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Metabolite assignment of ultrafiltered synovial fluid extracted from knee joints of reactive arthritis patients using high resolution NMR spectroscopy
Author(s) -
Dubey Durgesh,
Chaurasia Smriti,
Guleria Anupam,
Kumar Sandeep,
Modi Dinesh Raj,
Misra Ramnath,
Kumar Dinesh
Publication year - 2019
Publication title -
magnetic resonance in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 72
eISSN - 1097-458X
pISSN - 0749-1581
DOI - 10.1002/mrc.4763
Subject(s) - metabolomics , chemistry , metabolite , nuclear magnetic resonance spectroscopy , synovial fluid , heteronuclear single quantum coherence spectroscopy , metabolome , homonuclear molecule , proton nmr , nuclear magnetic resonance , chromatography , stereochemistry , biochemistry , pathology , medicine , molecule , physics , alternative medicine , organic chemistry , osteoarthritis
Abstract Currently, there are no reliable biomarkers available that can aid early differential diagnosis of reactive arthritis (ReA) from other inflammatory joint diseases. Metabolic profiling of synovial fluid (SF)—obtained from joints affected in ReA—holds great promise in this regard and will further aid monitoring treatment and improving our understanding about disease mechanism. As a first step in this direction, we report here the metabolite specific assignment of 1 H and 13 C resonances detected in the NMR spectra of SF samples extracted from human patients with established ReA. The metabolite characterization has been carried out on both normal and ultrafiltered (deproteinized) SF samples of eight ReA patients ( n = 8) using high‐resolution (800 MHz) 1 H and 1 H─ 13 C NMR spectroscopy methods such as one‐dimensional 1 H CPMG and two‐dimensional J‐resolved 1 H NMR and homonuclear 1 H─ 1 H TOCSY and heteronuclear 1 H─ 13 C HSQC correlation spectra. Compared with normal SF samples, several distinctive 1 H NMR signals were identified and assigned to metabolites in the 1 H NMR spectra of ultrafiltered SF samples. Overall, we assigned 53 metabolites in normal filtered SF and 64 metabolites in filtered pooled SF sample compared with nonfiltered SF samples for which only 48 metabolites (including lipid/membrane metabolites as well) have been identified. The established NMR characterization of SF metabolites will serve to guide future metabolomics studies aiming to identify/evaluate the SF‐based metabolic biomarkers of diagnostic/prognostic potential or seeking biochemical insights into disease mechanisms in a clinical perspective.