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Skin Microbe Colonies Used for Human Identification

Humans transfer trace signatures of unique colonies of microbes on our skin to objects we touch. The tiny size of that signature make it difficult for investigators to identify an individual. Research now has made that identification more likely.
Date Published
November 4, 2019

Scientists have long known that microbial communities on human skin can be unique to specific people, families, and regions, and that they can stay stable over time. Although people generally have the same species of microbes on their skin, the microbial communities on an individual evolve and accumulate unique mutations that vary in predictable ways from person to person and, researchers have discovered, on different parts of the body. Those differences are of interest to forensic investigators because of the advent of fast, cheap, and large-scale DNA sequencing that allows the identification of whole communities of bacteria, fungi, and viruses without the need to first isolate and culture them.

Geneticist Bruce Budowle, director of the University of North Texas Center for Human Identification, realized that not only does human skin have a large number of bacterial cells on its surface, but that those cells would be transferred to an object when it is handled.  Efforts to sequence human DNA from touch samples face the challenge of amplifying and analyzing highly dilute concentrations, but including an analysis of DNA from the bacterial cells, Budowle surmised, could provide much more information from a single sample.

Budowle’s research team, supported by a 2015 NIJ grant, developed methods for identifying a core set of skin organisms consisting of ten species of bacteria, one fungus species, and one bacteria-attacking virus. Their methods combined the best features of current approaches used to identify microbial communities, which, for bacteria, have traditionally been based on either sequencing the single gene “16S,” or entire genomes. They developed primers for 286 lineage-informative genomic sites, used them to amplify sample DNA, and then sequenced the products on a next-generation sequencing platform.

Using this panel of markers (which the team named “hidSkinPlex”), triplicate samples taken from the hands, feet, and chests of eight volunteers were sequenced, and those data used to train and evaluate an automated matching system. Samples were correctly matched to individuals with 94% accuracy and to the body site from which they came with 86% accuracy. Samples from hands were the most useful, being correctly matched to individuals with 100% accuracy.

The ability of hand microbes to identify host donors is important for forensic investigators because touch DNA is most likely to be found on handled items. It is consistent with earlier experiments done by Budowle and his team, as well as research by other groups. Indeed, earlier work has shown that microbial samples can not only identify a person’s hands, but also distinguish the left or the right, as each hand hosts host different bacterial lineages on the same person. The mechanism behind this is poorly understood, for a person’s daily activities would seem to provide ample opportunities for repeated mixing of left and right hand microbial colonies. .

Budowle also found that microbial communities may be used to accurately identify individuals over time spans of three years or more. Using 23 billion publicly available reads of DNA sequences collected by another study, Budowle’s team asked not only whether microbial communities can accurately be matched to donor hosts and body locations, but also whether such matches could be stable over time. The data set, taken from 17 body sites on 12 individuals over 30 months, was screened for core microbial species, and the bacteria Propionibacterium acnes was identified from all individuals and body sites (confirming its potential as a forensically important species). When trees of relationships among P. acnes samples taken at different times were analyzed, the researchers found that samples taken from the same individual at different times were more closely related to each other than samples taken at the same time from different individuals, with rare exception. Indeed, match accuracies remained as high over time, significantly better than chance and as high as 100% for samples from certain body areas.

Despite the findings that support the utility of microbial genomics in forensic investigation, the researchers remain cautious. The fact that microbial communities vary among people gives them promise as a tool for identification, but variations across time frames and body regions (which, by their very nature, are subject to unknown contact with individuals over time) could complicate human identification in forensic applications.

Much of the team’s success comes from their method of targeting a large number of taxonomically informative genomic markers, and they noted that further methodological improvements could improve accuracy. For example, in another study Budowle found that the composition of microbial communities was more identifying than mutational histories and historical relationships. That is, the ratios of different types of microbes on an individual’s skin may be more unique than the specific lineages that compose each type.

Budowle concluded by saying his research was “highly successful and met the goals of the project to develop an initial targeted multiplex skin microbiome panel that potentially could be used for human identification.” Continued evaluation and optimization of hidSkinPlex and host-biome matching algorithms may help bring microbiome analysis to “forensic fruition,” he said.

About This Article

The research described in this article was funded by NIJ grant 2015-NE-BX-K006 awarded to the University of North Texas Health Science Center. This article is based on the grantee report "Human Microbiome Species and Genes for Human Identification," by Bruce Budowle, principal investigator, Graduate School of Biomedical Sciences and UNT Center for Human Identification, University of North Texas.

Date Published: November 4, 2019