Histone marks are drivers of the splicing changes necessary for an epithelial-to-mesenchymal transition
Bibliographic record
Abstract
Abstract Cell differentiation and reprogramming depend on coordinated changes in specific alternative splicing events. How these cell type-specific splicing patterns are dynamically modified in response to a stimulus remains elusive. Taking advantage of the epithelial-to-mesenchymal transition (EMT), a reversible cell reprogramming intimately involved in cancer cell invasiveness and metastasis, we found a strong correlation between changes in the alternative splicing of key exons for EMT, such as at the Fgfr2 and Cnntd1 loci, and changes in the enrichment levels of specific histone modifications, namely H3K27ac and H3K27me3. Localised CRISPR epigenome editing of these exon-specific histone marks was sufficient to induce changes in splicing capable of recapitulating important aspects of EMT, such as a motile and invasive cell phenotype. Whereas, impairment of the changes in H3K27 marks observed during EMT, using histone deacetylase inhibitors, repressed inclusion of the mesenchymal isoform despite an EMT induction, supporting a driving effect for H3K27 modifications in establishing the new cell type-specific splicing patterns necessary for EMT cell reprogramming. Finally, H3K27 marks were shown to impact splicing by modulating recruitment of the splicing factor PTB to its RNA binding sites, suggesting a direct link between chromatin modifications and the splicing machinery. Taken together, these results prove the causal role of H3K27 marks in driving the dynamic splicing changes necessary for induction of important aspects of EMT. They also prove that chromatin-mediated splicing changes are sufficient to impact the cell’s phenotype, which expands the cell’s toolkit to adapt and respond to diverse stimuli, such as EMT induction.
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) | 0.002 | 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".