Suppression of arbuscular mycorrhizal colonization and nodulation in split-root systems of alfalfa after pre-inoculation and treatment with Nod factors
Bibliographic record
Abstract
Roots of legumes establish symbiosis with arbuscular mycorrhizal fungi (AMF) and nodule-inducing rhizobia. The existing nodules systemically suppress subsequent nodule formation in other parts of the root, a phenomenon termed autoregulation. Similarly, mycorrhizal roots reduce further AMF colonization on other parts of the root system. In this work, split- root systems of alfalfa (Medicago sativa) were used to study the autoregulation of symbiosis with Sinorhizobium meliloti and the mycorrhizal fungus Glomus mosseae. It is shown that nodulation systemically influences AMF root colonization and vice versa. Nodules on one half of the split-root system suppressed subsequent AMF colonization on the other half. Conversely, root systems pre-colonized on one side by AMF exhibited reduced nodule formation on the other side. An inhibition effect was also observed with Nod factors (lipo-chito-oligosaccharides). NodSm-IV(C16:2, S) purified from S. meliloti systemically suppressed both nodule formation and AMF colonization. The application of Nod factors, however, did not influence the allocation of (14)C within the split-root system, excluding competition for carbohydrates as the regulatory mechanism. These results indicate a systemic regulatory mechanism in the rhizobial and the arbuscular mycorrhizal association, which is similar in both symbioses.
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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) | 0.001 | 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".