Cold-inducible promoter-driven knockdown of <i>Brachypodium</i> antifreeze proteins confers freeze sensitivity
Bibliographic record
Abstract
Abstract The model forage crop, Brachypodium distachyon , has a family of ice recrystallization inhibition ( BdIRI ) genes, which encode antifreeze proteins that function by adsorbing to ice crystals and inhibiting their growth. The genes were previously targeted for knockdown using a constitutive CaMV 35S promoter and the resulting transgenic Brachypodium showed reduced antifreeze activity and a greater susceptibility to freezing. However, the transgenic plants also showed developmental defects with shortened stem lengths and were almost completely sterile, raising the possibility that their reduced freeze tolerance could be attributed to developmental deficits. A cold-induced promoter from rice (pr OsMYB1R35 ) has now been substituted for the constitutive promoter to generate temporal miRNA-mediated Brachypodium antifreeze protein knockdowns. Although transgenic lines showed no apparent pleiotropic developmental defects, they demonstrated reduced antifreeze activity as assessed by assays for ice-recrystallization inhibition, thermal hysteresis, electrolyte leakage, leaf infrared thermography, and leaf damage after infection with an ice nucleating phytopathogen. Strikingly, the number of cold-acclimated transgenic plants that survived freezing at -8 °C was reduced by half or killed entirely, depending on the line, compared to cold-acclimated wild type plants. Although these proteins have been studied for almost 60 years, this is the first unequivocal demonstration in any organism of the utility of antifreeze protein function and their contribution to freeze protection, independent of obvious developmental defects. These proteins are thus of potential interest in a wide range of biotechnological applications from accessible cryopreservation, to frozen product additives, to the engineering of transgenic crops with enhanced freezing tolerance.
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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 | 0.001 |
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".