Dissecting the Herbicidal Mechanism of Microbial Natural Product Lydicamycins Using a Deep Learning-Based Nonlinear Regression Model

利用基于深度学习的非线性回归模型解析微生物天然产物利迪霉素的除草机制

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Abstract

The plant microbiome significantly influences plant-microbe interactions, but the mechanisms are often complex and nonlinear. Here we show the nonlinear regulatory effects of Streptomyces ginsengnesis G7 on Arabidopsis thaliana growth. We focused on lydicamycin, a molecule from this bacterium that interferes with auxin polar transport. Using a deep learning approach with a feedforward neural network, we integrated multiomics data to elucidate the mechanism of lydicamycin on plant growth and development. We also examined the impact of flavonol metabolites, particularly isorhamnetin from A. thaliana, on the PIN protein family's role in auxin transport. Our findings indicate that lydicamycin regulates auxin transport by inducing flavonol overaccumulation in A. thaliana, affecting plant development. This study identifies potential molecular targets for crop enhancement and improved agricultural productivity.

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