Bipartite regulation of cellular communication network factor 2 and fibroblast growth factor 1 genes by fibroblast growth factor 1 through histone deacetylase 1 and fork head box protein A1
Bibliographic record
Abstract
Fibroblast growth factor 1 (FGF-1) is the first FGF family member, and it induces proliferation of fibroblasts and other types of the cells. However, recent studies are uncovering unexpected functions of this molecule. Our previous study redefined this growth factor as a catabolic molecule produced in cartilage upon metabolic insult. Indeed, FGF-1 was found to repress the gene expression of cellular communication network factor 2 (CCN2), which protects and regenerates cartilage, amplifying its own production through positive feedback regulation. In the present study, we investigated the molecular mechanism of this bipartite CCN2 repression and FGF1 activation by FGF-1 in chondrocytes. Repression of CCN2 and induction of FGF1 in human chondrocytic cells were both partly abolished by valproic acid, an inhibitor of histone deacetylase 1 (HDAC1), indicating the involvement of chromatin remodeling by histone acetylation in this system. In contrast, RNA degradation analysis suggested no contribution of post-transcriptional regulation of the mRNA stability to the effects conferred by FGF-1. Suspecting a regulation by a specific transcription factor, we next sought a candidate in silico from a large dataset. As a result, we found fork head box protein A1 (FOXA1) as the transcription factor that bound to both CCN2 and FGF1 loci. Functional analysis demonstrated that FOXA1 silencing significantly attenuated the CCN2 repression and FGF1 induction caused by FGF1. These findings collectively indicate that the bipartite regulation by FGF-1 is enabled by the combination of chromatin remodeling by HDACs and transcriptional modulation by FOXA1 with unknown transcriptional coactivators of opposite functionalities.
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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".