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The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing

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Abstract

Decomposition studies of vertebrate remains primarily focus on data that can be seen with the naked eye, such as arthropod or vertebrate scavenger activity, with little regard for what might be occurring with the microorganism community. Here, we discuss the necrobiome, or community of organisms associated with the decomposition of remains, specifically, the “epinecrotic” bacterial community succession throughout decomposition of vertebrate carrion. Pyrosequencing was used to (1) detect and identify bacterial community abundance patterns that described discrete time points of the decomposition process and (2) identify bacterial taxa important for estimating physiological time, a time–temperature metric that is often commensurate with minimum post-mortem interval estimates, via thermal summation models. There were significant bacterial community structure differences in taxon richness and relative abundance patterns through the decomposition process at both phylum and family taxonomic classification levels. We found a significant negative linear relationship for overall phylum and family taxon richness as decomposition progressed. Additionally, we developed a statistical model using high throughput sequencing data of epinecrotic bacterial communities on vertebrate remains that explained 94.4 % of the time since placement of remains in the field, which was within 2–3 h of death. These bacteria taxa are potentially useful for estimating the minimum post-mortem interval. Lastly, we provide a new framework and standard operating procedure of how this novel approach of using high throughput metagenomic sequencing has remarkable potential as a new forensic tool. Documenting and identifying differences in bacterial communities is key to advancing knowledge of the carrion necrobiome and its applicability in forensic science.

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Acknowledgments

We thank A. Lewis, T. Blair, and J. White for their help during the study. The Blair family is gratefully acknowledged for allowing access to their property for this research. We thank B. Singh for assistance in the metagenomic data sequence classification processing. Financial support was given to JLP from the Department of Entomology and the Whole Systems Genome Initiative at Texas A&M University. AMT and JKT would like to thank the Department of Entomology at Texas A&M University and Texas A&M University AgriLife Research for financial support of this project. MEB was supported by the University of Dayton Research Council and the Department of Biology.

Disclaimer

This project was funded (TLC, MEB, AMT, and JKT), in part, by the National Institute of Justice, Office of Justice Programs, US Department of Justice through Grant 2010-DN-BX-K243. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of the U.S. Department of Justice. Mention of trade names, companies, or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement of the products by the US Department of Agriculture

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Authors and Affiliations

  1. Department of Entomology, 2475 TAMU, Texas A&M University, College Station, TX, 77843, USA

    Jennifer L. Pechal

  2. Department of Biology, 300 College Park, University of Dayton, Dayton, OH, 45469-2320, USA

    Jennifer L. Pechal

  3. Southern Plains Agricultural Research Center, USDA-ARS, 2881 F and B Road, College Station, TX, 77845, USA

    Tawni L. Crippen

  4. Department of Biology, 300 College Park, University of Dayton, Dayton, OH, 45469-2320, USA

    M. Eric Benbow

  5. Department of Entomology, 2475 TAMU, Texas A&M University, College Station, TX, 77843, USA

    Aaron M. Tarone

  6. Molecular Research LP, 503 Clovis Rd, Shallowater, TX, 79363, USA

    Scot Dowd

  7. Department of Entomology, 2475 TAMU, Texas A&M University, College Station, TX, 77843, USA

    Jeffery K. Tomberlin

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  1. Jennifer L. Pechal
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  2. Tawni L. Crippen
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  3. M. Eric Benbow
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  4. Aaron M. Tarone
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  5. Scot Dowd
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  6. Jeffery K. Tomberlin
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Correspondence to Jennifer L. Pechal, Tawni L. Crippen or M. Eric Benbow.

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Pechal, J.L., Crippen, T.L., Benbow, M.E. et al. The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. Int J Legal Med 128, 193–205 (2014). https://doi.org/10.1007/s00414-013-0872-1

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