Molecular characterization of gut microbial shift in SD rats after death for 30 days

Exploring the potential effects of forest urbanization on the interplay between small mammal communities and their gut microbiota

Urbanization significantly impacts wild populations, favoring urban dweller species over those that are unable to adapt to rapid changes. These differential adaptative abilities could be mediated by the microbiome, which may modulate the host phenotype rapidly through a high degree of flexibility. Conversely, under anthropic perturbations, the microbiota of some species could be disrupted, resulting in dysbiosis and negative impacts on host fitness. The links between the impact of urbanization on host communities and their gut microbiota (GM) have only been scarcely explored. In this study, we tested the hypothesis that the bacterial composition of the GM could play a role in host adaptation to urban environments. We described the GM of several species of small terrestrial mammals sampled in forested areas along a gradient of urbanization, using a 16S metabarcoding approach. We tested whether urbanization led to changes in small mammal communities and in their GM, considering the presence and abundance of bacterial taxa and their putative functions. This enabled to decipher the processes underlying these changes. We found potential impacts of urbanization on small mammal communities and their GM. The urban dweller species had a lower bacterial taxonomic diversity but a higher functional diversity and a different composition compared to urban adapter species. Their GM assembly was mostly governed by stochastic effects, potentially indicating dysbiosis. Selection processes and an overabundance of functions were detected that could be associated with adaptation to urban environments despite dysbiosis. In urban adapter species, the GM functional diversity and composition remained relatively stable along the urbanization gradient. This observation can be explained by functional redundancy, where certain taxa express the same function. This could favor the adaptation of urban adapter species in various environments, including urban settings. We can therefore assume that there are feedbacks between the gut microbiota and host species within communities, enabling rapid adaptation.

Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans

The microbial communities may undergo a meaningful successional change during the progress of decay and decomposition that could aid in determining the post-mortem interval (PMI). However, there are still challenges to applying microbiome-based evidence in law enforcement practice. In this study, we attempted to investigate the principles governing microbial community succession during decomposition of rat and human corpse, and explore their potential use for PMI of human cadavers. A controlled experiment was conducted to characterize temporal changes in microbial communities associated with rat corpses as they decomposed for 30 days. Obvious differences of microbial community structures were observed among different stages of decomposition, especially between decomposition of 0-7d and 9-30d. Thus, a two-layer model for PMI prediction was developed based on the succession of bacteria by combining classification and regression models using machine learning algorithms. Our results achieved 90.48% accuracy for discriminating groups of PMI 0-7d and 9-30d, and yielded a mean absolute error of 0.580d within 7d decomposition and 3.165d within 9-30d decomposition. Furthermore, samples from human cadavers were collected to gain the common succession of microbial community between rats and humans. Based on the 44 shared genera of rats and humans, a two-layer model of PMI was rebuilt to be applied for PMI prediction of human cadavers. Accurate estimates indicated a reproducible succession of gut microbes across rats and humans. Together these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating PMI.

Dissecting the microbial community structure of internal organs during the early postmortem period in a murine corpse model

BACKGROUND

Microorganisms distribute and proliferate both inside and outside the body, which are the main mediators of decomposition after death. However, limited information is available on the postmortem microbiota changes of extraintestinal body sites in the early decomposition stage of mammalian corpses.

RESULTS

This study investigated microbial composition variations among different organs and the relationship between microbial communities and time since death over 1 day of decomposition in male C57BL/6 J mice by 16S rRNA sequencing. During 1 day of decomposition, Agrobacterium, Prevotella, Bacillus, and Turicibacter were regarded as time-relevant genera in internal organs at different timepoints. Pathways associated with lipid, amino acid, carbohydrate and terpenoid and polyketide metabolism were significantly enriched at 8 h than that at 0.5 or 4 h. The microbiome compositions and postmortem metabolic pathways differed by time since death, and more importantly, these alterations were organ specific.

CONCLUSION

The dominant microbes differed by organ, while they tended toward similarity as decomposition progressed. The observed thanatomicrobiome variation by body site provides new knowledge into decomposition ecology and forensic microbiology. Additionally, the microbes detected at 0.5 h in internal organs may inform a new direction for organ transplantation.

Microbial community analyses provide a differential diagnosis for the antemortem and postmortem injury of decayed cadaver: An animal model

Differentiating antemortem injury from postmortem injury of decayed cadavers is one of the difficult issues in forensic science. Forensic pathologists identify antemortem injury according to the macroscopic and microscopic vital reactions taken place after being injured. However, the decomposition would render those vital reactions ineffective. Microbiomes have been widely used in forensic science due to their succession with time and sensitivity to vary of environment. In this study, microbiomes were introduced to determine whether the bacterial communities can be used to distinguish between the ante- and postmortem injuries through an animal experiment. Our findings showed that the differences of bacterial community were increasingly apparent from the 6th to 9th day after the wound created when the types of wounds were unidentified by morphological examination due to decomposition. The biomarkers at the genus level could effectively distinguish between injury types, Among them, Enterococcus and Enterobacter were only observed in the antemortem injured group, while Staphylococcus and Acinetobacter were only in the postmortem injured group. It is possible to tell whether cadaveric injuries developed before or after death by detecting differences in the bacterial communities of putrefying wounds. This study provides a new perspective for the differences between ante- and postmortem injuries and provides a promising method for us to identify the ante- and postmortem wounds, especially in decomposed cadavers.

Advances in artificial intelligence-based microbiome for PMI estimation

Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regularity, the microbiome has been shown to be a potentially effective tool for PMI estimation in the last decade. Recently, artificial intelligence (AI) technologies shed new lights on forensic medicine through analyzing big data, establishing prediction models, assisting in decision-making, etc. With the application of next-generation sequencing (NGS) and AI techniques, it is possible for forensic practitioners to improve the dataset of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, descriptions of their ecological function, and even their application in legal medicine. This review describes the postmortem succession of the microbiome in cadavers and their surroundings, and summarizes the application, advantages, problems, and future strategies of AI-based microbiome analysis for PMI estimation.

Predicting the postmortem interval using human intestinal microbiome data and random forest algorithm

Gradual changes in microbial communities in a human body after death can be used to determine postmortem interval (PMI). In this study, gut microflora samples were collected from the vermiform appendix and the transverse colon of human cadavers with PMIs between 5 and 192 h. The results revealed that the appendix might be an excellent intestinal sampling site and the appendix flora had an inferred succession rule during human body decomposition. Firmicutes, Bacteroidetes, and their respective subclasses showed a predictable successionrule in relative abundance over time. A Random Forest regression model was developed to correlate human gut microbiota with PMI. We believe that our findings have increased the knowledge of the composition and abundance of the gut microbiota in human corpses, and suggest that the use of the human appendix microbial succession may be a potential method for forensic estimation of the time of death.

NGS plus bacterial culture: A more accurate method for diagnosing forensic-related nosocomial infections

Traditional autopsy and microscopic examination of pathological sections are the "gold standard" for the cause of death diagnosis. However, in some special cases, such as the deaths caused by bacterial infections, pathological sections are not always sufficient to provide convincing evidences for determining the causes of death. In recent years, with the development of Next Generation Sequencing (NGS), clinical medicine has already introduced it into the diagnosis of difficult diseases, which is rare in forensic pathological diagnoses. Here, we applied an NGS-based method combined with bacterial culture to examine a special case in which the deceased was suspected of having suffered from nosocomial infections. Results of the NGS and bacterial culture showed that Enterococcus and Acinetobacter baumannii, which are the most common bacteria causing nosocomial infections, were abundant in blood and hydropericardium of the deceased. Combining medical records and the results of the dissections, we proved that the death was actually caused by MODS which was the adverse consequence of nosocomial infections. In this case, the combination of NGS and bacterial culture was used to identify the pathogen which had caused the death. The results of NGS not only shorten the period of diagnosis, but also greatly increase the credibility of traditional anatomy and results of bacterial culture, which is expected to be further applied for forensic practices in the near future.