Differential contributions of <i>de novo</i> and maintenance <scp>DNA</scp> methyltransferases to object memory processing in the rat hippocampus and perirhinal cortex – a double dissociation
Bibliographic record
Abstract
Epigenetic mechanisms are increasingly acknowledged as major players in memory formation. Specifically, DNA methylation is necessary for the formation of long-term memory in various brain regions, including the hippocampus (HPC); however, its role in the perirhinal cortex (PRh), a structure critical for object memory, has not been characterized. Moreover, the mnemonic effects of selective DNA methyltransferase (DNMT) inhibition have not yet been investigated systematically, despite distinct roles for de novo (DNMT3a, 3b) and maintenance (DNMT1) methyltransferases. Consequently, we assessed the effects of various DNMT inhibitors within the HPC and PRh of rats using the object-in-place paradigm, which requires both brain regions. The non-nucleoside DNA methyltransferase inhibitor RG-108 impaired long-term object-in-place memory in both regions. Furthermore, intracranial administration of Accell short-interference RNA sequences to inhibit the expression of individual DNMTs implicated DNMT3a and DNMT1 in the HPC and PRh effects, respectively. mRNA expression analyses revealed a complementary pattern of results, as only de novo DNMT3a and DNMT3b mRNA was upregulated in the HPC (dentate gyrus) following object-in-place learning, whereas DNMT1 mRNA was selectively upregulated in the PRh. These results reinforce the established functional double dissociation between the HPC and PRh and imply the operation of different epigenetic mechanisms in brain regions dedicated to long-term memory processing for different types of information.
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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 | 0.000 |
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".