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

Area of Science:

• Metabolomics
• Systems Biology
• Infectious Disease Research

Background:

• Influenza virus outbreaks pose significant public health challenges.
• Traditional metabolomics struggles with identifying unknown metabolites ("dark matter") in influenza research.
• There is a need for advanced analytical methods to discover novel metabolic markers.

Purpose of the Study:

• To establish and validate a structure-based comparative metabolomics approach for influenza research.
• To identify and annotate candidate metabolic markers in H1N1-infected mice.
• To overcome limitations of database-dependent metabolomic analysis.

Main Methods:

• Utilized liquid chromatography-mass spectrometry (LC-MS) on fecal samples from a C57BL/6J mouse model of H1N1 infection.
• Integrated quantitative MS¹ data with MS²-derived fragmentation trees and molecular fingerprints.
• Employed structure-based comparative metabolomics, including Mirror plot, CFM-ID, and sim-Rank-Network for annotation and validation.

Main Results:

• Identified 40 differential metabolites through quantitative MS¹ analysis.
• Successfully annotated metabolite structures using qualitative MS² data, enabling library spectra-free analysis.
• Discovered and validated LysoPE 15:0 as a candidate metabolite marker for H1N1 infection.

Conclusions:

• Structure-based comparative metabolomics effectively annotates metabolomic "dark matter" without relying on spectral databases.
• This methodology provides a robust workflow for discovering novel metabolite biomarkers in infectious diseases.
• The study highlights a promising avenue for advancing influenza research and diagnostics.