Cage effects on synaptic plasticity and its modulation in a mouse model of fragile X syndrome
Bibliographic record
Abstract
Fragile X syndrome (FXS) is characterized by impairments in executive function including different types of learning and memory. Long-term potentiation (LTP), thought to underlie the formation of memories, has been studied in the Fmr1 mouse model of FXS. However, there have been many discrepancies in the literature with inconsistent use of littermate and non-littermate Fmr1 knockout (KO) and wild-type (WT) control mice. Here, the influence of the breeding strategy (cage effect) on short-term potentiation (STP), LTP, contextual fear conditioning (CFC), expression of N -methyl- d -aspartate receptor (NMDAR) subunits and the modulation of NMDARs, were examined. The largest deficits in STP, LTP and CFC were found in KO mice compared with non-littermate WT. However, the expression of NMDAR subunits was unchanged in this comparison. Rather, NMDAR subunit (GluN1, 2A, 2B) expression was sensitive to the cage effect, with decreased expression in both WT and KO littermates compared with non-littermates. Interestingly, an NMDAR-positive allosteric modulator, UBP714, was only effective in potentiating the induction of LTP in non-littermate KO mice and not the littermate KO mice. These results suggest that commonly studied phenotypes in Fmr1 KOs are sensitive to the cage effect and therefore the breeding strategy may contribute to discrepancies in the literature. This article is part of a discussion meeting issue ‘Long-term potentiation: 50 years on’.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".