Genome-wide CIITA-binding profile identifies sequence preferences that dictate function versus recruitment
Bibliographic record
Abstract
The class II transactivator (CIITA) is essential for the expression of major histocompatibility complex class II (MHC-II) genes; however, the role of CIITA in gene regulation outside of MHC-II biology is not fully understood. To comprehensively map CIITA-bound loci, ChIP-seq was performed in the human B lymphoblastoma cell line Raji. CIITA bound 480 sites, and was significantly enriched at active promoters and enhancers. The complexity of CIITA transcriptional regulation of target genes was analyzed using a combination of CIITA-null cells, including a novel cell line created using CRISPR/Cas9 tools. MHC-II genes and a few novel genes were regulated by CIITA; however, most other genes demonstrated either diminished or no changes in the absence of CIITA. Nearly all CIITA-bound sites were within regions containing accessible chromatin, and CIITA's presence at these sites was associated with increased histone H3K27 acetylation, suggesting that CIITA's role at these non-regulated loci may be to poise the region for subsequent regulation. Computational genome-wide modeling of the CIITA bound XY box motifs provided constraints for sequences associated with CIITA-mediated gene regulation versus binding. These data therefore define the CIITA regulome in B cells and establish sequence specificities that predict activity for an essential regulator of the adaptive immune response.
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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) | 0.001 | 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".