Estimating postmortem interval based on oral microbial community succession in rat cadavers.

The accurate estimation of the postmortem interval has been one of the crucial issues to be solved in forensic research, and it is influenced by various factors in the process of decay. With the development of high-throughput sequencing technology, forensic microbiology has become the major hot topic in forensic science, which provides new research options for postmortem interval estimation. The oral microbial community is one of the most diverse of microbiomes, ranking as the second most abundant microbiota following the gastrointestinal tract. It is remarkable that oral microorganisms have a significant function in the decay process of cadavers. Therefore, we collected outdoor soil to simulate the death environment and focused on the relationship between oral microbial community succession and PMI in rats above the soil. In addition, linear regression models and random forest regression models were developed for the relationship between the relative abundance of oral microbes and PMI. We also identified a number of microorganisms that may be important to estimate PMI, including: Ignatzschineria, Morganella, Proteus, Lysinibacillus, Pseudomonas, Globicatella, Corynebacterium, Streptococcus, Rothia, Aerococcus, Staphylococcus, and so on.

Succession changes of microbial community for inferring the time since deposition of saliva.

Saliva is a common biological examination material at crime scenes and has high application value in forensic case investigations. It can reflect the suspect's time of crime at the scene and provide evidence of the suspect's criminal facts. Even though many researchers have proposed their experimental protocols for estimating the time since deposition (TsD) of saliva, there is still a relative lack of research on the use of microorganisms to estimate TsD. In the current study, the succession change of microbial community in saliva with different TsD values was explored to discern the microbial markers related to TsD of saliva. We gathered saliva samples from six unrelated healthy Han individuals living in Guizhou, China and exposed these samples to indoor conditions at six time points (0, 1, 3, 7, 15, and 28 days). Temporal changes of microbial compositions in these samples were investigated by 16S rRNA sequencing (V3-V4 regions). By assessing temporal variation patterns of microbial abundance at the genus level, four bacteria (Brucella, Prevotella, Pseudomonas, and Fusobacterium) were observed to show good time dependence in these samples. In addition, the hierarchical clustering and principal co-ordinates analysis results revealed that these saliva samples could be classified into t-short (≤7 days) and t-long (>7 days) groups. In the end, the random forest model was developed to predict the TsD of these samples. For the model, the root mean square error, R2, and mean absolute error between predicted and actual TsD values were 1.5213, 0.9851, and 1.1969, respectively. To sum up, we identified TsD-related microbial markers in saliva samples, which could be viewed as valuable markers for inferring the TsD of saliva.