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Use of GC–MS based metabolic fingerprinting for fast exploration of fungicide modes of action
BACKGROUND: The widespread occurrence of fungicide resistance in fungal plant pathogens requires the development of new compounds with different mode(s) of action (MOA) to avoid cross resistance. This will require a rapid method to identify MOAs. RESULTS: Here, gas chromatography–mass spectrometry (...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591849/ https://www.ncbi.nlm.nih.gov/pubmed/31234789 http://dx.doi.org/10.1186/s12866-019-1508-5 |
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author | Hu, Zhihong Dai, Tan Li, Lei Liu, Pengfei Liu, Xili |
author_facet | Hu, Zhihong Dai, Tan Li, Lei Liu, Pengfei Liu, Xili |
author_sort | Hu, Zhihong |
collection | PubMed |
description | BACKGROUND: The widespread occurrence of fungicide resistance in fungal plant pathogens requires the development of new compounds with different mode(s) of action (MOA) to avoid cross resistance. This will require a rapid method to identify MOAs. RESULTS: Here, gas chromatography–mass spectrometry (GC–MS) based metabolic fingerprinting was used to elucidate the MOAs of fungicides. Botrytis cinerea, an important pathogen of vegetables and flowers, can be inhibited by a wide range of chemical fungicides with different MOAs. A sensitive strain of B. cinerea was exposed to EC(50) concentrations of 13 fungicides with different known MOAs and one with unknown MOA. The mycelial extracts were analyzed for their “metabolic fingerprint” using GC–MS. A comparison among the GC–MS vector’ profiles of cultures treated with fungicides were performeded. A model based on hierarchical clustering was established which allowed these antifungal compounds to be distinguished and classified coinciding with their MOAs. Thus, metabolic fingerprinting represents a rapid, convenient, and information-rich method for classifying the MOAs of antifungal substances. The biomarkers of fungicide MOAs were also established by an analysis of variance and included succinate for succinate dehydrogenase inhibitors and cystathionine for methionine synthesis inhibitors. Using the metabolic model and the common perturbation of metabolites, the new fungicide SYP-14288 was identified as having the same MOA as fluazinam. CONCLUSION: This study provides a comprehensive database of the metabolic perturbations of B. cinerea induced by diverse MOA inhibitors and highlights the utility of metabolic fingerprinting for defining MOAs, which will assist in the development and optimization of new fungicides. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12866-019-1508-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6591849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65918492019-07-08 Use of GC–MS based metabolic fingerprinting for fast exploration of fungicide modes of action Hu, Zhihong Dai, Tan Li, Lei Liu, Pengfei Liu, Xili BMC Microbiol Research Article BACKGROUND: The widespread occurrence of fungicide resistance in fungal plant pathogens requires the development of new compounds with different mode(s) of action (MOA) to avoid cross resistance. This will require a rapid method to identify MOAs. RESULTS: Here, gas chromatography–mass spectrometry (GC–MS) based metabolic fingerprinting was used to elucidate the MOAs of fungicides. Botrytis cinerea, an important pathogen of vegetables and flowers, can be inhibited by a wide range of chemical fungicides with different MOAs. A sensitive strain of B. cinerea was exposed to EC(50) concentrations of 13 fungicides with different known MOAs and one with unknown MOA. The mycelial extracts were analyzed for their “metabolic fingerprint” using GC–MS. A comparison among the GC–MS vector’ profiles of cultures treated with fungicides were performeded. A model based on hierarchical clustering was established which allowed these antifungal compounds to be distinguished and classified coinciding with their MOAs. Thus, metabolic fingerprinting represents a rapid, convenient, and information-rich method for classifying the MOAs of antifungal substances. The biomarkers of fungicide MOAs were also established by an analysis of variance and included succinate for succinate dehydrogenase inhibitors and cystathionine for methionine synthesis inhibitors. Using the metabolic model and the common perturbation of metabolites, the new fungicide SYP-14288 was identified as having the same MOA as fluazinam. CONCLUSION: This study provides a comprehensive database of the metabolic perturbations of B. cinerea induced by diverse MOA inhibitors and highlights the utility of metabolic fingerprinting for defining MOAs, which will assist in the development and optimization of new fungicides. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12866-019-1508-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-24 /pmc/articles/PMC6591849/ /pubmed/31234789 http://dx.doi.org/10.1186/s12866-019-1508-5 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Research Article Hu, Zhihong Dai, Tan Li, Lei Liu, Pengfei Liu, Xili Use of GC–MS based metabolic fingerprinting for fast exploration of fungicide modes of action |
title | Use of GC–MS based metabolic fingerprinting for fast exploration of fungicide modes of action |
title_full | Use of GC–MS based metabolic fingerprinting for fast exploration of fungicide modes of action |
title_fullStr | Use of GC–MS based metabolic fingerprinting for fast exploration of fungicide modes of action |
title_full_unstemmed | Use of GC–MS based metabolic fingerprinting for fast exploration of fungicide modes of action |
title_short | Use of GC–MS based metabolic fingerprinting for fast exploration of fungicide modes of action |
title_sort | use of gc–ms based metabolic fingerprinting for fast exploration of fungicide modes of action |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591849/ https://www.ncbi.nlm.nih.gov/pubmed/31234789 http://dx.doi.org/10.1186/s12866-019-1508-5 |
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