A memory transcriptome time course reveals essential long-term memory transcription factors
Bibliographic record
Abstract
Long-term memory (LTM) requires transcription and translation of new proteins, yet the transcriptional control of memory remains poorly understood. Here, we performed a transcriptome time-course during LTM formation in Drosophila melanogaster exposed to courtship conditioning. We identified a mushroom body-specific transcriptional memory trace that becomes activated during memory consolidation. Using scRNAseq of CREB-activated cells we were able to detect a persistent transcriptional response in MB neurons after LTM consolidation and retrieval. As a proof of causality, we conducted a loss-of-function screen for genes comprising the transcriptional memory trace, finding 16 positive hits whose disruption impaired LTM. Among them, we identified two neuron activity-regulated genes, Hr38 and sr, which encode transcription factors that are activated by courtship LTM training, required for LTM, and bind to many genes comprising the transcriptional memory trace. Overall, we further define the transcriptional response to LTM and identify transcription factors that may help shape it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".