Literature DB >> 32559614

Detecting personal microbiota signatures at artificial crime scenes.

Jarrad T Hampton-Marcell1, Peter Larsen2, Tifani Anton2, Lauren Cralle3, Naseer Sangwan4, Simon Lax5, Neil Gottel6, Mariana Salas-Garcia6, Candace Young7, George Duncan8, Jose V Lopez8, Jack A Gilbert9.   

Abstract

When mapped to the environments we interact with on a daily basis, the 36 million microbial cells per hour that humans emit leave a trail of evidence that can be leveraged for forensic analysis. We employed 16S rRNA amplicon sequencing to map unique microbial sequence variants between human skin and building surfaces in three experimental conditions: over time during controlled and uncontrolled incidental interactions with a door handle, and during multiple mock burglaries in ten real residences. We demonstrate that humans (n = 30) leave behind microbial signatures that can be used to track interaction with various surfaces within a building, but the likelihood of accurately detecting the specific burglar for a given home was between 20-25%. Also, the human microbiome contains rare microbial taxa that can be combined to create a unique microbial profile, which when compared to 600 other individuals can improve our ability to link an individual 'burglar' to a residence. In total, 5512 discriminating, non-singleton unique exact sequence variants (uESVs) were identified as unique to an individual, with a minimum of 1 and a maximum of 568, suggesting some people maintain a greater degree of unique taxa compared to our population of 600. Approximate 60-77% of the unique exact sequence variants originated from the hands of participants, and these microbial discriminators spanned 36 phyla but were dominated by the Proteobacteria (34%). A fitted regression generated to determine whether an intruder's uESVs found on door handles in an office decayed over time in the presence or absence of office workers, found no significant shift in proportion of uESVs over time irrespective of the presence of office workers. While it was possible to detect the correct burglars' microbiota as having contributed to the invaded space, the predictions were very weak in comparison to accepted forensic standards. This suggests that at this time 16S rRNA amplicon sequencing of the built environment microbiota cannot be used as a reliable trace evidence standard for criminal investigations.
Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Built-environment; Forensic microbiology; Host-microbe; Human microbiome; Trace evidence

Mesh:

Substances:

Year:  2020        PMID: 32559614     DOI: 10.1016/j.forsciint.2020.110351

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  5 in total

Review 1.  Microbial source tracking using metagenomics and other new technologies.

Authors:  Shahbaz Raza; Jungman Kim; Michael J Sadowsky; Tatsuya Unno
Journal:  J Microbiol       Date:  2021-02-10       Impact factor: 3.422

2.  Population Informative Markers Selected Using Wright's Fixation Index and Machine Learning Improves Human Identification Using the Skin Microbiome.

Authors:  Allison J Sherier; August E Woerner; Bruce Budowle
Journal:  Appl Environ Microbiol       Date:  2021-08-11       Impact factor: 4.792

3.  Determining Informative Microbial Single Nucleotide Polymorphisms for Human Identification.

Authors:  Allison J Sherier; August E Woerner; Bruce Budowle
Journal:  Appl Environ Microbiol       Date:  2022-03-14       Impact factor: 5.005

4.  Diurnal variation in the human skin microbiome affects accuracy of forensic microbiome matching.

Authors:  David Wilkins; Xinzhao Tong; Marcus H Y Leung; Christopher E Mason; Patrick K H Lee
Journal:  Microbiome       Date:  2021-06-05       Impact factor: 14.650

Review 5.  Challenges in Human Skin Microbial Profiling for Forensic Science: A Review.

Authors:  Ana Neckovic; Roland A H van Oorschot; Bianca Szkuta; Annalisa Durdle
Journal:  Genes (Basel)       Date:  2020-08-28       Impact factor: 4.096

  5 in total

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