Glutamate Acting on N-Methyl-d-aspartate Receptors Attenuates Insulin-like Growth Factor-1 Receptor Tyrosine Phosphorylation and Its Survival Signaling Properties in Rat Hippocampal Neurons
Bibliographic record
Abstract
Impairing intracellular signaling induced by survival factors and excess glutamate have recently been suggested to play important role in neurodegenerative processes. However, the underlying mechanism(s) and interrelationships between these factors mostly remain to be established. In the present study, we show that glutamate attenuates the tyrosine phosphorylation of the insulin-like growth factor-1 (IGF-1) receptor and the survival effect of IGF-1 (100 nm) in hippocampal cultured neurons. Pretreatment of cultured hippocampal neurons with glutamate concentration dependently inhibited the tyrosine phosphorylation of IGF-1 receptors as well as that of IRS-1 and Shc, two IGF-1 receptor adapter proteins. The effect of glutamate was also evident on the phosphorylation of Akt, as well as its upstream kinase PI3K/PDK1 and downstream targets, GSK3beta and FOXO3a. The inhibitory effect of glutamate (1 mm) was blocked by antagonists of the N-methyl-d-aspartate (NMDA) receptor, including MK801 (20 microm) and AP5 (100 microm), but not by blockers of other ionotropic or metabotropic glutamate receptor sub-types demonstrating the involvement of the NMDA receptor. This hypothesis is supported further by the observation that treatment with NMDA concentration dependently inhibited the activation and phosphorylation of IGF-1 receptors and downstream targets induced by IGF-1 (100 nm). These findings demonstrate that glutamate can block the effect of IGF-1 by decreasing IGF-1 receptor signaling and responsiveness, hence attenuating the survival properties of this trophic factor in neuronal cells. Our results also suggest a novel mechanism by which glutamate can reduce cell viability and induce neurotoxicity.
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 | 0.000 | 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 | 0.001 |
| 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".