The Effect of GLT-1 Upregulation on Extracellular Glutamate Dynamics
Bibliographic record
Abstract
Pharmacological upregulation of glutamate transporter-1 (GLT-1), commonly achieved using the beta-lactam antibiotic ceftriaxone, represents a promising therapeutic strategy to accelerate glutamate uptake and prevent excitotoxic damage in neurological conditions. While excitotoxicity is indeed implicated in numerous brain diseases, it is typically restricted to select vulnerable brain regions, particularly in early disease stages. In healthy brain tissue, the speed of glutamate uptake is not constant and rather varies in both an activity- and region-dependent manner. Despite the widespread use of ceftriaxone in disease models, very little is known about how such treatments impact functional measures of glutamate uptake in healthy tissue, and whether GLT-1 upregulation can mask the naturally occurring activity-dependent and regional heterogeneities in uptake. Here, we used two different compounds, ceftriaxone and LDN/OSU-0212320 (LDN), to upregulate GLT-1 in healthy wild-type mice. We then used real-time imaging of the glutamate biosensor iGluSnFR to investigate functional consequences of GLT-1 upregulation on activity- and regional-dependent variations in glutamate uptake capacity. We found that while both ceftriaxone and LDN increased GLT-1 expression in multiple brain regions, they did not prevent activity-dependent slowing of glutamate clearance nor did they speed basal clearance rates, even in areas characterized by slow uptake (e.g., striatum). Unexpectedly, ceftriaxone but not LDN decreased glutamate release in the cortex, suggesting that ceftriaxone may alter release properties independent of its effects on GLT-1 expression. In sum, our data demonstrate the complexities of glutamate uptake by showing that GLT-1 expression does not necessarily translate to accelerated uptake. Furthermore, these data suggest that the mechanisms underlying activity- and regional-dependent differences in glutamate dynamics are independent of GLT-1 expression levels.
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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 | 0.001 |
| 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".