ArticlePlant cell reports2025
AI-integrated metabolomics maps functional divergence of microbial consortia in field-grown maize.
Article in Plant cell reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Challenges and Future Directives of Synthetic Biology in Engineering Plant-Microbe Partnerships for Sustainable Agriculture.Biotechnology and bioengineering · 2026Review
- Seed-microbiome interactions: Mechanistic insights and utilization toward seed performance for sustainable agriculture.Plant communications · 2026Review
- Adaptive reprogramming of carbon-nitrogen metabolism inFrontiers in microbiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
key messageThe study provides actionable insights into modes of action of the three microbial biostimulants on maize plants under field conditions. The postulated framework indicates a divergence model involving (i) photoprotection, structural reinforcement, and defense priming, (ii) systemic metabolic reprograming for growth and defense, and (iii) hormonal signalling modulation for stress response. These insights offer a data-driven blueprint for the next generation of sustainable, effective, and field-ready bioformulations. Microbial consortia are currently recognized as a promising strategy for sustainable agriculture due to their ability to enhance plant growth, improve soil health, and mitigate environmental stresses. However, the biochemical and molecular mechanisms governing these beneficial effects on crops under field conditions remain poorly understood, and differential effects due to different microbial formulations are enigmatic. This study, therefore, aims to unravel the metabolic alterations, in maize plants, induced by three microbial biostimulants, under field conditions at different growth stages. Leaves from biostimulant-treated and untreated control maize plants were harvested at different time points. Metabolites were extracted using methanol. The extracts were analyzed on LC-MS/MS system. Computational metabolomics workflows and AI-driven strategies such as molecular networking and machine learning methods (PCA and OPLS-DA) were applied to mine and interpret spectral data. Machine learning models revealed the common and unique significant metabolites among the consortia at the vegetative stage. One of the key findings was that hydroxycinnamic acid (HCA) derivatives are the discriminatory metabolites differentiating the effects of the three microbial consortia on maize plants. Moreover, the results showed that consortia application significantly impacted primary and secondary maize metabolism, reprogramming biological pathways such as phenylalanine, tyrosine, and tryptophan biosynthesis, tyrosine metabolism, the citrate cycle (TCA cycle), flavone and flavonol biosynthesis, and flavonoid biosynthesis. These pathways are associated with plant defense, priming and development. Thus, this study sheds light on the complex molecular interactions between maize and microbial biostimulants under real-world conditions. It reveals that distinct microbial formulations differentially influence plant metabolism by reprogramming defense- and growth-related pathways. These actionable insights establish a foundational framework for functionally characterizing microbial consortia and pave the way for the rational design of next generation biostimulants tailored to specific crop needs and growth stages.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.