Connecting the dots: chromatin and alternative splicing in EMT
Bibliographic record
Abstract
Nature has devised sophisticated cellular machinery to process mRNA transcripts produced by RNA Polymerase II, removing intronic regions and connecting exons together, to produce mature RNAs. This process, known as splicing, is very closely linked to transcription. Alternative splicing, or the ability to produce different combinations of exons that are spliced together from the same genomic template, is a fundamental means of regulating protein complexity. Similar to transcription, both constitutive and alternative splicing can be regulated by chromatin and its associated factors in response to various signal transduction pathways activated by external stimuli. This regulation can vary between different cell types, and interference with these pathways can lead to changes in splicing, often resulting in aberrant cellular states and disease. The epithelial to mesenchymal transition (EMT), which leads to cancer metastasis, is influenced by alternative splicing events of chromatin remodelers and epigenetic factors such as DNA methylation and non-coding RNAs. In this review, we will discuss the role of epigenetic factors including chromatin, chromatin remodelers, DNA methyltransferases, and microRNAs in the context of alternative splicing, and discuss their potential involvement in alternative splicing during the EMT process.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".