Microvesicles derived from dermal myofibroblasts modify the integrity of the blood and lymphatic barriers using distinct endocytosis pathways
Bibliographic record
Abstract
Microvesicles (MVs) are a subtype of extracellular vesicles that can transfer biological information from their producer cells to target cells. This communication can in turn affect both normal and pathological processes. Mounting evidence has revealed that dermal wound myofibroblasts (Wmyo) produce MVs, which can transfer biomolecules impacting receptor cells such as human dermal microvascular endothelial cells (HDMECs). While the effects of MVs on HDMECs are generally well described in the literature, little is known about the transport of MVs across the HDMEC barrier, and their potential effect on the barrier integrity remains unknown. Here, we investigated these roles of Wmyo-derived MVs on two sub-populations of HDMECs, blood endothelial cells (BECs) and lymphatic endothelial cells (LECs). Using an in vitro model to mimic the endothelial barrier, we showed that MVs crossed the LEC barrier but not the BEC barrier. In addition, we demonstrated that MVs were able to influence the cell-cell junctions of HDMECs. Specifically, we observed that after internalization via the predominantly caveolin-dependent pathway, MVs induced the opening of junctions in BECs. Conversely, in LECs, MVs mainly use the macropinocytosis pathway and induce closure of these junctions. Moreover, proteins in the MV membrane were responsible for this effect, but not specifically those belonging to the VEGF family. Finally, we found that once the LEC barrier permeability was reduced by MV stimuli, MVs ceased to cross the barrier. Conversely, when the BEC barrier was rendered permeable following stimulation with MVs, they were subsequently able to cross the barrier via the paracellular pathway. Taken together, these results suggest that the study of Wmyo-derived MVs offers valuable insights into their interaction with the HDMEC barrier in the context of wound healing. They highlight the potential significance of these MVs in the overall process.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".