Targeting HSP70 to motoneurons protects locomotor activity from hyperthermia in <i>Drosophila</i>
Bibliographic record
Abstract
Heat shock preconditioning can enhance locomotor and synaptic performance during subsequent hyperthermia. The molecular basis underlying this neural phenotypic modification is largely unknown. Here we report that directing the expression of the 70 kDa heat shock protein (HSP70) to motoneurons protected larval locomotor activity of Drosophila. Tissue-specific expression showed that motoneurons were critical for developing HSP70-mediated thermoprotection of locomotor activity, whereas peripheral sensory neurons, dopaminergic neurons, serotonergic neurons, and muscle cells alone were insufficient. Targeting HSP70 to motoneurons caused structural plasticity of axonal terminals associated with increased transmitter release at neuromuscular junctions at high temperature. The thermoprotection induced by motoneuronal expression of HSP70 mimicked the protective effect of a prior heat shock (36 degrees C, 1 h; 25 degrees C, 1 h) but the effects of heat shock and motoneuronal expression of HSP70 were not additive. In the absence of heat shock pretreatment, ubiquitously expressed transgenic HSP70 activated the transcription of endogenous hsp70 genes. These results demonstrate that motoneurons were critical for HSP70-mediated thermoprotection, and that transgenic HSP70 activated the transcription of endogenous hsp70 in motoneurons with the result that a mix of transgenic and endogenous HSP70 conferred thermoprotection in Drosophila larva.
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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".