Improving salt tolerance in potato through overexpression of AtHKT1 gene
Bibliographic record
Abstract
Survival of plants in response to salinity stress is typically related to Na + toxicity, but little is known about how heterologous high-affinity potassium transporter (HKT) may help alleviate salt-induced damages in potato ( Solanum tuberosum L.). In this study, we used the Arabidopsis thaliana high-affinity potassium transporter gene ( AtHKT1 ) to enhance the capacity of potato plants to tolerate salinity stress by decreasing Na + content and improving K + /Na + ratio in plant leaves, while maintaining osmotic balance. Seven AtHKT1 transformed potato lines (namely T1, T2, T3, T5, T11, T13 and T15) were compared with non-transgenic control plant at molecule and whole-plant levels. The lines T3 and T13 had the highest AtHKT1 expression with the tolerance index (an quantitative assessment) being 6.8 times that of the control. At 30 days under 100 and 150 mmol L − 1 NaCl stress treatments, the T3 and T13 lines had least reductions in net photosynthetic rate, stomatal conductance and transpiration rate among the seven lines, leading to the increased water use efficiency and decreased yield loss. We conclude that the constitutive overexpression of AtHKT1 reduces Na + accumulation in potato leaves and promotes the K + /Na + homeostasis that minimizes osmotic imbalance, maintains photosynthesis and stomatal conductance, and increases plant productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".