Astrocytes are GABAergic cells that modulate microglial activity
Bibliographic record
Abstract
GABA is assumed to function in brain only as an inhibitory neurotransmitter. Here we report a much broader CNS role. We show that human astrocytes are GABAergic cells, and that human microglia are GABAceptive cells. We show that in adult human brain tissue, astrocytes immunostain for the GABA synthesizing enzyme GAD 67, the GABA metabolizing enzyme GABA-T and the GABA(A) and GABA(B) receptors. The intensity of staining is comparable or greater to that observed for known inhibitory neurons. We show that cultured human astrocytes strongly express the mRNA and protein for GAD 67, as well as GABA-T, and the GABA(A) and GABA(B) receptors. We further show that cultured human microglia express the mRNA and protein for GABA-T, in addition to the GABA(A) and GABA(B) receptors characterizing them as GABAceptive cells. We demonstrate that GABA suppresses the reactive response of both astrocytes and microglia to the inflammatory stimulants lipopolysaccharide (LPS) and interferon-γ by inhibiting induction of inflammatory pathways mediated by NFκB and P38 MAP kinase. This results in a reduced release of the inflammatory cytokines TNFα and IL-6 and an attenuation of conditioned medium neurotoxicity toward neuroblastoma SH-SY5Y cells. These inhibitory reactions are partially mimicked by the GABA(A) receptor agonist muscimol and the GABA(B) receptor agonist baclofen, indicating that GABA can stimulate both types of receptors in astrocytes as well as microglia. We conclude that the antiinflammatory actions of GABA offer new therapeutic opportunities since agonists should enhance the effectiveness of other antiinflammatory agents that operate through non-GABA pathways.
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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 | 0.001 |
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".