Integrated Microbiome and Metabolome Analysis for Characterization and Discrimination of Saliva, Semen, Vaginal Secretions, and Their Mixtures

Body fluid identification (BFID) and estimation of time
since deposition
(TsD) are valuable yet challenging in forensic practice. Previous
studies have demonstrated that integrating microbial and metabolomic
profiles provides complementary biological insights. Therefore, this
study performed untargeted metabolomic profiling and full-length 16S
rRNA sequencing on fresh saliva (SA), semen (SE), vaginal secretions
(VF), and their mixtures (SA-VF and SE-VF), with additional microbial
analysis after 15 and 30 days of indoor exposure. Results showed the
single-fluid samples exhibited specific dominant bacterial taxa, whereas
the two mixture samples contained detectable bacterial signatures
from both constituent fluids. Untargeted UHPLC-QTOF/MS analysis revealed
unique metabolic signatures for each body fluid, enriched in biologically
relevant pathways like steroid and bile acid metabolism. Moreover,
we putatively identified characteristic metabolites, including α-solanine,
candicidin, and megalomicin C1, some of which are rare microbial antibiotics.
Owing to the exploratory nature and associated constraints of nontargeted
approaches, these results serve as a provisional reference for identifying
potential candidates. Integration of metabolomic and microbiome data
uncovered strong metabolite-microbe correlations, highlighting microbially
influenced metabolic networks unique to each body fluid type. Using
differential microbes and metabolites individually as input features,
the random forest model achieved BFID accuracies of 80 and 83.1%,
respectively; however, integrating both sets of features increased
accuracy to 100%. In contrast, microbial-based TsD prediction performed
well for single-fluid samples but showed reduced effectiveness for
mixed samples. Overall, our research highlights the powerful predictive
potential and improved predictive accuracy of the integration of microbiome
and metabolome data in BFID.