Osteogenesis requires FAK‐dependent collagen synthesis by fibroblasts and osteoblasts
Bibliographic record
Abstract
ABSTRACT Focal adhesion kinase (FAK) is critical in adhesion‐dependent signaling, but its role in osteogenesis in vivo is ill defined. We deleted Fak in fibroblasts and osteoblasts in Floxed‐Fak mice bred with those expressing Crerecombinase driven by 3.6‐kb α1(I)‐collagen promoter. Compared with wild‐type (WT), conditional FAK‐knockout (CFKO) mice were shorter (2‐fold; P < 0.0001) and had crooked, shorter tails (50%; P < 0.0001). Microcomputed tomography analysis showed reduced bone volume (4‐fold in tails; P < 0.0001; 2‐fold in mandibles; P < 0.0001), whereas bone surface area/bone volume increased (3‐fold in tails; P < 0.0001; 2.5‐fold in mandibles; P < 0.001). Collagen density and fiber alignment in periodontal ligament were reduced by 4‐fold ( P < 0.0001) and 30% ( P < 0.05), respectively, in CFKO mice. In cultured CFKO osteoblasts, mineralization at d 7 and mineralizing colony‐forming units at d 21 were 30% ( P < 0.0001) and >3‐fold less than WT, respectively. Disruptions of FAK function in osteoblasts by conditional knockout, siRNA‐knockdown, or FAK inhibitor reduced mRNA and protein expression of Runx2 (>30%), Osterix (>25%), and collagen‐1 (2‐fold). Collagen synthesis was abrogated in WT osteoblasts with Runx2 knockdown and in Fak ‐null fibroblasts transfected with an FAK kinase domain mutant or a kinase‐impaired mutant (Y397F). These data indicate that FAK regulates osteogenesis through transcription factors that regulate collagen synthesis.—Rajshankar, D., Wang, Y., McCulloch, C. A. Osteogenesis requires FAK‐dependent collagen synthesis by fibroblasts and osteoblasts. FASEB J. 31, 937–953 (2017). www.fasebj.org
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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".