Differential Effects of Voclosporin and Tacrolimus on Insulin Secretion From Human Islets
Bibliographic record
Abstract
The incidence of new onset diabetes after transplant (NODAT) has increased over the past decade, likely due to calcineurin inhibitor-based immunosuppressants, including tacrolimus (TAC) and cyclosporin. Voclosporin (VCS), a next-generation calcineurin inhibitor, is reported to cause fewer incidences of NODAT but the reason is unclear. While calcineurin signaling plays important roles in pancreatic β-cell survival, proliferation, and function, its effects on human β-cells remain understudied. In particular, we do not understand why some calcineurin inhibitors have more profound effects on the incidence of NODAT. We compared the effects of TAC and VCS on the dynamics of insulin secretory function, programmed cell death rate, and the transcriptomic profile of human islets. We studied 2 clinically relevant doses of TAC (10 ng/mL, 30 ng/mL) and VCS (20 ng/mL, 60 ng/mL), meant to approximate the clinical trough and peak concentrations. TAC, but not VCS, caused a significant impairment of 15 mM glucose-stimulated and 30 mM KCl-stimulated insulin secretion. This points to molecular defects in the distal stages of exocytosis after voltage-gated Ca2+ entry. No significant effects on islet cell survival or total insulin content were identified. RNA sequencing showed that TAC significantly decreased the expression of 17 genes, including direct and indirect regulators of exocytosis (SYT16, TBC1D30, PCK1, SMOC1, SYT5, PDK4, and CREM), whereas VCS has less broad, and milder, effects on gene expression. Clinically relevant doses of TAC, but not VCS, directly inhibit insulin secretion from human islets, likely via transcriptional control of exocytosis machinery.
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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".