Identification of eNOS‐based Megakaryocyte Subpopulations and Their Pharmacological Characterization by IFNγ and IL‐10
Bibliographic record
Abstract
Purpose Recently, our laboratory identified in human blood two platelet subpopulations based on the presence or absence of a functional endothelial nitric oxide synthase (eNOS)‐signalling pathway and the ability to produce nitric oxide (NO). We have found that eNOS‐negative (eNOS neg ) platelets, although less abundant are more reactive than eNOS‐positive (eNOS pos ) platelets and initiate aggregate/thrombus formation, while eNOS‐positive platelets through their ability to generate NO limit aggregate size. As platelets derive from bone marrow megakaryocytes, we hypothesized that eNOS neg and eNOS pos subpopulations of megakaryocytes exist and give rise to their respective eNOS‐based platelet subpopulations. Additionally, we hypothesized that interferon‐γ (IFNγ) and interleukin‐10 (IL‐10), which are known to counter‐regulate eNOS expression and megakaryocyte differentiation, would determine in a concentration manner the ratio of eNOS neg to eNOS pos megakaryocytes. Methods The human megakaryoblastic cell line (Meg‐01) and megakaryocytes isolated from eNOS‐GFP transgenic mice were studied. RT‐PCR and DNA sequencing were performed to validate eNOS presence within Meg‐01. Flow cytometry was used to identify eNOS neg and eNOS pos megakaryocytes, and NO production was measured using DAF‐FM, a cell‐permeable fluorescent probe. Further, Meg‐01 cells were treated for 48 hours with IFNγ (0 – 100 ng/ml) and IL‐10 (0 – 100 ng/ml), and eNOS as well as inducible NOS (iNOS) were measured by flow cytometry and immunoblot, respectively. Results The presence of eNOS was confirmed in Meg‐01 and eNOS‐GFP transgenic mice. Similar to human platelets, the majority of Meg‐01 cells were eNOS pos (91.5%±1.2 vs. 8.5%±1.2 eNOS neg , P<0.05 ) and NO‐producing (92.0%±3.34 vs. 8.0%±6.68 non‐producing, P < 0.05 ). Conversely, similar to mouse platelets, the majority of mouse megakaryocytes were eNOS neg (95.8%±0.7 vs. 4.2%±0.7 eNOS pos , P < 0.05 ). The proinflammatory cytokine IFNγ decreased the percent eNOS pos Meg‐01 in a concentration‐dependent manner and also increased the level of iNOS within Meg‐01. IL‐10 in a concentration‐dependent manner (10–100ng/ml) reversed IFNγ‐induced iNOS expression, but not the decrease in percent eNOS pos Meg‐01. The changes in eNOS expression within Meg‐01 were further confirmed by qPCR within the total Meg‐01 population and demonstrated that IFNγ (10 ng/ml) caused a 6.4 fold decrease of eNOS mRNA but only 2.2 fold decrease in the presence IL‐10 (100ng/ml). Conclusions eNOS‐based megakaryocyte subpopulations exist within the Meg‐01 cell line and in eNOS‐GFP transgenic mice. Similar to previously shown for platelets, major species differences exist in eNOS pos to eNOS neg ratios between human and mouse megakaryocytes. IFNγ decreases eNOS expression and percent eNOS pos Meg‐01; while increasing iNOS expression. However, high concentrations of IL‐10 attenuated this effect. Our data show that pro‐inflammatory IFN‐γ may promote growth/differentiation of megakaryocytes with decreased levels/absence of eNOS, which may lead to formation of higher numbers of more reactive eNOS neg platelets potentially leading to higher risk of a pro‐thrombotic state. Further experiments are required to confirm whether eNOS pos and eNOS neg megakaryocytes give rise to their respective platelet subtypes. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".