A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Wildlife parasitology: sample collection and processing, diagnostic constraints, and methodological challenges in terrestrial carnivores

Wild terrestrial carnivores play a crucial role as reservoir, maintenance, and spillover hosts for a wide parasite variety. They may harbor, shed, and transmit zoonotic parasites and parasites of veterinary importance for domestic hosts. Although wild carnivores are globally distributed and comprise many different species, some living in close proximity to human settlements, only a few studies have investigated parasites of wild terrestrial carnivores using non-specific techniques. Access to samples of wild carnivores may be challenging as some species are protected, and others are secretive, possibly explaining the data paucity. Considering the importance of wild carnivores' health and ecological role, combined with the lack of specific diagnostic methodologies, this review aims to offer an overview of the diagnostic methods for parasite investigation in wild terrestrial carnivores, providing the precise techniques for collection and analysis of fecal, blood, and tissue samples, the environmental impact on said samples, and the limitations researchers currently face in analyzing samples of wild terrestrial carnivores. In addition, this paper offers some crucial information on how different environmental factors affect parasite detection postmortem and how insects can be used to estimate the time of death with a specific highlight on insect larvae. The paper contains a literature review of available procedures and emphasizes the need for diagnostic method standardization in wild terrestrial carnivores.

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.

Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology

Due to the complex nature of microbiome data, the field of microbial ecology has many current and potential uses for machine learning (ML) modeling. With the increased use of predictive ML models across many disciplines, including microbial ecology, there is extensive published information on the specific ML algorithms available and how those algorithms have been applied. Thus, our goal is not to summarize the breadth of ML models available or compare their performances. Rather, our goal is to provide more concrete and actionable information to guide microbial ecologists in how to select, run, and interpret ML algorithms to predict the taxa or genes associated with particular sample categories or environmental gradients of interest. Such microbial data often have unique characteristics that require careful consideration of how to apply ML models and how to interpret the associated results. This review is intended for practicing microbial ecologists who may be unfamiliar with some of the intricacies of ML models. We provide examples and discuss common opportunities and pitfalls specific to applying ML models to the types of data sets most frequently collected by microbial ecologists.

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.

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.

Microbiology and postmortem interval: a systematic review

This systematic review aims to learn if and how it is possible to use the human microbiome to indicate the time elapsed after death. Articles were searched on the PubMed database using predefined data fields and keywords; reviews, systematic reviews, and meta-analyses were excluded. The final selection included 14 papers (out of 144). The results indicated that the microorganisms present in the cadaveric island succeed predictably over time, with markers between the stages of decomposition constituting a potential innovative tool for postmortem interval (PMI) estimation. The human microbiome has the potential to be used for PMI estimation and may present advantages as microbes are present in all seasons, in all habitats, including the most extreme ones, and because microbial communities respond predictably to environmental changes.

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.

Gut bacterial communities in Atlantic bottlenose dolphins (Tursiops truncatus) throughout a disease-driven (Morbillivirus) unusual mortality event

Gut microbiomes are important determinants of animal health. In sentinel marine mammals where animal and ocean health are connected, microbiome impacts can scale to ecosystem-level importance. Mass mortality events affect cetacean populations worldwide, yet little is known about the contributory role of their gut bacterial communities to disease susceptibility and progression. Here, we characterized bacterial communities from fecal samples of common bottlenose dolphins, Tursiops truncatus, across an unusual mortality event (UME) caused by dolphin Morbillivirus (DMV). 16S rRNA gene sequence analysis revealed similar diversity and structure of bacterial communities in individuals stranding before, during, and after the 2013-2015 Mid-Atlantic Bottlenose Dolphin UME and these trends held in a subset of dolphins tested by PCR for DMV infection. Fine-scale shifts related to the UME were not common (10 of 968 bacterial taxa) though potential biomarkers for health monitoring were identified within the complex bacterial communities. Accordingly, acute DMV infection was not associated with a distinct gut bacterial community signature in T. truncatus. However, temporal stratification of DMV-positive dolphins did reveal changes in bacterial community composition between early and late outbreak periods, suggesting that gut community disruptions may be amplified by the indirect effects of accumulating health burdens associated with chronic morbidity.

A synthesis of carcass decomposition studies conducted at a tropical (Aw) taphonomy facility: 2013-2022

Decomposition studies have been conducted in several regions of the world, but relatively few have investigated taphonomy in tropical environments. Even fewer have explored carcass decomposition during multiple tropical seasons, leaving the relationships between season and decomposition in tropical environments poorly understood. Ten decomposition studies using 30 carcasses were conducted in Honolulu, Hawaii, USA to start addressing this knowledge gap. These studies show that some postmortem processes were observed regardless of season. Carcass temperature and chemistry were spatiotemporally variable. Fly larval masses were consistently observed within 3 days (∼75 ADD) postmortem and carcasses lost 60%-90% of mass by 10 days (∼250 ADD) postmortem (Total Body Score ∼26). Season had a significant effect on decomposition, yet the warmest and most humid seasons did not always result in the most rapid and extensive decomposition. Seasonal variation appears to be less pronounced than at other tropical decomposition sites.

Postmortem skin microbiome signatures associated with human cadavers within the first 12 h at the morgue

Introduction

Forensic microbiome studies expanded during the last decade, aiming to identify putative bacterial biomarkers to be used for the postmortem interval (PMI) estimation. Bacterial diversity and dynamics during decomposition are influenced by each individual's micro and macroenvironment, ante and postmortem conditions, varying across body sites and time. The skin, the largest organ of the human body, hosts a diverse microbial diversity, representing the first line of defense of a living individual. Targeting the investigation of the postmortem skin microbiome could help understanding the role of microbes during decomposition, and association with the ante and postmortem conditions.

Methods

The current study aimed to identify the postmortem skin microbiome signatures associated with eight human bodies, received at the Institute of Legal Medicine Iasi, Romania, during April and May 2021. A total of 162 samples (including triplicate) representing face and hands skin microbiome were investigated via Illumina MiSeq, upon arrival at the morgue (T0) and after 12 hours (T1).

Results

The taxonomic characteristics of the skin microbiota varied across different body sites. However, there were no significant differences in taxonomic profiles between collection time, T0 and T1, except for some dynamic changes in the abundance of dominant bacteria. Moreover, different microbial signatures have been associated with a specific cause of death, such as cardiovascular disease, while an elevated blood alcohol level could be associated with a decrease in bacterial richness and diversity.

Discussion

The places where the bodies were discovered seemed to play an important role in explaining the bacterial diversity composition. This study shows promising results towards finding common postmortem bacterial signatures associated with human cadavers within the first 12h at the morgue.

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.

Mammalian carcass decay increases carbon storage and temporal turnover of carbon-fixing microbes in alpine meadow soil

Corpse decomposition is of great significance to the carbon cycle of natural ecosystem. Carbon fixation is a carbon conversion process that converts carbon dioxide into organic carbon, which greatly contributes to carbon emission reduction. However, the effects of wild animal carcass decay on carbon-fixing microbes in grassland soil environment are still unknown. In this research, thirty wild mammal (Ochotona curzoniae) corpses were placed on alpine meadow soil to study the carbon storage and carbon-fixing microbiota succession for a 94-day decomposition using next-generation sequencing. Our results revealed that 1) the concentration of total carbon increased approximately 2.24-11.22% in the corpse group. 2) Several carbon-fixing bacterial species (Calothrix parietina, Ancylobacter rudongensis, Rhodopseudomonas palustris) may predict the concentration of total carbon. 3) Animal cadaver degradation caused the differentiation of carbon-fixing microbiota structures during succession and made the medium-stage networks of carbon-fixing microbes more complicated. 4) The temporal turnover rate in the experimental groups was higher than that in the control groups, indicating a quick change of gravesoil carbon-fixing microbiota. 5) The deterministic process dominates the assembly mechanism of experimental groups (ranging from 53.42% to 94.94%), which reflects that the carbon-fixing microbial community in gravesoil can be regulated. Under global climate change, this study provides a new perspective for understanding the effects of wild animal carcass decay on soil carbon storage and carbon-fixing microbes.

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.

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.

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.

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

Background

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

Materials and methods

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

Results

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

Conclusion

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

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

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

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.

Exploring the value of microorganisms in the appendix for inferring postmortem interval in Sprague-Dawley rats using high-throughput sequencing

Various microorganisms play an important role in daily functions in the body and continue to flourish after death. Our prior investigation using frozen cadavers revealed that the appendix, rather than the transverse colon, was a superior sampling site for intestinal bacteria because the appendiceal flora had higher diversity than that in the transverse colon in the majority of experimental periods after death. We sought to explore out more about whether the appendicular flora is significantly related to postmortem interval (PMI) at natural temperatures following the host's death. In this work, we employed high-throughput sequencing to evaluate the contents of rats' appendices within 2 weeks after death and then utilized the random forest algorithm to build a PMI prediction model after completing basic visual analyses on the sequencing data. The findings revealed that Firmicutes was the absolute dominant species of appendicular flora; alpha-diversity of appendix flora first increased and then decreased, with the highest point appearing at 36 h after death; and the primary metabolic functions were carbohydrate metabolism, amino acid metabolism, as well as cofactors and vitamin metabolism. Finally, a random forest regression model for PMI prediction was built by the training data at the family level, with the mean absolute error of 10.27 h for prediction within 14 days postmortem, and the test set data subsequently proved the model's reliability. Changes in appendicular flora were strongly related to the PMI following rats' deaths, so we have reason to believe that the appendicular flora is valuable in predicting PMI.

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.

Microbial communities in the liver and brain are informative for postmortem submersion interval estimation in the late phase of decomposition: A study in mouse cadavers recovered from freshwater

Introduction

Bodies recovered from water, especially in the late phase of decomposition, pose difficulties to the investigating authorities. Various methods have been proposed for postmortem submersion interval (PMSI) estimation and drowning identification, but some limitations remain. Many recent studies have proved the value of microbiota succession in viscera for postmortem interval estimation. Nevertheless, the visceral microbiota succession and its application for PMSI estimation and drowning identification require further investigation.

Methods

In the current study, mouse drowning and CO₂ asphyxia models were developed, and cadavers were immersed in freshwater for 0 to 14 days. Microbial communities in the liver and brain were characterized via 16S rDNA high-throughput sequencing.

Results

Only livers and brains collected from 5 to 14 days postmortem were qualified for sequencing. There was significant variation between microbiota from liver and brain. Differences in microbiota between the cadavers of mice that had drowned and those only subjected to postmortem submersion decreased over the PMSI. Significant successions in microbial communities were observed among the different subgroups within the late phase of the PMSI in livers and brains. Eighteen taxa in the liver which were mainly related to Clostridium_sensu_stricto and Aeromonas, and 26 taxa in the brain which were mainly belonged to Clostridium_sensu_stricto, Acetobacteroides, and Limnochorda, were selected as potential biomarkers for PMSI estimation based on a random forest algorithm. The PMSI estimation models established yielded accurate prediction results with mean absolute errors ± the standard error of 1.282 ± 0.189 d for the liver and 0.989 ± 0.237 d for the brain.

Conclusions

The present study provides novel information on visceral postmortem microbiota succession in corpses submerged in freshwater which sheds new light on PMSI estimation based on the liver and brain in forensic practice.

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.

Post-Mortem Interval and Microbiome Analysis through 16S rRNA Analysis: A Systematic Review

The determination of the Post-Mortem Interval (PMI) is an issue that has always represented a challenge in the field of forensic science. Different innovative approaches, compared to the more traditional ones, have been tried over the years, without succeeding in being validated as successful methods for PMI estimation. In the last two decades, innovations in sequencing technologies have made it possible to generate large volumes of data, allowing all members of a bacterial community to be sequenced. The aim of this manuscript is to provide a review regarding new advances in PMI estimation through cadaveric microbiota identification using 16S rRNA sequencing, in order to correlate specific microbiome profiles obtained from different body sites to PMI. The systematic review was performed according to PRISMA guidelines. For this purpose, 800 studies were identified through database searching (Pubmed). Articles that dealt with PMI estimation in correlation with microbiome composition and contained data about species, body site of sampling, monitoring time and sequencing method were selected and ultimately a total of 25 studies were considered. The selected studies evaluated the contribution of the various body sites to determine PMI, based on microbiome sequencing, in human and animal models. The results of this systematic review highlighted that studies conducted on both animals and humans yielded results that were promising. In order to fully exploit the potential of the microbiome in the estimation of PMI, it would be desirable to identify standardized body sampling sites and specific sampling methods in order to align data obtained by different research groups.

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.

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.

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.

Microbiota succession throughout life from the cradle to the grave

Associations between age and the human microbiota are robust and reproducible. The microbial composition at several body sites can predict human chronological age relatively accurately. Although it is largely unknown why specific microorganisms are more abundant at certain ages, human microbiota research has elucidated a series of microbial community transformations that occur between birth and death. In this Review, we explore microbial succession in the healthy human microbiota from the cradle to the grave. We discuss the stages from primary succession at birth, to disruptions by disease or antibiotic use, to microbial expansion at death. We address how these successions differ by body site and by domain (bacteria, fungi or viruses). We also review experimental tools that microbiota researchers use to conduct this work. Finally, we discuss future directions for studying the microbiota's relationship with age, including designing consistent, well-powered, longitudinal studies, performing robust statistical analyses and improving characterization of non-bacterial microorganisms.

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.

Estimating the time since deposition (TsD) in saliva stains using temporal changes in microbial markers

Determining the time since deposition (TsD) of traces could be helpful in the investigation of criminal offenses. However, there are no reliable markers and models available for the inference of short-term TsD. The goal of this study was to investigate the potential of the succession pattern of human salivary microbial communities to serve as an efficiency TsD prediction tool in the resolution of the forensic cases. Saliva stains exposed to indoor conditions up to 20 days were collected and analyzed by 16S rRNA profiling using high-throughput sequencing technique. Noticeable differences in microbial composition were observed between different time points, and the indoor exposure time of saliva stains were inversely correlated with alpha diversity estimates across the measured time period. The sequencing results were used to identify TsD-dependent bacterial indicators to regress a generalized random forest model, resulting in a mean absolute deviation (MAD) of 1.41 days. Furthermore, a simplified TsD predictive model was also developed utilizing Enhydrobacter, Paenisporosarcina, and Janthinobacterium by quantitative PCR (qPCR) with a MAD of 1.32 days, and then forensic practice assessment were also performed by using mock samples with a MAD of 3.53 days. In conclusion, this study revealed significant changes in salivary microbial abundance as the prolongation of TsD. It demonstrated that the microbial biomarkers could be invoked as a "clock" for TsD estimation in human dried saliva stains.

[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.

Identification of fungi found on desiccated human remains in an arid outdoor environment

Three fungi not previously reported on desiccated human remains were identified on cadavers at the Forensic Investigation Research Station (FIRS) in Whitewater, Colorado. The location of the FIRS provides the unique opportunity to observe the stages of decomposition in a high desert environment. The two cadavers used in the study were in the late stages of decomposition (PMI of approximately 1520 and 1820 days) to the point of desiccation and had developed an extensive black crust on the skin that remained. Skin samples of the two cadavers were taken and plated onto potato dextrose agar to determine whether fungi were present on the desiccated tissues. Three different fungi consistently dominated cultures grown from numerous samples taken from each cadaver. Based on morphological observations, nuclear rDNA sequence data, and phylogenetic analyses, two fungi were identified to species (Aureobasidium melanogenum and Didymella glomerata) and one fungus was identified to the genus level (Alternaria). These results will contribute to the understanding of the role that fungi might play in late-stage decomposition and the extended postmortem period.

Insights into the Differential Preservation of Bone Proteomes in Inhumed and Entombed Cadavers from Italian Forensic Caseworks

Bone is a hard biological tissue and a precious reservoir of information in forensic investigations as it retains key biomolecules commonly used for identification purposes. Bone proteins have recently attracted significant interest for their potential in estimating post-mortem interval (PMI) and age at death (AAD). However, the preservation of such proteins is highly dependent on intrinsic and extrinsic factors that can hinder the potential application of molecular techniques to forensic sciences. The present study aims at investigating the effects that two commonly used types of burial practices (entombment and inhumation) have on bone protein survival. The sample consists of 14 exhumed individuals from cemeteries in Southern Italy with different AADs (29-85 years) and PMIs (1-37 years). LC-MS/MS analyses show that 16 proteins are better preserved under the entombed conditions and 4 proteins are better preserved under the inhumed conditions, whereas no clear differences are detected for post-translational protein modifications. Furthermore, several potential "stable" protein markers (i.e., proteins not affected by the burial environment) are identified for PMI and AAD estimation. Overall, these results show that the two burial environments play a role in the differential preservation of noncollagenous proteins, confirming the potential of LC-MS/MS-based proteomics in forensic sciences.

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.

Analysis of Microbial Communities: An Emerging Tool in Forensic Sciences

The objective of forensic sciences is to find clues in a crime scene in order to reconstruct the scenario. Classical samples include DNA or fingerprints, but both have inherent limitations and can be uninformative. Another type of sample has emerged recently in the form of the microbiome. Supported by the Human Microbiome Project, the characteristics of the microbial communities provide real potential in forensics. They are highly specific and can be used to differentiate and classify the originating body site of a human biological trace. Skin microbiota is also highly specific and different between individuals, leading to its possibility as an identification tool. By extension, the possibilities of the microbial communities to be deposited on everyday objects has also been explored. Other uses include the determination of the post-mortem interval or the analysis of soil communities. One challenge is that the microbiome changes over time and can be influenced by many environmental and lifestyle factors. This review offers an overview of the main methods and applications to demonstrate the benefit of the microbiome to provide forensically relevant information.