A rice jacalin‐related mannose‐binding lectin gene, <i>Os<scp>JRL</scp></i>, enhances <i>Escherichia coli</i> viability under high salinity stress and improves salinity tolerance of rice
Bibliographic record
Abstract
Abstract Salinity, which is one of the most common abiotic stresses, may severely affect plant productivity and quality. Although plant lectins are thought to play important roles in plant defense signaling during pathogen attack, little is known about the contribution of plant lectins to stress resistance. We cloned and functionally characterized a rice jacalin‐related mannose‐binding lectin gene, Os JRL , from rice ‘Nipponbare’. We analyzed the expression patterns of Os JRL under various stress conditions in rice. Furthermore, we overexpressed Os JRL in Escherichia coli and rice. The cDNA of Os JRL contained a 438 bp open reading frame, which encodes a polypeptide of 145 amino acids. Os JRL was localized in the nucleus and cytoplasm. Real time PCR analyses revealed that Os JRL expression showed tissue specificity in rice and was upregulated under diverse stresses, namely salt, drought, cold, heat and abscisic acid treatments. Overexpression of Os JRL in E. coli enhanced cell viability and dramatically improved tolerance of high salinity. Overexpression of Os JRL in rice also enhanced salinity tolerance and increased the expression levels of a number of stress‐related genes, including three LEA (late embryogenesis abundant proteins) genes ( Os LEA 19a , Os LEA 23 and Os LEA 24 ), three Na + transporter genes ( Os HKT 1;3 , Os HKT 1;4 and Os HKT 1;5 ) and two DREB genes ( Os DREB 1A and Os DREB 2B ). Based on these results, we suggest that Os JRL plays an important role in cell protection and stress signal transduction.
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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) | 0.001 | 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".