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A large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry

MALDI-TOF MS-based microbial identification relies on reference spectral libraries, which limits the screening of diverse isolates, including uncultured lineages. We present a new strategy for broad-spectrum identification of bacterial and archaeal isolates by MALDI-TOF MS using a large-scale databa...

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Autores principales: Sekiguchi, Yuji, Teramoto, Kanae, Tourlousse, Dieter M., Ohashi, Akiko, Hamajima, Mayu, Miura, Daisuke, Yamada, Yoshihiro, Iwamoto, Shinichi, Tanaka, Koichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696839/
https://www.ncbi.nlm.nih.gov/pubmed/38049850
http://dx.doi.org/10.1186/s13059-023-03096-4
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author Sekiguchi, Yuji
Teramoto, Kanae
Tourlousse, Dieter M.
Ohashi, Akiko
Hamajima, Mayu
Miura, Daisuke
Yamada, Yoshihiro
Iwamoto, Shinichi
Tanaka, Koichi
author_facet Sekiguchi, Yuji
Teramoto, Kanae
Tourlousse, Dieter M.
Ohashi, Akiko
Hamajima, Mayu
Miura, Daisuke
Yamada, Yoshihiro
Iwamoto, Shinichi
Tanaka, Koichi
author_sort Sekiguchi, Yuji
collection PubMed
description MALDI-TOF MS-based microbial identification relies on reference spectral libraries, which limits the screening of diverse isolates, including uncultured lineages. We present a new strategy for broad-spectrum identification of bacterial and archaeal isolates by MALDI-TOF MS using a large-scale database of protein masses predicted from nearly 200,000 publicly available genomes. We verify the ability of the database to identify microorganisms at the species level and below, achieving correct identification for > 90% of measured spectra. We further demonstrate its utility by identifying uncultured strains from mouse feces with metagenomics, allowing the identification of new strains by customizing the database with metagenome-assembled genomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03096-4.
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spelling pubmed-106968392023-12-06 A large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry Sekiguchi, Yuji Teramoto, Kanae Tourlousse, Dieter M. Ohashi, Akiko Hamajima, Mayu Miura, Daisuke Yamada, Yoshihiro Iwamoto, Shinichi Tanaka, Koichi Genome Biol Method MALDI-TOF MS-based microbial identification relies on reference spectral libraries, which limits the screening of diverse isolates, including uncultured lineages. We present a new strategy for broad-spectrum identification of bacterial and archaeal isolates by MALDI-TOF MS using a large-scale database of protein masses predicted from nearly 200,000 publicly available genomes. We verify the ability of the database to identify microorganisms at the species level and below, achieving correct identification for > 90% of measured spectra. We further demonstrate its utility by identifying uncultured strains from mouse feces with metagenomics, allowing the identification of new strains by customizing the database with metagenome-assembled genomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03096-4. BioMed Central 2023-12-05 /pmc/articles/PMC10696839/ /pubmed/38049850 http://dx.doi.org/10.1186/s13059-023-03096-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Sekiguchi, Yuji
Teramoto, Kanae
Tourlousse, Dieter M.
Ohashi, Akiko
Hamajima, Mayu
Miura, Daisuke
Yamada, Yoshihiro
Iwamoto, Shinichi
Tanaka, Koichi
A large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry
title A large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry
title_full A large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry
title_fullStr A large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry
title_full_unstemmed A large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry
title_short A large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry
title_sort large-scale genomically predicted protein mass database enables rapid and broad-spectrum identification of bacterial and archaeal isolates by mass spectrometry
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696839/
https://www.ncbi.nlm.nih.gov/pubmed/38049850
http://dx.doi.org/10.1186/s13059-023-03096-4
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