Regulation of Follicular B Cell Differentiation by the Related E26 Transformation-Specific Transcription Factors PU.1, Spi-B, and Spi-C
Bibliographic record
Abstract
Splenic B-2 cells can be divided into two major subsets: follicular (FO) and marginal zone (MZ) B cells. FO and MZ B cells are generated from immature transitional B cells. Few transcription factors have been identified that regulate FO B cell differentiation. The highly related proteins PU.1, Spi-B, and Spi-C are transcription factors of the E26-transformation-specific family and are important for B cell differentiation and function. To determine whether these proteins play a role in the differentiation of FO B cells, we performed a detailed analysis of splenic B cells in mice with inactivating mutations in the genes encoding PU.1 (Sfpi1) or Spi-B (Spib). Sfpi1(+/-) Spib(-/-) (PUB) mice had a 9-fold reduction in the frequency of CD23(+) FO B cells compared with that of wild-type mice. In contrast, PUB mice had a 2-fold increase in the frequency of MZ B cells that was confirmed by immunofluorescence staining. Expression of Spi-C in Eμ-Spi-C transgenic PUB mice partially rescued frequencies of CD23(+) B cells. Gene expression analysis, in vitro reporter assays, and chromatin immunoprecipitation experiments showed that transcription of the Fcer2a gene encoding CD23 is activated by PU.1, Spi-B, and Spi-C. These results demonstrate that FO B cell differentiation is regulated by the E26-transformation-specific transcription factors PU.1, Spi-B, and Spi-C.
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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 | 0.000 |
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".