Evidence map›Paper›PMID 41116203›Full record

ReviewJournal of Zhejiang University. Science. B2025

Harnessing chemical communication in plant-microbiome and intra-microbiome interactions.

Hongfu Li, Yaxin Hu, Siqi Chen, Yusufjon Gafforov, Mengcen Wang, Xiaoyu Liu

Abstract readReview
In one paragraph

Review in Journal of Zhejiang University. Science. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Hongfu LiCollege of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.
Yaxin HuCollege of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.
Siqi ChenCollege of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China.
Yusufjon GafforovCentral Asian Center for Development Studies, New Uzbekistan University, Tashkent 100007, Uzbekistan.
Mengcen WangCollege of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China. wmctz@zju.edu.cn.
Xiaoyu LiuAustralian Research Council Centre of Excellence in Plant Energy Biology, The University of Western Australia, Perth, WA 6430, Australia. wmctz@zju.edu.cn, Xiaoyu.liu@research.uwa.edu.au.

Funding

the National Key R&D Program of China 2025YFE0104500the National Natural Science Foundation of China U21A20219 and 32122074the Natural Science Foundation of Hangzhou 2024SZRZDC 130001the Zhejiang Provincial Natural Science Foundation of China LD25C140002
6 · The paper itself

Abstract

Chemical communication in plant-microbiome and intra-microbiome interactions weaves a complex network, critically shaping ecosystem stability and agricultural productivity. This non-contact interaction is driven by small-molecule signals that orchestrate crosstalk dynamics and beneficial association. Plants leverage these signals to distinguish between pathogens and beneficial microbes, dynamically modulate immune responses, and secrete exudates to recruit a beneficial microbiome, while microbes in turn influence plant nutrient acquisition and stress resilience. Such bidirectional chemical dialogues underpin nutrient cycling, co-evolution, microbiome assembly, and plant resistance. However, knowledge gaps persist regarding validating the key molecules involved in plant-microbe interactions. Interpreting chemical communication requires multi-omics integration to predict key information, genome editing and click chemistry to verify the function of biomolecules, and artificial intelligence (AI) models to improve resolution and accuracy. This review helps advance the understanding of chemical communication and provides theoretical support for agriculture to cope with food insecurity and climate challenges.

Indexed as

MicrobiotaPlantsArtificial IntelligenceEcosystemArtificial intelligence (AI)Chemical communicationClick chemistryGenome editingIntra-microbiome interactionPlant–microbiome interaction

Identifiers

PMID41116203
PMCPMC12537646

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.