The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing

Environmental predictors impact microbial-based postmortem interval (PMI) estimation models within human decomposition soils

Microbial succession has been suggested to supplement established postmortem interval (PMI) estimation methods for human remains. Due to limitations of entomological and morphological PMI methods, microbes are an intriguing target for forensic applications as they are present at all stages of decomposition. Previous machine learning models from soil necrobiome data have produced PMI error rates from two and a half to six days; however, these models are built solely on amplicon sequencing of biomarkers (e.g., 16S, 18S rRNA genes) and do not consider environmental factors that influence the presence and abundance of microbial decomposers. This study builds upon current research by evaluating the inclusion of environmental data on microbial-based PMI estimates from decomposition soil samples. Random forest regression models were built to predict PMI using relative taxon abundances obtained from different biological markers (bacterial 16S, fungal ITS, 16S-ITS combined) and taxonomic levels (phylum, class, order, OTU), both with and without environmental predictors (ambient temperature, soil pH, soil conductivity, and enzyme activities) from 19 deceased human individuals that decomposed on the soil surface (Tennessee, USA). Model performance was evaluated by calculating the mean absolute error (MAE). MAE ranged from 804 to 997 accumulated degree hours (ADH) across all models. 16S models outperformed ITS models (p = 0.006), while combining 16S and ITS did not improve upon 16S models alone (p = 0.47). Inclusion of environmental data in PMI prediction models had varied effects on MAE depending on the biological marker and taxonomic level conserved. Specifically, inclusion of the measured environmental features reduced MAE for all ITS models, but improved 16S models at higher taxonomic levels (phylum and class). Overall, we demonstrated some level of predictability in soil microbial succession during human decomposition, however error rates were high when considering a moderate population of donors.

Seasonal mouse cadaver microbial study: rupture time and postmortem interval estimation model construction

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.

A preliminary study characterizing temporal changes in soil bacterial communities after dismembered bones were buried

Determining the burial time of skeletal remains is one of the most important issues of forensic medicine. We speculated that the microbiome of gravesoil may be a promising method to infer burial time by virtue of time-dependent. As we know, forensic scientists have established various models to predict the postmortem interval of a decedent based on the changes in body and soil microbiome communities. However, limited data are available on the burial time prediction for bones, especially dismembered bones. In this exploratory study, we initially conducted 16S rRNA amplicon high-throughput sequencing on the burial soil of 10 porcine femurs within a 120-day period and analyzed the changes in soil microbial communities. Compared with the control soil, a higher Shannon index in the microbial diversity of burial soil containing bones was observed. Correlation analysis identified 61 time-related bacterial families and the best subset selection method obtained best subset, containing Thermomonosporaceae, Clostridiaceae, 0319-A21, and Oxalobacteraceae, which were used to construct a simplified multiple linear regression model with a mean absolute error (MAE) of 56.69 accumulated degree day (ADD). An additional random forest model was established based on indicators for the minimum cross-validation error of Thermomonosporaceae, Clostridiaceae, 0319-A21, Oxalobacteraceae, and Syntrophobacteraceae, with an MAE of 55.65 ADD. The produced empirical data in this pilot study provided the evidence of feasibility that the microbial successional changes of burial soil will predict the burial time of dismembered bones and may also expand the current knowledge of the effects of bone burial on soil bacterial communities.

Microscopic fungi on the corpse - Promising tool requiring further research

Forensic microbiology is a relatively new area of forensic sciences. It considers the potential of microorganisms to be used in criminal investigations. As most studies involve the role of bacteria in fields like post-mortem interval estimation, personal identification or geolocation, the data on the role of fungi is comparatively scarce. Forensic mycology involves the application of fungi and their structures in forensic cases. The aim of this review is the evaluation of the current state of knowledge on fungi associated with human cadavers and their possible role in estimating the time since death. In accordance with the available reports, we focused on the relation between microscopic fungi isolated from human corpses and the cadaver condition e.g., the stage of decomposition. We also emphasised the contrast between the reported methodologies and attempted to standardise research methods in forensic mycology from sample collection to its storage, mycological analysis and identification of the obtained fungal cultures. Moreover, the potential usage of microscopic fungi in criminal cases was discussed based on various case reports.

Aquatic conditions & bacterial communities as drivers of the decomposition of submerged remains

Aquatic decomposition, as a forensic discipline, has been largely under-investigated as a consequence of the highly complex and influential variability of the water environment. The limitation to the adaptability of scenario specific results justifies the necessity for experimental research to increase our understanding of the aquatic environment and the development of post-mortem submersion interval (PMSI) methods of estimation. This preliminary research aims to address this contextual gap by assessing the variation in the bacterial composition of aquatic biofilms as explained by water parameter measurements over time, associated with clothed and bare decomposing remains. As part of three field investigations, a total of 9 still-born piglets (n = 3, per trial) were used as human analogues and were submerged bare or clothed in either natural cotton or synthetic nylon. Changes in the bacterial community composition of the water surrounding the submerged remains were assessed at 4 discrete time points post submersion (7, 14, 21 and 28 days) by 16 S rRNA gene Next Generation Sequencing analysis and compared to coinciding water parameter measurements (i.e. conductivity, total dissolved solids (TDS), salinity, pH, and dissolved oxygen (DO)). Bacterial diversity was found to change over time and relative to clothing type, where significant variation was observed between synthetic nylon samples and bare/cotton samples. Seasonality was a major driver of bacterial diversity, where substantial variation was found between samples collected in early winter to those collected in mid - late winter. Water parameter measures of pH, salinity and DO were identified to best explain the global bacterial community composition and their corresponding dynamic trajectory patterns overtime. Further investigation into bacterial community dynamics in accordance with varying environmental conditions could potentially lead to the determination of influential extrinsic factors that may drive bacterial activity in aquatic decomposition. Together with the identification of potential bacterial markers that complement the different stages of decomposition, this may provide a future approach to PMSI estimations.

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.

Decomposition in an extreme cold environment and associated microbiome-prediction model implications for the postmortem interval estimation

Introduction

The accurate estimation of postmortem interval (PMI), the time between death and discovery of the body, is crucial in forensic science investigations as it impacts legal outcomes. PMI estimation in extremely cold environments becomes susceptible to errors and misinterpretations, especially with prolonged PMIs. This study addresses the lack of data on decomposition in extreme cold by providing the first overview of decomposition in such settings. Moreover, it proposes the first postmortem microbiome prediction model for PMI estimation in cold environments, applicable even when the visual decomposition is halted.

Methods

The experiment was conducted on animal models in the second-coldest region in the United States, Grand Forks, North Dakota, and covered 23 weeks, including the winter months with temperatures as low as -39°C. Random Forest analysis models were developed to estimate the PMI based either uniquely on 16s rRNA gene microbial data derived from nasal swabs or based on both microbial data and measurable environmental parameters such as snow depth and outdoor temperatures, on a total of 393 samples.

Results

Among the six developed models, the best performing one was the complex model based on both internal and external swabs. It achieved a Mean Absolute Error (MAE) of 1.36 weeks and an R2 value of 0.91. On the other hand, the worst performing model was the minimal one that relied solely on external swabs. It had an MAE of 2.89 weeks and an R2 of 0.73. Furthermore, among the six developed models, the commonly identified predictors across at least five out of six models included the following genera: Psychrobacter (ASV1925 and ASV1929), Carnobacterium (ASV2872) and Pseudomonas (ASV1863).

Discussion

The outcome of this research provides the first microbial model able to predict PMI with an accuracy of 9.52 days over a six-month period of extreme winter conditions.

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

Artificial intelligence-driven microbiome data analysis for estimation of postmortem interval and crime location

Microbial communities, demonstrating dynamic changes in cadavers and the surroundings, provide invaluable insights for forensic investigations. Conventional methodologies for microbiome sequencing data analysis face obstacles due to subjectivity and inefficiency. Artificial Intelligence (AI) presents an efficient and accurate tool, with the ability to autonomously process and analyze high-throughput data, and assimilate multi-omics data, encompassing metagenomics, transcriptomics, and proteomics. This facilitates accurate and efficient estimation of the postmortem interval (PMI), detection of crime location, and elucidation of microbial functionalities. This review presents an overview of microorganisms from cadavers and crime scenes, emphasizes the importance of microbiome, and summarizes the application of AI in high-throughput microbiome data processing in forensic microbiology.

Complexity of human death: its physiological, transcriptomic, and microbiological implications

Human death is a complex, time-governed phenomenon that leads to the irreversible cessation of all bodily functions. Recent molecular and genetic studies have revealed remarkable experimental evidence of genetically programmed cellular death characterized by several physiological processes; however, the basic physiological function that occurs during the immediate postmortem period remains inadequately described. There is a paucity of knowledge connecting necrotic pathologies occurring in human organ tissues to complete functional loss of the human organism. Cells, tissues, organs, and organ systems show a range of differential resilience and endurance responses that occur during organismal death. Intriguingly, a persistent ambiguity in the study of postmortem physiological systems is the determination of the trajectory of a complex multicellular human body, far from life-sustaining homeostasis, following the gradual or sudden expiry of its regulatory systems. Recent groundbreaking investigations have resulted in a paradigm shift in understanding the cell biology and physiology of death. Two significant findings are that (i) most cells in the human body are microbial, and (ii) microbial cell abundance significantly increases after death. By addressing the physiological as well as the microbiological aspects of death, future investigations are poised to reveal innovative insights into the enigmatic biological activities associated with death and human decomposition. Understanding the elaborate crosstalk of abiotic and biotic factors in the context of death has implications for scientific discoveries important to informing translational knowledge regarding the transition from living to the non-living. There are important and practical needs for a transformative reestablishment of accepted models of biological death (i.e., artificial intelligence, AI) for more precise determinations of when the regulatory mechanisms for homeostasis of a living individual have ceased. In this review, we summarize mechanisms of physiological, genetic, and microbiological processes that define the biological changes and pathways associated with human organismal death and decomposition.

Alterations in microbiome of COVID-19 patients and its impact on forensic investigations

The human microbiome is vital for maintaining human health and has garnered substantial attention in recent years, particularly in the context of the coronavirus disease 2019 (COVID-19) outbreak. Studies have underscored significant alterations in the microbiome of COVID-19 patients across various body niches, including the gut, respiratory tract, oral cavity, skin, and vagina. These changes manifest as shifts in microbiota composition, characterized by an increase in opportunistic pathogens and a decrease in beneficial commensal bacteria. Such microbiome transformations may play a pivotal role in influencing the course and severity of COVID-19, potentially contributing to the inflammatory response. This ongoing relationship between COVID-19 and the human microbiome serves as a compelling subject of research, underscoring the necessity for further investigations into the underlying mechanisms and their implications for patient health. Additionally, these alterations in the microbiome may have significant ramifications for forensic investigations, given the microbiome's potential in establishing individual characteristics. Consequently, changes in the microbiome could introduce a level of complexity into forensic determinations. As research progresses, a more profound understanding of the human microbiome within the context of COVID-19 may offer valuable insights into disease prevention, treatment strategies, and its potential applications in forensic science. Consequently, this paper aims to provide an overarching review of microbiome alterations due to COVID-19 and the associated impact on forensic applications, bridging the gap between the altered microbiome of COVID-19 patients and the challenges forensic investigations may encounter when analyzing this microbiome as a forensic biomarker.

Changes in Microbial Communities Using Pigs as a Model for Postmortem Interval Estimation

Microbial communities can undergo significant successional changes during decay and decomposition, potentially providing valuable insights for determining the postmortem interval (PMI). The microbiota produce various gases that cause cadaver bloating, and rupture releases nutrient-rich bodily fluids into the environment, altering the soil microbiota around the carcasses. In this study, we aimed to investigate the underlying principles governing the succession of microbial communities during the decomposition of pig carcasses and the soil beneath the carcasses. At early decay, the phylum Firmicutes and Bacteroidota were the most abundant in both the winter and summer pig rectum. However, Proteobacteria became the most abundant in the winter pig rectum in late decay. Using genus as a biomarker to estimate the PMI could get the MAE from 1.375 days to 2.478 days based on the RF model. The abundance of bacterial communities showed a decreasing trend with prolonged decomposition time. There were statistically significant differences in microbial diversity in the two periods (pre-rupture and post-rupture) of the four groups (WPG 0-8Dvs. WPG 16-40D, p < 0.0001; WPS 0-16Dvs. WPS 24-40D, p = 0.003; SPG 0D vs. SPG 8-40D, p = 0.0005; and SPS 0D vs. SPS 8-40D, p = 0.0208). Most of the biomarkers in the pre-rupture period belong to obligate anaerobes. In contrast, the biomarkers in the post-rupture period belong to aerobic bacteria. Furthermore, the genus Vagococcus shows a similar increase trend, whether in winter or summer. Together, these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating the PMI.

The Future Is Now: Unraveling the Expanding Potential of Human (Necro)Microbiome in Forensic Investigations

The relevance of postmortem microbiological examinations has been controversial for decades, but the boom in advanced sequencing techniques over the last decade is increasingly demonstrating their usefulness, namely for the estimation of the postmortem interval. This comprehensive review aims to present the current knowledge about the human postmortem microbiome (the necrobiome), highlighting the main factors influencing this complex process and discussing the principal applications in the field of forensic sciences. Several limitations still hindering the implementation of forensic microbiology, such as small-scale studies, the lack of a universal/harmonized workflow for DNA extraction and sequencing technology, variability in the human microbiome, and limited access to human cadavers, are discussed. Future research in the field should focus on identifying stable biomarkers within the dominant Bacillota and Pseudomonadota phyla, which are prevalent during postmortem periods and for which standardization, method consolidation, and establishment of a forensic microbial bank are crucial for consistency and comparability. Given the complexity of identifying unique postmortem microbial signatures for robust databases, a promising future approach may involve deepening our understanding of specific bacterial species/strains that can serve as reliable postmortem interval indicators during the process of body decomposition. Microorganisms might have the potential to complement routine forensic tests in judicial processes, requiring robust investigations and machine-learning models to bridge knowledge gaps and adhere to Locard's principle of trace evidence.

Composition and functional diversity of bacterial communities during swine carcass decomposition

OBJECTIVE

This study investigated the changes in bacterial communities within decomposing swine microcosms, comparing soil with or without intact microbial communities, and under aerobic and anaerobic conditions.

METHODS

The experimental microcosms consisted of four conditions: UA, unsterilized soil-aerobic condition; SA, sterilized soil-aerobic condition; UAn, unsterilized soil-anaerobic condition; and San, sterilized soil-anaerobic condition. The microcosms were prepared by mixing 112.5 g of soil and 37.5 g of ground carcass, which were then placed in sterile containers. The carcass-soil mixture was sampled at day 0, 5, 10, 30, and 60 of decomposition, and the bacterial communities that formed during carcass decomposition were assessed using Illumina MiSeq sequencing of the 16S rRNA gene.

RESULTS

A total of 1,687 amplicon sequence variants representing 22 phyla and 805 genera were identified in the microcosms. The Chao1 and Shannon diversity indices varied in between microcosms at each period (p<0.05). Metagenomic analysis showed variation in the taxa composition across the burial microcosms during decomposition, with Firmicutes being the dominant phylum, followed by Proteobacteria. At the genus level, Bacillus and Clostridium were the main genera within Firmicutes. Functional prediction revealed that the most abundant Kyoto encyclopedia of genes and genomes metabolic functions were carbohydrate and amino acid metabolisms.

CONCLUSION

This study demonstrated a higher bacteria diversity in UA and UAn microcosms than in SA and SAn microcosms. In addition, the taxonomic composition of the microbial community also exhibited changes, highlighting the impact of soil sterilization and oxygen on carcass decomposition. Furthermore, this study provided insights into the microbial communities associated with decomposing swine carcasses in microcosm.

Forensic Microbiology in India: A missing piece in the puzzle of criminal investigation system

BACKGROUND

Forensic Microbiology is an emerging branch of science that has great potential to assist criminal investigations. Having said that, microbial analysis is not performed routinely during forensic investigations in India. This could be attributed to lack of specific training and lack of evidence-based standard protocol.

OBJECTIVES

The authors attempt to highlight the key areas in forensic microbiology that need to be explored in a developing nation like India.

CONTENT

Forensic microbiology could help in linking a person to a crime, determining the cause of death, estimating postmortem interval (PMI), etc. Additionally, applications are being developed by forensic microbiologists across the globe to investigate the coordinated and dynamic changes in microbial activity which occur after the death of a human host. Such evidence from the human postmortem microbiome can aid in criminal investigations and administration of justice. These recent advances and developments have the potential to transform the field of forensic microbiology in a developing country.

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.

Toward Oral Thanatomicrobiology-An Overview of the Forensic Implications of Oral Microflora

Introduction

The oral cavity is home to numerous microorganisms including bacteria, fungi, and viruses which together form the oral microflora. It is the second most diverse microbial site in the human body after the gastrointestinal tract. Microbial degradation is a common phenomenon that occurs after death, with the early and advanced stages of decomposition being closely associated with oral microbial activity.

Methods

This article reviews the current state of knowledge on the role of the oral microflora in postmortem events, and highlights the growing importance of terms such as forensic microbiology and thanatomicrobiome. This article also discusses next-generation sequencing, metagenomic sequencing studies, and RNA sequencing to study the oral thanatomicrobiome and epinecrotic communities in forensic oral genetics.

Results

The indigenous microorganisms in the oral cavity are among the first to respond to the process of decomposition. DNA/RNA sequencing is a relatively simple, precise, and cost-effective method to estimate biological diversity during various stages of postmortem decomposition. The field of thanatomicrobiology is rapidly evolving into a key area in forensic research.

Conclusion

This article briefly narrates oral microflora and its implications in forensic odontology. The role of microbial activity in postmortem events is gaining importance in forensic research, and further studies are needed to fully understand the potential applications of advanced technology in the study of the oral thanatomicrobiome.

Microbes in fingerprints: A source for dating crime evidence?

Interest in the human microbiome has grown in recent years because of increasing applications to biomedicine and forensic science. However, the potential for dating evidence at a crime scene based upon time-dependent changes in microbial signatures has not been established, despite a relatively straightforward scientific process for isolating the microbiome. We hypothesize that modifications in microbial diversity, abundance, and succession can provide estimates of the time a surface was touched for investigative purposes. In this proof-of-concept research, the sequencing and analysis of the 16 S rRNA gene from microbes present in fresh and aged latent fingerprints deposited by three donors with pre- and post-washed hands is reported. The stability of major microbial phyla is confirmed while the dynamics of less abundant groups is described up to 21 days post-deposition. Most importantly, a phylum is suggested as the source for possible biological markers to date fingerprints: Deinococcus-Thermus.

Forty years of research and development on forensic genetics: A bibliometric analysis

The current study aims to investigate the research publication trends in the field of forensic genetics using Bibliometric analysis. An extensive search of the Scopus database was conducted to identify scholarly articles on forensic genetics published between 1977 and 2022, and a data set comprising 2945 articles was obtained. The analysis was carried out using VOSviewer, RStudio, MS Excel and MS Access to investigate the annual publication trend, most productive journals, organizations/authors/countries, authorship and citation patterns, most cited documents/articles and co-occurrence of keywords. The results revealed the first article in the field of forensic genetics was published in 1977. By the end of 1999, only 15 articles were published. Since then, there has been a considerable increase in the yearly number of publications and post-2006, there were more than 100 yearly published articles. USA, China, Spain, Germany and United Kingdom were found to be the most productive countries. Among various organizations, the Institute of Legal Medicine, Innsbruck Medical University, Austria was found to be the most productive organization. In terms of the number of publications and citations, Morling N. was found to be the most prolific author. The highest number of articles were published in Forensic Science International: Genetics, contributing about 34% of the total articles published in different sources/journals. The document with the highest number of citations was "HOMER N, 2008, PLOS GENET", with a total of 750 citations. The most frequent keywords were forensic genetics and forensic science, followed by STR, population genetics, DNA, mt-DNA and DNA-typing. The results also revealed that there had been collaborative research among countries, organizations and authors, which helps in the exchange of ideas across disciplines, developing new skills, getting access to financial resources and generating quality results.

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

In forensic medical examinations, estimating the postmortem interval (PMI) is an important factor. Methamphetamine (MA) is a synthetic stimulant that is commonly abused, and estimation of the PMI after MA abuse has become one of the main tasks in forensic investigation. Microorganisms play a vital role in carrion decomposition. Analysing the bacterial succession patterns can be used as a forensic tool to estimate the PMI. The present study aimed to analyse bacterial succession changes during the decomposition of MA to estimate the PMI. We analysed bacterial communities in rabbits treated with three different concentrations of MA (0, 22.5, and 90 mg/kg) under the natural conditions of 20 °C and 70% humidity by sequencing 16S rRNA gene amplicons using the Illumina MiSeq system. We obtained 2 374 209 high-quality sequences and 2 937 operational taxonomic units (OTUs). The relative abundances of the bacterial communities varied markedly in response to different MA concentrations. Interestingly, in response to the different concentrations of MA, Bacteroidetes became disparate in the rectum in the late PMI. Increased numbers of bacterial taxa were identified in the rectum and buccal cavity samples, except at the highest concentration of MA in the rectum samples when PMI was 0-h, than were present in live rabbits. Meanwhile, the PMI correlated significantly with bacterial succession at different taxonomic levels. Our results suggested that bacterial community succession could be used as a "microbial clock" to estimate the PMI in cases of MA-related death; however, further study is required to gain a deeper understanding of this concept.

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.

Time-dependent microbial shifts during crayfish decomposition in freshwater and sediment under different environmental conditions

Fossilization processes and especially the role of bacterial activity during the preservation of organic material has not yet been well understood. Here, we report the results of controlled taphonomic experiments with crayfish in freshwater and sediment. 16S rRNA amplicon analyzes showed that the development of the bacterial community composition over time was correlated with different stages of decay and preservation. Three dominating genera, Aeromonas, Clostridium and Acetobacteroides were identified as the main drivers in the decomposition of crayfish in freshwater. Using micro-computed tomography (µ-CT), scanning electron microscopy (SEM) and confocal Raman spectroscopy (CRS), calcite clusters were detected after 3-4 days inside crayfish carcasses during their decomposition in freshwater at 24 °C. The precipitation of calcite clusters during the decomposition process was increased in the presence of the bacterial genus Proteocatella. Consequently, Proteocatella might be one of the bacterial genera responsible for fossilization.

Microbial ecology of vertebrate decomposition in terrestrial ecosystems

Vertebrate decomposition results in an ephemeral disturbance of the surrounding environment. Microbial decomposers are recognized as key players in the breakdown of complex organic compounds, controlling carbon and nutrient fate in the ecosystem and potentially serving as indicators of time since death for forensic applications. As a result, there has been increasing attention on documenting the microbial communities associated with vertebrate decomposition, or the 'necrobiome'. These necrobiome studies differ in the vertebrate species, microhabitats (e.g. skin vs. soil), and geographic locations studied, but many are narrowly focused on the forensic application of microbial data, missing the larger opportunity to understand the ecology of these communities. To further our understanding of microbial dynamics during vertebrate decomposition and identify knowledge gaps, there is a need to assess the current works from an ecological systems perspective. In this review, we examine recent work pertaining to microbial community dynamics and succession during vertebrate (human and other mammals) decomposition in terrestrial ecosystems, through the lens of a microbial succession ecological framework. From this perspective, we describe three major microbial microhabitats (internal, external, and soil) in terms of their unique successional trajectories and identify three major knowledge gaps that remain to be addressed.

Trends in forensic microbiology: From classical methods to deep learning

Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.

Utility and diagnostic value of postmortem microbiology associated with histology for forensic purposes

Nowadays, the diagnostic value of postmortem microbiological investigations is still a debated topic, but postmortem microbiology (PMM) remains a discipline with great forensic potential. To evaluate the usefulness and diagnostic-forensic value of postmortem microbiological cultures, it has been conducted a study on cadaveric material sampled during autopsy aiming to identify the correct cause of death. The study analyzed 45 cadavers subjected to judicial autopsy, divided into two groups based on the presence or absence of external or internal macroscopic autopsy signs suggesting infectious pathology. In the same cases, both the microbiological and conventional histological investigations have been simultaneously carried out. From the investigations, mono-bacterial, mono-fungal, mixed and negative cultures were observed. In mono-species microbiological growth, the histological epicrisis confirmed an infectious cause of death due to the presence of signs of acute infection with an aggressive infectious agent. In cases where growth was mixed, it was possible to distinguish between simple postmortal contamination and perimortem colonization. Finally, in some cases where the microbiology was negative, this has been essential in highlighting signs of a vital reaction to viral or parasitic infection. The joint and integrated evaluation of the laboratory results made it possible to correctly understand even those peculiar situations in which the PMM results alone would not have been significant. These methods, when combined, constitute an optimal forensic approach for the identification of the real cause of death and thus reduce the number of unsolved cases.

Predicting the Postmortem Interval Based on Gravesoil Microbiome Data and a Random Forest Model

The estimation of a postmortem interval (PMI) is particularly important for forensic investigations. The aim of this study was to assess the succession of bacterial communities associated with the decomposition of mouse cadavers and determine the most important biomarker taxa for estimating PMIs. High-throughput sequencing was used to investigate the bacterial communities of gravesoil samples with different PMIs, and a random forest model was used to identify biomarker taxa. Redundancy analysis was used to determine the significance of environmental factors that were related to bacterial communities. Our data showed that the relative abundance of Proteobacteria, Bacteroidetes and Firmicutes showed an increasing trend during decomposition, but that of Acidobacteria, Actinobacteria and Chloroflexi decreased. At the genus level, Pseudomonas was the most abundant bacterial group, showing a trend similar to that of Proteobacteria. Soil temperature, total nitrogen, NH₄⁺-N and NO₃^--N levels were significantly related to the relative abundance of bacterial communities. Random forest models could predict PMIs with a mean absolute error of 1.27 days within 36 days of decomposition and identified 18 important biomarker taxa, such as Sphingobacterium, Solirubrobacter and Pseudomonas. Our results highlighted that microbiome data combined with machine learning algorithms could provide accurate models for predicting PMIs in forensic science and provide a better understanding of decomposition processes.

The devil is in the details: Variable impacts of season, BMI, sampling site temperature, and presence of insects on the post-mortem microbiome

Background

Post-mortem microbial communities are increasingly investigated as proxy evidence for a variety of factors of interest in forensic science. The reported predictive power of the microbial community to determine aspects of the individual's post-mortem history (e.g., the post-mortem interval) varies substantially among published research. This observed variation is partially driven by the local environment or the individual themselves. In the current study, we investigated the impact of BMI, sex, insect activity, season, repeat sampling, decomposition time, and temperature on the microbial community sampled from donated human remains in San Marcos, TX using a high-throughput gene-fragment metabarcoding approach.

Materials and methods

In the current study, we investigated the impact of BMI, sex, insect activity, season, repeat sampling, decomposition time, and temperature on the microbial community sampled from donated human remains in San Marcos, TX using a high-throughput gene-fragment metabarcoding approach.

Results

We found that season, temperature at the sampling site, BMI, and sex had a significant effect on the post-mortem microbiome, the presence of insects has a homogenizing influence on the total bacterial community, and that community consistency from repeat sampling decreases as the decomposition process progresses. Moreover, we demonstrate the importance of temperature at the site of sampling on the abundance of important diagnostic taxa.

Conclusion

The results of this study suggest that while the bacterial community or specific bacterial species may prove to be useful for forensic applications, a clearer understanding of the mechanisms underpinning microbial decomposition will greatly increase the utility of microbial evidence in forensic casework.

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.

Estimation of the post-mortem interval by modelling the changes in oral bacterial diversity during decomposition

AIMS

Decomposition, a complicated process, depends on several factors, including carrion insects, bacteria and the environment. However, the composition of and variation in oral bacteria over long periods of decomposition remain unclear. The current study aims to illustrate the composition of oral bacteria and construct an informative model for estimating the post-mortem interval (PMI) during decomposition.

METHODS AND RESULTS

Samples were collected from rats' oral cavities for 59 days, and 12 time points in the PMI were selected to detect bacterial community structure by sequencing the V3-V4 region of the bacterial 16S ribosomal RNA (16S rRNA) gene on the Ion S5 XL platform. The results indicated that microorganisms in the oral cavity underwent great changes during decomposition, with a tendency for variation to first decrease and then increase at day 24. Additionally, to predict the PMI, an informative model was established using the random forest algorithm. Three genera of bacteria (Atopostipes, Facklamia and Cerasibacillus) were linearly correlated at all 12 time points in the 59-day period. Planococcaceae was selected as the best feature for the last 6 time points. The R² of the model reached 93.94%, which suggested high predictive accuracy. Furthermore, to predict the functions of the oral microbiota, PICRUSt results showed that energy metabolism was increased on day 3 post-mortem and carbohydrate metabolism surged significantly on days 3 and 24 post-mortem.

CONCLUSIONS

Overall, our results suggested that post-mortem oral microbial community data can serve as a forensic resource to estimate the PMI over long time periods.

SIGNIFICANCE AND IMPACT OF THE STUDY

The results of the present study are beneficial for estimating the PMI. Identifying changes in the bacterial community is of great significance for further understanding the applicability of oral flora in forensic medicine.

Metagenomics Next Generation Sequencing (mNGS): An Exciting Tool for Early and Accurate Diagnostic of Fungal Pathogens in Plants

Crop output is directly impacted by infections, with fungi as the major plant pathogens, making accurate diagnosis of these threats crucial. Developing technology and multidisciplinary approaches are turning to genomic analyses in addition to traditional culture methods in diagnostics of fungal plant pathogens. The metagenomic next-generation sequencing (mNGS) method is preferred for genotyping identification of organisms, identification at the species level, illumination of metabolic pathways, and determination of microbiota. Moreover, the data obtained so far show that this new approach is promising as an emerging new trend in fungal disease detection. Another approach covered by mNGS technologies, known as metabarcoding, enables use of specific markers specific to a genetic region and allows for genotypic identification by facilitating the sequencing of certain regions. Although the core concept of mNGS remains constant across applications, the specific sequencing methods and bioinformatics tools used to analyze the data differ. In this review, we focus on how mNGS technology, including metabarcoding, is applied for detecting fungal pathogens and its promising developments for the future.

Analysis of Postmortem Intestinal Microbiota Successional Patterns with Application in Postmortem Interval Estimation

Microorganisms play a vital role in the decomposition of vertebrate remains in natural nutrient cycling, and the postmortem microbial succession patterns during decomposition remain unclear. The present study used hierarchical clustering based on Manhattan distances to analyze the similarities and differences among postmortem intestinal microbial succession patterns based on microbial 16S rDNA sequences in a mouse decomposition model. Based on the similarity, seven different classes of succession patterns were obtained. Generally, the normal intestinal flora in the cecum was gradually decreased with changes in the living conditions after death, while some facultative anaerobes and obligate anaerobes grew and multiplied upon oxygen consumption. Furthermore, a random forest regression model was developed to predict the postmortem interval based on the microbial succession trend dataset. The model demonstrated a mean absolute error of 20.01 h and a squared correlation coefficient of 0.95 during 15-day decomposition. Lactobacillus, Dubosiella, Enterococcus, and the Lachnospiraceae NK4A136 group were considered significant biomarkers for this model according to the ranked list. The present study explored microbial succession patterns in terms of relative abundances and variety, aiding in the prediction of postmortem intervals and offering some information on microbial behaviors in decomposition ecology.

Body Mass Index (BMI) Impacts Soil Chemical and Microbial Response to Human Decomposition

Microorganisms are key decomposers of vertebrate mortalities, breaking down body tissues and impacting decomposition progress. During human decomposition, both extrinsic environmental factors and intrinsic cadaver-related factors have the potential to impact microbial decomposers either directly or indirectly via altered physical or chemical conditions. While extrinsic factors (e.g., temperature, humidity) explain some variation in microbial response during human decomposition in terrestrial settings, recent work has noted that even under the same environmental conditions, individuals can have different decomposition patterns, highlighting the potential for intrinsic factors to impact microbial decomposers. The goal of this study was to investigate the effects of several intrinsic factors (age, sex, diseases at time of death, and body mass index [BMI]) on chemical and microbial changes in decomposition-impacted soils. In a field study conducted at the University of Tennessee Anthropology Research Facility, soils were collected from the decomposition-impacted area surrounding 19 deceased human individuals through the end of active decomposition. Soil physicochemical parameters were measured, and microbial (bacterial and fungal) communities were assessed via amplicon sequencing. BMI was shown to explain some variation in soil pH and microbial response to human decomposition. Hierarchical linear mixed (HLM) effects models revealed that BMI category significantly explained variation in pH response within decomposition-impacted soils over time (HLM F = 9.647; P < 0.001). Additionally, the relative abundance of soil Saccharomycetes in decomposition soils under underweight donors displayed little to no changes (mean maximum change in relative abundance, +6.6%), while all other BMI categories displayed an increased relative abundance of these organisms over time (normal, +50.6%; overweight, +64.4%; and obese, +64.6%) (HLM F = 3.441; P = 0.11). Together, these results reveal intrinsic factors influencing decomposition patterns, especially within the soil environment, and suggest BMI is an important factor for controlling decomposition processes. IMPORTANCE This work begins to address questions about interindividual variation in vertebrate decomposition attributed to intrinsic factors, that is, properties of the carcass or cadaver itself. Most research on factors affecting decomposition has focused on the extrinsic environment, such as temperature or humidity. While these extrinsic factors do explain some variation in decomposition patterns, interindividual variability is still observed. Understanding how intrinsic factors influence microbial decomposers will help reveal the ecological impacts of decomposition. This work also has forensic applications, as soil chemical and biological changes have been suggested as indicators of postmortem interval. We reveal factors that explain variation in the decomposition environment that should be considered in these estimates. This is particularly important as we consider the implications of variations in human populations due to diet, age, BMI, disease, toxicological loading, etc. on forensic investigations dealing with decomposing remains.

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.

Application of artificial intelligence and machine learning technology for the prediction of postmortem interval: A systematic review of preclinical and clinical studies

BACKGROUND /PURPOSE

Establishing an accurate postmortem interval (PMI) is exceptionally crucial in forensic investigation. Artificial intelligence (AI) and Machine learning (ML) models are widely employed in forensic practice. ML is a part of AI, both terms are highly associated and sometimes used interchangeably. This systematic review aims to evaluate the application and performance of AI technology for the prediction of PMI.

METHODS

Systematic literature search across different electronic databases using PubMed/Google Scholar/EMBASE/Scopus/CINAHL/Web of Science/Cochrane library was conducted from inception to 3 December 2021 for preclinical and clinical studies reported ML models for PMI estimation.

RESULTS

We identified 18 studies (12 preclinical and 06 clinical) that met the inclusion criteria in the qualitative analysis. Most of the studies employed supervised learning (N = 15), and others employed unsupervised learning (N = 3). Due to the heterogeneity of the samples, quantitative analysis was not performed.

CONCLUSION

In this systematic review, we discussed the performance of AI-based automated systems in PMI estimation. ML models have demonstrated accuracy and precision and the ability to overcome human errors and bias. However, the research is limited, conducted in primarily small, selected human populations. In addition, we suggest further research in larger population-based studies is needed to fully understand the extent of integrated ML models.

Application of Microbiome in Forensics

Recent advances in next-generation sequencing technology and improvements in bioinformatics have expanded the scope of microbiome analysis as a forensic tool. Microbiome research is concerned with the study of the compositional profile and diversity of microbial flora as well as the interactions between microbes, hosts, and the environment. It has opened up many new possibilities for forensic analysis. In this review, we discuss various applications of microbiomes in forensics, including identification of individuals, geolocation inference, post-mortem interval (PMI) estimation, and others.

Linking bacteria, volatiles and insects on carrion: the role of temporal and spatial factors regulating inter-kingdom communication via volatiles

Multi-kingdom community complexity and the chemically mediated dynamics between bacteria and insects have recently received increased attention in carrion research. However, the strength of these inter-kingdom interactions and the factors that regulate them are poorly studied. We used 75 piglet cadavers across three forest regions to survey the relationship between three actors (epinecrotic bacteria, volatile organic compounds (VOCs) and flies) during the first 4 days of decomposition and the factors that regulate this interdependence. The results showed a dynamic bacterial change during decomposition (temperature-time index) and across the forest management gradient, but not between regions. Similarly, VOC emission was dynamic across a temperature-time index and the forest management gradient but did not differ between regions. However, fly occurrence was dynamic across both space and time. The strong interdependence between the three actors was mainly regulated by the temperature-time index and the study regions, thereby revealing regulation at temporal and spatial scales. Additionally, the actor interdependence was stable across a gradient of forest management intensity. By combining different actors of decomposition, we have expanded our knowledge of the holistic mechanisms regulating carrion community dynamics and inter-kingdom interactions, an important precondition for better describing food web dynamics and entire ecosystem functions.

Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation

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.

[Estimation of time since death with the postmortem microbiome: a modern view and approaches to solving the problem]

The aim of the review is to summarize and update the data of modern studies devoted to determining the post-mortem interval (PMI) with the use of microorganisms, as well as disclosing prospects for further study in the presented direction. Estimating the time elapsed since death based on the postmortem microbiome has great potential for accurate determination of PMI, but all methods currently used have their limitations. The dynamics of changes in microbial communities due to the influence of many external and internal factors significantly complicates the process of interpreting the results. The change of microbial communities in the human corpse has shown promising results for the assessment of PMI, but to date there is no evidence of the repeatability of such a continuity in various geographic and ecological conditions. The question of conducting new, large-scale studies, taking in all the factors that could affect the posthumous microbiome, is becoming urgent.

Microbial community signatures for estimation of postmortem time intervals

The human body provides a complex ecosystem for symbiotic habitation of a huge number of microorganisms. These commensal microorganisms provide a huge benefit to the living host by acting against many deadly infections. Once the host dies, many changes in the complex ecosystem of the human body take place. The personalized microbes of a human body undergo successional change as many exogenous microbes attack the nutrient-rich cadaver after death. The succession pattern change of microbes in human cadaver allows postulating different models for estimation of Postmortem time interval (PMI). Estimation of PMI has a broad prospect from the criminal investigation point of view. Though many techniques are being used nowadays to estimate PMI, all of them have their pros and cons. With the advent of advanced molecular biological techniques, studies on the thanatomicrobiome of a human cadaver have gained pace and provide a superior alternative for conventional methods of PMI estimation. This chapter summarizes the recent advancements in the changes in signature microflora postmortem with change in human microenvironment to postulate a consensus model for estimation of PMI.

MALDI-TOF Mass Spectrometry Analysis and Human Post-Mortem Microbial Community: A Pilot Study

Introduction: The human post-mortem microbiome (HPM) plays a major role in the decomposition process. Successional changes in post-mortem bacterial communities have been recently demonstrated using high throughput metagenomic sequencing techniques, showing great potential as a post-mortem interval (PMI) predictor. The aim of this study is to verify the application of the mass spectrometry technique, better known as MALDI-TOF MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry), as a cheap and quick method for microbe taxonomic identification and for studying the PM microbiome. Methods: The study was carried out on 18 human bodies, ranging from 4 months to 82 years old and with a PMI range from 24 h up to 15 days. The storage time interval in the coolers was included in the final PMI estimates. Using the PMI, the sample study was divided into three main groups: seven cases with a PMI < 72 h; six cases with a PMI of 72−168 h and five cases with a PMI > 168 h. For each body, microbiological swabs were sampled from five external anatomical sites (eyes, ears, nose, mouth, and rectum) and four internal organs (brain, spleen, liver, and heart). Results: The HPM became increasingly different from the starting communities over time in the internal organs as well as at skin sites; the HPM microbiome was mostly dominated by Firmicutes and Proteobacteria phyla; and a PM microbial turnover existed during decomposition, evolving with the PMI. Conclusions: MALDI-TOF is a promising method for PMI estimation, given its sample handling, good reproducibility, and high speed and throughput. Although several intrinsic and extrinsic factors can affect the structure of the HPM, MALDI-TOF can detect the overall microbial community turnover of most prevalent phyla during decomposition. Limitations are mainly related to its sensitivity due to the culture-dependent method and bias in the identification of new isolates.

Microbial community succession of submerged bones in an aquatic habitat

After death, microbes (including bacteria and fungi) colonize carrion from a variety of sources during the decomposition process. The predictable succession of microbes could be useful for forensics, such as postmortem submersion interval estimation (PMSI) for aquatic deaths. However, gaps exist in our understanding of microbial succession on submerged bone, particularly regarding longer-term decomposition (>1 year), fungal composition, and differences between internal and external microbial communities. To further explore this potential forensic tool, we described the postmortem microbial communities (bacteria and fungi) on and within submerged bones using targeted amplicon sequencing. We hypothesized predictable successional patterns of microbial colonization would be detected on the surface and within submerged bones, which would eventually converge to a similar microbial community. To best replicate forensic contexts, we sampled bones from replicate swine (Sus scrofa domesticus) carcasses submerged in a freshwater pond, every three months for nearly two years. Microbial bone (internal vs. external) community structure (taxa abundance and diversity) of bones differed for both bacteria and fungi, but internal and external communities did not converge to a similar structure. PMSI estimation models built with random forest regression of postmortem microbiomes were highly accurate (>80% variation explained in PMSI) and showed promise for forensic purposes. Overall, we provide further evidence that internal and external bone microbial communities submerged in an aquatic habitat are distinct and each community undergoes predictable succession, demonstrating potential utility in forensics for modeling PMSI in unattended deaths and/or cold cases.

Successions of rare and abundant microbial subcommunities during fish carcass decomposition in a microcosm under the influence of variable factors

Animal carcasses are hotspots of ecological activity. The study of the role of microbes in carcass decomposition has been exclusively focused on microbes with higher abundance. The comparative study of abundant and rare subcommunities associated with decomposition needs in-depth exploration. The current experiment has been conducted on the decomposition of a fish carcass in a microcosm. We conducted 16S rRNA gene sequencing of the microbial communities. The correlation of the physicochemical properties of tap and Yellow river water with the microbial communities was evaluated. Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria were found to be the dominant phyla in both abundant and rare subcommunities. Among bacteria, the Acidobacteria, Planctomycetes, and Cyanobacteria were found only in the rare subcommunity. In both subcommunities, the abundance of Proteobacteria was found to increase over time, and that of Firmicutes to decrease. The rare subcommunity shows higher alpha diversity than the abundant one. The variation in the abundant subcommunity was influenced by time and water type, and that in the rare subcommunity was influenced by pH and water type. These results have implications for future research on the ecological role of rare and abundant subcommunities in the decomposition of carcasses in the aquatic ecosystem.

Microbial DNA in human nucleic acid extracts: Recoverability of the microbiome in DNA extracts stored frozen long-term and its potential and ethical implications for forensic investigation

Human DNA samples can remain unaltered for years and preserve important genetic information for forensic investigations. In fact, besides human genetic information, these extracts potentially contain additional valuable information: microbiome signatures. Forensic microbiology is rapidly becoming a significant tool for estimating post-mortem interval (PMI), and establishing cause of death and personal identity. To date, the possibility to recover unaltered microbiome signatures from human DNA extracts has not been proven. This study examines the microbiome signatures within human DNA extracts obtained from six cadavers with different PMIs, which were stored frozen for 5-16 years. Results demonstrated that the microbiome can be co-extracted with human DNA using forensic kits designed to extract the human host's DNA from different tissues and fluids during decomposition. We compared the microbial communities identified in these samples with microbial DNA recovered from two human cadavers donated to the Forensic Anthropology Center at Texas State University (FACTS) during multiple decomposition stages, to examine whether the microbial signatures recovered from "old" (up to 16 years) extracts are consistent with those identified in recently extracted microbial DNA samples. The V4 region of 16 S rRNA gene was amplified and sequenced using Illumina MiSeq for all DNA extracts. The results obtained from the human DNA extracts were compared with each other and with the microbial DNA from the FACTS samples. Overall, we found that the presence of specific microbial taxa depends on the decomposition stage, the type of tissue, and the depositional environment. We found no indications of contamination in the microbial signatures, or any alterations attributable to the long-term frozen storage of the extracts, demonstrating that older human DNA extracts are a reliable source of such microbial signatures. No shared Core Microbiome (CM) was identified amongst the total 18 samples, but we identified certain species in association with the different decomposition stages, offering potential for the use of microbial signatures co-extracted with human DNA samples for PMI estimation in future. Unveiling the new significance of older human DNA extracts brings with it important ethical-legal considerations. Currently, there are no shared legal frameworks governing the long-term storage and use of human DNA extracts obtained from crime scene evidence for additional research purposes. It is therefore important to create common protocols on the storage of biological material collected at crime scenes. We review existing legislation and guidelines, and identify some important limitations for the further development and application of forensic microbiomics.

Microbial traces and their role in forensic science

Forensic microbiology, also known as the microbiology of death, is an emerging branch of science that is still underused in criminal investigations. Some of the cases might be difficult to solve with commonly-used forensic methods, and then they become an operational field for microbiological and mycological analysis. The aim of our review is to present significant achievements of selected studies on the thanatomicrobiome (microorganisms found in the body, organs and fluids after death) and epinecrotic community (microorganisms found on decaying corpses) that can be used in forensic sciences. Research carried out as a part of the forensic microbiology deals with the thanatomicrobiome and the necrobiome - communities of microorganisms that live inside and outside of a putrefying corpse. Change of species composition observed in each community is a valuable feature that gives a lot of information related to the crime. It is mainly used in the estimation of post-mortem interval (PMI). In some criminal investigations, such noticeable changes in the microbiome and mycobiome can determine the cause or the actual place of death. The microbial traces found at the crime scene can also provide clear evidence of guilt. Nowadays, identification of microorganisms isolated from the body or environment is based on metagenome analysis and 16S rRNA gene amplicon-based sequencing for bacteria and ITS rRNA gene amplicon-based sequencing for fungi. Cultivation methods are still in use and seem to be more accurate; however, they require much more time to achieve a final result, which is an unwanted feature in any criminal investigation.

Dynamics of insects, microorganisms and muscle mRNA on pig carcasses and their significances in estimating PMI

The accurate estimate of the postmortem interval (PMI) is of vital significance in the investigation of homicide cases. In this study, three pig carcasses were placed in the field to study the pattern of insect succession, the change of microorganisms and the degradation of muscle tissue RNA during the decomposition process. The results showed that insects could quickly colonize the carcasses and still exist on them until the end of the experiment (41 days). Their development and succession patterns are useful indicators for PMI estimation. The diversity of rectal microorganisms decreased with the decomposition time. In different decomposition periods, significant differences in the rectal and soil microbial composition and relative abundance were found, which could be used to estimate the PMI with an accuracy of 3-4 days. The RNA of muscle tissue was found to have a time-dependent relationship with the PMI. Ppia and Gapdh showed a linear upward trend within 10 h after the death, followed by a gradual downward trend from 10 to 240 h. The expression of β-actin gene showed a gradual downward trend during 0-240 h. This is the first study in China to analyze the changes of insects, muscle RNA and microorganisms on pig carcasses in the same natural environment, which provide basic data for the PMI estimation.

Is Resource Change a Useful Predictor of Carrion Insect Succession on Pigs and Humans?

Carrion is a dynamic and nutrient-rich resource that attracts numerous insect species that undergo succession due to the rapid change in the carrion resource. Despite this process being well-understood, few studies have examined resource change as a driver of carrion insect succession, and instead have focused on the effects of time per se, or on coarse, qualitative measures such as decay stage. Here we report on three field succession experiments using pig carcasses and human cadavers encompassing two winters and one summer. We quantified the effects of resource change (measured as total body score, TBS), carrion type, initial carrion mass, ambient temperature, and season on insect species richness and community composition. We found that all variables had an effect on different taxonomic or trophic components of the insect community composition, with the exception of initial carrion mass which had no effect. We found significant positive effects of TBS on beetle species richness and composition, while fly species richness was not significantly affected by TBS, but was by ambient temperature. TBS had a significant positive effect on all trophic groups, while ambient temperature also had a significant positive effect on the necrophages and predator/parasitoids. Our study indicates that resource change, as indicated by TBS, is an important driver of carrion insect species turnover and succession on carrion, and that TBS can provide information about insect ecological patterns on carrion that other temporal measures of change cannot.

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.

Microbial community succession on submerged vertebrate carcasses in a tidal river habitat: Implications for aquatic forensic investigations

Death investigations in aquatic ecosystems are challenging due to abiotic and biotic factors that may influence the estimation of a postmortem submersion interval (PMSI). In this study, we examined bacterial changes throughout the decomposition process on porcine carcasses submerged in a tidal-influenced river and identified predictors of epinecrotic community succession. Fetal porcine (Sus scrofa) carcasses (N = 6) were submerged with epinecrotic samples collected every 3 days (6 collections) over a period of 19 days (~7415 accumulated degree hours (ADH)). Amplicon sequencing was performed using the Illumina MiSeq platform (16S V4 region, 2 × 250 bp format) to identify changes in bacterial relative abundance and diversity. To match bacterial succession with rough taphonomy, carcasses were visually assessed at each sampling time point to determine the decomposition stage. Notably, the three most abundant families were Moraxellaceae, Burkholderiaceae (Proteobacteria), and Clostridiaceae (Firmicutes), though communities composition varied significantly across decomposition stages. Greater bacterial phylogenetic diversity was observed in in latter decomposition stages (advanced floating decay, sunken remains). Random Forest Models were built to predict ADH and explained 77%-80.8% of variation in ADH with an error rate of +/-1943.2 ADH (Root Mean Square Error) or approx. ±2.7 days at the mean water temperature of this study. This study provided a useful model that could be used to estimate a PMSI in this river system utilizing bacterial community succession, and thus, potentially improve the accuracy of such estimations to be used in the court of law.

A Pilot Study of Microbial Succession in Human Rib Skeletal Remains during Terrestrial Decomposition

The 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.

A walk on the dirt: soil microbial forensics from ecological theory to the crime lab

Forensics aims at using physical evidence to solve investigations with science-based principles, thus operating within a theoretical framework. This however is often rather weak, the exception being DNA-based human forensics that is well anchored in theory. Soil is a most commonly encountered, easily and unknowingly transferred evidence but it is seldom employed as soil analyses require extensive expertise. In contrast, comparative analyses of soil bacterial communities using nucleic acid technologies can efficiently and precisely locate the origin of forensic soil traces. However, this application is still in its infancy, and is very rarely used. We posit that understanding the theoretical bases and limitations of their uses is essential for soil microbial forensics to be judiciously implemented. Accordingly, we review the ecological theory and experimental evidence explaining differences between soil microbial communities, i.e. the generation of beta diversity, and propose to integrate a bottom-up approach of interactions at the microscale, reflecting historical contingencies with top-down mechanisms driven by the geographic template, providing a potential explanation as to why bacterial communities map according to soil types. Finally, we delimit the use of soil microbial forensics based on the present technologies and ecological knowledge, and propose possible venues to remove existing bottlenecks.