GABA Protects Human Islet Cells Against the Deleterious Effects of Immunosuppressive Drugs and Exerts Immunoinhibitory Effects Alone
Bibliographic record
Abstract
BACKGROUND: We recently found that γ-aminobutyric acid (GABA) protects mouse islet β cells. It prevented autoimmune type 1 diabetes in mice, induced islet β-cell regeneration, and exerted immunoinhibitory effects. However, it is not known whether GABA would be equally active on human islet and immune cells. METHODS: In vitro culture of human islets and immune cells with or without GABA and immunosuppressive drugs. In vitro analysis of apoptosis, proliferation, nuclear factor (NF)-κB activation, calcium signaling, and insulin secretion. RESULTS: GABA reduced human islet cell apoptosis in culture, such that the yield of live cells was approximately tripled after 1 week, and it stimulated insulin secretion. It protected against the deleterious effects of rapamycin, tacrolimus, and mycophenolate mofetil. In human immune cells, GABA had inhibitory effects similar to mouse cells, such as suppressed anti-CD3-stimulated T-cell proliferation, in a GABA type A receptor-dependent fashion. The immunosuppressive mechanisms have been unclear, but we found that GABA blocked calcium influx, which is a key activation signal. GABA also suppressed NF-κB activation in both human islet cells and immune cells. We found that it could be combined with rapamycin to increase its suppressive effects. CONCLUSIONS: GABA improved human islet cell survival and had suppressive effects on human immune cells. It inhibited canonical NF-κB activation in both islet and immune cells. This is important because activation of this pathway is detrimental to islet cells and likely promotes damaging autoimmunity and alloreactivity against transplanted islets. These findings suggest that GABA might find applications in clinical islet transplantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".