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Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning

Downy mildew (caused by Plasmopara viticola) and gray mold (caused by Botrytis cinerea) are fungal diseases that significantly impact grape production globally. Cytochrome b plays a significant role in the mitochondrial respiratory chain of the two fungi that cause these diseases and is a key target...

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Autores principales: Zhang, Junrui, Fernando, Sandun D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221559/
https://www.ncbi.nlm.nih.gov/pubmed/37317315
http://dx.doi.org/10.3390/microorganisms11051341
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author Zhang, Junrui
Fernando, Sandun D.
author_facet Zhang, Junrui
Fernando, Sandun D.
author_sort Zhang, Junrui
collection PubMed
description Downy mildew (caused by Plasmopara viticola) and gray mold (caused by Botrytis cinerea) are fungal diseases that significantly impact grape production globally. Cytochrome b plays a significant role in the mitochondrial respiratory chain of the two fungi that cause these diseases and is a key target for quinone outside inhibitor (QoI)-based fungicide development. Since the mode of action (MOA) of QoI fungicides is restricted to a single active site, the risk of developing resistance to these fungicides is deemed high. Consequently, using a combination of fungicides is considered an effective way to reduce the development of QoI resistance. Currently, there is little information available to help in the selection of appropriate fungicides. This study used a combination of in silico simulations and quantitative structure–activity relationship (QSAR) machine learning algorithms to screen the most potent QoI-based fungicide combinations for wild-type (WT) and the G143A mutation of fungal cytochrome b. Based on in silico studies, mandestrobin emerged as the top binder for both WT Plasmopara viticola and WT Botrytis cinerea cytochrome b. Famoxadone appeared to be a versatile binder for G143A-mutated cytochrome b of both Plasmopara viticola and Botrytis cinerea. Thiram emerged as a reasonable, low-risk non-QoI fungicide that works on WT and G143A-mutated versions of both fungi. QSAR analysis revealed fenpropidin, fenoxanil, and ethaboxam non-QoIs to have a high affinity for G143A-mutated cytochrome b of Plasmopara viticola and Botrytis cinerea. Above-QoI and non-QoI fungicides can be considered for field studies in a fungicide management program against Plasmopara viticola- and Botrytis cinerea-based fungal infections.
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spelling pubmed-102215592023-05-28 Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning Zhang, Junrui Fernando, Sandun D. Microorganisms Article Downy mildew (caused by Plasmopara viticola) and gray mold (caused by Botrytis cinerea) are fungal diseases that significantly impact grape production globally. Cytochrome b plays a significant role in the mitochondrial respiratory chain of the two fungi that cause these diseases and is a key target for quinone outside inhibitor (QoI)-based fungicide development. Since the mode of action (MOA) of QoI fungicides is restricted to a single active site, the risk of developing resistance to these fungicides is deemed high. Consequently, using a combination of fungicides is considered an effective way to reduce the development of QoI resistance. Currently, there is little information available to help in the selection of appropriate fungicides. This study used a combination of in silico simulations and quantitative structure–activity relationship (QSAR) machine learning algorithms to screen the most potent QoI-based fungicide combinations for wild-type (WT) and the G143A mutation of fungal cytochrome b. Based on in silico studies, mandestrobin emerged as the top binder for both WT Plasmopara viticola and WT Botrytis cinerea cytochrome b. Famoxadone appeared to be a versatile binder for G143A-mutated cytochrome b of both Plasmopara viticola and Botrytis cinerea. Thiram emerged as a reasonable, low-risk non-QoI fungicide that works on WT and G143A-mutated versions of both fungi. QSAR analysis revealed fenpropidin, fenoxanil, and ethaboxam non-QoIs to have a high affinity for G143A-mutated cytochrome b of Plasmopara viticola and Botrytis cinerea. Above-QoI and non-QoI fungicides can be considered for field studies in a fungicide management program against Plasmopara viticola- and Botrytis cinerea-based fungal infections. MDPI 2023-05-19 /pmc/articles/PMC10221559/ /pubmed/37317315 http://dx.doi.org/10.3390/microorganisms11051341 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Junrui
Fernando, Sandun D.
Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning
title Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning
title_full Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning
title_fullStr Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning
title_full_unstemmed Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning
title_short Identification of Fungicide Combinations Targeting Plasmopara viticola and Botrytis cinerea Fungicide Resistance Using Machine Learning
title_sort identification of fungicide combinations targeting plasmopara viticola and botrytis cinerea fungicide resistance using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221559/
https://www.ncbi.nlm.nih.gov/pubmed/37317315
http://dx.doi.org/10.3390/microorganisms11051341
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