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Updated: Jan 12, 2026
4D Multimodality Imaging of Citrobacter rodentium Infections in Mice
Published on: August 13, 2013
Effects of Combined Bacterial Infection and Radiation Injury on Biofluid Metabolite Profiles in the Murine Model
Evan L Pannkuk1,2,3, Anika Kot1, Lorreta Yun-Tien Lin1
1Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, D.C., District of Columbia 20057, United States.
Abstract:
Rapid biodosimetry tools are needed to assess radiation exposure in scenarios complicated by secondary infections. This study evaluated how Listeria monocytogenes infection impacts metabolite-based biodosimetry in male C57BL/6 mice. The mice were infected and exposed to 0, 2, or 6 Gy X-rays at 4 days postinfection. Untargeted metabolomics was performed on serum and urine at 1 day postirradiation. We found that the effect of bacterial infection increased white blood cell counts and altered metabolomic signatures in a biofluid- and compound-specific manner. Infection alone altered select serum lipids and urinary TCA intermediates. Some urinary metabolites displayed additive effects in infected animals exposed to 6 Gy. The best model for combined biofluids (serum: lysophosphatidylcholines [14:0] and [22:5], glycerophosphatidylcholines [42:8] and [42:11] and citrate; urine: glutamic acid, creatine, propionylcarnitine, acetylspermidine, and hexanoylglycine) was determined with a multivariate random forest analysis model. A combined biofluid random forest model predicted the radiation dose and infection status with 90% accuracy (RMSE = 1.31 Gy). These findings support the development of robust, multiplexed biodosimetry panels capable of accounting for real-world confounders like infection. Such models can improve the precision of triage decisions following radiological emergencies (raw data available at Metabolomics Workbench Study IDs ST004101 and ST004100).
Insights
Assessing radiation exposure with Listeria infection requires advanced biodosimetry. This study shows a metabolomics model combining serum and urine accurately predicts radiation dose and infection status in mice.
Area of Science:
- * Radiation biology
- * Infectious disease
- * Metabolomics
Background:
- * Rapid biodosimetry is crucial for assessing radiation exposure, especially when complicated by secondary infections.
- * Listeria monocytogenes infection can confound existing biodosimetry methods.
- * Evaluating the impact of infection on metabolite-based biodosimetry is essential for accurate dose assessment.
Purpose of the Study:
- * To investigate how Listeria monocytogenes infection affects metabolite profiles in serum and urine.
- * To develop and validate a biodosimetry model that accounts for radiation exposure and bacterial infection.
- * To assess the accuracy of a multivariate random forest model for predicting radiation dose and infection status.
Main Methods:
- * Male C57BL/6 mice were infected with Listeria monocytogenes and exposed to 0, 2, or 6 Gy X-rays.
- * Untargeted metabolomics was performed on serum and urine samples collected one day post-irradiation.
- * A multivariate random forest analysis was used to build a predictive model using combined biofluid data.
Main Results:
- * Listeria infection increased white blood cell counts and altered metabolite signatures in serum and urine.
- * Specific serum lipids and urinary TCA intermediates were affected by infection alone.
- * A combined biofluid model accurately predicted radiation dose and infection status with 90% accuracy (RMSE = 1.31 Gy).
Conclusions:
- * Metabolite-based biodosimetry can be adapted to account for confounding factors like Listeria infection.
- * A multiplexed biodosimetry panel using serum and urine metabolites shows promise for accurate dose assessment in complex scenarios.
- * These findings support the development of robust biodosimetry tools for improved triage decisions in radiological emergencies.
