Estimation of postmortem submersion interval based on microbial community composition in human remains recovered from aquatic environments.

Estimating the postmortem submersion interval (PMSI) remains a major challenge in forensic science. Although microbiome-based approaches to postmortem interval estimation have advanced considerably in recent years, studies focusing specifically on aquatic environments, particularly involving human remains and region-specific conditions, remain limited. This study aimed to investigate microbial community composition in human remains recovered from freshwater ecosystems using 16S rRNA gene sequencing and to evaluate the potential use of stage-associated microbial community patterns for estimating PMSI. Gut swabs and rib bones were collected to study the changes in the bacterial community with time. To estimate PMSI, we established regression models using random forest algorithms based on postmortem microbial community composition. The full model, incorporating 1129 bacterial genera, explained 40.4% of the variance in PMSI estimation. Furthermore, we identified 15 key genera and aquatic-specific biomarkers to construct a simplified predictive model, which significantly improved performance, explaining 81.8% of the variance with a mean absolute error of 2.66 days. To our knowledge, this study represents one of the first investigations in India to characterize microbial community composition in human cadavers recovered from freshwater environments. This research provides evidence that microbial community composition and stage-associated bacterial patterns serve as valuable biological markers for estimating PMSI in corpses recovered from aquatic habitats, offering a robust tool for forensic investigations involving submerged remains.