Roles for the long non-coding RNA Pax6os1/PAX6-AS1 in pancreatic beta cell function
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of beta cell function. Here, we show that an lncRNA-transcribed antisense to Pax6, annotated as Pax6os1/PAX6-AS1, was upregulated by high glucose concentrations in human as well as murine beta cell lines and islets. Elevated expression was also observed in islets from mice on a high-fat diet and patients with type 2 diabetes. Silencing Pax6os1 / PAX6-AS1 in MIN6 or EndoC-βH1 cells increased several beta cell signature genes' expression. Pax6os1/PAX6-AS1 was shown to bind to EIF3D, indicating a role in translation of specific mRNAs, as well as histones H3 and H4, suggesting a role in histone modifications. Important interspecies differences were found, with a stronger phenotype in humans. Only female Pax6os1 null mice fed a high-fat diet showed slightly enhanced glucose clearance. In contrast, silencing PAX6-AS1 in human islets enhanced glucose-stimulated insulin secretion and increased calcium dynamics, whereas overexpression of the lncRNA resulted in the opposite phenotype.
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 | 0.000 |
| 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".