Pharmacologic stimulation of insulin granule acidification increases β-cell zinc content and augments β-cell-targeted drug delivery
Bibliographic record
Abstract
Pathologic loss of insulin-producing pancreatic β-cells is a hallmark of diabetes that is potentially reversible through regenerative therapy. However, existing replication-promoting compounds lack β-cell specificity, limiting their clinical application. To overcome this challenge, we generated βRepZnC, a zinc-chelating replication compound designed to leverage the uniquely high zinc content of β-cells for targeted delivery. Herein, we identify pharmacological agents that boost β-cell zinc and improve the targeted delivery and bioactivity of βRepZnC. Using a high-content, image-based screen with the zinc fluorophore TSQ, we identified GR-46611 as a pharmacologic enhancer of β-cell zinc levels. Time-lapse TSQ imaging revealed that GR-46611 rapidly elevated intracellular zinc, prompting further mechanistic studies that showed increased zinc accumulation through the transporter ZnT8. This effect was mediated by enhanced V-ATPase-driven vesicle acidification via cAMP-PKA signaling inhibition. Supporting this mechanism, multiple protein kinase A (PKA) inhibitors also increased β-cell zinc content. Importantly, zinc enhancement significantly increased βRepZnC accumulation in both mouse and human primary islets, with fluorescence-activated cell sorting and mass spectrometry confirming selective drug retention in β-cells over non-β-cells. To evaluate effects on bioactivity, we performed complementary on-treatment and post treatment islet replication assays, measuring replication either concurrent with or 48 h after drug exposure, respectively. Zinc elevation via GR-46611 or the PKA inhibitor H89 selectively potentiated βRepZnC-induced β-cell replication in both contexts. Notably, only βRepZnC-unlike non-zinc-binding replication compounds-elicited a sustained replication response after drug withdrawal. This work defines a new pharmacologic strategy for manipulating β-cell zinc levels that can be exploited for durable β-cell-targeted therapeutic delivery.
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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 | 0.001 |
| 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".