Expanding and validating microbiome database for estimating the postmortem interval

Time of death is important for death investigations, particularly criminal investigations for which alibis must be verified. However, it can be difficult to estimate the postmortem interval (PMI) after the first several days of death, especially in the absence of other physical evidence such as last known communications or insect activity. Over the last decade, the proposal investigators have demonstrated the power of microbiome-based estimates of PMI with a mouse model system (2011-DN-BX-K533), and then translated estimates to humans by utilizing multiple U.S. anthropological forensic research facilities (2015-DN-BX-K016, 2016-DN-BX-0194). Recent research by the proposal investigators generated a machine learning model utilizing 16S rRNA gene amplicon data from skin and soil samples associated with 36 human cadavers collected daily for 21 days from three forensic facilities, which predicts PMI within approximately +/- 3 days over the first 21 days postmortem. Thus, this new tool provides useful accuracy for crime scene investigations. In the proposed research, the first goal is to expand the 36-body PMI microbiome database by collecting similar sample types from an additional 18 human cadavers from two additional facilities, which are in a climate type not yet represented in the PMI database. This additional collection will bring Köppen-Geiger classified climate types in the database to include three of the major U.S. climate types. The new and existing data will together train a new machine learning model for estimating PMI that will be more generalizable across climates represented in the U.S. Second, an independent test set of samples from cadavers that are not part of the 54-body training set will be used to validate the model generated in Goal 1. Samples will be collected from cadavers from locations represented in the training set, and also from new locations not represented in the training set. From these data, generalizability and accuracy of the model will be assessed for predicting PMI across climates, individual locations, and decomposition time frames in the U.S. The purpose of the proposed research is to create and validate a microbial-based model to predict PMI across locations in the U.S. The proposed applied research seeks to improve forensic science for criminal justice purposes by increasing knowledge about a potential new type of physical evidence (microbes) and focuses on developing a tool in which the microbiome present on skin or in nearby soils is used as physical evidence to estimate PMI.

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