-limiting disease course

We profiled gene expression signatures to distinguish rheumatoid arthritis (RA) from non-inflammatory arthralgia (NIA), self-limiting arthritis (SLA), and undifferentiated arthritis (UA) as compared to healthy controls as novel potential biomarkers for therapeutic responsiveness. Global gene expression profiles of PBMCs from 43 drug-naïve patients presenting with joint symptoms were evaluated and differentially expressed genes identified by comparative analysis with 24 healthy volunteers. Patients were assessed at presentation with follow up at 6 and 12 months. Gene ontology and network pathway analysis were performed using DAVID Bioinformatics Resources v6.7. Gene expression profiles were also determined after disease-modifying anti-rheumatic drug (DMARD) treatment in the inflammatory arthritis groups (i.e. RA and UA) and confirmed by qRT-PCR. Receiver operating characteristic (ROC) curves analysis and Area Under the Curve (AUC) estimation were performed to assess the diagnostic value of candidate gene expression signatures. A type I interferon (IFN) gene signature distinguished DMARD-naïve patients who will subsequently develop persistent inflammatory arthritis (i.e. RA and UA) from those with NIA. In patients with RA, the IFN signature is characterised by up-regulation of SIGLEC1 (p = 0.00597) and MS4A4A (p = 0.00000904). We also identified, EPHB2 (p = 0.000542) and PDZK1IP1 (p = 0.0206) with RA-specific gene expression profiles and elevated expression of the ST6GALNAC1 (p = 0.0023) gene in UA. ROC and AUC risk score analysis suggested that MSA4A (AUC: 0.894, 0.644, 0.720), PDZK1IP1 (AUC: 0.785, 0.806, 0.977), and EPHB2 (AUC: 0.794, 0.723, 0.620) at 0, 6, and 12 months follow-up can accurately discriminate patients with RA from healthy controls and may have practical value for RA diagnosis. In patients with early inflammatory arthritis, ST6GALNAC1 is a potential biomarker for UA as compared with healthy controls whereas EPHB2, MS4A4A, and particularly PDZK1IP1 may discriminate RA patients. SIGLEC1 may also be a useful marker of disease activity in UA.

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.

Restoring T Cell Tolerance, Exploring the Potential of Histone Deacetylase Inhibitors for the Treatment of Juvenile Idiopathic Arthritis

Juvenile Idiopathic Arthritis (JIA) is characterized by a loss of immune tolerance. Here, the balance between the activity of effector T (Teff) cells and regulatory T (Treg) cells is disturbed resulting in chronic inflammation in the joints. Presently, therapeutic strategies are predominantly aimed at suppressing immune activation and pro-inflammatory effector mechanisms, ignoring the opportunity to also promote tolerance by boosting the regulatory side of the immune balance. Histone deacetylases (HDACs) can deacetylate both histone and non-histone proteins and have been demonstrated to modulate epigenetic regulation as well as cellular signaling in various cell types. Importantly, HDACs are potent regulators of both Teff cell and Treg cell function and can thus be regarded as attractive therapeutic targets in chronic inflammatory arthritis. HDAC inhibitors (HDACi) have proven therapeutic potential in the cancer field, and are presently being explored for their potential in the treatment of autoimmune diseases. Specific HDACi have already been demonstrated to reduce the secretion of pro-inflammatory cytokines by Teff cells, and promote Treg numbers and suppressive capacity in vitro and in vivo. In this review, we outline the role of the different classes of HDACs in both Teff cell and Treg cell function. Furthermore, we will review the effect of different HDACi on T cell tolerance and explore their potential as a therapeutic strategy for the treatment of oligoarticular and polyarticular JIA.

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.

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.

Pathway analysis based on Monte Carlo Cross-Validation in polyarticular juvenile idiopathic arthritis

INTRODUCTION

Juvenile idiopathic arthritis (JIA) is a common chronic disease with onset before the 16 years old in a child. Polyarticular JIA has been reported as the main form of JIA in several locations. Until now, understanding of the genetic basis of JIA is incomplete. The purpose of this study was to identify pathway pairs of great potential functional relevance in the progression of polyarticular JIA.

MATERIALS AND METHODS

Microarray data of 59 peripheral blood samples from healthy children and 61 samples from polyarticular JIA were transformed to gene expression data. Differential expressed genes (DEG) between patients and normal controls were identified using Linear Models for Microarray Analysis. After performed enrichment of DEG, differential pathways were identified with Fisher's test and false discovery rate. Differential pathway pairs were constructed with random two differential pathways, and were evaluated by Random Forest classification. Monte Carlo Cross-Validation was introduced to screen the best pathway pair.

RESULTS

42 DEG with P-values<0.01 were identified. 19 differential pathways with P-values<0.01 were identified. Area under the curve (AUC) of pathway pairs was generated with RF classification. After 50 bootstraps of Monte Carlo Cross-Validation, the best pathway pair with the highest AUC value was identified, and it was the pair of tumoricidal function of hepatic natural killer cells pathway and erythropoietin signaling pathway.

CONCLUSION

These identified pathway pairs may play pivotal roles in the progress of polyarticular JIA and can be applied for diagnosis. Particular attention can be focused on them for further research.

A Novel Method for Pathway Identification Based on Attractor and Crosstalk in Polyarticular Juvenile Idiopathic Arthritis

Background

Juvenile idiopathic arthritis (JIA) is one of the most common inflammatory disorders of unknown etiology. We introduced a novel method to identify dysregulated pathways associated with polyarticular JIA (pJIA).

Material/Methods

Gene expression profiling of 61 children with pJIA and 59 healthy controls were collected from E-GEOD-13849; 300 pathways were obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG) database and 787,896 protein-protein interaction sets were gathered from the Retrieval of Interacting Genes. Attractor and crosstalk were designed to complement each other to increase the integrity of pathways assessment. Then, impact factor was used to assess the interactions inter-pathways, and RP-value was used to evaluate the comprehensive influential ability of attractors.

Results

There were seven attractors with p<0.01 and 14 pathways with RP<0.01. Finally, two significantly dysfunctional pathways were found, which were related to pJIA progression: p53 signaling pathway (KEGG ID: 04115) and non-alcoholic fatty liver disease (NAFLD) (KEGG ID: 04932).

Conclusions

A novel approach that identified the dysregulated pathways in pJIA was constructed based on attractor and crosstalk. The new process is expected to be efficient in the upcoming era of medicine.

Enhancing translational research in paediatric rheumatology through standardization

The past decade has seen many successes in translational rheumatology, from dramatic improvements in outcomes brought about by novel biologic therapies, to the discovery of new monogenic inflammatory disorders. Advances in molecular medicine, combined with progress towards precision care, provide an excellent opportunity to accelerate the translation of biological understanding to the bedside. However, although the field of rheumatology is a leader in the standardization of data collection and measures of disease activity, it lags behind in standardization of biological sample collection and assay performance. Uniform approaches are necessary for robust collaborative research, particularly in rare diseases. Standardization is also critical to increase reproducibility between centres, a prerequisite for clinical implementation of translational research. This Perspectives article emphasizes the need for standardization and implementation of best practices, presented in the context of lessons learned from international biorepository networks.

Antinuclear antibody-positive cohort constitutes homogeneous entity in juvenile idiopathic arthritis

OBJECTIVE

To identify a homogeneous entity for antinuclear antibody (ANA)-positive patients suffering from juvenile idiopathic arthritis (JIA).

METHODS

All of the clinical features were recorded retrospectively. ANA positivity was defined as more than twice positive results at a titer of > 1:100. The correlation between ANA positivity and clinical parameters was assessed by multiple logistic regression analysis.

RESULT

Of 120 patients, 49 patients were ANA positive (31 oligoarthritis, 18 rheumatoid factor [RF]-negative polyarthritis) and 71 patients were ANA negative (48 oligoarthritis, 23 RF-negative polyarthritis), and were recruited retrospectively to this study according to the International League of Associations for Rheumatology (ILAR) criteria. In ANA-positive cohort, the characteristics of early-onset age, female predominance, and asymmetric arthritis were observed compared with ANA-negative cohort including oligoarthritis and RF-negative polyarthritis. Correspondingly, we found that ANA-positive cohort had higher cumulative number of joints affected at 9 and 12 months after disease presentation than ANA-negative cohort, had lower frequency of occurrence of image change, and had a different pattern of affected arthritis than ANA-negative cohort, which was more likely to have knee involvement and less likely to have hip and shoulder involvement. ANA positivity correlated strongly with asymmetric arthritis, female predominance and wrist involvement.

CONCLUSION

This study demonstrates that ANA-positive cohort divided into different subgroups by present ILAR criteria share the similar features and suggests that ANA positivity might serve as a novel potential value for JIA classification.

Association of a mutation in LACC1 with a monogenic form of systemic juvenile idiopathic arthritis

OBJECTIVE

The pathologic basis of systemic juvenile idiopathic arthritis (JIA) is a subject of some controversy, with evidence for both autoimmune and autoinflammatory etiologies. Several monogenic autoinflammatory disorders have been described, but thus far, systemic JIA has only been attributed to a mutation of MEFV in rare cases and has been weakly associated with the HLA class II locus. This study was undertaken to identify the cause of an autosomal-recessive form of systemic JIA.

METHODS

We studied 13 patients with systemic JIA from 5 consanguineous families, all from the southern region of Saudi Arabia. We used linkage analysis, homozygosity mapping, and whole-exome sequencing to identify the disease-associated gene and mutation.

RESULTS

Linkage analysis localized systemic JIA to a region on chromosome 13 with a maximum logarithm of odds score of 11.33, representing the strongest linkage identified to date for this disorder. Homozygosity mapping reduced the critical interval to a 1.02-Mb region defined proximally by rs9533338 and distally by rs9595049. Whole-exome sequencing identified a homoallelic missense mutation in LACC1, which encodes the enzyme laccase (multicopper oxidoreductase) domain-containing 1. The mutation was confirmed by Sanger sequencing and segregated with disease in all 5 families based on an autosomal-recessive pattern of inheritance and complete penetrance.

CONCLUSION

Our findings provide strong genetic evidence of an association of a mutation in LACC1 with systemic JIA in the families studied. Association of LACC1 with Crohn's disease and leprosy has been reported and justifies investigation of its role in autoinflammatory disorders.

The biologic basis of clinical heterogeneity in juvenile idiopathic arthritis

Objective

Childhood arthritis encompasses a heterogeneous family of diseases. Significant variation in clinical presentation remains despite consensus-driven diagnostic classifications. Developments in data analysis provide powerful tools for interrogating large heterogeneous data sets. We report a novel approach to integrating biologic and clinical data toward a new classification for childhood arthritis, using computational biology for data-driven pattern recognition.

Methods

Probabilistic principal components analysis was used to transform a large set of data into 4 interpretable indicators or composite variables on which patients were grouped by cluster analysis. Sensitivity analysis was conducted to determine key variables in determining indicators and cluster assignment. Results were validated against an independent validation cohort.

Results

Meaningful biologic and clinical characteristics, including levels of proinflammatory cytokines and measures of disease activity, defined axes/indicators that identified homogeneous patient subgroups by cluster analysis. The new patient classifications resolved major differences between patient subpopulations better than International League of Associations for Rheumatology subtypes. Fourteen variables were identified by sensitivity analysis to crucially determine indicators and clusters. This new schema was conserved in an independent validation cohort.

Conclusion

Data-driven unsupervised machine learning is a powerful approach for interrogating clinical and biologic data toward disease classification, providing insight into the biology underlying clinical heterogeneity in childhood arthritis. Our analytical framework enabled the recovery of unique patterns from small cohorts and addresses a major challenge, patient numbers, in studying rare diseases.

Development of positive antinuclear antibodies and rheumatoid factor in systemic juvenile idiopathic arthritis points toward an autoimmune phenotype later in the disease course

BACKGROUND

Systemic juvenile idiopathic arthritis (sJIA) is commonly considered an autoinflammatory disease. However, sJIA patients may develop aggressive arthritis without systemic inflammation later in the disease, resembling an autoimmune phenotype similar to other subtypes of JIA. The objective of this study was to determine whether antinuclear antibodies (ANA) and rheumatoid factor (RF) will develop in patients with sJIA over the course of the disease.

FINDINGS

A single center sample of sJIA patients with follow-up of more than one year was obtained. A retrospective chart survey was used to extract demographic and clinical data as well as presence and titers of ANA and RF at diagnosis and during follow-up. 32 patients were included in the study, with a median age of 4.2 years and median follow-up of 6.0 years. 8/32 patients had ANA titers ≥ 1:80 at diagnosis, with 22/32 patients showing rising ANA titers with titers ≥ 1:80 at last follow-up (p =0.001). 10/32 patients had a positive RF at least once during follow-up, compared to 0/32 at diagnosis (p = 0.001). In 5/10 patients, positive RF was documented at least twice, more than twelve weeks apart. Patients treated with TNF antagonists were not significantly more likely to develop positive ANA titers (p = 0.425) or positive RF (p = 0.703).

CONCLUSIONS

Patients with sJIA developed increased ANA titers and positive RF over the course of the disease, independent of treatment with TNF antagonists. This might point towards an autoimmune, rather than an autoinflammatory phenotype later in the course of sJIA.

Two randomized trials of canakinumab in systemic juvenile idiopathic arthritis

BACKGROUND

Interleukin-1 is pivotal in the pathogenesis of systemic juvenile idiopathic arthritis (JIA). We assessed the efficacy and safety of canakinumab, a selective, fully human, anti-interleukin-1β monoclonal antibody, in two trials.

METHODS

In trial 1, we randomly assigned patients, 2 to 19 years of age, with systemic JIA and active systemic features (fever; ≥2 active joints; C-reactive protein, >30 mg per liter; and glucocorticoid dose, ≤1.0 mg per kilogram of body weight per day), in a double-blind fashion, to a single subcutaneous dose of canakinumab (4 mg per kilogram) or placebo. The primary outcome, termed adapted JIA ACR 30 response, was defined as improvement of 30% or more in at least three of the six core criteria for JIA, worsening of more than 30% in no more than one of the criteria, and resolution of fever. In trial 2, after 32 weeks of open-label treatment with canakinumab, patients who had a response and underwent glucocorticoid tapering were randomly assigned to continued treatment with canakinumab or to placebo. The primary outcome was time to flare of systemic JIA.

RESULTS

At day 15 in trial 1, more patients in the canakinumab group had an adapted JIA ACR 30 response (36 of 43 [84%], vs. 4 of 41 [10%] in the placebo group; P<0.001). In trial 2, among the 100 patients (of 177 in the open-label phase) who underwent randomization in the withdrawal phase, the risk of flare was lower among patients who continued to receive canakinumab than among those who were switched to placebo (74% of patients in the canakinumab group had no flare, vs. 25% in the placebo group, according to Kaplan-Meier estimates; hazard ratio, 0.36; P=0.003). The average glucocorticoid dose was reduced from 0.34 to 0.05 mg per kilogram per day, and glucocorticoids were discontinued in 42 of 128 patients (33%). The macrophage activation syndrome occurred in 7 patients; infections were more frequent with canakinumab than with placebo.

CONCLUSIONS

These two phase 3 studies show the efficacy of canakinumab in systemic JIA with active systemic features. (Funded by Novartis Pharma; ClinicalTrials.gov numbers, NCT00889863 and NCT00886769.).

Correlation analyses of clinical and molecular findings identify candidate biological pathways in systemic juvenile idiopathic arthritis

BACKGROUND

Clinicians have long appreciated the distinct phenotype of systemic juvenile idiopathic arthritis (SJIA) compared to polyarticular juvenile idiopathic arthritis (POLY). We hypothesized that gene expression profiles of peripheral blood mononuclear cells (PBMC) from children with each disease would reveal distinct biological pathways when analyzed for significant associations with elevations in two markers of JIA activity, erythrocyte sedimentation rate (ESR) and number of affected joints (joint count, JC).

METHODS

PBMC RNA from SJIA and POLY patients was profiled by kinetic PCR to analyze expression of 181 genes, selected for relevance to immune response pathways. Pearson correlation and Student's t-test analyses were performed to identify transcripts significantly associated with clinical parameters (ESR and JC) in SJIA or POLY samples. These transcripts were used to find related biological pathways.

RESULTS

Combining Pearson and t-test analyses, we found 91 ESR-related and 92 JC-related genes in SJIA. For POLY, 20 ESR-related and 0 JC-related genes were found. Using Ingenuity Systems Pathways Analysis, we identified SJIA ESR-related and JC-related pathways. The two sets of pathways are strongly correlated. In contrast, there is a weaker correlation between SJIA and POLY ESR-related pathways. Notably, distinct biological processes were found to correlate with JC in samples from the earlier systemic plus arthritic phase (SAF) of SJIA compared to samples from the later arthritis-predominant phase (AF). Within the SJIA SAF group, IL-10 expression was related to JC, whereas lack of IL-4 appeared to characterize the chronic arthritis (AF) subgroup.

CONCLUSIONS

The strong correlation between pathways implicated in elevations of both ESR and JC in SJIA argues that the systemic and arthritic components of the disease are related mechanistically. Inflammatory pathways in SJIA are distinct from those in POLY course JIA, consistent with differences in clinically appreciated target organs. The limited number of ESR-related SJIA genes that also are associated with elevations of ESR in POLY implies that the SJIA associations are specific for SJIA, at least to some degree. The distinct pathways associated with arthritis in early and late SJIA raise the possibility that different immunobiology underlies arthritis over the course of SJIA.

Genome-wide association analysis of juvenile idiopathic arthritis identifies a new susceptibility locus at chromosomal region 3q13

OBJECTIVE

In a genome-wide association study of Caucasian patients with juvenile idiopathic arthritis (JIA), we have previously described findings limited to autoimmunity loci shared by JIA and other diseases. The present study was undertaken to identify novel JIA-predisposing loci using genome-wide approaches.

METHODS

The discovery cohort consisted of Caucasian JIA cases (n = 814) and local controls (n = 658) genotyped on the Affymetrix Genome-Wide SNP 6.0 Array, along with 2,400 out-of-study controls. In a replication study, we genotyped 10 single-nucleotide polymorphisms (SNPs) in 1,744 cases and 7,010 controls from the US and Europe.

RESULTS

Analysis within the discovery cohort provided evidence of associations at 3q13 within C3orf1 and near CD80 (rs4688011) (odds ratio [OR] 1.37, P = 1.88 × 10(-6) ) and at 10q21 near JMJD1C (rs647989 [OR 1.59, P = 6.1 × 10(-8) ], rs12411988 [OR 1.57, P = 1.16 × 10(-7) ], and rs10995450 [OR 1.31, P = 6.74 × 10(-5) ]). Meta-analysis provided further evidence of association for these 4 SNPs (P = 3.6 × 10(-7) for rs4688011, P = 4.33 × 10(-5) for rs6479891, P = 2.71 × 10(-5) for rs12411988, and P = 5.39 × 10(-5) for rs10995450). Gene expression data on 68 JIA cases and 23 local controls showed cis expression quantitative trait locus associations for C3orf1 SNP rs4688011 (P = 0.024 or P = 0.034, depending on the probe set) and JMJD1C SNPs rs6479891 and rs12411988 (P = 0.01 or P = 0.04, depending on the probe set and P = 0.008, respectively). Using a variance component liability model, it was estimated that common SNP variation accounts for approximately one-third of JIA susceptibility.

CONCLUSION

Genetic association results and correlated gene expression findings provide evidence of JIA association at 3q13 and suggest novel genes as plausible candidates in disease pathology.

What do cytokine profiles tell us about subsets of juvenile idiopathic arthritis?

Classification of juvenile idiopathic arthritis is an ongoing process and up to now has been predominantly based on clinical manifestations--mainly number of joints at onset of disease. In the meantime, basic studies have advanced our knowledge regarding the disease pathogenesis. Unfortunately, studies of cytokines and cytokine polymorphisms have not followed the predominantly clinical International League of Associations for Rheumatology classification in that no significant biological differences among the different disease categories have been demonstrated with robust associations. Only systemic-onset disease seems to be quite different from other disease categories with regard to biologic mechanisms; indeed, it now seems closer to autoinflammatory than to classic autoimmune diseases. New players in the immunologic basis of juvenile idiopathic arthritis (eg, interleukin-17 and regulatory T cells) are also discussed in this review.

Synovial fluid mononuclear cell gene expression profiling suggests dysregulation of innate immune genes in enthesitis-related arthritis patients

OBJECTIVE

Microarray studies have provided insight into the pathogenesis of systemic JIA and have opened new avenues for therapy. Data on the pathogenesis of the enthesitis-related arthritis (ERA) category of JIA are limited, thus we studied the expression profile of ERA patients' peripheral blood and SF mononuclear cells (PBMCs and SFMCs, respectively). PBMCs from healthy subjects were used as controls.

METHODS

RNA from PBMCs of ERA patients (n=17) and healthy controls (n=8) and seven ERA SFMCs were converted to labelled cRNA and hybridized to Illumina Human WG-6_v3_BeadChip chips. Expression profiles were analysed using GeneSpring software. Selected genes of interest were validated by real-time PCR.

RESULTS

There was no significant difference in PBMC gene expression of ERA and control groups. However, there was a significant difference between expression profiles of SFMCs and PBMCs of patients with ERA, with 131 genes being overexpressed and 216 being underexpressed in SFMCs. Among genes involved with immune function, cluster of differentiation (CD)1b, CD1d, MHC class II alpha and beta chain, and soluble CD163 were overexpressed, whereas genes related to NK cell function, namely, Granzyme H, killer cell lectin-like receptor subfamily F member 1, killer cell immunoglobulin-like receptor, three domains, long cytoplasmic tail (KIR3DL3), natural killer group 7 (NKG7) and other genes like CD244, CD248 and Fas apoptotic inhibitory molecule 3 (FAIM3) were underexpressed.

CONCLUSION

ERA SFMCs had a distinct gene expression profile from PBMCs and had higher expression of genes associated with antigen presentation, scavenger function, chemotaxis and proteases, whereas genes involved in NK cell function, cell adhesion and inhibitors of apoptosis were underexpressed.

An Interspecies Regulatory Network Inferred from Simultaneous RNA-seq of Candida albicans Invading Innate Immune Cells

The ability to adapt to diverse micro-environmental challenges encountered within a host is of pivotal importance to the opportunistic fungal pathogen Candida albicans. We have quantified C. albicans and M. musculus gene expression dynamics during phagocytosis by dendritic cells in a genome-wide, time-resolved analysis using simultaneous RNA-seq. A robust network inference map was generated from this dataset using NetGenerator, predicting novel interactions between the host and the pathogen. We experimentally verified predicted interdependent sub-networks comprising Hap3 in C. albicans, and Ptx3 and Mta2 in M. musculus. Remarkably, binding of recombinant Ptx3 to the C. albicans cell wall was found to regulate the expression of fungal Hap3 target genes as predicted by the network inference model. Pre-incubation of C. albicans with recombinant Ptx3 significantly altered the expression of Mta2 target cytokines such as IL-2 and IL-4 in a Hap3-dependent manner, further suggesting a role for Mta2 in host-pathogen interplay as predicted in the network inference model. We propose an integrated model for the functionality of these sub-networks during fungal invasion of immune cells, according to which binding of Ptx3 to the C. albicans cell wall induces remodeling via fungal Hap3 target genes, thereby altering the immune response to the pathogen. We show the applicability of network inference to predict interactions between host-pathogen pairs, demonstrating the usefulness of this systems biology approach to decipher mechanisms of microbial pathogenesis.

Current medical treatments for juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) differs markedly from adult rheumatoid arthritis. It is not a single disease, but an exclusion diagnosis that gather together all forms of arthritis that begin before the age of 16 years, persist for more than 6 weeks, and are of unknown origin. The advent of the new biological treatments has dramatically changed both the observed responses to treatment and the expectations of therapies. The implementation of an adequate legislation as well as the presence of international research networks of pediatric rheumatology have contributed to foster the conduct of controlled clinical trials and the development of validated outcome measures. This review will currently describe the methodological approach for performing clinical trials in JIA as well as the current available drug treatment.

Prednisolone-induced changes in gene-expression profiles in healthy volunteers

Background: Prednisolone and other glucocorticoids (GCs) are potent anti-inflammatory and immunosuppressive drugs. However, prolonged use at a medium or high dose is hampered by side effects of which the metabolic side effects are most evident. Relatively little is known about their effect on gene-expression in vivo, the effect on cell subpopulations and the relation to the efficacy and side effects of GCs. Aim: To identify and compare prednisolone-induced gene signatures in CD4+ T lymphocytes and CD14+ monocytes derived from healthy volunteers and to link these signatures to underlying biological pathways involved in metabolic adverse effects. Materials & methods: Whole-genome expression profiling was performed on CD4+ T lymphocytes and CD14+ monocytes derived from healthy volunteers treated with prednisolone. Text-mining analyses was used to link genes to pathways involved in metabolic adverse events. Results: Induction of gene-expression was much stronger in CD4+ T lymphocytes than in CD14+ monocytes with respect to fold changes, but the number of truly cell-specific genes where a strong prednisolone effect in one cell type was accompanied by a total lack of prednisolone effect in the other cell type, was relatively low. Subsequently, a large set of genes was identified with a strong link to metabolic processes, for some of which the association with GCs is novel. Conclusion: The identified gene signatures provide new starting points for further study into GC-induced transcriptional regulation in vivo and the mechanisms underlying GC-mediated metabolic side effects.

Original submitted 5 January 2011; Revision submitted 24 February 2011

Juvenile idiopathic arthritis

Juvenile idiopathic arthritis is a heterogeneous group of diseases characterised by arthritis of unknown origin with onset before age of 16 years. Pivotal studies in the past 5 years have led to substantial progress in various areas, ranging from disease classification to new treatments. Gene expression profiling studies have identified different immune mechanisms in distinct subtypes of the disease, and can help to redefine disease classification criteria. Moreover, immunological studies have shown that systemic juvenile idiopathic arthritis is an acquired autoinflammatory disease, and have led to successful studies of both interleukin-1 and interleukin-6 blockade. In other forms of the disease, synovial inflammation is the consequence of a disturbed balance between proinflammatory effector cells (such as T-helper-17 cells), and anti-inflammatory regulatory cells (such as FOXP3-positive regulatory T cells). Moreover, specific soluble biomarkers (S100 proteins) can guide individual treatment. Altogether these new developments in genetics, immunology, and imaging are instrumental to better define, classify, and treat patients with juvenile idiopathic arthritis.

Transcriptional profiling to identify biomarkers of disease and drug response

The discovery, biological qualification and analytical validation of genomic biomarkers requires extensive collaborations between individuals with expertise in biology, statistics, bioinformatics, chemistry, clinical medicine, regulatory science and so on. For clinical utility, blood-borne biomarkers (e.g., mRNA and miRNA) of organ damage, drug toxicity and/or response would be preferred to those that are tissue based. Currently used biomarkers such as serum creatinine (indicating renal dysfunction) denote organ damage whether caused by disease, physical injury or drugs. Therefore, it is anticipated that studies of disease will discover biomarkers that can also be used to identify drug-induced injury and vice versa. This article describes transcriptomic blood-borne biomarkers that have been reported to be connected with disease and drug toxicity. Much more qualification and validation needs to be carried out before many of these biomarkers can prove useful. Discussed here are some of the lessons learned and roadblocks to success.

Juvenile idiopathic arthritis: new insights into classification, measures of outcome, and pharmacotherapy

Significant advances have taken place in recent years in our understanding of the aetiopathogenesis, management, and clinical outcome of juvenile idiopathic arthritis (JIA). Fundamental to this advancement has been international collaborative efforts of the clinical scientific community and all those involved in the multidisciplinary care of children and young people with JIA. A key factor has been facing the challenge of developing a robust classification system for JIA, a clinically very heterogeneous group of conditions. JIA illustrates the necessity of disease classification to enable scientific progress but also the iterative and evolving process this entails. What is emerging is the imperative to improve our understanding of the biologic and genetic basis of JIA to underpin classification systems. Growing emphasis is centered on improved holistic care and outcome of children and young people with JIA. The expectation of patients, their families, and clinicians is the goal of inactive disease, remission off treatment, and the health and psychosocial well-being of young people emerging into adulthood. Validated tools that reflect these challenges are being developed, including those measuring disease improvement, flare, remission and minimal disease activity, health-related quality of life, and composite scores of activity and damage. Clinical research networks have driven success in developing an evidence-base for the treatment of JIA. Randomized comparative trials have demonstrated the benefit of early use of intra-articular corticosteroid injections, and the importance of methotrexate as the first-line, disease-modifying antirheumatic drug in JIA. The introduction of biologic therapies has opened a major new epoch in the medical management of JIA, with recent trials published on etanercept, infliximab, adalimumab, abatacept, tocilizumab, and anakinra. This review focuses on recent advances in JIA, especially developments in its classification, validation of appropriate measures of holistic outcome, and the specific contribution of established and newer pharmacologic agents available for treating children and young people.

Generation of novel pharmacogenomic candidates in response to methotrexate in juvenile idiopathic arthritis: correlation between gene expression and genotype

OBJECTIVES

Little is known about the mechanisms of efficacy of methotrexate (MTX) in childhood arthritis, or genetic influences upon response to MTX. The aims of this study were to use gene expression profiling to identify novel pathways/genes altered by MTX and then investigate these genes for genotype associations with response to MTX treatment.

METHODS

Gene expression profiling before and after MTX treatment was performed on 11 children with juvenile idiopathic arthritis (JIA) treated with MTX, in whom response at 6 months of treatment was defined. Genes showing the most differential gene expression after the treatment were selected for single nucleotide polymorphism (SNP) genotyping. Genotype frequencies were compared between nonresponders and responders (ACR-Ped70). An independent cohort was available for validation.

RESULTS

Gene expression profiling before and after MTX treatment revealed 1222 differentially expressed probes sets (fold change >1.7, P<0.05) and 1065 when restricted to full responder cases only. Six highly differentially expressed genes were analyzed for genetic association in response to MTX. Three SNPs in the SLC16A7 gene showed significant association with MTX response. One SNP showed validated association in an independent cohort.

CONCLUSION

This study is the first, to our knowledge, to evaluate gene expression profiles in children with JIA before and after MTX, and to analyze genetic variation in differentially expressed genes. We have identified a gene, which may contribute to genetic variability in MTX response in JIA, and established as proof of principle that genes that are differentially expressed at mRNA level after drug administration may also be good candidates for genetic analysis.

Biologic similarities based on age at onset in oligoarticular and polyarticular subtypes of juvenile idiopathic arthritis

OBJECTIVE

To explore biologic correlates to age at onset in patients with juvenile idiopathic arthritis (JIA) using peripheral blood mononuclear cell (PBMC) gene expression analysis.

METHODS

PBMCs were isolated from 56 healthy controls and 104 patients with recent-onset JIA (39 with persistent oligoarticular JIA, 45 with rheumatoid factor-negative polyarticular JIA, and 20 with systemic JIA). RNA was amplified and labeled using NuGEN Ovation, and gene expression was assessed with Affymetrix HG-U133 Plus 2.0 GeneChips.

RESULTS

A total of 832 probe sets revealed gene expression differences (false discovery rate 5%) in PBMCs from children with oligoarticular JIA whose disease began before age 6 years (early-onset disease) compared with those whose disease began at or after age 6 years (late-onset disease). In patients with early-onset disease, there was greater expression of genes related to B cells and less expression of genes related to cells of the myeloid lineage. Support vector machine analyses identified samples from patients with early- or late-onset oligoarticular JIA (with 97% accuracy) or from patients with early- or late-onset polyarticular JIA (with 89% accuracy), but not from patients with systemic JIA or healthy controls. Principal components analysis showed that age at onset was the major classifier of samples from patients with oligoarticular JIA and patients with polyarticular JIA.

CONCLUSION

PBMC gene expression analysis reveals biologic differences between patients with early-and late-onset JIA, independent of classification based on the number of joints involved. These data suggest that age at onset may be an important parameter to consider in JIA classification. Furthermore, pathologic mechanisms may vary with age at onset, and understanding these processes may lead to improved treatment of JIA.

Gene Expression Profiles from Peripheral Blood Mononuclear Cells Are Sensitive to Short Processing Delays

In the analysis of peripheral blood gene expression, timely processing of samples is essential to ensure that measurements reflect in vivo biology, rather than ex vivo sample processing variables. The effect of processing delays on global gene expression patterns in peripheral blood mononuclear cells (PBMCs) was assessed by isolating and stabilizing PBMC-derived RNA from 3 individuals either immediately after phlebotomy or after a 4 h delay. RNA was labeled using NuGEN Ovation labeling and probed using the Affymetrix HG U133 Plus 2.0 GeneChip(®). Comparison of gene expression levels (≥2-fold expression change and P < 0.05) identified 307 probe sets representing genes with increased expression and 46 indicating decreased expression after 4 h. These differentially expressed genes include many that are important to inflammatory, immunologic, and cancer pathways. Among others, CCR2, CCR5, TLR10, CD180, and IL-16 have decreased expression, whereas VEGF, IL8, SOCS2, SOCS3, CD69, and CD83 have increased expression after a 4 h processing delay. The trends in expression patterns associated with delayed processing were also apparent in an independent set of 276 arrays of RNA from human PBMC samples with varying processing times. These data indicate that the time between sample acquisition, initiation of processing, and when the RNA is stabilized should be a prime consideration when designing protocols for translational studies involving PBMC gene expression analysis.

Emerging roles of SUMO modification in arthritis

Dynamic modification involving small ubiquitin-like modifier (SUMO) has emerged as a new mechanism of protein regulation in mammalian biology. Sumoylation is an ATP-dependent, reversible post-translational modification which occurs under both basal and stressful cellular conditions. Sumoylation profoundly influences protein functions and pertinent biological processes. For example, sumoylation modulates multiple components in the NFkappaB pathway and exerts an anti-inflammatory effect. Likewise, sumoylation of peroxisome proliferator-activated receptor gamma (PPARgamma) augments its anti-inflammatory activity. Current evidence suggests a role of sumoylation for resistance to apoptosis in synovial fibroblasts. Dynamic SUMO regulation controls the biological outcomes initiated by various growth factors involved in cartilage homeostasis, including basic fibroblast growth factors (bFGF or FGF-2), transforming growth factor-beta (TGF-beta) and insulin-like growth factor-1 (IGF-1). The impact of these growth factors on cartilage are through sumoylation-dependent control of the transcription factors (e.g., Smad, Elk-1, HIF-1) that are key regulators of matrix components (e.g., aggrecan, collagen) or cartilage-degrading enzymes (e.g., MMPs, aggrecanases). Thus, SUMO modification appears to profoundly affect chondrocyte and synovial fibroblast biology, including cell survival, inflammatory responses, matrix metabolism and hypoxic responses. More recently, evidence suggests that, in addition to their nuclear roles, the SUMO pathways play crucial roles in mitochondrial activity, cellular senescence, and autophagy. With an increasing number of reports linking SUMO to human diseases like arthritis, it is probable that novel and equally important functions of the sumoylation pathway will be elucidated in the near future.

Juvenile idiopathic arthritis and HLA class I and class II interactions and age-at-onset effects

OBJECTIVE

The aim of this study was to quantitate risk and to examine heterogeneity for HLA at high resolution in patients with the most common subtypes of juvenile idiopathic arthritis (JIA), IgM rheumatoid factor-negative polyarticular JIA and oligoarticular JIA. Use of 4-digit comprehensive HLA typing enabled great precision, and a large cohort allowed for consideration of both age at disease onset and disease subtype.

METHODS

Polymerase chain reaction-based high-resolution HLA typing for class I and class II loci was accomplished for 820 patients with JIA and 273 control subjects. Specific HLA epitopes, potential interactions of alleles at specific loci and between loci (accounting for linkage disequilibrium and haplotypic associations), and an assessment of the current International League of Associations for Rheumatology classification criteria were considered.

RESULTS

An HLA-DRB1/DQB1 effect was shown to be exclusively attributable to DRB1 and was similar between patients with oligoarticular JIA and a younger subgroup of patients with polyarticular JIA. Furthermore, patients with polyarticular JIA showed age-specific related effects, with disease susceptibility in the group older than age 6 years limited to an effect of the HLA-DRB1*08 haplotype, which is markedly different from the additional susceptibility haplotypes, HLA-DRB1*1103/1104, found in the group with oligoarticular JIA and the group of younger patients with polyarticular JIA. Also in contrast to findings for oligoarticular JIA, patients with polyarticular arthritis had no evidence of an HLA class I effect. Markers associated with a reduced risk of disease included DRB1*1501, DRB1*0401, and DRB1*0701. DRB1*1501 was shown to reduce risk across the whole cohort, whereas DRB1*0401 and DRB1*0701 were protective for selected JIA subtypes. Surprisingly, the disease predisposition mediated by DPB1*0201 in individuals without any disease-predisposing DRB1 alleles was great enough to overcome even the very strong protective effect observed for DRB1*1501.

CONCLUSION

Inherited HLA factors in JIA show similarities overall as well as differences between JIA subtypes.

Developmental pharmacogenetics in pediatric rheumatology: utilizing a new paradigm to effectively treat patients with juvenile idiopathic arthritis with methotrexate

Although methotrexate is widely used in clinical practice there remains significant lack of understanding of its mechanisms of action and the factors that contribute to the variability in toxicity and response seen clinically. In addition to differences in drug administration, factors that affect pharmacokinetics and pharmacodynamics such as genetic variation may explain individual differences in drug biotransformation. However, the pediatric population has an additional factor to consider, namely the ontogeny of gene expression which may result in variation throughout growth and development. We review the current understanding of methotrexate biotransformation and the concept of ontogeny, with further discussion of how to implement a developmental pharmacogenomics approach in future studies.

Understanding the relative roles of pharmacogenetics and ontogeny in pediatric drug development and regulatory science

Understanding the dose-exposure-response relationship across the pediatric age spectrum from preterm and term newborns to infants, children, adolescents, and adults is a major challenge for clinicians, pharmaceutical companies, and regulatory agencies. Over the past 3 decades, clinical investigations of many drugs commonly used in pediatric therapeutics have provided valuable insights into age-associated differences in drug disposition and action. However, our understanding of the contribution of genetic variation to variability in drug disposition and response in children generally has lagged behind that of adults. This article proposes a systematic approach that can be used to assess the relative contributions of ontogeny and genetic variation for a given compound. Application of the strategy is illustrated using the current regulatory dilemma posed by the safety and effectiveness of over-the-counter cough and cold remedies as an example. The results of the analysis can be used to aid in the design of studies to yield maximally informative data in pediatric populations of different ages and developmental stages and thereby improve the efficiency of study design.

An overview of genetics of paediatric rheumatic diseases

The evidence so far suggests that the paediatric inflammatory diseases encountered in rheumatology practice may be largely genetic in origin, where common single nucleotide polymorphisms (SNPs) in multiple genes contribute to risk, with real but variable environmental components. As far as genetic susceptibility to common paediatric rheumatic diseases is concerned, only juvenile idiopathic arthritis (JIA) has been investigated in any substantial way so far. This article discusses susceptibility for different types of JIA, the different methods used and their advantages and disadvantages. The genetic code is also modifiable by epigenetic mechanisms and examples of these in immunity and rheumatoid arthritis are given to indicate another area of research in the elucidation of the genetics of paediatric rheumatic diseases.