Learning-induced mRNA alterations in olfactory bulb mitral cells in neonatal rats
Bibliographic record
Abstract
In the olfactory bulb, a cAMP/PKA/CREB-dependent form of learning occurs in the first week of life that provides a unique mammalian model for defining the epigenetic role of this evolutionarily ancient plasticity cascade. Odor preference learning in the week-old rat pup is rapidly induced by a 10-min pairing of odor and stroking. Memory is demonstrable at 24 h, but not 48 h, posttraining. Using this paradigm, pups that showed peppermint preference 30 min posttraining were sacrificed 20 min later for laser microdissection of odor-encoding mitral cells. Controls were given odor only. Microarray analysis revealed that 13 nonprotein-coding mRNAs linked to mRNA translation and splicing and 11 protein-coding mRNAs linked to transcription differed with odor preference training. MicroRNA23b, a translation inhibitor of multiple plasticity-related mRNAs, was down-regulated. Protein-coding transcription was up-regulated for Sec23b, Clic2, Rpp14, Dcbld1, Magee2, Mstn, Fam229b, RGD1566265, and Mgst2. Gng12 and Srcg1 mRNAs were down-regulated. Increases in Sec23b, Clic2, and Dcbld1 proteins were confirmed in mitral cells in situ at the same time point following training. The protein-coding changes are consistent with extracellular matrix remodeling and ryanodine receptor involvement in odor preference learning. A role for CREB and AP1 as triggers of memory-related mRNA regulation is supported. The small number of gene changes identified in the mitral cell input/output link for 24 h memory will facilitate investigation of the nature, and reversibility, of changes supporting temporally restricted long-term memory.
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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) | 0.002 | 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".