Stimulation of nitrogen removal in the rhizosphere of aquatic duckweed by root exudate components
Bibliographic record
Abstract
Plants can stimulate bacterial nitrogen (N) removal by secretion of root exudates that may serve as carbon sources as well as non-nutrient signals for denitrification. However, there is a lack of knowledge about the specific non-nutrient compounds involved in this stimulation. Here, we use a continuous root exudate-trapping system in two common aquatic duckweed species, Spirodela polyrrhiza (HZ1) and Lemna minor (WX3), under natural and aseptic conditions. An activity-guided bioassay using denitrifying bacterium Pseudomonas fluorescens showed that crude root exudates of the two species strongly enhanced the nitrogen-removal efficiency (NRE) of P. fluorescens (P < 0.05) under both conditions. Water-insoluble fractions (F) obtained under natural conditions stimulated NRE to a significant extent, promoting rates by about 30%. Among acidic, neutral and basic fractions, a pronounced stimulatory effect was also observed for the neutral fractions from HZ1 and WX3 under both conditions, whereas the acidic fractions from WX3 displayed an inhibitory effect. Analysis of the active fractions using gas chromatography/mass spectrometry (GC/MS) revealed that duckweed released fatty acid methyl esters and fatty acid amides, specifically: methyl hexadecanoate, methyl (Z)-7-hexadecenoate, methyl dodecanoate, methyl-12-hydroxystearate, oleamide, and erucamide. Methyl (Z)-7-hexadecenoate and erucamide emerged as the effective N-removal stimulants (maximum stimulation of 25.9 and 33.4%, respectively), while none of the other tested compounds showed stimulatory effects. These findings provide the first evidence for a function of fatty acid methyl esters and fatty acid amides in stimulating N removal of denitrifying bacteria, affording insight into the "crosstalk" between aquatic plants and bacteria in the rhizosphere.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 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 source (direct Gemma or distilled Codex), 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".