A Biosensor-Based Assay (<i>GlnLux</i>-Agar) Shows Defoliation Triggers Rapid Release of Glutamine from Nodules and Young Roots of Forage Legumes
Bibliographic record
Abstract
Forage legumes experience defoliation from grazing and injury in both natural and agricultural ecosystems. Defoliation induces rhizodeposition of nitrogen (N) compounds from root systems that can feed microbes and plants that depend on the rhizosphere. The literature suggests that N exudates are primarily released from root tips, and those from legume nodules are released via slow nodule decomposition. However, the early timing and precise locations of N release postdefoliation are poorly characterized. The objectives of this study were to directly image tissue-specific N exudation sites in forage legumes, specifically for glutamine, and to do so at early time points postdefoliation. Glutamine is the primary assimilate of symbiotic nitrogen fixation in nodules and a key transport form of fixed N in amide-exporting legumes. Three amide-exporting forages, alfalfa (Medicago sativa), red clover (Trifolium pretense), and white clover (Trifolium repens), were defoliated or not, and placed on agar embedded with whole cell biosensor cells (GlnLux) that detect glutamine. There were two unexpected findings. First, Gln release occurred rapidly, starting within 2 h postdefoliation, depleting rapidly. Second, the sources of early Gln release were primarily nodules in addition to the expected young lateral roots/root tips. Lux quantification statistically confirmed the key findings. These observations suggest that N exudate release should be added to the list of defoliation stress early responses in nodules, and may have implications for our understanding of how defoliation impacts the rhizosphere microbiome. Furthermore, GlnLux-agar imaging represents a new assay to explore the proposed but yet unknown mechanisms underlying organic N exudation in plants.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".