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Discovery and Mechanism of Novel 7-Aliphatic Amine Tryptanthrin Derivatives against Phytopathogenic Bacteria.

ABSTRACT: Rice bacterial leaf blight is a destructive bacterial disease caused by Xanthomonas oryzae pv. oryzae (Xoo) that seriously threatens crop yields and their associated economic benefits. In this study, a series of improved dissolubility 7-aliphatic amine tryptanthrin derivatives was designed and synthesized, and their potency in antibacterial applications was investigated. Notably, compound 6e exhibited excellent activity against Xoo, with an EC₅₀ value of 2.55 μg/mL, compared with the positive control bismerthiazol (EC₅₀ = 35.0 μg/mL) and thiodiazole copper (EC₅₀ = 79.4 μg/mL). In vivo assays demonstrated that 6e exhibited a significant protective effect on rice leaves. After exposure, the morphology of the bacteria was partially atrophied by SEM. Furthermore, 6e increased the accumulation of intracellular reactive oxygen species, causing cell apoptosis and the formation of bacterial biofilms. All the results indicated that 6e could be a potential agrochemical bactericide for controlling phytopathogenic bacteria.

SUBMITTER: Long X

PROVIDER: S-EPMC10341529 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Discovery and Mechanism of Novel 7-Aliphatic Amine Tryptanthrin Derivatives against Phytopathogenic Bacteria.

Long Xuesha X Zhang Guanglong G Long Haitao H Wang Qin Q Wang Congyu C Zhu Mei M Wang Wenhang W Li Chengpeng C Wang Zhenchao Z Ouyang Guiping G

International journal of molecular sciences 20230630 13

Rice bacterial leaf blight is a destructive bacterial disease caused by Xanthomonas oryzae pv. oryzae (Xoo) that seriously threatens crop yields and their associated economic benefits. In this study, a series of improved dissolubility 7-aliphatic amine tryptanthrin derivatives was designed and synthesized, and their potency in antibacterial applications was investigated. Notably, compound 6e exhibited excellent activity against Xoo, with an EC50 value of 2. ...[more]

PMID: 37446077

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Project description:IntroductionAntimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models for predicting and selecting AMPs active against plant pathogenic bacteria.MethodsN-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluate their predictive accuracy and robustness.ResultsThe models were applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. Approximately 7% of the 10,000-peptide dataset from the intergenic region and 7% of the 685,924-peptide dataset from the whole genome were predicted as probable AMPs. The prediction accuracy of the reported models range from 0.72 to 0.91. A subset of the predicted AMPs was selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models.DiscussionHydrophobic amino acid residues and positively charged amino acid residues are among the key features in predicting AMPs by the Random Forest Algorithm. Aggregation propensity appears to be correlated with the effectiveness of the AMPs. The described models would contribute to the development of effective AMP-based strategies for plant disease management in agricultural and environmental settings. To facilitate broader accessibility, our model is publicly available on the AGRAMP (Agricultural Ngrams Antimicrobial Peptides) server.

Dataset Information

Discovery and Mechanism of Novel 7-Aliphatic Amine Tryptanthrin Derivatives against Phytopathogenic Bacteria.

Publications

Discovery and Mechanism of Novel 7-Aliphatic Amine Tryptanthrin Derivatives against Phytopathogenic Bacteria.

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