The anti-aging protein Klotho is induced by GABA therapy and exerts protective and stimulatory effects on pancreatic beta cells
Bibliographic record
Abstract
Systemic gamma-aminobutyric acid (GABA) therapy prevents or ameliorates type 1 diabetes (T1D), by suppressing autoimmune responses and stimulating pancreatic beta cells. In beta cells, it increases insulin secretion, prevents apoptosis, and induces regeneration. It is unclear how GABA mediates these effects. We hypothesized that Klotho is involved. It is a multi-functional protein expressed in the kidneys, brain, pancreatic beta cells, other tissues, and is cell-bound or soluble. Klotho knockout mice display accelerated aging, and in humans Klotho circulating levels decline with age, renal disease and diabetes. Here, we report that GABA markedly increased circulating levels of Klotho in streptozotocin (STZ)-induced diabetes. GABA also increased Klotho in the islet of Langerhans of normal mice, as well as the islets and kidneys of STZ-treated mice. In vitro, GABA stimulated production and secretion of Klotho by human islet cells. Knockdown (KD) of Klotho with siRNA in INS-1E insulinoma cells abrogated the protective effects of GABA against STZ toxicity. Following KD, soluble Klotho reversed the effects of Klotho deficiency. In human islet cells soluble Klotho protected against cell death, and stimulated proliferation and insulin secretion. NF-κB activation triggers beta-cell apoptosis, and both GABA and Klotho suppress this pathway. We found Klotho KD augmented NF-κB p65 expression, and abrogated the ability of GABA to block NF-κB activation. This is the first report that GABAergic stimulation increases Klotho expression. Klotho protected and stimulated beta cells and lack of Klotho (KD) was reversed by soluble Klotho. These findings have important implications for the treatment of T1D.
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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 | 0.000 |
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".