New Sequence of Strategies to Define Gene Transcriptional Territories in Lymphocytes: The Perforin Example (35.32)
Bibliographic record
Abstract
Abstract Distal regulatory regions control transcription by promoter interactions that are limited to a territory to avoid non-target genes. Few loci are defined with the scrutiny needed to fully understand how a territory is established, maintained and fenced off. Here we approach the human perforin gene (PRF1). First, we transferred an epigenetically silenced PRF1 within its chromosome into a rodent CTL cell line. Afterward PRF1 expression was turned on equivalently to the endogenous mouse gene without perturbing bordering genes. Ultra long-range DHS assays showed epigenetic remodeling during chromosome transfer and delineated a 150 kb territory with 16 DHS sites for PRF1. When a corresponding BAC was introduced into the CTL, transgene expression proceeded with locus control region activity dependent on 4 distal DHS sites that also protected PRF1 from transcriptional perturbations by bordering genes. Finally, differentiation of transgenic ES cells showed the area contained all regulatory domains required for developmentally appropriate PRF1 expression during in vitro lymphopoiesis. Thus, we defined a new sequence of strategies that reliably characterizes autonomous transcriptional territories in lymphocytes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".