CRISPR-induced null alleles show that <i>Frost</i> protects <i>Drosophila melanogaster</i> reproduction after cold exposure
Bibliographic record
Abstract
The ability to survive and reproduce after cold exposure is important in all kingdoms of life. However, even in a sophisticated genetic model system like Drosophila melanogaster, few genes have been identified as functioning in cold tolerance. The accumulation of the Frost (Fst) gene transcript increases after cold exposure, making it a good candidate for a gene that has a role in cold tolerance. However, despite extensive RNAi knockdown analysis, no role in cold tolerance has been assigned to Fst. CRISPR is an effective technique for completely knocking down genes, and less likely to produce off-target effects than GAL4-UAS RNAi systems. We have used CRISPR-mediated homologous recombination to generate Fst null alleles, and these Fst alleles uncovered a requirement for FST protein in maintaining female fecundity following cold exposure. However, FST does not have a direct role in survival following cold exposure. FST mRNA accumulates in the Malpighian tubules, and the FST protein is a highly disordered protein with a putative signal peptide for export from the cell. Future work is needed to determine whether FST is exported from the Malpighian tubules and directly interacts with female reproductive tissues post-cold exposure, or if it is required for other repair/recovery functions that indirectly alter energy allocation to reproduction.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".