Platelet microparticles reprogram macrophage gene expression and function
Bibliographic record
Abstract
Platelet microparticles (MPs) represent the most abundant MPs subtype in the circulation, and can mediate intercellular communication through delivery of bioactives molecules, such as cytokines, proteins, lipids and RNAs. Here, we show that platelet MPs can be internalised by primary human macrophages and deliver functional miR-126-3p. The increase in macrophage miR-126-3p levels was not prevented by actinomycin D, suggesting that it was not due to de novo gene transcription. Platelet MPs dose-dependently downregulated expression of four predicted mRNA targets of miR-126-3p, two of which were confirmed also at the protein level. The mRNA downregulatory effects of platelet MPs were abrogated by expression of a neutralising miR-126-3p sponge, implying the involvement of miR-126-3p. Transcriptome-wide, microarray analyses revealed that as many as 66 microRNAs and 653 additional RNAs were significantly and differentially expressed in macrophages upon exposure to platelet MPs. More specifically, platelet MPs induced an upregulation of 34 microRNAs and a concomitant downregulation of 367 RNAs, including mRNAs encoding for cytokines/chemokines CCL4, CSF1 and TNF. These changes were associated with reduced CCL4, CSF1 and TNF cytokine/chemokine release by macrophages, and accompanied by a marked increase in their phagocytic capacity. These findings demonstrate that platelet MPs can modify the transcriptome of macrophages, and reprogram their function towards a phagocytic phenotype.
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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".