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Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification
The highly personalized human skin microbiome may serve as a viable marker in personal identification. Amplicon sequencing resolution using 16S rRNA cannot identify bacterial communities sufficiently to discriminate between individuals. Thus, novel higher-resolution genetic markers are required for...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857535/ https://www.ncbi.nlm.nih.gov/pubmed/33531409 http://dx.doi.org/10.1128/mSystems.01255-20 |
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author | Toyomane, Kochi Yokota, Ryo Watanabe, Ken Akutsu, Tomoko Asahi, Ai Kubota, Satoshi |
author_facet | Toyomane, Kochi Yokota, Ryo Watanabe, Ken Akutsu, Tomoko Asahi, Ai Kubota, Satoshi |
author_sort | Toyomane, Kochi |
collection | PubMed |
description | The highly personalized human skin microbiome may serve as a viable marker in personal identification. Amplicon sequencing resolution using 16S rRNA cannot identify bacterial communities sufficiently to discriminate between individuals. Thus, novel higher-resolution genetic markers are required for forensic purposes. The clustered regularly interspaced short palindromic repeats (CRISPRs) are prokaryotic genetic elements that can provide a history of infections encountered by the bacteria. The sequencing of CRISPR spacers may provide phylogenetic information with higher resolution than other markers. However, using spacer sequencing for discrimination of personal skin microbiome is difficult due to limited information on CRISPRs in human skin microbiomes. It remains unclear whether personal microbiome discrimination can be achieved using spacer diversity or which CRISPRs will be forensically relevant. We identified common CRISPRs in the human skin microbiome via metagenomic reconstruction and used amplicon sequencing for deep sequencing of spacers. We successfully reconstructed 24 putative CRISPR arrays using metagenomic data sets. A total of 1,223,462 reads from three CRISPR arrays revealed that spacers in the skin microbiome were highly personalized, and conserved repeats were commonly shared between individuals. These individual specificities observed using CRISPR typing were confirmed by comparing the CRISPR diversity to microbiome diversity assessed using 16S rRNA amplicon sequencing. CRISPR typing achieved 95.2% accuracy in personal classification, whereas 16S rRNA sequencing only achieved 52.6%. These results suggest that sequencing CRISPRs in the skin microbiome may be a more powerful approach for personal identification and ecological studies compared to conventional 16S rRNA sequencing. IMPORTANCE Microbial community diversity analysis can be utilized to characterize the personal microbiome that varies between individuals. CRISPR sequences, which reflect virome structure, in the human skin environment may be highly personalized similar to the structures of individual viromes. In this study, we identified 24 putative CRISPR arrays using a shotgun metagenome data set of the human skin microbiome. The findings of this study expand our understanding of the nature of CRISPRs by identifying novel CRISPR candidates. We developed a method to efficiently determine the diversity of three CRISPR arrays. Our analysis revealed that the CRISPR spacer diversity in the human skin microbiome is highly personalized compared with the microbiome diversity assessed by 16S rRNA sequencing, providing a new perspective on the study of the skin microbiome. |
format | Online Article Text |
id | pubmed-7857535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-78575352021-02-11 Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification Toyomane, Kochi Yokota, Ryo Watanabe, Ken Akutsu, Tomoko Asahi, Ai Kubota, Satoshi mSystems Research Article The highly personalized human skin microbiome may serve as a viable marker in personal identification. Amplicon sequencing resolution using 16S rRNA cannot identify bacterial communities sufficiently to discriminate between individuals. Thus, novel higher-resolution genetic markers are required for forensic purposes. The clustered regularly interspaced short palindromic repeats (CRISPRs) are prokaryotic genetic elements that can provide a history of infections encountered by the bacteria. The sequencing of CRISPR spacers may provide phylogenetic information with higher resolution than other markers. However, using spacer sequencing for discrimination of personal skin microbiome is difficult due to limited information on CRISPRs in human skin microbiomes. It remains unclear whether personal microbiome discrimination can be achieved using spacer diversity or which CRISPRs will be forensically relevant. We identified common CRISPRs in the human skin microbiome via metagenomic reconstruction and used amplicon sequencing for deep sequencing of spacers. We successfully reconstructed 24 putative CRISPR arrays using metagenomic data sets. A total of 1,223,462 reads from three CRISPR arrays revealed that spacers in the skin microbiome were highly personalized, and conserved repeats were commonly shared between individuals. These individual specificities observed using CRISPR typing were confirmed by comparing the CRISPR diversity to microbiome diversity assessed using 16S rRNA amplicon sequencing. CRISPR typing achieved 95.2% accuracy in personal classification, whereas 16S rRNA sequencing only achieved 52.6%. These results suggest that sequencing CRISPRs in the skin microbiome may be a more powerful approach for personal identification and ecological studies compared to conventional 16S rRNA sequencing. IMPORTANCE Microbial community diversity analysis can be utilized to characterize the personal microbiome that varies between individuals. CRISPR sequences, which reflect virome structure, in the human skin environment may be highly personalized similar to the structures of individual viromes. In this study, we identified 24 putative CRISPR arrays using a shotgun metagenome data set of the human skin microbiome. The findings of this study expand our understanding of the nature of CRISPRs by identifying novel CRISPR candidates. We developed a method to efficiently determine the diversity of three CRISPR arrays. Our analysis revealed that the CRISPR spacer diversity in the human skin microbiome is highly personalized compared with the microbiome diversity assessed by 16S rRNA sequencing, providing a new perspective on the study of the skin microbiome. American Society for Microbiology 2021-02-02 /pmc/articles/PMC7857535/ /pubmed/33531409 http://dx.doi.org/10.1128/mSystems.01255-20 Text en Copyright © 2021 Toyomane et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Toyomane, Kochi Yokota, Ryo Watanabe, Ken Akutsu, Tomoko Asahi, Ai Kubota, Satoshi Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification |
title | Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification |
title_full | Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification |
title_fullStr | Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification |
title_full_unstemmed | Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification |
title_short | Evaluation of CRISPR Diversity in the Human Skin Microbiome for Personal Identification |
title_sort | evaluation of crispr diversity in the human skin microbiome for personal identification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857535/ https://www.ncbi.nlm.nih.gov/pubmed/33531409 http://dx.doi.org/10.1128/mSystems.01255-20 |
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