Soil Fungal Communities Investigated by Metabarcoding within Simulated Forensic Burial Contexts

Abstract

Background One of the most debated questions in forensic science is the estimation of the post-mortem interval (PMI). Despite the large amount of research currently performed to improve the PMI estimation, there is still the need for additional improvements, particularly in cases of severely decomposed buried remains. A novel alternative to the morphological examination of the remains is the analysis of the soil microbial communities. Bacteria and fungi are ubiquitous and can be found in the soil and in/on the corpses, and their shifts in populational compositions present at different PMIs may reveal insights for PMI estimation. Despite it already having been revealed that bacteria might be good candidates for this type of analysis, there are knowledge gaps for this type of application when dealing with fungal communities. For this reason, we performed the metabarcoding analysis of the mycobiome present in the soil after prolonged decomposition times, from one- to six-months, targeting both the Internal Transcribed Spacer (ITS) 1 and 2, to elucidate which of the two was more suitable for this purpose. Results Our results showed a decrease in the fungal taxonomic richness associated with increasing PMIs and the presence of specific trends associated with specific PMIs, such as the increase of the Mortierellomycota taxa after four- and six-months post-mortem and of Ascomycota particularly after two months, and the decrease of Basidiomycota from the first to the last time point. We have found a limited amount of taxa specifically associated with the presence of the mammalian carcasses and not present in the control soil, showing that the overall the taxa which are contributing the most to the changes in the community originate from the soil and are not introduced by the carrion, extending the potential to perform comparisons with other experimental studies with different carrion species. Conclusions This study has been the first one conducted on gravesoil, and sets the baseline for additional studies, showing the potential to use fungal biomarkers in combination with bacterial ones to improve the accuracy of the PMI predictive model based on the shifts in the soil microbial communities.