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Abstract Number: 2701

An Expansion of Rare Lineage Intestinal Microbes Characterize Rheumatoid Arthritis

Jun Chen1, John M. Davis III2, Eric L. Matteson3 and Veena Taneja4, 1Health Sciences Research, Mayo Clinic, Rochesert, MN, 2Division of Rheumatology, Mayo Clinic, Rochester, MN, 3Division of Rheumatology, Department of Internal Medicine and Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 4Immunology, Mayo Clinic, Rochester, MN

Meeting: 2015 ACR/ARHP Annual Meeting

Date of first publication: September 29, 2015

Keywords: Biomarkers, metabolomics, microbiome, rheumatoid arthritis (RA) and therapeutic targeting

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Session Information

Date: Tuesday, November 10, 2015

Session Title: Rheumatoid Arthritis - Human Etiology and Pathogenesis Poster III

Session Type: ACR Poster Session C

Session Time: 9:00AM-11:00AM

Background/Purpose: The adaptive immune response in Rheumatoid arthritis (RA) is influenced by an interaction between host genetics and environment, particularly the host microbiome. To define the role of host microbiome, we characterized intestinal microbiome signatures in patients with RA.

Methods: To identify an RA biomarker profile, fecal samples from 40 RA patients and 32 healthy non-RA comparator subjects were sequenced for the 16S ribosomal DNA.  Bray-Curtis distances were constructed and PERMANOVA was used to test for an association between variables of interest and the overall microbiota composition.PICRUSt was used to infer the abundance of functional categories (KEGG pathways) based on the 16S rRNA data, depending upon which differential abundance analysis was performed. Differential abundance analysis was also performed using LEfSe software. The machine learning algorithm ‘Random Forest’ was used to build a predictive model, and identify the most discriminatory taxa between patients and controls.

Results: Patients with RA exhibited decreased gut microbial diversity compared to controls. Increased rheumatoid factor levels and disease duration were associated with decreased species richness and diversity (P<0.05 and P<0.1 respectively) (Fig 1A). PERMANOVA based on Bray-Curtis distance showed that the structure of the microbiota of patients with RA differed significantly from control subjects (Fig 1B). A taxon-level analysis suggested an expansion of the rare taxa, Actinobacteria, with a decrease in abundant taxa in patients with RA compared to controls. The abundance of Actinobacteria correlated strongly with high levels of IL-17A. Prediction models based on the Random Forests algorithm suggested that 3 genera segregated with RA. Increased abundance of Actinobacteria was associated with decrease in the expression of tight junction protein in epithelial cells and abundance of metabolite alpha aminoadipic, a marker for age-associated changes in collagen.  

Conclusion: These observations suggest dysbiosis in patients with RA resulting from the abundance of certain rare bacterial lineages. A correlation between the intestinal microbiota and metabolic signatures could determine a predictive profile for disease causation, progression, and drug efficacy.


Disclosure: J. Chen, None; J. M. Davis III, None; E. L. Matteson, Novartis/Sanofi/Centocor-Jansen/Celgene/Amgen/Roche/Genentech/Mesoblast/Pfizer, 2; V. Taneja, None.

To cite this abstract in AMA style:

Chen J, Davis JM III, Matteson EL, Taneja V. An Expansion of Rare Lineage Intestinal Microbes Characterize Rheumatoid Arthritis [abstract]. Arthritis Rheumatol. 2015; 67 (suppl 10). https://acrabstracts.org/abstract/an-expansion-of-rare-lineage-intestinal-microbes-characterize-rheumatoid-arthritis/. Accessed June 25, 2022.
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