Distinct Proteomic Brain States Underlying Long-Term Memory Formation in Aversive Operant Conditioning
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Long-term memory (LTM) formation relies on de novo protein synthesis; however, the full complement of proteins crucial to LTM formation remains unknown in any system. Using an aversive operant conditioning model of aerial respiratory behavior in the pond snail mollusk, Lymnaea stagnalis ( L. stagnalis ), we conducted a transcriptome-guided proteomic analysis on the central nervous system (CNS) of LTM, no LTM, and control animals. We identified 366 differentially expressed proteins linked to LTM formation, with 88 upregulated and 36 downregulated in LTM compared to both no LTM and controls. Functional annotation highlighted the importance of balancing protein synthesis and degradation for LTM, as indicated by the upregulation of proteins involved in proteasome activity and translation initiation, including EIF2D, mRNA levels of which were confirmed to be upregulated by conditioning and implicated nuclear factor Y as a potential regulator of LTM-related transcription in this model. This study represents the first transcriptome-guided proteomic analysis of LTM formation ability in this model and lays the groundwork for discovering orthologous proteins critical to LTM in mammals.
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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".