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

Abstract

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.

WITHDRAWN: Microbial Image Deciphering: Navigating Challenges with Machine and Deep Learning

“…To prevent the spread of toxic microorganisms and ensure food safety, the study emphasises the importance of developing accessible methods for pre-testing microbial contamination in food production. Zhang and Fernando (2023) [15]address the issue of fungicide resistance in the ght against Plasmopara viticola and Botrytis cinerea grape fungal diseases. They investigate the use of machine learning and in silico simulations to identify effective fungicide combinations, focusing on the cytochrome b site in the fungi's mitochondrial respiratory chain.…”

Transcriptome Analysis of mfs2-Defective Penicillium digitatum Mutant to Reveal Importance of Pdmfs2 in Developing Fungal Prochloraz Resistance

“…According to studies in the past years, more and more evidence has emerged to support comprehensive metabolism backgrounds underlying fungicide resistance. Such backgrounds include various ERG-encoding genes in fungal ergosterol biosynthesis pathways [36,41,42], acetyl-CoA carboxylase (ACCase)-encoding genes in the lipid and fatty acid oxidation path-ways [45][46][47], reactive oxygen species (ROS)-metabolizing enzyme-encoding genes in the cell wall maintenance and oxidative-stress-responsive processes [50,51], mitochondrial respiratory chain protein-encoding genes in the cellular energy metabolisms [53][54][55], ubiquitin-encoding genes in the post-translational modification processes [57][58][59], and protein kinase-encoding genes involved in mitogen-activated signal transductions [55,60,61]. In the present study, RNA-seq analysis revealed that Pdmfs2 knockout led to the down-regulation of genes involved in peroxisome (ko04146) and oxidative phosphorylation (ko00190) at no prochloraz treatment (Tables 1 and 2, and Figures 1 and 2).…”

“…These metabolisms and cellular stimuli processes in response to specific fungicide(s) have been studied, including ergosterol biosynthesis pathways, lipid and fatty acid oxidation pathways, cell wall maintenance, oxidative-stress-responsive processes, carbohydrate and amino acid metabolisms, cellular energy metabolisms, post-translational modification processes, and signal transduction pathways. All these pathways are highly dependent on many stress-responsive genes, including various ERG-encoding genes (such as erg1, erg3, erg11, erg24, erg25 and so on) [36,[40][41][42], acetyl-CoA carboxylase (ACCase)-encoding genes [43][44][45][46][47], reactive oxygen species (ROS)-metabolizing enzyme-encoding genes [48][49][50][51], mitochondrial respiratory chain protein-encoding genes [52][53][54][55], ubiquitin-encoding genes [56][57][58][59], and a series of protein kinase-encoding genes involved in mitogen-activated signal transductions [55,60,61]. As reported, MFS and ABC transporters played multiple roles in the transport of a diverse range of metabolic substrates and intermediates [62,63].…”

Identification of Fungicide Combinations for Overcoming Plasmopara viticola and Botrytis cinerea Fungicide Resistance

“…The in silico methods described in Sections 2.1 and 2.2 are similar to those used in our previous studies [25].…”

“…Only a few studies have addressed the selection of fungicide combinations for QoIs based on molecular structures and the molecular-level affinity of the fungicides to the cytochrome b active site [25]. In one of our previous studies [25], we identified some fungicide combinations using a machine learning algorithm. However, none of the com-binations were experimentally tested.…”