Fused Omics Data Models Reveal Gut Microbiome Signatures Specific of Inactive Stage of Juvenile Idiopathic Arthritis in Pediatric Patients.

Pamela Vernocchi,Alfredo Miccheli,Alberta Tomassini,Lorenza Putignani,Federico Marini,Giorgio Capuani,Bruno Dallapiccola,Giorgia Conta,Alberto Martini,Federica Del Chierico,E H Pieter Van Dijkhuizen,Clara Malattia,Fabrizio De Benedetti

doi:10.3390/microorganisms8101540

Abstract

Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in children. Herein, we evaluated the relationship between the gut microbiome (GM) and disease phenotype by an integrated omics fused approach. In a multicenter, observational cohort study, stools from Italian JIA patients were collected at baseline, active, and inactive disease stages, and their GM compared to healthy controls (CTRLs). The microbiota metabolome was analyzed to detect volatile- and non-volatile organic compounds (VOCs); the data were fused with operational taxonomic units (OTUs) from 16S RNA targeted-metagenomics and classified by chemometric models. Non-VOCs did not characterize JIA patients nor JIA activity stages compared to CTRLs. The core of VOCs, (Ethanol, Methyl-isobutyl-ketone, 2,6-Dimethyl-4-heptanone and Phenol) characterized patients at baseline and inactive disease stages, while the OTUs represented by Ruminococcaceae, Lachnospiraceae and Clostridiacea discriminated between JIA inactive stage and CTRLs. No differences were highlighted amongst JIA activity stages. Finally, the fused data discriminated inactive and baseline stages versus CTRLs, based on the contribution of the invariant core of VOCs while Ruminococcaceae concurred for the inactive stage versus CTRLs comparison. In conclusion, the GM signatures enabled to distinguish the inactive disease stage from CTRLs.

Highlights

In recent years, many evidences have highlighted that alterations in the gut microbiome (GM) were frequently associated to the development of local and systemic inflammatory and autoimmune diseases [1], and the role of GM in the pathogenesis of juvenile idiopathic arthritis (JIA) has been hypothesized [2]
The present study highlights a progression of a previous clinical study [3]; in particular, a well-defined Italian cohort of JIA patients at baseline, inactive and persistent stage conditions compared to CTRLs was studied to find possible associations amongst composition and content of volatile organic compound (VOCs) and low weight molecules (Non-volatile organic compounds (VOCs)) in the stools
The integrated metabolomics and targeted-metagenomics results suggest that the observed GM functional and compositional profiles can be specific for the subjects affected by JIA, rather than for each specific disease stage, and regardless of inflammation status, in agreement with previous results obtained by van Dijkhuizen et al [3]

Summary

Introduction

Many evidences have highlighted that alterations in the gut microbiome (GM) were frequently associated to the development of local and systemic inflammatory and autoimmune diseases [1], and the role of GM in the pathogenesis of juvenile idiopathic arthritis (JIA) has been hypothesized [2]. The study of van Dijkhuizen et al [3] included a large cohort of treated-naive JIA Dutch and Italian patients in longitudinal follow up. This experimental design, which allowed us to compare the GM amongst patients versus CTRLs, revealed that age, geographic origin and disease status appeared to be determinant factors for the GM signature, regardless of the disease activity stage (i.e., baseline, inactive, active) and inflammation markers. The present study highlights a progression of a previous clinical study [3]; in particular, a well-defined Italian cohort of JIA patients at baseline, inactive and persistent stage conditions compared to CTRLs was studied to find possible associations amongst composition (operational taxonomic units OTUs at bacterial family level) and content of volatile organic compound (VOCs) and low weight molecules (Non-VOCs) in the stools. The multi-omic approach, achieved by the integration of different metabolomics and targeted-metagenomics data, might provide different profiles to better highlight the role of GM in the JIA disease

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Discussion

Conclusion