Ascophyllum nodosum based plant biostimulant shapes the bacterial community in the rhizosphere of corn
Bibliographic record
Abstract
BACKGROUND: Plant biostimulants are an emerging class of agricultural inputs known to enhance plant growth and improve their tolerance to abiotic stress. While biostimulants are widely used, their mechanisms of action remain poorly understood. This study investigates the effects of an Ascophyllum nodosum-based biostimulant (ANE) on the rhizosphere bacterial communities of corn (Zea mays). RESULTS: Root exudates from ANE root-treated plants promoted the swarming motility of Pseudomonas protegens (CHA0), a plant growth-promoting rhizobacterium. Gene expression analysis showed that root exudates from 0.01% ANE-treated plants up-regulated P. protegens CHA0 genes associated with chemotaxis (cheW, cheV), pyoverdine (pvdS), pyrrolnitrin (prnD), and hydrogen cyanide (hcnA) biosynthesis compared to controls. ANE also significantly altered rhizosphere microbiome composition, increasing the abundance of genera such as Chryseolinea, Pseudoxanthomonas, Novosphingobium, Quadrisphaera, Turneriella, and Kitasatospora. Liquid chromatography-high resolution mass spectrometry (LC-HRMS) and partial least squares-discriminant analysis (PLS-DA) revealed distinct chemical profiles in the root extracts of ANE-treated plants. Specifically, ANE increased the concentrations of benzoxazinoids, including 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) and 6-methoxybenzoxazolinone (MBOA) in maize roots by approximately 1.4-fold and 1.76-fold, respectively. CONCLUSION: Overall, these findings suggest that ANE modifies the rhizosphere microbiome by influencing the chemical composition of both root tissue and root exudates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".