Metabolic and genetic responses to simulated overwintering conditions of alfalfa-rhizobia associations contrasted in their freezing tolerance
Bibliographic record
Abstract
Abstract The study of winter stress tolerance in perennial legumes needs to consider the complete symbiotic system including both plants and bacteria since these two partners are differentially affected by stress conditions. Here, we compared the regrowth after a freezing stress of four different associations of two alfalfa populations differing in freezing tolerance (A-TF0 and A-TF7) inoculated with two Sinorhizobium (Ensifer) meliloti strains (B399 and NRG34) of contrasted adaptation to cold. To understand the contribution of each partner to a better regrowth performance of an association after freezing, we identified molecular traits having major roles in cold acclimation, freezing tolerance, and those involved in the crosstalk between alfalfa and its symbiotic partner. Regrowth after exposure to a freezing stress was 35% larger in the A-TF7 × NRG34 than in the A-TF0 × B399 association. The metabolomic study of roots, crowns and, more specifically, nodules, revealed profound changes in these organs, switching from a sink to support cold acclimation to a source of reserves enabling regrowth after deacclimation. Marked increases in concentrations of stachyose and raffinose, two sugars of the raffinose-family oligosaccharides (RFO), and in the expression level of a gene of the RFO synthetic pathway were observed in response to cold acclimation supporting the importance of a protective role for RFO in alfalfa. Both cold-adapted partners of the symbiotic association contributed to increases in arginine concentration in nodules in response to cold acclimation and deacclimation underscoring the importance of N storage and remobilization for a successful overwintering in alfalfa.
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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".