Microbial Signatures of Cadaver Gravesoil During Decomposition

Abstract

Genomic studies have estimated there are approximately 10(3)-10(6) bacterial species per gram of soil. The microbial species found in soil associated with decomposing human remains (gravesoil) have been investigated and recognized as potential molecular determinants for estimates of time since death. The nascent era of high-throughput amplicon sequencing of the conserved 16S ribosomal RNA (rRNA) gene region of gravesoil microbes is allowing research to expand beyond more subjective empirical methods used in forensic microbiology. The goal of the present study was to evaluate microbial communities and identify taxonomic signatures associated with the gravesoil human cadavers. Using 16S rRNA gene amplicon-based sequencing, soil microbial communities were surveyed from 18 cadavers placed on the surface or buried that were allowed to decompose over a range of decomposition time periods (3-303 days). Surface soil microbial communities showed a decreasing trend in taxon richness, diversity, and evenness over decomposition, while buried cadaver-soil microbial communities demonstrated increasing taxon richness, consistent diversity, and decreasing evenness. The results show that ubiquitous Proteobacteria was confirmed as the most abundant phylum in all gravesoil samples. Surface cadaver-soil communities demonstrated a decrease in Acidobacteria and an increase in Firmicutes relative abundance over decomposition, while buried soil communities were consistent in their community composition throughout decomposition. Better understanding of microbial community structure and its shifts over time may be important for advancing general knowledge of decomposition soil ecology and its potential use during forensic investigations.

Function of bacterial community dynamics in the formation of cadaveric semiochemicals during in situ carcass decomposition

“…Indeed, the considerable dynamics of bacterial richness increase concomitant with homogenization of community composition in the first four days of decomposition has not been reported before (Metcalf et al ., ; Pechal et al ., ; Finley et al ., ; Guo et al ., ). The initial heterogeneous bacterial community of piglets directly after the birth is most likely related to the individual composition of the microbiome of each mother sow and the physical contact to different inoculation sources during delivery (Dominguez‐Bello et al ., ).…”

A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval

“…Studies by Finley, et al, and Cobaugh, et al have reported that cadaver-derived microbes can be detected in the nearby soil for up to a year and possibly much longer. [10, 11] Importantly, the detection of microbes in nearby soil appears to follow a steady progression and could prove fruitful for forensic estimation of the postmortem interval, even long after a body has been removed the scene.…”

Microbial forensics: new breakthroughs and future prospects

“…While the first is composed microorganisms, mainly bacteria and fungi, present in the blood and internal organs (e.g., brain, heart, liver, lungs, spleen); the second is composed by the microorganisms residing in and/or moving on the surface (e.g., superficial epithelial tissues, oral mucosal membranes and distal orifices of the digestive tract) of decomposing cadavers. Contrary to the epinecrotic community, the thanatomicrobiome is more stable being unbiased by either abiotic (e.g., humidity, temperature and pH) or biotic factors (e.g., gasses, insects and scavenger activities) (Javan et al 2016, b).…”

“…For many years, forensic entomology, based on the postmortem succession of insects colonizing the cadaver, has been used for PMI estimation. In the recent years, evidence has been accumulated, proving that microbial communities can also go through successional changes postmortem, paving the ways for its use as PMI indicators (Dickson et al 2011;Hauther et al 2015;Heimesaat et al 2012;Javan et al 2016, b;Javan et al 2017;Metcalf et al 2013Metcalf et al , b, 2017Parkinson et al 2009).…”