A Human Microglial Cell Line Expresses γ-Aminobutyric Acid (GABA) Receptors and Responds to GABA and Muscimol by Increasing Production of IL-8
Bibliographic record
Abstract
Gamma-aminobutyric acid (GABA) is an essential neurotransmitter and an important regulator of neuroinflammation and disease. Microglia are important immune cells in the brain that express GABA receptors (GABAR) and respond to both GABA and GABAR agonists, yet the effect of GABA on microglial inflammatory responses is unclear. We hypothesized that GABA and GABAR agonists might modify the activation of a human microglial cell line (HMC3). We further hypothesized that Amanita muscaria extract (AME-1), which contained GABAR agonists (GABA and muscimol), would similarly stimulate HMC3. Ligand-gated GABAR (GABAAR) and G protein-coupled GABAR (GABABR) subunit expression was analyzed by qRT-PCR, metabolic activity was determined by nicotinamide adenine dinucleotide (NADH)-dependent oxidoreductase assay (XTT), reactive oxygen species (ROS) generation was analyzed by 2′,7′-dichlorodihydrofluorescein diacetate (DCFDA), and interleukin-8 (IL-8) production was analyzed by an enzyme-linked immunosorbent assay (ELISA). HMC3 expressed several neuroreceptors such as subunits of the GABAA receptor (GABAAR). HMC3 constitutively produce IL-8 and ROS. Both muscimol and GABA stimulated HMC3 to produce more IL-8 but had no effect on constitutive ROS production. GABA and muscimol altered the morphology and Iba1 localization of HMC3. GABA, but not muscimol, increased HMC3 metabolic activity. Similarly, AME-1 induced HMC3 to produce more IL-8 but not ROS and altered cell morphology and Iba1 localization. GABA induction of IL-8 was blocked by bicuculline, an antagonist of GABAAR. AME-1-induced production of IL-8 was not blocked by bicuculline, suggesting that AME-1’s effect on HMC3 was independent of GABAAR. In conclusion, these data show that GABA and GABA agonists stimulate HMC3 to increase their production of IL-8. Mixtures that contain GABA and muscimol, such as AME-1, have similar effects on HMC3 that are independent of GABAAR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".