Microbial community profiling for forensic drowning diagnosis across locations and submersion times

Abstract

Background: Drowning diagnosis has long been a critical issue in forensic research, influenced by various factors such as the environment and decomposition time. While traditional methods such as diatom analysis have limitations in decomposed remains, microbial community profiling offers a promising alternative. With the advancement of high-throughput sequencing technology, forensic microbiology has become a prominent focus in the field, providing new research avenues for drowning diagnosis. During drowning, microbial communities enter the lung tissue along with the water.

Methods: In this study, using a murine model, we collected samples from three rivers at random sites at postmortem intervals (PMI) of 1, 4, and 7 days‌ to comprehensively evaluate the differences in microbial communities between mice subjected to drowning versus postmortem immersion.

Results: The α-diversity analysis revealed that the observed Operational Taxonomic Units (OTUs) for the drowning group on day 1 was 234.77 ± 16.60, significantly higher than the postmortem immersion group (171.32 ± 9.22), indicating greater initial microbial richness in the drowning group. Additionally, Shannon index analysis showed a significant decline in evenness in the postmortem immersion group on day 7 (1.46 ± 0.09), whereas the drowning group remained relatively stable (2.38 ± 0.15), further indicating a rapid decrease in microbial diversity in the postmortem immersion group over time. PCoA analysis demonstrated that differences in microbial community composition between drowning and postmortem immersion groups were notably stable. Key microbial taxa differentiating the groups were identified through LEfSe analysis, with Enterococcaceae (family), Escherichia-Shigella (genus), and Proteus (genus), emerging as significant markers in drowning cases. A random forest model, trained using microbial community data, exhibited high predictive accuracy (AUC = 0.96) across locations and immersion times and identified microbial markers, including Enterococcaceae (family), Lactobacillales (order), Morganellaceae (family), as critical features influencing model performance.

Conclusion: These findings underscore the potential of combining 16 S rRNA sequencing with machine learning as a powerful tool for drowning diagnosis, offering novel insights into forensic microbiology.

Keywords: 16S rRNA sequencing; Drowning diagnosis; Machine learning; Random forest.

Fig. 1

Alpha diversity analysis of the drowning and postmortem submersion groups. (A) Observed OTUs. (B) Shannon index. D, drowning group; PS, postmortem submersion group. * p < 0.05, *** p < 0.001

Fig. 2

Principal Coordinate Analysis (PCoA) of lung samples (different colors represent different groups, different sizes represent different PMIs, and different shapes represent different water collection points). (A) PCoA results based on PCo1 and PCo2; (B) PCoA results based on PCo1 and PCo3; (C) PCoA results based on PCo2 and PCo3. D, drowning group; PS, postmortem submersion group

Fig. 3

Cladogram based on Linear Discriminant Analysis Effect Size (LEfSe). D, drowning group; PS, postmortem submersion group. Darker colors indicate a higher degree of decomposition

Fig. 4

LEfSe analysis with LDA values (left) of top 30 and relative abundance (right). The labels (p, c, o, f, g, s) before each taxon represented phylum, class, order, family, genus, and species respectively. D, drowning group; PS, postmortem submersion group. ** p < 0.01, *** p < 0.001

Fig. 5

Random forest model establishment and prediction. (A) Learning curve of the RF model. (B) Variable importance ranking of the RF model (top 10). (C) ROC curve for predicting L3 samples with the gray area representing the 95% confidence interval. (D) Graphical representation of SHAP feature importance. The labels (c, o, f, g) before each taxon represented class, order, family, and genus respectively