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Estimating relative biomasses of organisms in microbiota using “phylopeptidomics”
BACKGROUND: There is an important need for the development of fast and robust methods to quantify the diversity and temporal dynamics of microbial communities in complex environmental samples. Because tandem mass spectrometry allows rapid inspection of protein content, metaproteomics is increasingly...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060547/ https://www.ncbi.nlm.nih.gov/pubmed/32143687 http://dx.doi.org/10.1186/s40168-020-00797-x |
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author | Pible, Olivier Allain, François Jouffret, Virginie Culotta, Karen Miotello, Guylaine Armengaud, Jean |
author_facet | Pible, Olivier Allain, François Jouffret, Virginie Culotta, Karen Miotello, Guylaine Armengaud, Jean |
author_sort | Pible, Olivier |
collection | PubMed |
description | BACKGROUND: There is an important need for the development of fast and robust methods to quantify the diversity and temporal dynamics of microbial communities in complex environmental samples. Because tandem mass spectrometry allows rapid inspection of protein content, metaproteomics is increasingly used for the phenotypic analysis of microbiota across many fields, including biotechnology, environmental ecology, and medicine. RESULTS: Here, we present a new method for identifying the biomass contribution of any given organism based on a signature describing the number of peptide sequences shared with all other organisms, calculated by mathematical modeling and phylogenetic relationships. This so-called “phylopeptidomics” principle allows for the calculation of the relative ratios of peptide-specified taxa by the linear combination of such signatures applied to an experimental metaproteomic dataset. We illustrate its efficiency using artificial mixtures of two closely related pathogens of clinical interest, and with more complex microbiota models. CONCLUSIONS: This approach paves the way to a new vision of taxonomic changes and accurate label-free quantitative metaproteomics for fine-tuned functional characterization. |
format | Online Article Text |
id | pubmed-7060547 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70605472020-03-12 Estimating relative biomasses of organisms in microbiota using “phylopeptidomics” Pible, Olivier Allain, François Jouffret, Virginie Culotta, Karen Miotello, Guylaine Armengaud, Jean Microbiome Methodology BACKGROUND: There is an important need for the development of fast and robust methods to quantify the diversity and temporal dynamics of microbial communities in complex environmental samples. Because tandem mass spectrometry allows rapid inspection of protein content, metaproteomics is increasingly used for the phenotypic analysis of microbiota across many fields, including biotechnology, environmental ecology, and medicine. RESULTS: Here, we present a new method for identifying the biomass contribution of any given organism based on a signature describing the number of peptide sequences shared with all other organisms, calculated by mathematical modeling and phylogenetic relationships. This so-called “phylopeptidomics” principle allows for the calculation of the relative ratios of peptide-specified taxa by the linear combination of such signatures applied to an experimental metaproteomic dataset. We illustrate its efficiency using artificial mixtures of two closely related pathogens of clinical interest, and with more complex microbiota models. CONCLUSIONS: This approach paves the way to a new vision of taxonomic changes and accurate label-free quantitative metaproteomics for fine-tuned functional characterization. BioMed Central 2020-03-06 /pmc/articles/PMC7060547/ /pubmed/32143687 http://dx.doi.org/10.1186/s40168-020-00797-x Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 | Methodology Pible, Olivier Allain, François Jouffret, Virginie Culotta, Karen Miotello, Guylaine Armengaud, Jean Estimating relative biomasses of organisms in microbiota using “phylopeptidomics” |
title | Estimating relative biomasses of organisms in microbiota using “phylopeptidomics” |
title_full | Estimating relative biomasses of organisms in microbiota using “phylopeptidomics” |
title_fullStr | Estimating relative biomasses of organisms in microbiota using “phylopeptidomics” |
title_full_unstemmed | Estimating relative biomasses of organisms in microbiota using “phylopeptidomics” |
title_short | Estimating relative biomasses of organisms in microbiota using “phylopeptidomics” |
title_sort | estimating relative biomasses of organisms in microbiota using “phylopeptidomics” |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060547/ https://www.ncbi.nlm.nih.gov/pubmed/32143687 http://dx.doi.org/10.1186/s40168-020-00797-x |
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