Artemether and aspterric acid induce pancreatic alpha cells to transdifferentiate into beta cells in zebrafish
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recently, the antimalarial drug, artemether and the neurotransmitter GABA were identified to convert alpha cells into beta-like cells in vivo. However, some of these observations were challenged by other studies. To help address the controversy, we took advantage of zebrafish as a model to perform this study. EXPERIMENTAL APPROACH: First, we performed a small-molecule screening for artemether and its skeleton analogues. Second, we used the Cre-LoxP system for lineage tracing to indicate the conversion of alpha cells into beta cells in vivo. The stable transgenic ins2:eGFP αTC1-6-cell line was used for evaluation of alpha-cell transdifferentiation in vitro. We further used multiple zebrafish transgenic and mutation lines to demonstrate beta-cell differentiation, beta-cell ablation and alpha-cell hyperplasia in this study. KEY RESULTS: We showed that artemether and another sesquiterpene, aspterric acid, induced alpha-cell transdifferentiation into beta cells, both in zebrafish as well as using αTC1-6 cells. Furthermore, these two compounds also converted alpha cells into beta cells when beta cells were lost or alpha cells were hyperplastic in zebrafish. Unlike the previous report, the conversion of alpha cells to beta cells was mediated by increasing Pax4 expression, but not suppression of Arx expression. CONCLUSION AND IMPLICATIONS: Our data suggest that in zebrafish and αTC1-6 cells, both artemether and aspterric acid induce alpha-cell transdifferentiation. Our data, along with those of Li et al. (2017), suggested that artemether and aspterric acid were able to induce alpha-cell transdifferentiation, at least in zebrafish and αTC1-6 cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
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".