PLGF-1 contained in normal wound myofibroblast-derived microvesicles stimulated collagen production by dermal fibroblasts
Bibliographic record
Abstract
During the last stages of wound healing, myofibroblasts differentiate mainly from fibroblasts. Myofibroblasts from normal skin wounds (Wmyo) can communicate with its surrounding using secreted factors. They also have the capacity to produce microvesicles (MVs), a type of extracellular vesicles, as mediators of intercellular communication. MVs cargo are potentially capable of regulating the behavior of targeted cells and tissues. The aim of this study is to evaluate the effect of Wmyo-derived MVs on dermal fibroblasts and to determine the responsible signaling molecule. Microvesicles were obtained from culture media of myofibroblasts and characterized using protein quantification, dynamic light scattering and transmission electron microscopy. Uptake of fluorescent MVs in fibroblasts was assessed by flow cytometry. Cytokines concentrations were quantified in MV samples by a multiplex ELISA. Different concentration of MVs or a selected cytokine were used as treatments over fibroblasts culture for 5 days. Following the treatments, parameters linked to the extracellular matrix were studied. Lastly, the selected cytokine was neutralized within MVs before evaluating collagen production. We showed that Wmyo derived-MVs were internalized by dermal fibroblasts. Cytokine array analysis revealed that a large amount of placental growth factor 1 (PLGF-1) (0.88 ± 0.63 pg/μg proteins in MVs) could be detected in MVs samples. Cutaneous fibroblasts treated with MVs or PLGF-1 showed significantly stimulated procollagen I level production (Fold change of 1.80 ± 0.18 and 2.07 ± 0.18, respectively). Finally, the neutralization of PLGF-1 in MVs significantly inhibited the production of procollagen I by fibroblasts. Our study shows that Wmyo derived-MVs are involved in intercellular communication by stimulating collagen production by fibroblasts during wound healing. This effect is possibly attained through PLGF-1 signalling. These findings represent a promising opportunity to gain insight into how MVs and Wmyo may mediate the healing of a skin wound.
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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".