Dicer Cleavage by Calpain Determines Platelet microRNA Levels and Function in Diabetes
Bibliographic record
Abstract
RATIONALE: MicroRNAs (miRNAs) are short noncoding RNA species generated by the processing of longer precursors by the ribonucleases Drosha and Dicer. Platelets contain large amounts of miRNA that are altered by disease, in particular diabetes mellitus. OBJECTIVE: This study determined why platelet miRNA levels are attenuated in diabetic individuals and how decreased levels of the platelet-enriched miRNA, miR-223, affect platelet function. METHODS AND RESULTS: Dicer levels were altered in platelets from diabetic mice and patients, a change that could be attributed to the cleavage of the enzyme by calpain, resulting in loss of function. Diabetes mellitus in human subjects as well as in mice resulted in decreased levels of platelet miR-142, miR-143, miR-155, and miR-223. Focusing on only 1 of these miRNAs, miR-223 deletion in mice resulted in modestly enhanced platelet aggregation, the formation of large thrombi and delayed clot retraction compared with wild-type littermates. A similar dysregulation was detected in platelets from diabetic patients. Proteomic analysis of platelets from miR-223 knockout mice revealed increased levels of several proteins, including kindlin-3 and coagulation factor XIII-A. Whereas, kindlin-3 was indirectly regulated by miR-223, factor XIII was a direct target and both proteins were also altered in diabetic platelets. Treating diabetic mice with a calpain inhibitor prevented loss of platelet dicer as well as the diabetes mellitus-induced decrease in platelet miRNA levels and the upregulation of miR-223 target proteins. CONCLUSIONS: Thus, calpain inhibition may be one means of normalizing platelet miRNA processing as well as platelet function in diabetes mellitus.
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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 | 0.001 |
| 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".