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Linking Internal Organ Microbiome and Metabolome Composition to Cause of Death in Medicolegal Investigations

Award Information

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Funding First Awarded
Total funding (to date)

Description of original award (Fiscal Year 2020, $393,503)

As submitted by the proposer: Does life end at death? Not for microbial activity on and in cadavers. Microbes become abundant in the corpse following death, and evidence is growing that they may assist in medicolegal investigations. Determination of the precise time since death, or postmortem interval (PMI), and the cause of death, is critical for forensic science when criminal deaths are not witnessed, or when conflicting accounts are reported. While there is compelling evidence that PMI can be predicted from changes in the microbiome of the human body during decomposition, new approaches to predict cause of death are required, and the researchers believe that the microbiome and associated metabolome of internal organs may hold valuable trace evidence of cause of death. The researchers hypothesize that the microbiome and metabolite profile associated with decaying liver and brain tissue maintains a signature of the cause of death. For example, drug overdose or rupturing of body cavity due to knife or bullet entry, can alter the chemical dynamic of internal organs which alters the microbial community composition and metabolism, providing a unique signature of these activities. The researchers propose to determine how microbiome composition and associated metabolism correlates with cause of death in 100 cadavers for which this information is known. The researchers will then build a neural network model that can be used to predict cause of death based on microbial and metabolic signatures associated with liver and heart tissue. This study will acquire tissue from cadavers through collaboration with both national (Alabama and Florida), and international morgues (Tempere University, Finland). The researchers will extract DNA and metabolites using standardized methods, and then sequence the bacterial, archaeal and fungal taxonomic markers (16S and ITS rRNA), and determine the metabolite profile of these samples. Statistical analyses will then be performed including two-tailed t-test, Mann-Whitney test, PERMANOVA, correlative network analysis, and machine learning approaches. The researchers will also build an artificial neural network model for liver and brain tissue samples, trained with data from 50 randomly select cadavers from the study, and validated with the other 50 cadavers. The model developed during this study will be uploaded to the Department of Energys KBASE resource with user friendly instructions and example files. Results will also be presented as oral talks or poster presentations at regional, national, and international forensic meetings, and will be developed into peer-reviewed articles for publication. Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14). ca/ncf

Date Created: September 21, 2020