A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system
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REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| ERR344870.fastq.gz | Fastqsanger.gz | |||
| ERR344871.fastq.gz | Fastqsanger.gz | |||
| ERR344872.fastq.gz | Fastqsanger.gz | |||
| ERR344873.fastq.gz | Fastqsanger.gz |
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Project description:Establishing the time since death is critical in every death investigation, yet existing techniques are susceptible to a range of errors and biases. For example, forensic entomology is widely used to assess the postmortem interval (PMI), but errors can range from days to months. Microbes may provide a novel method for estimating PMI that avoids many of these limitations. Here we show that postmortem microbial community changes are dramatic, measurable, and repeatable in a mouse model system, allowing PMI to be estimated within approximately 3 days over 48 days. Our results provide a detailed understanding of bacterial and microbial eukaryotic ecology within a decomposing corpse system and suggest that microbial community data can be developed into a forensic tool for estimating PMI. DOI:http://dx.doi.org/10.7554/eLife.01104.001.
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Project description:Mouse_Decomp_Forensics_2011_16S
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Project description:A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system
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Project description:A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system
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Project description:The estimation of postmortem interval (PMI) has long been a focal point in the field of forensic science. Following the death of an organism, microorganisms exhibit a clock-like proliferation pattern during the course of cadaver decomposition, forming the foundation for utilizing microbiology in PMI estimation. The establishment of PMI estimation models based on datasets from different seasons is of great practical significance. In this experiment, we conducted microbiota sequencing and analysis on gravesoil and mouse intestinal contents collected during both the winter and summer seasons and constructed a PMI estimation model using the Random Forest algorithm. The results showed that the MAE of the gut microbiota model in summer was 0.47 ± 0.26 d, R2 = 0.991, and the MAE of the gravesoil model in winter was 1.04 ± 0.22 d, R2 = 0.998. We propose that, in practical applications, it is advantageous to selectively build PMI estimation models based on seasonal variations. Additionally, through a combination of morphological observations, gravesoil microbiota sequencing results, and soil physicochemical data, we identified the time of cadaveric rupture for mouse cadavers, occurring at around days 24-27 in winter and days 6-9 in summer. This study not only confirms previous research findings but also introduces novel insights, contributing to the foundational knowledge necessary to advance the utilization of microbiota for PMI estimation.
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Project description:The establishment of postmortem interval is one of the most important aspects of forensic expertise. Microbes may provide a novel way to estimate the postmortem intervals in order to avoid many of these limitations. The oral cavity harbors one of the most diverse microbiomes that play a key role in the decomposition of corpses. In this study, the oral bacterial community showed obvious changes in relative abundance during the process of mice decomposition. Meanwhile, at different taxonomic levels, specific bacteria were found to be significantly correlated with the postmortem interval. Linear regression models between relative abundance and the postmortem interval were constructed. Among these species, Gamma-proteobacteria and Proteus were the best ones that can be used to infer the postmortem interval, especially late postmortem interval. Therefore, we suggest that succession of oral microbial community can be developed as a forensic tool for estimating the postmortem interval.
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Project description:Estimation of the postmortem interval (PMI) is a poorly studied field in veterinary pathology. The development of field-applicable methods is needed given that animal cruelty investigations are increasing continually. We evaluated various histologic criteria in equine brain, liver, and muscle tissue to aid the estimation of PMI in horses, which is central to forensic investigations of suspicious death. After death, autolysis proceeds predictably, depending on environmental conditions. Currently, no field-applied methods exist that accurately estimate the PMI using histology in animals or humans through quantification of autolysis. Brain, liver, and skeletal muscle from 12 freshly euthanized horses were held at 22°C and 8°C for 72 h. Tissues were sampled at T0h, T1h, T2h, T4h, T6h, T12h, T24h, T36h, T48h, T60h, and T72h. For each tissue, we quantified 5 to 7 criteria associated with autolysis, based on the percentage of microscopic field involved. Each criterion was modeled, with temperature and time as independent variables. Changes were most predictable in liver and muscle over the first 72 h postmortem. The criteria for autolysis that were present most extensively at both temperatures were hepatocyte individualization and the separation of bile duct epithelium from the basement membrane. The changes that were present next most extensively were disruption of myofiber continuity, hypereosinophilia, and loss of striation. Brain changes were highly variable. The high statistical correlation between the parameter "autolysis" and the variables "time/temperature", indicates that autolysis is progressive and predictable. Further investigation of these criteria is needed to establish histologic algorithms for PMI.
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Project description: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.
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Project description:Human thanatomicrobiota studies have shown that microorganisms inhabit and proliferate externally and internally throughout the body and are the primary mediators of putrefaction after death. Yet little is known about the source and diversity of the thanatomicrobiome or the underlying factors leading to delayed decomposition exhibited by reproductive organs. The use of the V4 hypervariable region of bacterial 16S rRNA gene sequences for taxonomic classification ("barcoding") and phylogenetic analyses of human postmortem microbiota has recently emerged as a possible tool in forensic microbiology. The goal of this study was to apply a 16S rRNA barcoding approach to investigate variation among different organs, as well as the extent to which microbial associations among different body organs in human cadavers can be used to predict forensically important determinations, such as cause and time of death. We assessed microbiota of organ tissues including brain, heart, liver, spleen, prostate, and uterus collected at autopsy from criminal casework of 40 Italian cadavers with times of death ranging from 24 to 432 h. Both the uterus and prostate had a significantly higher alpha diversity compared to other anatomical sites, and exhibited a significantly different microbial community composition from non-reproductive organs, which we found to be dominated by the bacterial orders MLE1-12, Saprospirales, and Burkholderiales. In contrast, reproductive organs were dominated by Clostridiales, Lactobacillales, and showed a marked decrease in relative abundance of MLE1-12. These results provide insight into the observation that the uterus and prostate are the last internal organs to decay during human decomposition. We conclude that distinct community profiles of reproductive versus non-reproductive organs may help guide the application of forensic microbiology tools to investigations of human cadavers.
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