Characterization of Lipid Epi-Metabolites/Reactions as Diagnostic Biomarkers for Idiopathic Pulmonary Fibrosis by Lipidepifind

Lipidomics
enables comprehensive profiling of lipid species, providing
a powerful approach for studying disease pathogenesis and identifying
biomarkers. Conventional lipidomics workflows rely on the annotation
of lipid species using curated biochemical databases. However, many
unidentified lipids are absent from these databases, which limits
the discovery of functional or pathological biomarkers. To address
this limitation, we developed a method to systematically identify
structurally modified lipids, referred to as epi-metabolites, in biological
samples. First, 1479 parent lipids were identified across different
biological matrices using liquid chromatography–high-resolution
mass spectrometry. Next, the MS characteristics of all potential lipid
epi-metabolites were predicted using a metabolic network expansion
strategy comprising 62 reaction types and were subsequently screened
in raw test sample data. To ensure reproducibility and improve analytical
efficiency, we implemented this workflow in a user-friendly Shiny
app (https://xinguang-liu.shinyapps.io/metabolite_mz_predictor/)
and an open-source R package, Lipidepifind (https://github.com/Xinguang-Liu/Lipidepifind). Lipid epi-metabolites were then identified using four validation
criteria: MS/MS analysis, retention order filtering, retention time
calibration, and collision cross-section validation. Finally, differential
structural modifications and putative enzyme-mediated reactions were
characterized through multivariate statistics and epi-metabolic reaction
enrichment analysis. We demonstrated the utility of this approach
in idiopathic pulmonary fibrosis (IPF), identifying 725 lipid epi-metabolites
in patient serum. Two enriched lipid oxidative cleavage reactions
were observed, and two epi-metabolite biomarkers, phosphatidylcholine
(16:0/9:0 (CHO)) and (16:0/5:0 (COOH)), were identified in IPF patients.
This method outperformed conventional database-based strategies in
matching and identifying lipid epi-metabolites and revealed differential
lipid modifications and enzymatic processes in IPF.