Dataset Information

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables.

ABSTRACT: Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

SUBMITTER: Burcham ZM

PROVIDER: S-EPMC10914610 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables.

Burcham Zachary M ZM Belk Aeriel D AD McGivern Bridget B BB Bouslimani Amina A Ghadermazi Parsa P Martino Cameron C Shenhav Liat L Zhang Anru R AR Shi Pixu P Emmons Alexandra A Deel Heather L HL Xu Zhenjiang Zech ZZ Nieciecki Victoria V Zhu Qiyun Q Shaffer Michael M Panitchpakdi Morgan M Weldon Kelly C KC Cantrell Kalen K Ben-Hur Asa A Reed Sasha C SC Humphry Greg C GC Ackermann Gail G McDonald Daniel D Chan Siu Hung Joshua SHJ Connor Melissa M Boyd Derek D Smith Jake J Watson Jenna M S JMS Vidoli Giovanna G Steadman Dawnie D Lynne Aaron M AM Bucheli Sibyl S Dorrestein Pieter C PC Wrighton Kelly C KC Carter David O DO Knight Rob R Metcalf Jessica L JL

Nature microbiology 20240212 3

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to ident ...[more]

PMID: 38347104

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Project description: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.

Dataset Information

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables.

Publications

A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables.

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