JNK Signaling as a Key Modulator of Soft Connective Tissue Physiology, Pathology, and Healing
Bibliographic record
Abstract
In healthy individuals, the healing of soft tissues such as skin after pathological insult or post injury follows a relatively predictable and defined series of cell and molecular processes to restore tissue architecture and function(s). Healing progresses through the phases of hemostasis, inflammation, proliferation, remodeling, and concomitant with re-epithelialization restores barrier function. Soft tissue healing is achieved through the spatiotemporal interplay of multiple different cell types including neutrophils, monocytes/macrophages, fibroblasts, endothelial cells/pericytes, and keratinocytes. Expressed in most cell types, c-Jun N-terminal kinases (JNK) are signaling molecules associated with the regulation of several cellular processes involved in soft tissue wound healing and in response to cellular stress. A member of the mitogen-activated protein kinase family (MAPK), JNKs have been implicated in the regulation of inflammatory cell phenotype, as well as fibroblast, stem/progenitor cell, and epithelial cell biology. In this review, we discuss our understanding of JNKs in the regulation of cell behaviors related to tissue injury, pathology, and wound healing of soft tissues. Using models as diverse as Drosophila, mice, rats, as well as human tissues, research is now defining important, but sometimes conflicting roles for JNKs in the regulation of multiple molecular processes in multiple different cell types central to wound healing processes. In this review, we focus specifically on the role of JNKs in the regulation of cell behavior in the healing of skin, cornea, tendon, gingiva, and dental pulp tissues. We conclude that while parallels can be drawn between some JNK activities and the control of cell behavior in healing, the roles of JNK can also be very specific modes of action depending on the tissue and the phase of healing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".