Preliminary study on microeukaryotic community analysis using NGS technology to determine postmortem submersion interval (PMSI) in the drowned pig

[Determination of the corpse's stay in the water duration according to maceration degree of its skin]

A serious problem during the postmortem examination of a corpse extracted from the water can be a significant determination of its stay in the water duration. First of all, the signs indicating the presence of a corpse in the water include maceration, according to the severity of which forensic experts often determine how long the corpse stayed in the water. The aim of the study is to summarize the available literature data and propose ways to objectify the determination of a corpse's stay in water duration by the severity of skin maceration. In this article, based on the analysis of literature, the process of skin maceration is described, as well as the timing and speed of its development according to various authors. The presence of quite a large number of external and internal factors affecting the process of skin maceration and the subjectivity of its severity assessment is indicated. This article provides examples of the biophysical methods usage for the study of biological objects in forensic medical examination, allowing to objectively record changes in the researcher's parameter of interest. The use of skin impedancemetry to objectify the severity of skin maceration.

Predicting the Postmortem Interval Based on Gravesoil Microbiome Data and a Random Forest Model

The estimation of a postmortem interval (PMI) is particularly important for forensic investigations. The aim of this study was to assess the succession of bacterial communities associated with the decomposition of mouse cadavers and determine the most important biomarker taxa for estimating PMIs. High-throughput sequencing was used to investigate the bacterial communities of gravesoil samples with different PMIs, and a random forest model was used to identify biomarker taxa. Redundancy analysis was used to determine the significance of environmental factors that were related to bacterial communities. Our data showed that the relative abundance of Proteobacteria, Bacteroidetes and Firmicutes showed an increasing trend during decomposition, but that of Acidobacteria, Actinobacteria and Chloroflexi decreased. At the genus level, Pseudomonas was the most abundant bacterial group, showing a trend similar to that of Proteobacteria. Soil temperature, total nitrogen, NH₄⁺-N and NO₃^--N levels were significantly related to the relative abundance of bacterial communities. Random forest models could predict PMIs with a mean absolute error of 1.27 days within 36 days of decomposition and identified 18 important biomarker taxa, such as Sphingobacterium, Solirubrobacter and Pseudomonas. Our results highlighted that microbiome data combined with machine learning algorithms could provide accurate models for predicting PMIs in forensic science and provide a better understanding of decomposition processes.

Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation

Bacteria acts as the main decomposer during the process of biodegradation by microbial communities in the ecosystem. Numerous studies have revealed the bacterial succession patterns during carcass decomposition in the terrestrial setting. The machine learning algorithm-generated models based on such temporal succession patterns have been developed for the postmortem interval (PMI) estimation. However, the bacterial succession that occurs on decomposing carcasses in the aquatic environment is poorly understood. In the forensic practice, the postmortem submersion interval (PMSI), which approximately equals to the PMI in most of the common drowning cases, has long been problematic to determine. In the present study, bacterial successions in the epinecrotic biofilm samples collected from the decomposing swine cadavers submerged in water were analyzed by sequencing the variable region 4 (V4) of 16S rDNA. The succession patterns between the repeated experimental settings were repeatable. Using the machine learning algorithm for establishing random forest (RF) models, the microbial community succession patterns in the epinecrotic biofilm samples taken during the 56-day winter trial and 21-day summer trial were determined to be used as the PMSI predictors with the mean absolute error (MAE) of 17.87 ± 2.48 ADD (≈1.3 day) and 20.59 ± 4.89 ADD (≈0.7 day), respectively. Significant differences were observed between the seasons and between the substrates. The data presented in this research suggested that the influences of the environmental factors and the aquatic bacterioplankton on succession patterns of the biofilm bacteria were of great significance. The related mechanisms of such influence need to be further studied and clarified in depth to consider epinecrotic biofilm as a reliable predictor in the forensic investigations.

Microbial community succession of submerged bones in an aquatic habitat

After death, microbes (including bacteria and fungi) colonize carrion from a variety of sources during the decomposition process. The predictable succession of microbes could be useful for forensics, such as postmortem submersion interval estimation (PMSI) for aquatic deaths. However, gaps exist in our understanding of microbial succession on submerged bone, particularly regarding longer-term decomposition (>1 year), fungal composition, and differences between internal and external microbial communities. To further explore this potential forensic tool, we described the postmortem microbial communities (bacteria and fungi) on and within submerged bones using targeted amplicon sequencing. We hypothesized predictable successional patterns of microbial colonization would be detected on the surface and within submerged bones, which would eventually converge to a similar microbial community. To best replicate forensic contexts, we sampled bones from replicate swine (Sus scrofa domesticus) carcasses submerged in a freshwater pond, every three months for nearly two years. Microbial bone (internal vs. external) community structure (taxa abundance and diversity) of bones differed for both bacteria and fungi, but internal and external communities did not converge to a similar structure. PMSI estimation models built with random forest regression of postmortem microbiomes were highly accurate (>80% variation explained in PMSI) and showed promise for forensic purposes. Overall, we provide further evidence that internal and external bone microbial communities submerged in an aquatic habitat are distinct and each community undergoes predictable succession, demonstrating potential utility in forensics for modeling PMSI in unattended deaths and/or cold cases.