Circulating bone marrow-derived precursor cells modulate the wound healing outcome by cell transdifferentiation
Bibliographic record
Abstract
Normal wound healing process is a regulated biological response of multiple events whose common aim is to restore the integrity of injured tissues. Fibroblasts exert an important role in this process as target cells that allow either tissue remodeling through the predominant production of proteases (i.e., matrix metalloproteinases) or tissue fibrosis through the over-expression of extracellular matrix (ECM) components such as collagens. However, it remains unclear which factors activate such diversity of fibroblast responses and how this decision making process is made. Currently, there is no a well established model that integrally explains the diversity of responses from minimal to hypertropic scarring and keloids. Previous reports have demonstrated that some recruited cells can be locally transformed into fibrocytes, a pro-fibrogenic cells that stimulate resident fibroblasts to produce collagen accumulation and tissue fibrosis. However, recruited cells with anti-fibrogenic profile that can compete with and eventually reverse the local effects of fibrocytes have not been identified. How the skin maintains the cell transdifferentiation balance in normal wound healing and how this balance is modified in fibro-proliferative cutaneous disorders cannot be entirely explained by pro-fibrogenic fibrocytes without anti-fibrogenic counterparts. This doctoral thesis hypothesizes a novel mechanism in which bone marrow-derived cells recruited to the injured area modulate the expression of ECM components produced by resident fibroblasts. As a result of the tissue injury, a repertoire of systemic and local cytokines and growth factors induce epigenetic changes and cell transdifferentiation in circulating recruited cells. Thus, these locally transformed cells can acquire either pro- or anti-fibrogenic profile, and more importantly, they can induce the production of either collagens or MMPs by dermal fibroblasts. This study demonstrates that circulating stem cells and monocytes have the capacity to transdifferentiate into keratincoyte-like cells (KLCs), anti-fibrogenic cells that increase the expression MMPs by themselves and by the stimulation of dermal fibroblasts. The transdifferentiation of pro-fibrogenic fibrocytes into KLCs was also studied. In all cases the TGF-β deprivation was identified as crucial factor in the anti-fibrotic commitment of recruited cells. Finally, adipofascial groin flaps in rats were utilized as in vivo model to study the role of the tissue repair microenvironment in the cell transdifferentiation of recruited bone marrow-derived cells. The findings presented in this thesis are consistent with the existence of a "seesaw mechanism" in the regulation of MMPs/collagen production by dermal fibroblasts. Thus, during the wound-healing response, the local environment may induce epigenetic changes in recruited bone marrow-derived cells to follow either pro- or anti-fibrogenic pathways. Subsequently, these committed cells may trigger a fibro-proliferative switch on resident fibroblasts to predominantly develop either collagen accumulation with tissue fibrosis or collagen breakdown with tissues remodeling. Findings of this doctoral thesis provide new insights into the role of cell transdifferentiation and local environment not only in wound healing, but may also in other fibro-proliferative processes such as lung fibrosis, asthma, liver cirrhosis, chronic pancreatitis, and atherosclerosis, among others.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".