Analysis of long-range interactions in primary human cells identifies cooperative<i>CFTR</i>regulatory elements
Bibliographic record
Abstract
A mechanism by which control DNA elements regulate transcription over large linear genomic distances is by achieving close physical proximity with genes, and looping of the intervening chromatin paths. Alterations of such regulatory 'chromatin looping' systems are likely to play a critical role in human genetic disease at large. Here, we studied the spatial organization of a ≈790 kb locus encompassing the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Dysregulation of CFTR is responsible for cystic fibrosis, which is the most common lethal genetic disorder in Caucasian populations. CFTR is a relatively large gene of 189 kb with a rather complex tissue-specific and temporal expression profile. We used chromatin conformation at the CFTR locus to identify new DNA sequences that regulate its transcription. By comparing 5C chromatin interaction maps of the CFTR locus in expressing and non-expressing human primary cells, we identified several new contact points between the CFTR promoter and its surroundings, in addition to regions featuring previously described regulatory elements. We demonstrate that two of these novel interacting regions cooperatively increase CFTR expression, and suggest that the new enhancer elements located on either side of the gene are brought together through chromatin looping via CTCF.
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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) | 0.002 | 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".