Spi-C and PU.1 Counterregulate <i>Rag1</i> and <i>Ig</i>κ Transcription to Effect Vκ-Jκ Recombination in Small Pre-B Cells
Bibliographic record
Abstract
B cell development requires the ordered rearrangement of Ig genes encoding H and L chain proteins that assemble into BCRs or Abs capable of recognizing specific Ags. Igκ rearrangement is promoted by chromatin accessibility and by relative abundance of RAG1/2 proteins. Expression of the E26 transformation-specific transcription factor Spi-C is activated in response to dsDNA double-stranded breaks in small pre-B cells to negatively regulate pre-BCR signaling and Igκ rearrangement. However, it is not clear if Spi-C regulates Igκ rearrangement through transcription or by controlling RAG expression. In this study, we investigated the mechanism of Spi-C negative regulation of Igκ L chain rearrangement. Using an inducible expression system in a pre-B cell line, we found that Spi-C negatively regulated Igκ rearrangement, Igκ transcript levels, and Rag1 transcript levels. We found that Igκ and Rag1 transcript levels were increased in small pre-B cells from Spic-/- mice. In contrast, Igκ and Rag1 transcript levels were activated by PU.1 and were decreased in small pre-B cells from PU.1-deficient mice. Using chromatin immunoprecipitation analysis, we identified an interaction site for PU.1 and Spi-C located in the Rag1 promoter region. These results suggest that Spi-C and PU.1 counterregulate Igκ transcription and Rag1 transcription to effect Igκ recombination in small pre-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) | 0.001 | 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 | 0.000 |
| 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".