Age-Dependent Resistance to Excitotoxicity in Htt CAG140 Mice and the Effect of Strain Background
Bibliographic record
Abstract
Mouse strain background can influence vulnerability to excitotoxic neuronal cell death and potentially modulate phenotypes in transgenic mouse models of human disease. Evidence supports a contribution of excitotoxicity to the selective death of medium spiny neurons in Huntington's disease (HD). Here, we assess whether strain differences in excitotoxic vulnerability influence striatal cell death in a knock-in mouse model of HD. Previous studies that evaluated resistance to excitotoxic lesions in several mouse models of HD had variable outcomes. In the present study, we directly compare one model on two different background strains to test the contribution of strain to excitotoxicity-mediated neurodegeneration. Mice of the FVB/N strain, which are highly vulnerable to excitotoxicity, become extremely resistant to quinolinic acid-induced striatal neurodegeneration with age, when carrying a huntingtin (Htt) allele expressing a HD transgene (CAG140). The resistance is much greater than the age-dependent resistance that has been previously reported in YAC128 mice. By 12 months of age, both heterozygous and homozygous FVB.CAG140 mice displayed virtually complete resistance to quinolinic acid-induced striatal neurodegeneration. A similar resistance develops in CAG140 mice on a C57BL/6N background although the effect size is smaller because C57BL/6N mice are already resistant due to genetic background. In a direct comparison with the YAC128 mice, FVB.CAG140 mice have greater resistance. FVB.CAG140 mice are also resistant to neurodegeneration following kainic acid-induced status epilepticus suggesting the existence of a common cellular mechanism that provides protection against multiple types of excitotoxic insult. These findings establish FVB.CAG140 mice as a useful model to investigate the cellular and molecular mechanisms that confer neuroprotection against excitotoxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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) | 0.000 | 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".