Choline transporter‐like protein 4 (<scp>CTL</scp>4) links to non‐neuronal acetylcholine synthesis
Bibliographic record
Abstract
Synthesis of acetylcholine (ACh) by non-neuronal cells is now well established and plays diverse physiologic roles. In neurons, the Na(+) -dependent, high affinity choline transporter (CHT1) is absolutely required for ACh synthesis. In contrast, some non-neuronal cells synthesize ACh in the absence of CHT1 indicating a fundamental difference in ACh synthesis compared to neurons. The aim of this study was to identify choline transporters, other than CHT1, that play a role in non-neuronal ACh synthesis. ACh synthesis was studied in lung and colon cancer cell lines focusing on the choline transporter-like proteins, a five gene family choline-transporter like protein (CTL)1-5. Supporting a role for CTLs in choline transport in lung cancer cells, choline transport was Na(+) -independent and CTL1-5 were expressed in all cells examined. CTL1, 2, and 5 were expressed at highest levels and knockdown of CTL1, 2, and 5 decreased choline transport in H82 lung cancer cells. Knockdowns of CTL1, 2, 3, and 5 had no effect on ACh synthesis in H82 cells. In contrast, knockdown of CTL4 significantly decreased ACh secretion by both lung and colon cancer cells. Conversely, increasing expression of CTL4 increased ACh secretion. These results indicate that CTL4 mediates ACh synthesis in non-neuronal cell lines and presents a mechanism to target non-neuronal ACh synthesis without affecting neuronal ACh synthesis.
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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 | 0.001 | 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".