5-azacytidine affects TET2 and histone transcription and reshapes morphology of human skin fibroblasts
Bibliographic record
Abstract
Phenotype definition is controlled by epigenetic regulations that allow cells to acquire their differentiated state. The process is reversible and attractive for therapeutic intervention and for the reactivation of hypermethylated pluripotency genes that facilitate transition to a higher plasticity state. We report the results obtained in human fibroblasts exposed to the epigenetic modifier 5-azacytidine (5-aza-CR), which increases adult cell plasticity and facilitates phenotype change. Although many aspects controlling its demethylating action have been widely investigated, the mechanisms underlying 5-aza-CR effects on cell plasticity are still poorly understood. Our experiments confirm decreased global methylation, but also demonstrate an increase of both Formylcytosine (5fC) and 5-Carboxylcytosine (5caC), indicating 5-aza-CR ability to activate a direct and active demethylating effect, possibly mediated via TET2 protein increased transcription. This was accompanied by transient upregulation of pluripotency markers and incremented histone expression, paralleled by changes in histone acetylating enzymes. Furthermore, adult fibroblasts reshaped into undifferentiated progenitor-like phenotype, with a sparse and open chromatin structure. Our findings indicate that 5-aza-CR induced somatic cell transition to a higher plasticity state is activated by multiple regulations that accompany the demethylating effect exerted by the modifier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".