Mechanisms of rapamycin toxicity in pancreatic beta cells
Bibliographic record
Abstract
Islet transplantation offers a potential cure for type I diabetes mellitus. The publication of the landmark Edmonton study in 2000, which reported insulin independence in seven consecutive patients, prompted increased interest in this therapy. However, enthusiasm was tempered when the 5 year follow up results of this study were published, with only 10% of recipients maintaining insulin independence. The cause for this graft loss is multifactorial, but there is in vivo and in vitro evidence that suggests immunosuppressive drug toxicity plays an important role. The aim of this thesis was to establish the effects of rapamycin, one of the primary immunosuppressants used in islet transplantation, on murine β cells and islets and elucidate the mechanisms of any toxicity seen. The intracellular target for rapamycin is mTOR which exists in two complexes, mTORC1 and mTORC2. mTORC1 primarily regulates cell size and proliferation; whereas mTORC2 plays a key role in regulating cell survival via protein kinase B (PKB). This thesis has demonstrated that rapamycin treatment results in significant reductions in glucose stimulated insulin secretion in the MIN6 mouse insulinoma cell line and isolated rat islets, as well as increased apoptosis in these cell types. Furthermore, it has shown that prolonged rapamycin results in inhibition of mTORC2 assembly, with resultant inhibition of PKB phosphorylation and activity. Overcoming rapamycin induced mTORC2 inhibition with an adenovirus encoding constitutively active PKB ameliorates the detrimental effects of rapamycin on both MIN6 cells and rat islets. This suggests that rapamycin toxicity is mediated predominantly via mTORC2 rather than mTORC1 inhibition. This work brings into question the use of rapamycin as an immunosuppressant in islet transplantation and also highlights the key role of PKB in β cell survival. Therapies resulting in PKB activation may have the potential to improve outcomes of islet transplantation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".