Catalytic-independent function of PARP-1 is essential in regulating human islet-cell differentiation from stem cells
Bibliographic record
Abstract
Abstract Poly (ADP) Ribose Polymerase-1 (PARP-1), a fundamental DNA repair enzyme is known to regulate β cell death, replication, and insulin secretion. PARP-1 knockout (KO) mice are resistant to diabetes while PARP-1 overactivation contributes to β cell death. Additionally, PARP-1 inhibition improves diabetes complications in patients with type-2 diabetes. Despite these beneficial effects, the use of PARP-1 modulating agents in diabetes treatment are largely neglected, primarily due to the poorly studied mechanistic action of PARP-1 catalytic function in human β cell development. In the present study, we evaluated PARP-1 regulatory action in human β cell differentiation using the human pancreatic stem cell line, PANC-1. We surveyed islet census and histology from PARP-1 wild-type versus KO mice pancreas in head-to-head comparison with PARP-1 regulatory action for in-vitro β cell differentiation following either PARP-1 depletion or its pharmacological inhibition in PANC-1-differentiated islet cells. shRNA mediated PARP-1 depleted (SiP), or control replete (U6) PANC-1 cells were differentiated into islet-like clusters using established protocols. We observed complete abrogation of new β cell formation with absolute PARP-1 depletion while its catalytic inactivation using the strong inhibitor, PJ34, promoted the endocrine β cell differentiation and maturation. Immunohistochemistry and proteomics for key endocrine differentiation factors in addition to β cell maturation markers highlighted potential regulatory and augmented β cell differentiation due to non-catalytic function of PARP-1 elicited p-38 MAPK phosphorylation and Neurogenin-3 (Ngn3) re-activation. In summary, the study reestablishes a novel ability of catalytic independent action of PARP-1 in regulating islet cell differentiation and potential avenues for pilot clinical testing of PARP-1 inhibitors for β cell replacement therapies for the treatment of diabetes.
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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) | 0.002 | 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".