Investigating Metabolic Pathways of Ankylosing Spondylitis via Compound Similarity Network-Assisted Metabolomics Analysis

LC-MS-based
metabolomics is a powerful tool in analyzing disease
molecular mechanisms. Because of its high sensitivity and throughput,
LC-MS-based metabolomics usually detects thousands of metabolites.
How to find disease-related metabolites and investigate metabolic
pathways is critical in metabolomics studies. Conventional statistics-guided
data mining looks only for mathematical relations between the detected
metabolites and the metadata. It is not enough to unveil biological
pathways of metabolites involved in disease progression. Compound
similarity network (CSN) is a spectral-independent technique to cluster
compounds based on their structural similarities and to investigate
potential chemical transformations. Herein, we developed a CSN-assisted
metabolic data mining strategy to quantitatively find key metabolites
in diseases through structural similarities and explore disease-regulating
metabolic pathways based on KEGG and RetroRules metabolic reaction
templates. The strategy was used in a metabolomics study of ankylosing
spondylitis (AS), in comparison with a healthy cohort and rheumatoid
arthritis (RA), a rheumatic disease having similar symptoms with early
AS. Using CSN-assisted data mining, a palmitic acid pathway was constructed,
which may be regulated in AS pathogenesis.