Structure-Based Comparative Metabolomics Identifies LysoPE 15:0 as a Candidate Metabolite Marker of Influenza Virus Infection Dynamics

Influenza virus outbreaks remain a persistent public health concern, yet traditional metabolomics methods are inadequate for addressing key analytical challenges of “dark matter” in influenza research. By integrating quantitative MS1 data, MS2-derived fragmentation trees and molecular fingerprints, structure-based comparative metabolomics enhances predictive capability for chemical structures, and enables the discovery of candidate metabolic markers without the need for database spectra. In this study, we established a C57BL/6J mouse model of H1N1 infection (with PBS as control) and performed structure-based comparative metabolomics on fecal samples using liquid chromatography–mass spectrometry (LC-MS). Quantitative analysis of MS1 data identified 40 differential metabolites, while qualitative analysis of MS2 data enabled their structural annotation. A candidate metabolite marker, LysoPE 15:0, along with other potential metabolic markers, was annotated and validated using Mirror plot, CFM-ID, and sim-Rank-Network. Our findings demonstrate that structure-based comparative metabolomics enables library spectra-free annotation of metabolomic “dark matter” and provides a methodological workflow for discovering candidate metabolite markers in other diseases.