Environmental predictors impact microbial-based postmortem interval (PMI) estimation models within human decomposition soils.

Autor: Mason AR; Department of Microbiology, University of Tennessee-Knoxville, Knoxville, TN, United States of America., McKee-Zech HS; Department of Anthropology, University of Tennessee-Knoxville, Knoxville, TN, United States of America., Steadman DW; Department of Anthropology, University of Tennessee-Knoxville, Knoxville, TN, United States of America., DeBruyn JM; Department of Microbiology, University of Tennessee-Knoxville, Knoxville, TN, United States of America.; Department of Biosystems Engineering and Soil Science, University of Tennessee-Knoxville, Knoxville, TN, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Oct 11; Vol. 19 (10), pp. e0311906. Date of Electronic Publication: 2024 Oct 11 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0311906
Abstrakt: Microbial succession has been suggested to supplement established postmortem interval (PMI) estimation methods for human remains. Due to limitations of entomological and morphological PMI methods, microbes are an intriguing target for forensic applications as they are present at all stages of decomposition. Previous machine learning models from soil necrobiome data have produced PMI error rates from two and a half to six days; however, these models are built solely on amplicon sequencing of biomarkers (e.g., 16S, 18S rRNA genes) and do not consider environmental factors that influence the presence and abundance of microbial decomposers. This study builds upon current research by evaluating the inclusion of environmental data on microbial-based PMI estimates from decomposition soil samples. Random forest regression models were built to predict PMI using relative taxon abundances obtained from different biological markers (bacterial 16S, fungal ITS, 16S-ITS combined) and taxonomic levels (phylum, class, order, OTU), both with and without environmental predictors (ambient temperature, soil pH, soil conductivity, and enzyme activities) from 19 deceased human individuals that decomposed on the soil surface (Tennessee, USA). Model performance was evaluated by calculating the mean absolute error (MAE). MAE ranged from 804 to 997 accumulated degree hours (ADH) across all models. 16S models outperformed ITS models (p = 0.006), while combining 16S and ITS did not improve upon 16S models alone (p = 0.47). Inclusion of environmental data in PMI prediction models had varied effects on MAE depending on the biological marker and taxonomic level conserved. Specifically, inclusion of the measured environmental features reduced MAE for all ITS models, but improved 16S models at higher taxonomic levels (phylum and class). Overall, we demonstrated some level of predictability in soil microbial succession during human decomposition, however error rates were high when considering a moderate population of donors.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Mason et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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