Glutamate Promotes Proliferation of Striatal Neuronal Progenitors by an NMDA Receptor-Mediated Mechanism
Bibliographic record
Abstract
Increasing evidence suggests that classical neurotransmitters play important roles in the development of the mammalian CNS. We used in vivo and in vitro models to identify a novel role for glutamate in striatal neurogenesis mediated by NMDA receptors. In utero exposure to NMDA receptor antagonists during striatal neurogenesis caused a dramatic reduction in the total number of adult striatal neurons. In contrast, embryos exposed to NMDA receptor antagonists immediately after the main period of neurogenesis showed no significant change in neuronal number in the adult striatum. In addition, examination of embryos shortly after NMDA receptor blockade revealed reduced proliferation in the lateral ganglionic eminence (LGE). In culture, dividing neuronal progenitors derived from the embryonic LGE showed marked reduction in 5'-bromodeoxyuridine (BrdU) uptake when exposed to NMDA receptor antagonists, indicating reduced DNA synthesis. Low concentrations of NMDA significantly increased proliferation, whereas high concentrations were toxic. AMPA-KA receptor antagonists had no significant effect on striatal neuroblast proliferation either in vivo or in vitro. These results support the hypothesis that glutamate plays a novel role during early development of the ventral telencephalon, promoting proliferation of striatal neuronal progenitors by an NMDA receptor-dependent mechanism. In contrast, previous findings suggest that proliferation of cortical progenitors derived from the dorsal telencephalon is regulated by activation of AMPA-KA but not NMDA receptors. Heterogeneous responses to glutamate in different germinal zones of the telencephalon may be an important mechanism contributing to generating neuronal diversity in the forebrain.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".