Shoot Extracts from Two Low Nodulation Mutants Significantly Reduce Nodule Number in Pea
Bibliographic record
Abstract
E107 and E132 are pea mutants that nodulate poorly. Because they have a shoot-controlled nodulation phenotype, we asked if their mutated genes were implicated in the autoregulation of nodulation (AON), a mechanism which consists of two systemic circuits, the positive CEP/CRA2 and the negative CLE/SUNN, coordinated via NIN and miR2111. We further characterized the mutants' phenotype by studying nodule distribution and nodulation efficiency. E107 was similar to wild-type (WT) in its nodule distribution, but E132 had an extended nodulation zone with nodules forming distally on its lateral roots. Moreover, we tested whether their shoots produced a compound inhibitory to nodulation. We made ethyl-acetate extracts of roots and shoots of both mutants and WT, which we applied to rhizobia-inoculated WT seedlings and to pure rhizobial cultures. Whereas free-living bacteria were unaffected by any of the extracts, WT treated with shoot extracts from either inoculated mutant had fewer nodules than that of control. E107 and E132 shoot extracts led to a 50% and a 35% reduction in nodule number, respectively. We propose that E107 and E132 belong to a new sub-class of AON mutants, i.e., hypo-nodulators, and that their respective gene products are acting in the AON descending branch, upstream of TML signaling.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".