Attenuated AMPA Receptor Expression Allows Glioblastoma Cell Survival in Glutamate-Rich Environment
Bibliographic record
Abstract
Background: Glioblastoma multiforme (GBM) cells secrete large amounts of glutamate that can trigger AMPA-type glutamate receptors (AMPARs). This commonly results in Na+ and Ca2+-permeability and thereby in excitotoxic cell death of the surrounding neurons. Here we investigated how the GBM cells themselves survive in a glutamate-rich environment. Methods and Findings: In silico analysis of published reports shows down-regulation of all ionotropic glutamate receptors in GBM as compared to normal brain. In vitro, in all GBM samples tested, mRNA expression of AMPAR subunit GluR1, 2 and 4 was relatively low compared to adult and fetal total brain mRNA and adult cerebellum mRNA. These findings were in line with primary GBM samples, in which protein expression patterns were down-regulated as compared to the normal tissue. Furthermore, mislocalized expression of these receptors was found. Sequence analysis of GluR2 RNA in primary and established GBM cell lines showed that the GluR2 subunit was found to be partly unedited. Conclusions: Together with the lack of functional effect of AMPAR inhibition by NBQX our results suggest that down-regulation and afunctionality of AMPARs, enable GBM cells to survive in a high glutamate environment without going into excitotoxic cell death themselves. It can be speculated that specific AMPA receptor inhibitors may protect normal neurons against the high glutamate microenvironment of GBM tumors
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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".