Antifreeze protein from Ammopiptanthus nanus functions in temperature-stress through domain A
Bibliographic record
Abstract
Temperature stress restricts plant growth and development. Antifreeze protein (AFP) can improve plants antifreeze ability. In our previous study, the AnAFP gene cloned from Ammopiptanthus nanus was confirmed to be an excellent candidate enhancing plant cold resistance. But, AnAFP protein shared similar structures with KnS type dehydrins including K, N and S domains except ice crystal binding domain A. Here, we generated AnAFPΔA, AnAFPΔK, AnAFPΔN and AnAFPΔS, and transformed them into ordinary and cold sensitive strains of E. coli, and Arabidopsis KS type dehydrin mutant to evaluate their function. Expression of AnAFPΔA decreases cold and heat tolerance in E. coli, meanwhile, AnAFP enhances heat tolerance in Arabidopsis, suggesting that domain A is a thermal stable functional domain. AnAFP, AnAFPΔA and AnAFPΔS localize in whole cell, but AnAFPΔK and AnAFPΔN only localizes in nucleus and cytoplasm, respectively, exhibiting that K and N domains control localization of AnAFP. Likewise, K domain blocks interaction between AnAFP and AnICE1. The result of RT-qPCR showed that expression of AnAFP, AnICE1 and AnCBF genes was significantly induced by high-temperature, indicating that the AnAFP is likely regulated by ICE1-CBF-COR signal pathway. Taken together, the study provides insights into understanding the mechanism of AnAFP in response to temperature stress and gene resource to improve heat or cold tolerance of plants in transgenic engineering.
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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".