Dicer1-mediated miRNA processing shapes the mRNA profile and function of murine platelets
Bibliographic record
Abstract
Human platelets contain microRNAs (miRNAs) and miRNA processing machinery, but their contribution to platelet function remains incompletely understood. Here, we show that murine megakaryocyte (MK)-specific knockdown of Dicer1, the ribonuclease that cleaves miRNA precursors into mature miRNAs, reduces the level of the majority of miRNAs in platelets. This leads to altered platelet messenger RNA (mRNA) expression profiles and mild thrombocytopenia. Fibrinogen receptor subunits Itga2b (αIIb) and Itgb3 (β3) mRNAs were among the differentially expressed transcripts that are increased in platelets lacking Dicer1. Argonaute 2 (Ago2), a member of the miRNA silencing complex, co-immunoprecipitated with αIIband β3mRNAs in wild-type platelets. Furthermore, co-immunoprecipitation experiments suggested reduced αIIb/β3/Ago2 complexes in miRNA-deficient platelets. These results suggested that miRNAs regulate both integrin subunits. Subsequent 3' untranslated region luciferase reporter assays confirmed that the translation of both αIIband β3mRNAs can be regulated by miRNAs miR-326, miR-128, miR-331, and miR-500. Consistent with these molecular changes, the deletion ofDicer1resulted in increased surface expression of integrins αIIband β3, and enhanced platelet binding to fibrinogen in vivo and in vitro. Heightened platelet reactivity, shortened tail-bleeding time, and reduced survival following collagen/epinephrine-induced pulmonary embolism were also observed in Dicer1-deficient animals. CombinedPf4-cre-mediated deletion of Drosha and Dicer1 did not significantly exacerbate phenotypes observed in single Dicer1 knockout mice. In summary, these findings indicate that Dicer1-dependent generation of mature miRNAs in late-stage MKs and platelets modulates the expression of target mRNAs important for the hemostatic and thrombotic function of platelets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".