The Zinc Finger Transcription Factor Zfp60 Is a Negative Regulator of Cartilage Differentiation
Bibliographic record
Abstract
The differentiation of many mesenchyme-derived cells, including cells that form bone and cartilage, is regulated at the level of gene transcription, but many of the factors involved in this regulation remain to be identified. In this study, a modified RNA fingerprinting technique was used to identify the KRAB domain zinc finger transcription factor Zfp60 as a candidate regulator of cell differentiation in mouse calvaria primary cultures. The highest expression of Zfp60 mRNA in vivo was found between embryonic day 11 (E11) and E15 during mouse embryonic development, coinciding with stages of active organ formation. The expression of Zfp60 mRNA and protein was analyzed further in mouse embryos during skeletal development. The most prominent expression was found in prehypertrophic chondrocytes, where it coincides with the expression of key regulators of chondrocyte maturation, Indian hedgehog (Ihh), and the parathyroid hormone-related peptide (PTHrP) receptor. Zfp60 mRNA was also found transiently expressed during chondrogenesis of C1 cells in vitro, preceding collagen type X expression and cellular hypertrophy. Overexpression of Zfp60 inhibited cartilage differentiation in the chondrogenic ATDC5 cell line. These results suggest a role for Zfp60 as a negative regulator of gene transcription, specifically during the development and/or differentiation of chondrocytes.
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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 | 0.001 |
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".