Construction of a succession model for the microbiome in water from submerged corpses based on single-molecule real-time sequencing

Abstract

Decomposition of corpses in aquatic environments is regulated by multiple factors, and traditional methods for estimating the postmortem submerged interval (PMSI) have clear limitations. To explore the microbial succession patterns in the water of submerged corpses and their potential application in PMSI estimation, this study submerged rat carcasses in both river water and tap water, collecting water samples on days 0, 2, 6, 9, 14, 20, 27, 40, 54, and 70 postmortem. The microbial succession dynamics in the water were analyzed using single-molecule real-time sequencing. The results indicated that, although the response patterns to decomposition differed between the two water sources, key microorganisms common to various decomposition stages were identified, suggesting their important role in the process. This study characterized the microbial community succession trajectory at the species level and identified several species with potential for PMSI indication. Based on this, we constructed a simple random forest prediction model. During the 70-day decomposition period, the mean absolute errors (MAE) of the river water model and the tap water model were 5.8745 days and 4.8599 days, respectively. This research elucidates the microbial community succession patterns induced by corpse submersion in water, constructs a methodological framework for PMSI estimation based on water microbiomes, and provides crucial support for the development of more versatile PMSI inference models.