Evidence map›Paper›PMID 40936028›Full record

ArticlePlant cell reports2025

AI-integrated metabolomics maps functional divergence of microbial consortia in field-grown maize.

Musiwalo Samuel Mulaudzi, Lerato Pertunia Nephali, Fidele Tugizimana

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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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

3 authors.

Musiwalo Samuel MulaudziResearch Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg, South Africa.
Lerato Pertunia NephaliResearch Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg, South Africa. Lerato.Nephali@omnia.co.za.
Fidele TugizimanaResearch Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg, South Africa. ftugizimana@uj.ac.za.ORCID http://orcid.org/0000-0001-8172-6599

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

MetabolomicsMicrobial ConsortiaZea maysArtificial IntelligenceMetabolomePlant LeavesArtificial intelligenceBiochemical and molecular mechanismsComputational metabolomicsMicrobial biostimulantsSustainable agriculture

Identifiers

PMID40936028
PMCPMC12426160

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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.