Role of PTPα in the Destruction of Periodontal Connective Tissues
Bibliographic record
Abstract
IL-1β contributes to connective tissue destruction in part by up-regulating stromelysin-1 (MMP-3), which in fibroblasts is a focal adhesion-dependent process. Protein tyrosine phosphatase-α (PTPα) is enriched in and regulates the formation of focal adhesions, but the role of PTPα in connective tissue destruction is not defined. We first examined destruction of periodontal connective tissues in adult PTPα(+/+) and PTPα(-/-) mice subjected to ligature-induced periodontitis, which increases the levels of multiple cytokines, including IL-1β. Three weeks after ligation, maxillae were processed for morphometry, micro-computed tomography and histomorphometry. Compared with unligated controls, there was ∼1.5-3 times greater bone loss as well as 3-fold reduction of the thickness of the gingival lamina propria and 20-fold reduction of the amount of collagen fibers in WT than PTPα(-/-) mice. Immunohistochemical staining of periodontal tissue showed elevated expression of MMP-3 at ligated sites. Second, to examine mechanisms by which PTPα may regulate matrix degradation, human MMP arrays were used to screen conditioned media from human gingival fibroblasts treated with vehicle, IL-1β or TNFα. Although MMP-3 was upregulated by both cytokines, only IL-1β stimulated ERK activation in human gingival fibroblasts plated on fibronectin. TIRF microscopy and immunoblotting analyses of cells depleted of PTPα activity with the use of various mutated constructs or with siRNA or PTPα(KO) and matched wild type fibroblasts were plated on fibronectin to enable focal adhesion formation and stimulated with IL-1β. These data showed that the catalytic and adaptor functions of PTPα were required for IL-1β-induced focal adhesion formation, ERK activation and MMP-3 release. We conclude that inflammation-induced connective tissue degradation involving fibroblasts requires functionally active PTPα and in part is mediated by IL-1β signaling through focal adhesions.
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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".