β-actin regulates a heterochromatin landscape essential for optimal induction of neuronal programs during direct reprograming
Bibliographic record
Abstract
During neuronal development, β-actin serves an important role in growth cone mediated axon guidance. Consistent with this notion, in vivo ablation of the β-actin gene leads to abnormalities in the nervous system. However, whether β-actin is involved in the regulation of neuronal gene programs is not known. In this study, we directly reprogramed β-actin+/+ WT, β-actin+/- HET and β-actin-/- KO mouse embryonic fibroblast (MEFs) into chemically induced neurons (CiNeurons). Using RNA-seq analysis, we profiled the transcriptome changes among the CiNeurons. We discovered that induction of neuronal gene programs was impaired in KO CiNeurons in comparison to WT ones, whereas HET CiNeurons showed an intermediate levels of induction. ChIP-seq analysis of heterochromatin markers demonstrated that the impaired expression of neuronal gene programs correlated with the elevated H3K9 and H3K27 methylation levels at gene loci in β-actin deficient MEFs, which is linked to the loss of chromatin association of the BAF complex ATPase subunit Brg1. Together, our study shows that heterochromatin alteration in β-actin null MEFs impedes the induction of neuronal gene programs during direct reprograming. These findings are in line with the notion that H3K9Me3-based heterochromatin forms a major epigenetic barrier during cell fate change.
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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".