Effect of Temperature on Microbial Succession in Different Tissues of Cadavers and Estimation of Postmortem Interval

ABSTRACTThe bones of decomposing vertebrates are colonized by a succession of diverse microbial communities. If this succession is similar across individuals, microbes may provide clues about the postmortem interval (PMI) during forensic investigations in which human skeletal remains are discovered. Here, we characterize the human bone microbial decomposer community to determine whether microbial succession is a marker for PMI. Six human donor subjects were placed outdoors to decompose on the soil surface at the Southeast Texas Applied Forensic Science facility. To also assess the effect of seasons, three decedents were placed each in the spring and summer. Once ribs were exposed through natural decomposition, a rib was collected from each body for eight time points at 3 weeks apart. We discovered a core bone decomposer microbiome dominated by taxa in the phylum Proteobacteria and evidence that these bone-invading microbes are likely sourced from the surrounding decomposition environment, including skin of the cadaver and soils. Additionally, we found significant overall differences in bone microbial community composition between seasons. Finally, we used the microbial community data to develop random forest models that predict PMI with an accuracy of approximately ±34 days over a 1- to 9-month time frame of decomposition. Typically, anthropologists provide PMI estimates based on qualitative information, giving PMI errors ranging from several months to years. Previous work has focused on only the characterization of the bone microbiome decomposer community, and this is the first known data-driven, quantitative PMI estimate of terrestrially decomposed human skeletal remains using microbial abundance information.IMPORTANCE Microbes are known to facilitate vertebrate decomposition, and they can do so in a repeatable, predictable manner. The succession of microbes in the skin and associated soil can be used to predict time since death during the first few weeks of decomposition. However, when remains are discovered after months or years, often the only evidence are skeletal remains. To determine if microbial succession in bone would be useful for estimating time since death after several months, human subjects were placed to decompose in the spring and summer seasons. Ribs were collected after 1 to 9 months of decomposition, and the bone microbial communities were characterized. Analysis revealed a core bone decomposer microbial community with some differences in microbial assembly occurring between seasons. These data provided time since death estimates of approximately ±34 days over 9 months. This may provide forensic investigators with a tool for estimating time since death of skeletal remains, for which there are few current methods.

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.

Accurate postmortem interval (PMI) estimation is critical for forensic investigations, aiding case classification and providing vital trial evidence. Early postmortem signs, such as body temperature and rigor mortis, are reliable for estimating PMI shortly after death. However, these indicators become less useful as decomposition progresses, making late-stage PMI estimation a significant challenge. Decomposition involves predictable microbial activity, which may serve as an objective criterion for PMI estimation. During decomposition, anaerobic microbes metabolize body tissues, producing gases and organic acids, leading to significant changes in skin and soil microbial communities. These shifts, especially the transition from anaerobic to aerobic microbiomes, can objectively segment decomposition into pre- and post-rupture stages according to rupture point. Microbial communities change markedly after death, with anaerobic bacteria dominating early stages and aerobic bacteria prevalent post-rupture. Different organs exhibit distinct microbial successions, providing valuable PMI insights. Alongside microbial changes, metabolic and volatile organic compound (VOC) profiles also shift, reflecting the body's biochemical environment. Due to insufficient information, unimodal models could not comprehensively reflect the PMI, so a muti-modal model should be used to estimate the PMI. Machine learning (ML) offers promising methods for integrating these multimodal data sources, enabling more accurate PMI predictions. Despite challenges such as data quality and ethical considerations, developing human-specific multimodal databases and exploring microbial-insect interactions can significantly enhance PMI estimation accuracy, advancing forensic science.

Circular RNAs (circRNAs) are conserved, abundant, stable, and specifically expressed in mammals. The postmortem interval (PMI) estimation is crucial in forensic medicine, particularly for case investigation and civil action. CircRNAs may serve as ideal PMI biomarkers. However, no research has explored PMI estimation in the brain using circRNAs. The total RNA, including circRNA, was sampled from mouse brain tissues at multiple temperatures (4℃, 25℃, and 35℃). The semi-quantitative reverse transcription (RT)-PCR and real-time quantitative PCR (RT-qPCR) were used to test the postmortem degradation levels at different PMIs. As a result, we found circFat3 is highly and specifically expressed in mouse brain tissue, with postmortem levels significantly correlated with PMI across multiple temperatures. In addition, mt-co1 and 28 S rRNA demonstrated stability under various temperature conditions, supporting their use as reliable reference genes for PMI models. Moreover, the error rates showed that the circFat3/28S rRNA model was more accurate at 4℃. The circFat3/mt-co1 and circFat3/28S rRNA models provided slightly better predictions for short-term and long-term PMI, respectively at 25℃, while the circFat3/mt-co1 model was more accurate at 35℃. The combined application of the two reference genes was beneficial primarily for long-term PMI estimation. Furthermore, the validation results confirmed that these models were more accurate for long-term PMI estimation. Thus, our mathematical models were constructed at multiple temperatures based on circFat3 and these two reference genes. Taken together, this is the first study to identify circRNA circFat3 as a novel biomarker that may serve as a complementary tool for PMI estimation.

Necrobiome is the main factor causing the cadaver decomposition. Studying the microbial succession during decomposition is one of the main tasks of forensic microbiology. The interactive relationships among cadaver, environment and microorganisms are complicated. The microbial succession study relies on macroscopic monitoring of community composition and the diversity change in each decomposition stage. With the maturity and development of high-throughput sequencing （HTS）, the structure and diversity of microbial communities in different environments have been successively revealed. A new breakthrough to explore the cadaveric microorganisms has been opened as well. It has become the research hotspots in forensic microbiology to reveal the microbial succession in the process of cadaver decomposition and to interpret the essence of various decomposition phenomena by using HTS, which can provide a new reference for postmortem interval （PMI） estimation. The present paper reviews studies on PMI estimation by using cadaveric microorganism. Problems and application prospects of forensic microbiology studies are discussed on the basis of the current application of HTS technology in the exploration of microbial succession.

BackgroundDrowning diagnosis has long been a critical issue in forensic research, influenced by various factors such as the environment and decomposition time. While traditional methods such as diatom analysis have limitations in decomposed remains, microbial community profiling offers a promising alternative. With the advancement of high-throughput sequencing technology, forensic microbiology has become a prominent focus in the field, providing new research avenues for drowning diagnosis. During drowning, microbial communities enter the lung tissue along with the water.MethodsIn this study, using a murine model, we collected samples from three rivers at random sites at postmortem intervals (PMI) of 1, 4, and 7 days‌ to comprehensively evaluate the differences in microbial communities between mice subjected to drowning versus postmortem immersion.ResultsThe α-diversity analysis revealed that the observed Operational Taxonomic Units (OTUs) for the drowning group on day 1 was 234.77 ± 16.60, significantly higher than the postmortem immersion group (171.32 ± 9.22), indicating greater initial microbial richness in the drowning group. Additionally, Shannon index analysis showed a significant decline in evenness in the postmortem immersion group on day 7 (1.46 ± 0.09), whereas the drowning group remained relatively stable (2.38 ± 0.15), further indicating a rapid decrease in microbial diversity in the postmortem immersion group over time. PCoA analysis demonstrated that differences in microbial community composition between drowning and postmortem immersion groups were notably stable. Key microbial taxa differentiating the groups were identified through LEfSe analysis, with Enterococcaceae (family), Escherichia-Shigella (genus), and Proteus (genus), emerging as significant markers in drowning cases. A random forest model, trained using microbial community data, exhibited high predictive accuracy (AUC = 0.96) across locations and immersion times and identified microbial markers, including Enterococcaceae (family), Lactobacillales (order), Morganellaceae (family), as critical features influencing model performance.ConclusionThese findings underscore the potential of combining 16 S rRNA sequencing with machine learning as a powerful tool for drowning diagnosis, offering novel insights into forensic microbiology.

Estimating the postmortem interval (PMI) is crucial in forensic science. Recent studies suggest microbial community succession patterns as a promising tool for PMI inference. This study examines how the cause of death, specifically mechanical asphyxia and hemorrhagic shock, influences microbial succession. By utilizing 16S amplicon sequencing, the study characterizes the succession patterns of microbial communities in different body parts (facial skin and cecal tissue) and applies random forest regression to develop PMI inference models. The results revealed significant differences in the decomposition processes between mechanical asphyxia and hemorrhagic shock. Determining the PMI based solely on postmortem phenomena proved challenging. Microbial communities in facial skin and cecal tissue-two distinct body parts from a decomposing corpse with the same cause of death-showed considerable variation, and the microbial composition in cecal tissue also differed between the two causes of death. The regression model, based on microbiota data at the family level, demonstrated the best performance. Specifically, eight bacterial families, including Enterobacteriaceae and Corynebacteriaceae, in facial skin were identified as predictors of PMI in corpses decomposed due to mechanical asphyxia, with an average absolute error of 2.15 ± 0.85 days. In contrast, 28 bacterial families, such as Lachnospiraceae and Clostridiales_NA, in cecal tissue were found to predict the PMI of corpses decomposed due to hemorrhagic shock, with an average absolute error of 2.52 ± 0.74 days. These findings provide a valuable microbial dataset for advancing forensic PMI studies.

The puparium is the hardened exoskeleton of the last larval instar of a fly, inside which a prepupa, a pupa and a pharate adult fly successively develop. Empty puparia are frequently collected at death scenes, especially in cases with a long post mortem interval (PMI). Although we are not able to estimate the interval between the eclosion of an adult fly and the collection of an empty puparium (i.e. the post-eclosion interval (PEI)), empty puparia may still provide valuable evidence about the minimum PMI. However, because of the unknown PEI, it is impossible to determine the time when the fly emerged, and thus when the retrospective calculation of the minimum PMI should start. In this study, the estimation of PMI (or minimum PMI) for empty puparia of Protophormia terraenovae Rob.-Desv. (Calliphoridae) and Stearibia nigriceps Meig. (Piophilidae) was simulated, to gain insight into the changes in estimates, when different PEIs and different temperature conditions were assumed. The simulations showed that the PEI (in a range of 0–90 days) had no effect on the PMI (or minimum PMI) when the puparium was collected in winter or early spring (December–April). In late spring, summer, or autumn (May–November) the PMI (or minimum PMI) increased with the PEI. The increase in PMI was large in the summer months, and surprisingly small in the autumn months, frequently smaller than the PEI used in the estimation. The shortest PMI was always obtained with a PEI of 0, indicating that the true minimum PMI is always estimated using a PEI of 0. When the puparium was collected during spring, simulations indicated that oviposition had occurred in the previous year, while in summer the previous-year oviposition has been indicated by the simulations only when longer PEIs had been assumed. These findings should guide estimation of the PMI (or minimum PMI) based on an empty puparium.

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Microbial communities play a crucial role in decomposition, yet their patterns in human tissues remain underexplored. Most previous research has often focused on animal models such as mice and swine, with limited studies on human samples, primarily targeting specific environments like the gut and skin. Consequently, gaps persist in understanding postmortem microbial dynamics within internal human organs. The 2bRAD-M sequencing technology offers a powerful approach for human thanatomicrobiome research, overcoming key limitations of 16S rRNA and metagenomic sequencing methods. In this study, we used 2bRAD-M to profile microbial succession across seven human tissues-heart, liver, spleen, lung, kidney, calf muscle, and gut-at various postmortem intervals (PMIs). Significant variations in microbial community composition were observed across organs and decomposition stages, with Proteobacteria dominating early and Firmicutes later. A comparison of frozen and unfrozen cadavers (PMI 1-7 days) revealed divergent microbial shifts in the liver and spleen, while other tissues exhibited limited variation. These findings highlight complex, organ-specific microbial trajectories and suggest that microbial signatures could serve as biomarkers for PMI estimation. This research deepens our understanding of the microbial succession within internal human organs postmortem and contributes to elucidating the identity and role of microorganisms in human decomposition.IMPORTANCEHumans host a diverse array of microbial communities that play a crucial role in the decomposition process after death. Understanding these postmortem microbial dynamics is essential, as they offer valuable insights into the progression of decomposition with significant implications for forensic science. The role of microorganisms in corpse decomposition has gained increasing attention in both forensic and ecological research, but studies in this area remain in their early stages, requiring further in-depth exploration. This work pioneers the use of 2bRAD-M sequencing to investigate microbial changes across various human organs over increasing postmortem intervals. By enhancing knowledge of postmortem microbiota dynamics, the study contributes to refining and improving the accuracy of forensic methodologies.

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.

Classical methods for postmortem interval (PMI) estimation have been applied for nearly a century. Contrary to the notion of being simple or easily accessible, these approaches require highly specialized training, including a medical degree, postgraduate specialization in forensic pathology, and extensive practical experience. Classical PMI estimation relies on observable physical and chemical changes in the human cadaver, such as rigor mortis, livor mortis, algor mortis, and transformative processes during decomposition. These methods are fundamental in medicolegal practice but remain largely influenced by environmental and individual variability. Recent advances in forensic research, particularly in microbiology and biochemistry, have introduced innovative approaches that complement traditional methods, offering greater accuracy and reliability, though resource-intensive. Emerging approaches leverage the predictable postmortem succession of microbial communities (thanatomicrobiome) and biochemical alterations in cadaver fluids and tissues. Techniques such as metagenomics, metatranscriptomics, and metabolomics enable detailed analysis of these changes, while computational models and machine learning further refine PMI estimates. Despite advancements, challenges persist, including variability due to environmental factors and limited access to human decomposition data. Integrating multi-omics approaches and artificial intelligence offers a path forward, addressing these limitations and enhancing the accuracy of PMI estimation. This review provides a comprehensive overview of PMI estimation, critically examining classical approaches and highlighting cutting-edge methodologies rooted in thanatomicrobiology and thanatochemistry. We emphasize the transformative potential of multi-omics integration and artificial intelligence in improving PMI accuracy. Importantly, we propose a paradigm shift: redefining PMI estimation through evidence-based, interdisciplinary research that bridges scientific rigor and judicial application. Transdisciplinary collaboration and standardized methodologies will be essential to translate emerging knowledge into robust forensic tools that serve both science and justice.

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

Background: In forensic investigations, accurate estimation of the postmortem interval (PMI) is an important task, but also an ongoing challenge. Especially in cases where the cadaver has been specially treated, for example, by boiling, the determination of PMI becomes extremely difficult. Previous studies have shown that the succession of the microbial community after decomposition of the cadaver can be used to infer PMI. However, the feasibility of determining the PMI of boiled cadavers has not yet been demonstrated. Aims and Objectives: The main objective of this study was to test whether we can infer PMI of boiled cadavers based on the succession of microbial communities. Materials and Methods: SD rats were killed by cervical dislocation. Subsequently, the rat cadavers were divided into the case (boiled cadavers) and control (unboiled cadavers) groups. Rectal samples were collected from the rats for 45 days and at nine time points. High-throughput sequencing of the 16S rRNA gene was performed to characterize the microbial community in the rectum. Results: The results showed that the composition and relative abundance of bacterial communities at the phylum level were significantly different between the case and control groups. The alpha diversity of the microbial community showed a decreasing trend with the decomposition process. Principal coordinate analysis showed that the case and control groups had obvious patterns along the succession of microbial communities. The rectal microbial communities showed a significant linear trend in the time course of decomposition. A random forest model was used to infer PMI. The goodness-of-fit (R2) of the model was 68.00% and 84.00%, and the mean absolute errors were 2.05 and 1.48 days within 45 days of decomposition for the case and control groups, respectively. Conclusions: Our results suggest that microbial community succession could be a potential method to infer PMI of boiled cadavers.

Linking soil depth to aridity effects on soil microbial community composition, diversity and resource limitation