Negative regulation of Activation-Induced Cytidine Deaminase gene transcription in developing B cells by a PU.1-interacting intronic region
Bibliographic record
Abstract
Abstract Activation-induced cytidine deaminase (AID, encoded by Aicda ) plays a key role in somatic hypermutation and class switch recombination in germinal center B cells. However, off-target effects of AID are implicated in human leukemia and lymphoma. A mouse model of precursor B cell acute lymphoblastic leukemia driven by deletion of the related transcription factors PU.1 and Spi-B revealed C->T transition mutations compatible with being induced by AID. Therefore, we hypothesized that PU.1 negatively regulates Aicda during B cell development. Aicda mRNA transcript levels were increased in leukemia cells and preleukemic bone marrow pre-B cells lacking PU.1 and/or Spi-B, relative to wild type cells. Using chromatin immunoprecipitation, PU.1 was found to interact with a negative regulatory region (R2-1) within the first intron of Aicda . CRISPR-Cas9-induced mutagenesis of R2-1 in cultured pre-B cells resulted in upregulation of Aicda in response to lipopolysaccharide stimulation. Mutation of the PU.1 interaction site and neighboring sequences resulted in reduced repressive ability of R2-1 in transient transfection analysis followed by luciferase assays. These results show that a PU.1-interacting intronic region negatively regulates Aicda transcription in developing B cells.
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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 | 0.001 |
| 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".