LC/TOF, LC/HRMS, LC/MS, LC/MS/MS
IndustriesClinical Research
ManufacturerShimadzu
Importance of the Topic
The gut microbiome produces metabolites that influence systemic inflammation and disease progression. Interleukin-18 is a key cytokine involved in immune signaling and metabolic regulation. Understanding how IL-18 modulates microbiome-derived compounds in the circulation can reveal mechanisms underlying metabolic and inflammatory disorders.
Study Objectives and Overview
This study applied an untargeted HILIC-LC-DIA-MS/MS metabolomics workflow to compare plasma profiles from four groups of mice: wild type and IL-18 knockout with or without a gut microbiome. The goal was to identify metabolites altered by IL-18 and microbial status and to explore their potential roles in health and disease.
Methodology and Instrumentation
- Chromatography: Shim-pack Velox HILIC column, gradient of ammonium formate with formic acid in water and acetonitrile, cycle time 18 minutes
- Detection: Shimadzu LCMS-9030 Q-TOF in positive and negative electrospray modes, DIA and DDA MS/MS scans covering m/z 60-1000
- Data Processing: Feature detection and alignment in MS-DIAL, statistical analysis (PCA, t-test) in MetaboAnalyst, compound identification in LabSolutions Insight using in-house and public MS/MS libraries
Main Results and Discussion
- Group Separation: PCA clearly distinguished specific pathogen free (spf) from germ free (gf) mice based on plasma metabolome.
- IL-18 Effects under SPF Conditions: IL-18 knockout spf mice showed elevated levels of amino acids including arginine, citrulline, methionine, phenylalanine, proline and valine compared to wild type spf (p<0.05).
- Role of the Microbiome: These differences were absent in germ free animals, indicating that IL-18 intertwined with microbial metabolism to shape the systemic metabolite profile.
- Metabolite Identification: Several compounds were confirmed at high confidence (MSI level 1) using authentic standards, highlighting the robustness of the workflow.
Benefits and Practical Applications
- The untargeted workflow enables comprehensive profiling of host-microbiome metabolic interactions.
- Identified metabolites may serve as biomarkers for IL-18 mediated inflammatory or metabolic states.
- The approach can be adapted to other cytokines or pathological models to discover novel mechanistic links.
Future Trends and Potential Applications
- Scaling to larger cohorts will validate early biomarkers and refine disease associations.
- Integration with multi-omics (transcriptomics, proteomics) could deepen mechanistic insights.
- Advances in spectral databases and AI-driven annotation will increase identification confidence and throughput.
- Clinical translation may leverage microbiome-targeted interventions based on metabolite signatures.
Conclusion
This proof-of-concept metabolomics study reveals that IL-18 exerts microbiome-dependent effects on systemic amino acid levels. The comprehensive HILIC-DIA-MS/MS workflow, combined with rigorous data analysis, provides a powerful platform for unraveling host-microbiome metabolic crosstalk and supports future investigations into therapeutic targets.
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