ANRIL regulates production of extracellular matrix proteins and vasoactive factors in diabetic complications
Bibliographic record
Abstract
noncoding RNAs (lncRNAs) have gained widespread interest due to their prevailing presence in various diseases. lncRNA ANRIL (a. k. a. CDKN2B-AS1) is located on human chromosome 9 (p21.3) and transcribed in opposite direction to the INK4b-ARF-INK4a gene cluster. It has been identified as a highly susceptible region for diseases such as coronary artery diseases and type 2 diabetes. Here, we explored its regulatory role in diabetic nephropathy (DN) and diabetic cardiomyopathy (DCM) in association with epigenetic modifiers p300 and polycomb repressive complex 2 (PRC2) complex. We used an ANRIL-knockout (ANRILKO) mouse model for this study. The wild-type and ANRILKO animals with or without streptozotocin-induced diabetes were monitored for 2 min. At the end of the time point, urine and tissues were collected. The tissues were measured for fibronectin (FN), type IV collagen (Col1α4), and VEGF mRNA and protein expressions. Renal function was determined by the measurement of 24-h urine volume and albumin/creatinine ratio at euthanasia. Renal and cardiac structures were investigated using periodic acid-Schiff stain and/or immunohistochemical analysis. Elevated expressions of extracellular matrix (ECM) proteins were prevented in ANRILKO diabetic animals. Furthermore, ANRILKO had a protective effect on diabetic mouse kidneys, as evidenced by lowering of urine volume and urine albumin levels in comparison with the wild-type diabetic animals. These alterations regulated by ANRIL may be mediated by p300 and enhancer of zeste 2 (EZH2) of the PRC2 complex. Our study concludes that ANRIL regulates functional and structural alterations in the kidneys and hearts in diabetes through controlling the expressions of ECM proteins and VEGF.
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".