Transcriptional programs of cell identity and p53-induced stress responses are associated with distinctive features of spatial genome organization
Bibliographic record
Abstract
To date, most studies explored changes in 3D-genome organization between different tissues or during differentiation, which involve massive reprogramming of transcriptional programs. Much fewer studies examined alterations in genome organization in response to cellular stress, which involves less pervasive transcriptional modulation. Here, we examined associations between spatial chromatin organization and gene expression in two different biological contexts: transcriptional programs determining cell identity and transcriptional responses to stress, using p53 activation as a model. We selected 10 cell lines of diverse tissues, and in each performed micro-C, RNA-seq, and p53 ChIP-seq, before and after p53 induction. In the comparison between cell types, we delineated marked correlations between gene expression and spatial genome organization and identified hundreds of active enhancer-promoter loops associated with the expression of cell-type marker genes. In contrast, within each cell type, no such links were observed for expression changes induced by p53 activation, even for enhancers and promoters activated by p53 binding. Our analysis points to a fundamental difference between chromatin interactions that define cell identity and those that are established in response to cellular stress. Our results on p53-induced transcriptional responses support the recently proposed TF activity gradient model, which speculated a contact-independent mechanism for enhancer-promoter communication.
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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 | 0.001 | 0.001 |
| 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".