The microbiome can be used to estimate the post-mortem interval, which is also known as the time elapsed since death, of human remains in the case of criminal investigations. This is because microbes are instrumental in the role of decomposition, with the stage of decay indicating time elapsed since death.
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The role of microbes is particularly important as communities undergo what is known as succession; this process is both predictable and timely, and high throughput DNA sequencing can be used to rapidly track these community shifts.
Current methods for estimating PMI have limited accuracy as in length of time increases. The study of insect activity (forensic entomology), can provide useful estimations of time elapsed since death if this has occurred between days and weeks. However, forensic entomology suffers limitations; insects are relatively absent indoors and during the winter. Consequently, new tools for estimating PMI are necessary.
Methods involving microbes have the potential to be used as forensic tools for the estimation of PMI. They are reliable means of PMI estimation as microbial communities shift in a predictably temporal manner. Microbial data gathered through high throughput characterization returns information about microbial communities associated with various decomposition states.
Coupled with time, this data can be developed into a form of ‘microbial clock’, that enables estimation of PMI. Several proofs of concept studies have been commissioned since 2013 to support the study of microbial community shifts as a forensic tool.
The link between decomposition and the post-mortem interval
Decomposition occurs as a product of the interaction between the ecosystem and external factors that affect the rate and mode of decomposition. Consequently, the measure of biotic and abiotic factors, in combination with the study of the remains, allows for the estimation of PMI.
There are several methods to achieve this, and the suitability of each of these is dependent on the extent to which decomposition is affected by external factors; this information can be compiled to produce a model of decomposition. As the PMI is a method that is dependent on working backward, the exact conditions of death are unlikely to be determined exactly. However, employing a method that has a clock-like dependency, will enable a more reliable estimation to be given.
There are several stages of decomposition. The first stage shows no outwardly visible signs, but at the level of the cell, oxygen deprivation results in the onset of rigor mortis; a drop in temperature to that which equals the ambient temperature results in algor mortis; and cells in contact with the surface discolor (livor mortis).
Bacterial overgrowth in the body occurs, and flies of the family Calliphoridae lay eggs in protected areas of the body or in thicker hair. Egg-laying propagates as other female flies do the same. In addition, beetles, ants, and wasps may be attracted.
Following earlier stages, skin and hair shed. Remains then putrefy as a result of fermentation and proteolysis, and this is accompanied by a change in color from pink white through to grey-green, and eventually black. The decomposition of the gut enables microbes here to enter into the circulatory system, breaking down blood which results in black residue visible under the skin as marbling.
Due to the increased acidity and decreased oxygen content (anoxia), anaerobic bacteria able to proliferate. The gas is released as a result of this process causes bloating and forces bodily fluids from the head and trunk. This marks the third stage of decomposition, and continual leakage of fluid through the head and trunk marks the transition to the fourth stage.
The third stage is also associated with bone exposure. This stimulates further attraction of flies. Diptera colonizes the body in waves, and the hatched mass of maggots (which are conglomerates of Larval Diptera and other species and families of insects), travel around the body.
Beetles are also common - from the family Silphidea - as well as predatory insects. Diptera colonization may be slowed as tissue dehydrates, and Coleoptera, a species that prefers dry conditions appear (from the families Cleridae, Silphidae, Dermestidae, Trogidae, and Scarabaeidae).
Like insects, different microbial communities can indicate PMI. This is because microbes undergo periods of succession, which can be accurately and comprehensively studied by high throughput sequencing. The succession of microbial communities is predictable, and as such, models based on regression of microbial data can be used to predict the PMI of a cadaver with accuracy.
Estimating PMI using regression models
Decomposition studies can build regression models - this requires data generated from studying several microbial communities. In decomposition studies, samples are collected at a series of time intervals from the same location on the body. DNA extraction, followed by the polymerase chain reaction (PCR), for DNA amplification, is conducted in the region of the 16S ribosomal RNA (rRNA).
rRNA Is a common region used to survey bacterial and archaeal populations, whereas the 18 S rRNA amplicon informs the characterization of eukaryotes. These amplicons are universal across species and can inform characterization based on taxa, a form of sub-classification. The relative abundance of each taxon is compiled into a table, which is amalgamated to form large and complex data sets.
Labs employ machine learning (ML) for the discovery of patterns in the data, and this can be used to build predictive models that enabled the PMI to be determined based on a time series of samples. The data is partitioned into training and testing data sets; the former is used to program the ML algorithm. This generates a model, and the testing data is then used to determine its accuracy.
Improvements in the model accuracy are achieved by factoring in environmental variables - the modeling process is iterative and continues until the lowest error of estimation is achieved. Once this best model is produced, swabs collected from remains with unknown PMIs can be inputted into the best model, which is based on the microbial clock, to generate a PMI with associated error rates.
Building the microbial clock: Mammalian models
The estimation of decomposition rates is most accurate when using mammalian models. These have been especially useful in demonstrating proof of concept for the predictability of succession. Typical mammalian models have included rats, fish, and swine. To corroborate the findings in mammalian models, donated human remains have been the subject of microbial succession tracking at specialized anthropological research facilities.
An important consideration of the microbial clock is the extent to which the sampling environment affects rates of microbial succession. The precise location from which samples are collected at various time points heavily influences decomposition, and so consideration of the limitations and benefits of each sampling location is necessary for the accurate prediction of PMI.
Sampling locations can be categorized into externally and internally accessible. Concerning externally accessible locations, the skin is the most common sample site and has proven to be an accurate site for the estimation of PMI as a result of a reliable microbial clock that can be constructed. Internally accessible sites are variable and microbial succession shows organ dependent differences.
The gut microbiome is the most promising site for clock-like determination of microbial succession. Studies demonstrated that the 16S rRNA gene sequencing could accurately determine bacterial populations that either increased or decreased reliably. While these sites can provide information a microbial succession and thus, PMI, sampling is destructive to the remains - therefore non-invasive sampling is thought to be essential.
The microbial clock: The role of the sampling environment
For researchers aiming to estimate PMI on a longer time scale, the bone is a good indicator of microbial succession. In addition to this, soil as a sample site holds great potential as an indicator for PMI as microbial succession is fundamental to microbial decomposers and is provided with a source of ammonia-rich fluids from the body.
For researchers conducting studies, sample type selection should be based on the invasiveness of sampling and the decomposition time frame. Additional studies focusing on other environmental factors, such as season, corroborate the finding that microbial communities associated with the decomposing body change in a significant and reproducible manner.
The pattern observed in this change has also been shown to be an accurate predictor of PMI at both early and late-stage decomposition. Through a series of independent studies in which researchers isolated a particular variable, an understanding of which factors that impact succession has been formed, providing the basis for the construction of an accurate microbial clock.
Unknowns and new technologies
New technologies that have provided a proof of concept for PMI estimation have been published in a series of studies. However, the adoption of new technologies by the judicial system requires a forensic science community following the proof of concept with the development of prototypes technology followed by legal validation and acceptance.
Currently, the next steps require a microbial PMI model with error rates and the creation of a prototype kit and trajectory for analysis. Following this, the technique must be accepted into the legal system by a judge. This is supported by the validity and reliability up there supporting science and technology as indicated by published pair viewed journals and teaching at higher education facilities.
The widespread acceptance of this technology can only be corroborated by the adoption of this new type of DNA sequencing for microbial succession by both the forensic and research communities – a feat supported by sharing the technology widely at conferences, training events and workshops, supported by accrediting organizations.
To conclude, the authors state that the microbial clock as an estimate of PMI provides a reproducible and accurate form of evidence for forensic investigation and beyond. The development of new technologies that have been validated and accredited is hoped to provide a means of realizing the potential of microbial succession and machine learning techniques.
One critical bottleneck in the implementation of the microbial clock as an estimator of PMI is there wide and numerous ranges of sample types (encompassing external and internal sites) to consider. With this in mind, the establishment of sample types and parameters are necessary additions to the models. These will ensure that the highest accuracy is obtained for PMI.
Moreover, these will enable researchers to determine whether PMI estimates should be based on models classified by the environment or via a single general model. Only when these knowledge gaps are filled can the integration of the microbial clock, as constructed by microbial succession, into the forensic toolbox can occur.
Deel, H. et al. (2020) Chapter 12 - Using microbiome tools for estimating the postmortem interval. Microbial Forensics (Third Edition) (pp.195-205). Cambridge, MA: Academic Press