Identification of islet-enriched long non-coding RNAs contributing to β-cell failure in type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: Non-coding RNAs constitute a major fraction of the β-cell transcriptome. While the involvement of microRNAs is well established, the contribution of long non-coding RNAs (lncRNAs) in the regulation of β-cell functions and in diabetes development remains poorly understood. The aim of this study was to identify novel islet lncRNAs differently expressed in type 2 diabetes models and to investigate their role in β-cell failure and in the development of the disease. METHODS: Novel transcripts dysregulated in the islets of diet-induced obese mice were identified by high throughput RNA-sequencing coupled with de novo annotation. Changes in the level of the lncRNAs were assessed by real-time PCR. The functional role of the selected lncRNAs was determined by modifying their expression in MIN6 cells and primary islet cells. RESULTS: We identified about 1500 novel lncRNAs, a number of which were differentially expressed in obese mice. The expression of two lncRNAs highly enriched in β-cells, βlinc2, and βlinc3, correlated to body weight gain and glycemia levels in obese mice and was also modified in diabetic db/db mice. The expression of both lncRNAs was also modulated in vitro in isolated islet cells by glucolipotoxic conditions. Moreover, the expression of the human orthologue of βlinc3 was altered in the islets of type 2 diabetic patients and was associated to the BMI of the donors. Modulation of the level of βlinc2 and βlinc3 by overexpression or downregulation in MIN6 and mouse islet cells did not affect insulin secretion but increased β-cell apoptosis. CONCLUSIONS: Taken together, the data show that lncRNAs are modulated in a model of obesity-associated type 2 diabetes and that variations in the expression of some of them may contribute to β-cell failure during the development of the disease.
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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) | 0.001 | 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".