A novel transgenic mouse model expressing primate-specific nuclear choline acetyltransferase: insights into potential cholinergic vulnerability
Bibliographic record
Abstract
The acetylcholine (ACh) synthesizing enzyme choline acetyltransferase (ChAT) is an important cholinergic neuronal marker whose levels and/or activity are reduced in physiological and pathological aging. One isoform of ChAT, 82-kDa ChAT, is expressed only in primates and found primarily in nuclei of cholinergic neurons in younger individuals, but this protein becomes mostly cytoplasmic with increasing age and in Alzheimer's disease (AD). Previous studies suggest that 82-kDa ChAT may be involved in regulating gene expression during cellular stress. Since it is not expressed in rodents, we developed a transgenic mouse model that expresses human 82-kDa ChAT under the control of an Nkx2.1 driver. Behavioral and biochemical assays were used to phenotype this novel transgenic model and elucidate the impact of 82-kDa ChAT expression. The 82-kDa ChAT transcript and protein were expressed predominantly in basal forebrain neurons and subcellular distribution of the protein recapitulated the age-related pattern found previously in human necropsy brains. Older 82-kDa ChAT-expressing mice presented with better age-related memory and inflammatory profiles. In summary, we established a novel transgenic mouse expressing 82-kDa ChAT that is valuable for studying the role of this primate-specific cholinergic enzyme in pathologies associated with cholinergic neuron vulnerability and dysfunction.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".