Effects of Combined Bacterial Infection and Radiation Injury on Biofluid Metabolite Profiles in the Murine Model

Rapid biodosimetry tools are needed to assess radiation exposure in scenarios complicated by secondary infections. This study evaluated how <i>Listeria monocytogenes</i> 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).