Proto Kranz-like leaf traits and cellular ionic regulation are associated with salinity tolerance in a halophytic wild rice
Bibliographic record
Abstract
Abstract Species of wild rice (Oryza spp.) possess a wide range of stress tolerance traits that can be potentially utilized in breeding climate-resilient cultivated rice cultivars (Oryza sativa) thereby aiding global food security. In this study, we conducted a greenhouse trial to evaluate the salinity tolerance of six wild rice species, one cultivated rice cultivar (IR64) and one landrace (Pokkali) using a range of electrophysiological, imaging, and whole-plant physiological techniques. Three wild species (O. latifolia, O. officinalis and O. coarctata) were found to possess superior salinity stress tolerance. The underlying mechanisms, however, were strikingly different. Na+ accumulation in leaves of O. latifolia, O. officinalis and O. coarctata were significantly higher than the tolerant landrace, Pokkali. Na+ accumulation in mesophyll cells was only observed in O. coarctata, suggesting that O. officinalis and O. latifolia avoid Na+ accumulation in mesophyll by allocating Na+ to other parts of the leaf. The finding also suggests that O. coarctata might be able to employ Na+ as osmolyte without affecting its growth. Further study of Na+ allocation in leaves will be helpful to understand the mechanisms of Na+ accumulation in these species. In addition, O. coarctata showed Proto Kranz-like leaf anatomy (enlarged bundle sheath cells and lower numbers of mesophyll cells), and higher expression of C4-related genes (e.g., NADPME, PPDK) and was a clear outlier with respect to salinity tolerance among the studied wild and cultivated Oryza species. The unique phylogenetic relationship of O. coarctata with C4 grasses suggests the potential of this species for breeding rice with high photosynthetic rate under salinity stress in the future.
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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".