Predicting the postmortem interval using human intestinal microbiome data and random forest algorithm

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.

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.

A computational approach to estimate postmortem interval using postmortem computed tomography of multiple tissues based on animal experiments

The estimation of postmortem interval (PMI) is a complex and challenging problem in forensic medicine. In recent years, many studies have begun to use machine learning methods to estimate PMI. However, research combining postmortem computed tomography (PMCT) with machine learning models for PMI estimation is still in early stages. This study aims to establish a multi-tissue machine learning model for PMI estimation using PMCT data from various tissues. We collected PMCT data of seven tissues, including brain, eyeballs, myocardium, liver, kidneys, erector spinae, and quadriceps femoris from 10 rabbits after death. CT images were taken every 12 h until 192 h after death, and HU values were extracted from the CT images of each tissue as a dataset. Support vector machine, random forest, and K-nearest neighbors were performed to establish PMI estimation models, and after adjusting the parameters of each model, they were used as first-level classification to build a stacking model to further improve the PMI estimation accuracy. The accuracy and generalized area under the receiver operating characteristic curve of the multi-tissue stacking model were able to reach 93% and 0.96, respectively. Results indicated that PMCT detection could be used to obtain postmortem change of different tissue densities, and the stacking model demonstrated strong predictive and generalization abilities. This approach provides new research methods and ideas for the study of PMI estimation.

Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus

Gut microbiota and circulating metabolite dysbiosis predate important pathological changes in glucose metabolic disorders; however, comprehensive studies on impaired glucose tolerance (IGT), a diabetes mellitus (DM) precursor, are lacking. Here, we perform metagenomic sequencing and metabolomics on 47 pairs of individuals with IGT and newly diagnosed DM and 46 controls with normal glucose tolerance (NGT); patients with IGT are followed up after 4 years for progression to DM. Analysis of baseline data reveals significant differences in gut microbiota and serum metabolites among the IGT, DM, and NGT groups. In addition, 13 types of gut microbiota and 17 types of circulating metabolites showed significant differences at baseline before IGT progressed to DM, including higher levels of Eggerthella unclassified, Coprobacillus unclassified, Clostridium ramosum, L-valine, L-norleucine, and L-isoleucine, and lower levels of Eubacterium eligens, Bacteroides faecis, Lachnospiraceae bacterium 3_1_46FAA, Alistipes senegalensis, Megaspaera elsdenii, Clostridium perfringens, α-linolenic acid, 10E,12Z-octadecadienoic acid, and dodecanoic acid. A random forest model based on differential intestinal microbiota and circulating metabolites can predict the progression from IGT to DM (AUC = 0.87). These results suggest that microbiome and metabolome dysbiosis occur in individuals with IGT and have important predictive values and potential for intervention in preventing IGT from progressing to DM.

Direct relationship between evapotranspiration rate (ETO) and vertebrate decomposition rate

When vertebrate scavenging is excluded, the Evapotranspiration Rate (ETo) of a given geographic region directly regulates the decomposition rate of unclothed vertebrate carrion, with any deviation attributed to insect activity. We conducted four decomposition experiments using pig carrion (Sus scrofa domesticus) over the span of two years (2018-2020) at a location in Davis, California. We used ETo, a variable that accounts for five climatic parameters (wind, temperature, humidity, solar radiation, and altitude) as the rate-determining variable of the decomposition process. We found ETo to have a strong (R2 = 0.98) predictive relationship with the decomposition rate. To account for maggot activity decomposing the carrion, we measured maggot weight in 2019 and 2020 using a novel method, and in 2020 we used FLIR imagery to measure maggot mass temperatures as a surrogate measurement of total maggot activity. Maggot activity was a significant predictor (p < 0.0001) of the decomposition rate, while maggot weight was not (p > 0.1). We hope to show the forensic entomology community the potential of using ETo. Future projects can incorporate ETo as a baseline to decomposition studies to determine if ETo remains the most accurate descriptor of decomposition and ultimately increase certainty in the Postmortem Interval (PMI).

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.

Zapico S. Partners in Postmortem Interval Estimation: X-ray Diffraction and Fourier Transform Spectroscopy

The postmortem interval (PMI) is difficult to estimate in later stages of decomposition. There is therefore a need to develop reliable methodologies to estimate late PMI. This study aims to assess whether there is a correlation between changes in the mineral composition of human teeth and the estimation of PMI. X-ray diffraction (XRD) and attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy techniques were performed to address this challenge. Forty healthy human teeth obtained from odontological clinics were stored at different times (0, 10, 25, 50 years; N = 10/group). XRD and ATR-FTIR parameters related to the structure and composition of teeth were studied. Our results showed that the crystallinity index, crystal size index, mineral-to-organic matrix ratio (M/M) and carbonate/phosphate ratio (C/P) had the strongest association with PMI. For larger PMIs, there was a significant increase in crystallinity, crystal size and M/M ratio, while the C/P ratio showed a specific decrease with increasing PMI. According to our results, the parameters of crystallinity, crystal size, M/M ratio and C/P ratio can be considered highly accurate in determining a PMI of 10 years of data; crystallinity and mineral maturity can be considered useful in determining a PMI of 25 years; and crystallinity and mineral maturity can be considered highly accurate in determining a PMI of 50 years. A particular XRD index was identified as the most suitable parameter to estimate PMI: crystallinity. The joint use of XRD and ATR-FTIR analyses could be a promising alternative for dating human teeth.

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.

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.

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.

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.

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.

A Timeframe for SARS-CoV-2 Genomes: A Proof of Concept for Postmortem Interval Estimations

Establishing the timeframe when a particular virus was circulating in a population could be useful in several areas of biomedical research, including microbiology and legal medicine. Using simulations, we demonstrate that the circulation timeframe of an unknown SARS-CoV-2 genome in a population (hereafter, estimated time of a queried genome [QG]; tE-QG) can be easily predicted using a phylogenetic model based on a robust reference genome database of the virus, and information on their sampling dates. We evaluate several phylogeny-based approaches, including modeling evolutionary (substitution) rates of the SARS-CoV-2 genome (~10-3 substitutions/nucleotide/year) and the mutational (substitutions) differences separating the QGs from the reference genomes (RGs) in the database. Owing to the mutational characteristics of the virus, the present Viral Molecular Clock Dating (VMCD) method covers timeframes going backwards from about a month in the past. The method has very low errors associated to the tE-QG estimates and narrow intervals of tE-QG, both ranging from a few days to a few weeks regardless of the mathematical model used. The SARS-CoV-2 model represents a proof of concept that can be extrapolated to any other microorganism, provided that a robust genome sequence database is available. Besides obvious applications in epidemiology and microbiology investigations, there are several contexts in forensic casework where estimating tE-QG could be useful, including estimation of the postmortem intervals (PMI) and the dating of samples stored in hospital settings.

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.

Applications of massively parallel sequencing in forensic genetics

Massively parallel sequencing, also referred to as next-generation sequencing, has positively changed DNA analysis, allowing further advances in genetics. Its capability of dealing with low quantity/damaged samples makes it an interesting instrument for forensics. The main advantage of MPS is the possibility of analyzing simultaneously thousands of genetic markers, generating high-resolution data. Its detailed sequence information allowed the discovery of variations in core forensic short tandem repeat loci, as well as the identification of previous unknown polymorphisms. Furthermore, different types of markers can be sequenced in a single run, enabling the emergence of DIP-STRs, SNP-STR haplotypes, and microhaplotypes, which can be very useful in mixture deconvolution cases. In addition, the multiplex analysis of different single nucleotide polymorphisms can provide valuable information about identity, biogeographic ancestry, paternity, or phenotype. DNA methylation patterns, mitochondrial DNA, mRNA, and microRNA profiling can also be analyzed for different purposes, such as age inference, maternal lineage analysis, body-fluid identification, and monozygotic twin discrimination. MPS technology also empowers the study of metagenomics, which analyzes genetic material from a microbial community to obtain information about individual identification, post-mortem interval estimation, geolocation inference, and substrate analysis. This review aims to discuss the main applications of MPS in forensic genetics.