Bacterial network for precise plant stress detection and enhanced crop resilience.

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS BMC Bioinformatics Pub Date : 2025-02-25 DOI:10.1186/s12859-025-06082-8
Shakeel Ahmed, Syed Muhammad Zaigham Abbas Naqvi, Muhammad Awais, Yongzhe Ren, Hao Zhang, Junfeng Wu, Linze Li, Vijaya Raghavan, Jiandong Hu
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Abstract

Understanding plant hormonal responses to stress and their transport dynamics remains challenging, limiting advancements in enhancing plant resilience. Our study presents a novel approach that utilizes genetically engineered bacteria (GEB) as molecular transceivers within plants, aiming to develop revolutionary agricultural biosensors. We focus on abscisic acid (ABA), a key hormone for plant growth and stress response. We propose using Escherichia coli (E. coli) engineered with PYR1-derived receptors that exhibit high affinity for ABA, triggering a bioluminescent response. Simulations investigate the detection time for ABA, bacterial diffusion within plant roots, advection effects through shoots, and chemotaxis in response to attractant gradients in leaves. Results indicate that higher ABA concentrations correlate with shorter response times, with an average of 431.52 s based on bioluminescence. The average internalization time for bacteria through a plant root area of 2 µm2 during the rhizophagy process is estimated at 1220.12 s. Simulations also assess bacterial movement through shoots, the impact of advection, and chemotactic responses. These findings highlight the complex interplay between plant signaling and microbial communities, validating the efficacy of our bacterial-based sensor approach and opening new avenues for agricultural biosensor technology.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
期刊最新文献
goloco: a web application to create genome scale information from surprisingly small experiments. scFTAT: a novel cell annotation method integrating FFT and transformer. Bacterial network for precise plant stress detection and enhanced crop resilience. Enhancing biomedical named entity recognition with parallel boundary detection and category classification. GRAMEP: an alignment-free method based on the maximum entropy principle for identifying SNPs.
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