<i>LcFIN2</i>, a novel chloroplast protein gene from sheepgrass, enhances tolerance to low temperature in Arabidopsis and rice
Bibliographic record
Abstract
Adverse environmental stresses affect plant growth and crop yields. Sheepgrass (Leymus chinensis (Trin.) Tzvel), an important forage grass that is widely distributed in the east of Eurasia steppe, has high tolerance to extreme low temperature. Many genes that respond to cold stress were identified in sheepgrass by RNA-sequencing, but more detailed studies are needed to dissect the function of those genes. Here, we found that LcFIN2, a sheepgrass freezing-induced protein 2, encoded a chloroplast-targeted protein. Expression of LcFIN2 was upregulated by freezing, chilling, NaCl and abscisic acid (ABA) treatments. Overexpression of LcFIN2 enhanced the survival rate of transgenic Arabidopsis after freezing stress. Importantly, heterologous expression of LcFIN2 in rice exhibited not only higher survival rate but also accumulated various soluble substances and reduced membrane damage in rice under chilling stress. Furthermore, the chlorophyll content, the quantum photochemistry efficiency of photosystem II (ΦPSII), the non-photochemical quenching (NPQ), the net photosynthesis rate (Pn) and the expression of some chloroplast ribosomal-related and photosynthesis-related genes were higher in the transgenic rice under chilling stress. These findings suggested that the LcFIN2 gene could potentially be used to improve low-temperature tolerance in crops.
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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".