C5L2 CRISPR KO enhances dental pulp stem cell-mediated dentinogenesis via TrkB under TNFα-induced inflammation
Bibliographic record
Abstract
Background and Objectives: Dental caries is one of the most common human pathological conditions resulting from the invasion of bacteria into the dentin. Current treatment options are limited. In many cases, endodontic therapy leads to permanent pulp tissue loss. Dentin–pulp complex regeneration involves dental pulp stem cells (DPSCs) that differentiate into odontoblast-like cells under an inflammatory context. However, limited information is available on how DPSC differentiation processes are affected under inflammatory environments. We identified the crucial role of complement C5a and its receptor C5aR in the inflammation-induced odontoblastic DPSC differentiation. Methodology: Here, we further investigated the role of a second and controversial C5a receptor, C5L2, in this process and explored the underlying mechanism. Human DPSCs were examined during 7-, 10-, and 14-day odontogenic differentiation treated with TNFα, C5L2 CRISPR, and tyrosine receptor kinase B (TrkB) antagonist [cyclotraxin-B (CTX-B)]. Results: Our data demonstrate that C5L2 CRISPR knockout (KO) enhances mineralization in TNFα-stimulated differentiating DPSCs. We further confirmed that C5L2 CRISPR KO significantly enhances dentin sialophosphoprotein (DSPP) and dentin matrix protein-1 (DMP-1) expression after 14-day odontoblastic DPSC differentiation, and treatment with CTX-B abolished the TNFα/C5L2 CRISPR KO-induced DSPP and DMP-1 increase, suggesting TrkB’s critical role in this process. Conclusion and Key applications: Our data suggest a regulatory role of C5L2 and TrkB in the TNFα-induced odontogenic DPSC differentiation. This study may provide a useful tool to understand the mechanisms of the role of inflammation in dentinogenesis that is required for successful DPSC engineering strategies.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".