Pharmacologic and genetic approaches define human pancreatic β cell mitogenic targets of DYRK1A inhibitors
Bibliographic record
Abstract
Small molecule inhibitors of dual specificity, tyrosine phosphorylation-regulated kinase 1A (DYRK1A), including harmine and others, are able to drive human β cell regeneration. While DYRK1A is certainly a target of this class, whether it is the only or the most important target is uncertain. Here, we employ a combined pharmacologic and genetic approach to refine the potential mitogenic targets of the DYRK1A inhibitor family in human islets. A combination of human β cell RNA sequencing, DYRK1A inhibitor kinome screens, pharmacologic inhibitors, and targeted silencing of candidate genes confirms that DYRK1A is a central target. Surprisingly, however, DYRK1B also proves to be an important target: silencing DYRK1A results in an increase in DYRK1B. Simultaneous silencing of both DYRK1A and DYRK1B yields greater β cell proliferation than silencing either individually. Importantly, other potential kinases, such as the CLK and the GSK3 families, are excluded as important harmine targets. Finally, we describe adenoviruses that are able to silence up to 7 targets simultaneously. Collectively, we report that inhibition of both DYRK1A and DYRK1B is required for induction of maximal rates of human β cell proliferation, and we provide clarity for future efforts in structure-based drug design for human β cell regenerative drugs.
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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 | 0.001 | 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".