Mechanisms of GABA release from human astrocytes
Bibliographic record
Abstract
We have previously demonstrated that human astrocytes are GABAergic cells. Throughout the adult human brain, they express the GABA synthesizing enzyme GAD 67, the GABA metabolizing enzyme GABA-T, and the GABA(A) and GABA(B) receptors. GABA modulates the actions of microglia, indicating an important role for astrocytes beyond that of influencing neurotransmitter function. Here we report on the mechanisms by which astrocytes release GABA. Astrocytes were found to express the mRNA and protein for multiple GABA transporters, and multiple receptors for glutamate, GABA, and glycine. In culture, untreated human astrocytes maintained an intracellular GABA level of 2.32 mM. They exported GABA into the culture medium so that an intracellular-extracellular gradient of 3.64 fold was reached. Inhibitors of the GABA transporters GAT1, GAT2, and GAT3, significantly reduced this export in a Ca(2+)-independent fashion. Intracellular GABA levels were enhanced by treatment with the GABA-T inhibitors gabaculine or vigabatrin. Treatment with glutamate increased GABA release in a concentration-dependent fashion. This was partially inhibited by blockers of N-methyl-D-aspartate and kainate receptors. Conversely, glycine and D-serine, co-agonists of NMDA receptors, enhanced the GABA release. GABA release was accompanied by an increase in intracellular Ca(2+) concentration ([Ca(2+)](i)) and was reduced by adding the Ca(2+) chelator, BAPTA-AM to the medium. These data indicate that astrocytes continuously synthesize GABA and that there are multiple mechanisms which can mediate its release. Each of these may play a role in the physiological functioning of astrocytes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".