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

IF 3.3 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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利用细菌网络精确检测植物逆境,提高作物抗逆性。
了解植物激素对胁迫的反应及其运输动力学仍然具有挑战性,限制了提高植物抗逆性的进展。我们的研究提出了一种利用基因工程细菌(GEB)作为植物分子收发器的新方法,旨在开发革命性的农业生物传感器。我们重点研究了植物生长和逆境反应的关键激素脱落酸(ABA)。我们建议使用大肠杆菌(E. coli)与pyr1衍生的对ABA具有高亲和力的受体进行工程改造,从而引发生物发光反应。模拟研究了ABA的检测时间、细菌在植物根内的扩散、通过芽的平流效应以及叶片对引诱剂梯度的趋化性反应。结果表明,ABA浓度越高,响应时间越短,根据生物发光平均为431.52 s。在根噬过程中,细菌通过2µm2植物根面积的平均内化时间估计为1220.12 s。模拟还评估了细菌通过芽的运动、平流的影响和趋化反应。这些发现强调了植物信号和微生物群落之间复杂的相互作用,验证了我们基于细菌的传感器方法的有效性,并为农业生物传感器技术开辟了新的途径。
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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.
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