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

ABSTRACT: 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.

SUBMITTER: Poppenberg KE

PROVIDER: S-EPMC6842535 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Poppenberg Kerry E KE Jiang Kaiyu K Li Lu L Sun Yijun Y Meng Hui H Wallace Carol A CA Hennon Teresa T Jarvis James N JN

Arthritis research & therapy 20191109 1

<h4>Background</h4>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.<h4>Methods</ ...[more]

PMID: 31706344

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Project description:BackgroundInterleukin (IL)-37 has emerged as a novel anti-inflammatory cytokine that play an immunosuppressive role in regulating inflammatory response. This study aimed to measure IL-37 levels in the plasma and peripheral blood mononuclear cells (PBMCs) of patients with systemic juvenile idiopathic arthritis (sJIA), and to establish the correlation between IL-37 levels and disease activity, laboratory parameters and inflammatory cytokines.MethodsThe mRNA levels of IL-37 in PBMCs and plasma IL-37 concentrations in 46 sJIA patients and 30 age- and sex-matched healthy controls were measured by real-time polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The correlations between plasma IL-37 levels and disease activity, laboratory parameters and inflammatory cytokines in sJIA were analyzed by Spearman correlation test. PBMCs from the sJIA patients were stimulated with recombinant human IL-37 (rhIL-37) protein, expressions of IL-1?, IL-6, TNF-? and IL-17 were detected by RT-PCR and ELISA.ResultsPlasma levels of IL-37 and relative IL-37 mRNA expression were significantly elevated in sJIA patients, especially in active sJIA patients, when compared with the healthy controls (P?<?0.001). Furthermore, patients with active disease showed higher IL-37 mRNAs and plasma protein levels than those with inactive disease as well as healthy controls. Plasma IL-37 levels were correlated with disease activity and inflammatory cytokines (IL-6, TNF-?, IL-17 and GM-CSF) in sJIA patients. The productions of inflammatory cytokines such as IL-6, TNF-?, IL-17 in PBMCs from sJIA patients were obviously decreased after recombinant IL-37 stimulation, whereas the production of IL-1? was not changed.ConclusionsOur results demonstrate that levels of IL-37 were higher in sJIA patients, which were correlated with disease activity and sJIA related inflammatory cytokines. In addition, rhIL-37 down-regulates the expressions of inflammatory cytokines form PBMCs in sJIA patients, suggesting that IL-37 may have the potential role as a natural inhibitor for the pathogenesis and therapy of sJIA.

Dataset Information

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

Publications

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

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