Serum metabolomic profiling predicts synovial gene expression in rheumatoid arthritis

• Main Authors: Rekha Narasimhan, Roxana Coras, Sara B. Rosenthal, Shannon R. Sweeney, Alessia Lodi, Stefano Tiziani, David Boyle, Arthur Kavanaugh, Monica Guma
• Format: Article
• Language: English
• Published: BMC 2018-08-01
• Series: Arthritis Research & Therapy
• Subjects: NMR; Metabolomics; Biomarkers; Synovium; Rheumatoid arthritis; Gene expression
• Online Access: http://link.springer.com/article/10.1186/s13075-018-1655-3

Abstract Background Metabolomics is an emerging field of biomedical research that may offer a better understanding of the mechanisms of underlying conditions including inflammatory arthritis. Perturbations caused by inflamed synovial tissue can lead to correlated changes in concentrations of certain metabolites in the synovium and thereby function as potential biomarkers in blood. Here, we explore the hypothesis of whether characterization of patients’ metabolomic profiles in blood, utilizing 1H-nuclear magnetic resonance (NMR), predicts synovial marker profiling in rheumatoid arthritis (RA). Methods Nineteen active, seropositive patients with RA, on concomitant methotrexate, were studied. One of the involved joints was a knee or a wrist appropriate for arthroscopy. A Bruker Avance 700 MHz spectrometer was used to acquire NMR spectra of serum samples. Gene expression in synovial tissue obtained by arthroscopy was analyzed by real-time PCR. Data processing and statistical analysis were performed in Python and SPSS. Results Analysis of the relationships between each synovial marker-metabolite pair using linear regression and controlling for age and gender revealed significant clustering within the data. We observed an association of serine/glycine/phenylalanine metabolism and aminoacyl-tRNA biosynthesis with lymphoid cell gene signature. Alanine/aspartate/glutamate metabolism and choline-derived metabolites correlated with TNF-α synovial expression. Circulating ketone bodies were associated with gene expression of synovial metalloproteinases. Discriminant analysis identified serum metabolites that classified patients according to their synovial marker levels. Conclusion The relationship between serum metabolite profiles and synovial biomarker profiling suggests that NMR may be a promising tool for predicting specific pathogenic pathways in the inflamed synovium of patients with RA.