Transcriptional Activation of the <i>Pirb</i> Gene in B Cells by PU.1 and Runx3
Bibliographic record
Abstract
Cells in the immune system are regulated positively or negatively by sets of receptor pairs that conduct balanced, activating, or inhibitory intracellular signaling. One such receptor pair termed paired Ig-like receptor (PIR) is composed of the inhibitory PIR-B and its activating isoform, PIR-A. Upon binding to their shared ligand, MHC class I molecules, these receptors control the threshold for immune cell activation. Gene-targeting studies on PIR-B in mice revealed the importance of the inhibition mediated by the PIR-B-MHC interaction in the immune system. Recent studies also revealed the significance of the interaction of PIR-B with neurite outgrowth inhibitors, including Nogo in the CNS. The coordinated regulation by PIR-B and PIR-A is considered to be primarily dependent on their expression balance in cells. However, the mechanism underlying transcriptional control of the genes for PIR-B and PIR-A (Pirb and Pira, respectively) remains to be clarified. In this study, we identified the major cis-acting promoter segment for Pirb and Pira in B cells as the -212 to -117 region upstream from the translation initiation codon. PU.1 and Runx3 were found to bind to this Pirb promoter. Truncation of the PU.1-binding motif significantly reduced the promoter activity, whereas the influence of elimination of the Runx3 site was marginal in B lymphoma BCL1-B20 cells. Unexpectedly, PU.1, but not Runx3, knockdown reduced the levels of both the Pirb and Pira transcripts. We conclude that the major promoter of Pirb, and probably Pira as well, is activated dominantly by PU.1 and marginally by Runx3 in 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 | 0.001 |
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".