Stachydrine Catabolism Contributes to an Optimal Root Nodule Symbiosis Between <i>Sinorhizobium meliloti</i> and <i>Medicago sativa</i>
Bibliographic record
Abstract
Sinorhizobium meliloti forms a robust N 2 -fixing root-nodule symbiosis with Medicago sativa. We are interested in identifying the minimal symbiotic genome of the model strain S. meliloti Rm1021. This gene set refers to the minimal genetic determinants required to form a robust N 2 -fixing symbiosis. Many symbiotic genes are located on the 1,354-kb pSymA megaplasmid of S. meliloti Rm1021. We recently constructed a minimalized pSymA, minSymA2.1, that lacked over 90% of the pSymA genes. Relative to the wild type, minSymA2.1 showed a reduction in M. sativa shoot biomass production and nodule size with an increase in total nodule number. Here, we show that the addition of either the stachydrine ( stc) or trigonelline ( trc) catabolism genes from pSymA to minSymA2.1 restores nodule size and total nodule number to levels indistinguishable from the wild type but does not restore reduced shoot biomass production. In the context of the complete Rm1021 genome, removing the stc genes reduced the nodule size and increased the total nodule number, whereas removal of the trc genes alone had no apparent effect. Together, these observations implicate stachydrine catabolism as an important determinant of root nodule symbiosis between S. meliloti and M. sativa, whereas trigonelline catabolism seems to contribute in a more conditional manner, in the context of the minimized genome. These findings highlight the minimal symbiotic genome as a tool for investigating the impact of individual genetic determinants in conferring an optimal symbiosis, factors whose impact, in the context of a complete genome, may be hidden or dampened due to redundancies. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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".