A metabolomics study into infuenza virus infection by HRAM Q-TOF analysis

Importance of the Topic

Analytical metabolomics provides insight into host–pathogen interactions by profiling small molecules in biological fluids. Influenza A virus infections cause complex metabolic changes tied to immune responses. Untargeted high-resolution metabolomics enables detection of diverse metabolites, facilitating understanding of disease progression and potential biomarkers.

Objectives and Study Overview

This study aimed to track serum metabolic alterations in swine following H1N1 influenza A infection over a 13-day period. The focus was on developing and applying a rapid, untargeted metabolomics workflow using high-resolution accurate-mass Q-TOF instrumentation to compare pre-infection and post-infection profiles and to characterize innate, adaptive, and recovery phases.

Methodology

• Animal model: Eight pigs were acclimatized and intranasally infected with H1N1pdm09; serum samples were collected pre-infection and at 1,2,3,4,5,6,7,9,11,13 days post-infection.
• Sample preparation: Serum extracts processed for untargeted LC-MS analysis.
• Data processing: Component detection via the Find algorithm in Insight Explore; compound table construction and automated peak integration; manual review ensured 716 reliable features.
• Identification: Formula prediction and MS/MS comparison against METLIN; isomer separation by retention time and diagnostic fragments.
• Statistical analysis: Univariate and multivariate trends assessed using MetaboAnalyst.

Instrumentation

• Shimadzu LCMS-9030 high-resolution accurate-mass Q-TOF.
• LC-MS settings: MS1 scan 100–1000 Da and DIA-MS/MS with 44 sequential windows over 75–1000 Da, cycle time <1 s.
• Quality control: Randomized injections, pooled QC samples, and external mass calibration.

Main Results and Discussion

Despite robust detection of 716 serum components and rigorous data analysis, metabolic profiles showed high inter-group variability. Expected perturbations around days 3–5 post-infection were not consistently observed. Representative lipids (sphingomyelin d18:0/18:1) and small molecules (hippuric acid) exhibited minor fluctuations without clear infection trends.

Practical Benefits and Applications

• Demonstrates a rapid, high-throughput untargeted metabolomics workflow suitable for infection studies.
• Provides a reference pipeline for serum analysis in veterinary and translational research.

Future Trends and Applications

• Increasing sample size and biological replicates to reduce variability.
• Integrating targeted assays for candidate biomarkers.
• Employing advanced chemometric and machine learning tools for pattern recognition.
• Expanding spectral libraries and in silico MS/MS prediction to improve identification coverage.
• Combining metabolomics with proteomics and transcriptomics for multi-omics insights.

Conclusion

A robust untargeted LC-Q-TOF metabolomics method was established and applied to track influenza A infection in swine serum. Although substantial metabolic shifts were not detected under current experimental conditions, the workflow offers a foundation for future infection metabolomics studies with optimized design and deeper analysis.