Evidence map›Paper›PMID 40000952›Full record

ArticleBMC bioinformatics2025

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

Shakeel Ahmed, Syed Muhammad Zaigham Abbas Naqvi, Muhammad Awais, Yongzhe Ren, Hao Zhang, Junfeng Wu, Linze Li, Vijaya Raghavan, Jiandong Hu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Shakeel AhmedCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
Syed Muhammad Zaigham Abbas NaqviCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
Muhammad AwaisCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
Yongzhe RenCollege of Agronomy, Henan Agricultural University, Zhengzhou, 450002, China.
Hao ZhangCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
Junfeng WuCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
Linze LiCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
Vijaya RaghavanDepartment of Bioresource Engineering, Faculty of Agriculture and Environmental Studies, McGill University, Sainte-Anne-de-Bellevue, QC, H9X 3V9, Canada.
Jiandong HuCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China. jdhu@henau.edu.cn.ORCID http://orcid.org/0000-0003-1325-7334

Funding

Henan Center for Outstanding Overseas Scientists GZS2021007Key Technologies Research and Development Program 2021YFD1700904Major Science and Technology projects of Henan Province 221100320200
6 · The paper itself

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 µm

Indexed as

Biosensing TechniquesCrops, AgriculturalEscherichia coliStress, PhysiologicalAbscisic AcidPlant Growth RegulatorsPlant RootsAbscisic AcidPlant Growth RegulatorsAbscisic acidBacterial-based sensorEngineered E. coli-PYR1 bacteriaMathematical modelingMolecular communicationSimulations

Identifiers

PMID40000952
PMCPMC11863917

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.