High-throughput sequencing to evaluate the effects of methamphetamine on the succession of the bacterial community to estimate the postmortem interval

Gut microbiota contribute to Methamphetamine-induced cardiotoxicity in mouse model

Methamphetamine (METH) is a psychotropic drug known to cause cardiotoxicity. The gut-heart axis is emerging as an important pathway linking gut microbiota to cardiovascular disease, but the precise association between METH-induced cardiotoxicity and gut microbiota has yet to be elucidated. In this study, we established an escalating dose-multiple METH administration model in male BALB/c mice, examined cardiac injury and gut microbiota, and investigated the contribution of gut microbiota to cardiotoxicity induced by METH. Additionally, we treated mice with antibiotics and fecal microbiota transplantation (FMT) to assess the impact of gut microbiota on cardiotoxicity. Our results showed that METH exposure altered the p53 and PI3K/Akt signaling pathways and modulated the apoptosis pathway in heart tissue, accompanied by elevated levels of Bax/BCL-2 expression and cleaved caspase-3 proteins. METH exposure increased the diversity and richness of gut microbiota, and significantly changed the microbial community composition, accompanied by elevated abundance of Lactobacillus, Bifidobacterium, and decreased abundance of Bacteroides, norank_f_Muribaculaceae and Alistipes. Eliminating gut microbiota by antibiotics treatment alleviated METH-induced cardiotoxicity, while FMT treatment transferred similar cardiac injury manifestations from METH-exposed mice to healthy recipient mice. Our study unveils the crucial involvement of gut microbiota in the development of cardiotoxicity induced by METH and provides potential strategies for treating cardiac complications caused by METH.

Combining with lab-on-chip technology and multi-organ fusion strategy to estimate post-mortem interval of rat

Background

The estimation of post-mortem interval (PMI) is one of the most important problems in forensic pathology all the time. Although many classical methods can be used to estimate time since death, accurate and rapid estimation of PMI is still a difficult task in forensic practice, so the estimation of PMI requires a faster, more accurate, and more convenient method.

Materials and methods

In this study, an experimental method, lab-on-chip, is used to analyze the characterizations of polypeptide fragments of the lung, liver, kidney, and skeletal muscle of rats at defined time points after death (0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27, and 30 days). Then, machine learning algorithms (base model: LR, SVM, RF, GBDT, and MLPC; ensemble model: stacking, soft voting, and soft-weighted voting) are applied to predict PMI with single organ. Multi-organ fusion strategy is designed to predict PMI based on multiple organs. Then, the ensemble pruning algorithm determines the best combination of multi-organ.

Results

The kidney is the best single organ for predicting the time of death, and its internal and external accuracy is 0.808 and 0.714, respectively. Multi-organ fusion strategy dramatically improves the performance of PMI estimation, and its internal and external accuracy is 0.962 and 0.893, respectively. Finally, the best organ combination determined by the ensemble pruning algorithm is all organs, such as lung, liver, kidney, and skeletal muscle.

Conclusion

Lab-on-chip is feasible to detect polypeptide fragments and multi-organ fusion is more accurate than single organ for PMI estimation.

Postmortem submersion interval estimation of cadavers recovered from freshwater based on gut microbial community succession

Microbial community succession during decomposition has been proven to be a useful tool for postmortem interval (PMI) estimation. Numerous studies have shown that the intestinal microbial community presented chronological changes after death and was stable in terrestrial corpses with different causes of death. However, the postmortem pattern of intestinal microbial community succession in cadavers retrieved from water remains unclear. For immersed corpses, the postmortem submersion interval (PMSI) is a useful indicator of PMI. To provide reliable estimates of PMSI in forensic investigations, we investigated the gut microbial community succession of corpses submersed in freshwater and explored its potential application in forensic investigation. In this study, the intestinal microbial community of mouse submersed in freshwater that died of drowning or CO2 asphyxia (i.e., postmortem submersion) were characterized by 16S rDNA amplification and high-throughput sequencing, followed by bioinformatic analyses. The results demonstrated that the chronological changes in intestinal bacterial communities were not different between the drowning and postmortem submersion groups. α-diversity decreased significantly within 14 days of decomposition in both groups, and the β-diversity bacterial community structure ordinated chronologically, inferring the functional pathway and phenotype. To estimate PMSI, a regression model was established by random forest (RF) algorithm based on the succession of postmortem microbiota. Furthermore, 15 genera, including Proteus, Enterococcus, and others, were selected as candidate biomarkers to set up a concise predicted model, which provided a prediction of PMSI [MAE (± SE) = 0.818 (± 0.165) d]. Overall, our present study provides evidence that intestinal microbial community succession would be a valuable marker to estimate the PMSI of corpses submerged in an aquatic habitat.