Characterization of the Human B Cell RAG-associated Gene,hBRAG, as a B Cell Receptor Signal-enhancing Glycoprotein Dimer That Associates with Phosphorylated Proteins in Resting B Cells
Bibliographic record
Abstract
Affinity-purified polyclonal antibodies against the hBRAG (human B cell RAG-associated gene) protein were generated to characterize hBRAG at the biochemical level. Immunoblotting and immunoprecipitation experiments with these antibody reagents demonstrate that this protein can be expressed in B cells as a membrane-integrated glycoprotein disulfide-linked dimer. However, both glycosylated and unglycosylated isoforms of hBRAG are detectable with these reagents. Additionally, their use in cell surface biotinylation and flow cytometry reveals subcellular hBRAG pools both at cell surface and intracellular locations. Co-immunoprecipitation experiments with hBRAG antisera detected the association of hBRAG with phosphorylated proteins in resting B cells, including the protein tyrosine kinase Hck, which is subsequently dephosphorylated upon B cell receptor (BCR) ligation. Consistent with its cell surface expression and possible link to BCR signaling, experiments in which alpha-hBRAG antibodies were used to generate early activation signals suggest a modest but specific element of tyrosine phosphorylation occurring through a putative hBRAG receptor. Additional experiments also suggest that hBRAG may be involved in positively enhancing BCR ligation-mediated early activation events. Collectively, these results are consistent with a function for hBRAG as a B cell surface signaling receptor molecule. Coupled with the earlier observation that hBRAG expression correlates with early and late B cell-specific RAG expression, we submit that hBRAG may mediate regulatory signals key to B cell development and/or regulation of B cell-specific RAG expression.
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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".