Biocontrol of strawberry gray mold caused by Botrytis cinerea with the termite associated Streptomyces sp. sdu1201 and actinomycin D

Yong, Daojing; Li, Yue; Gong, Kai; Yu, Yingying; Duan, Qiong; Ren, Cailing; Li, Aiying; Fu, Jun; Ni, Jinfeng; Zhang, Youming; Li, Ruijuan

doi:10.3389/fmicb.2022.1051730

Cited by 12 publications

(4 citation statements)

References 73 publications

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“…In particular, powdery mildew, a significant pest and disease of strawberries covered in this paper, results in up to 70% yield loss [ 5 , 6 ]. Also, gray mold, another major condition, generally damages 15–20% of strawberries and more than 50% in severe cases [ 7 ]. Pesticides used to prevent this are also used in trillions of US dollars, of which only 0.1% kills the target, and the rest is absorbed and distributed into the environment and causes pollution [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Lee,

Yun

2023

Plant Methods

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Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO2 concentration, using deep learning techniques. Using large-scale public data on crops of strawberry, pepper, grape, tomato, and paprika, we showed the model can predict the risk score of crop pests and diseases. It showed high predictive performance with an average AUROC of 0.917, and based on the predicted results, it can help prevent pests or post-processing. This environmental data-based crop disease prediction model and learning framework are expected to be universally applicable to various facilities and crops for disease/pest prevention.

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Section: Introductionmentioning

confidence: 99%

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Lee,

Yun

2023

Plant Methods

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“…The broad host range of S. sclerotiorum , its adaptability to varied environments, and its ability to produce long-lasting sclerotia that can survive in the soil for prolonged periods make it a formidable adversary ( Schwartz et al., 2005 ; Figueirêdo et al., 2010 ; Elsheshtawi et al., 2017 ). Although the biological control of S. sclerotiorum has been experimentally proofed recently ( Ali and Atallah, 2020 ; Atallah and Yassin, 2020 ; Atallah et al., 2022 ), conventional control measures mainly rely on chemical fungicides, which have several concerns about their disadvantageous environmental effect and the potential emergence of fungicide-resistant strains ( Zubrod et al., 2019 ; Yong et al., 2022 ). Therefore, it is necessary to introduce some eco-friendly alternatives to control S. sclerotiorum particularly, and soil-borne phytopathogens in general.…”

Section: Discussionmentioning

confidence: 99%

Non-proteinogenic amino acids mitigate oxidative stress and enhance the resistance of common bean plants against Sclerotinia sclerotiorum

Nehela,

Mazrou,

El_Gammal

et al. 2024

Front. Plant Sci.

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White mold, caused by the necrotrophic fungus Sclerotinia sclerotiorum, is a challenging disease to common bean cultivation worldwide. In the current study, two non-proteinogenic amino acids (NPAAs), γ-aminobutyric acid (GABA) and ß-alanine, were suggested as innovative environmentally acceptable alternatives for more sustainable management of white mold disease. In vitro, GABA and ß-alanine individually demonstrated potent dose-dependent fungistatic activity and effectively impeded the radial growth and development of S. sclerotiorum mycelium. Moreover, the application of GABA or ß-alanine as a seed treatment followed by three root drench applications efficiently decreased the disease severity, stimulated plant growth, and boosted the content of photosynthetic pigments of treated S. sclerotiorum-infected plants. Furthermore, although higher levels of hydrogen peroxide (H2O2), superoxide anion (O2•−), and malondialdehyde (MDA) indicated that S. sclerotiorum infection had markedly triggered oxidative stress in infected bean plants, the exogenous application of both NPAAs significantly reduced the levels of the three studied oxidative stress indicators. Additionally, the application of GABA and ß-alanine increased the levels of both non-enzymatic (total soluble phenolics and flavonoids), as well as enzymatic (catalase [CAT], peroxidases [POX], and polyphenol oxidase [PPO]) antioxidants in the leaves of S. sclerotiorum-infected plants and improved their scavenging activity and antioxidant efficiency. Applications of GABA and ß-alanine also raised the proline and total amino acid content of infected bean plants. Lastly, the application of both NPAAs upregulated the three antioxidant-related genes PvCAT1, PvCuZnSOD1, and PvGR. Collectively, the fungistatic activity of NPAAs, coupled with their ability to alleviate oxidative stress, enhance antioxidant defenses, and stimulate plant growth, establishes them as promising eco-friendly alternatives for white mold disease management for sustainable bean production.

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“… 7 However, people have gradually become aware of the numerous negative impacts of these chemicals, including the increasingly serious “3 R” (Resistance, Resurgence and Residue). 8 To address these concerns, biocontrol has emerged as a promising alternative to chemical treatments. 9 …”

Section: Introductionmentioning

confidence: 99%

Quasi-targeted metabolomics revealed isoliquiritigenin and lauric acid associated with resistance to tobacco black shank

Peng,

Shu,

et al. 2024

Plant Signaling & Behavior

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Tobacco black shank (TBS), caused by Phytophthora nicotianae , is a severe disease. Plant root exudates play a crucial role in mediating plant-pathogen interactions in the rhizosphere. However, the specific interaction between key secondary metabolites present in root exudates and the mechanisms of disease resistance remains poorly understood. This study conducted a comprehensive comparison via quasi-targeted metabolomic analysis on the root exudate metabolites from the tobacco cultivar Yunyan87 and K326, both before and after inoculation with P. nicotianae . The results showed that the root exudate metabolites changed after P. nicotianae inoculation, and the root exudate metabolites of different tobacco cultivar was significantly different. Furthermore, homovanillic acid, lauric acid, and isoliquiritigenin were identified as potential key compounds for TBS resistance based on their impact on the mycelium growth of the pathogens. The pot experiment showed that isoliquiritigenin reduced the incidence by 55.2%, while lauric acid reduced it by 45.8%. This suggests that isoliquiritigenin and lauric acid have potential applications in the management of TBS. In summary, this study revealed the possible resistance mechanisms of differential metabolites in resistance of commercial tobacco cultivar, and for the first time discovered the inhibitory effects of isoliquiritigenin and homovanillic acid on P. nictianae , and attempt to use plants secondary metabolites of for plant protection.

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Biocontrol of strawberry gray mold caused by Botrytis cinerea with the termite associated Streptomyces sp. sdu1201 and actinomycin D

Cited by 12 publications

References 73 publications

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Non-proteinogenic amino acids mitigate oxidative stress and enhance the resistance of common bean plants against Sclerotinia sclerotiorum

Quasi-targeted metabolomics revealed isoliquiritigenin and lauric acid associated with resistance to tobacco black shank

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