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 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).