Ezrin promotes antigen receptor diversity during B cell development by supporting immunoglobulin heavy chain variable gene recombination
Bibliographic record
Abstract
Abstract Genome-level rearrangements of immunoglobulin genes during B cell development are critical for generation of a diverse repertoire of B cell antigen receptors (BCRs) that bind to a multitude of foreign antigens and some self-antigens. Bone marrow B cell development involves a variety of cell-cell interactions, cell migration and receptor signaling that likely benefit from the activity of membrane-cytoskeletal reorganizing proteins. However, the specific contribution of such proteins towards BCR repertoire diversification is poorly understood. Ezrin is a membrane-cytoskeletal linker protein that regulates mature B cell activation through spatial organization of the BCR. We employed next generation sequencing to investigate if Ezrin plays a role in immunoglobulin heavy chain rearrangements and generation of BCR diversity in developing bone marrow B cells. BCR repertoire development occurred stochastically in B cell progenitors from both control and B cell conditional Ezrin-deficient mice. However, the loss of Ezrin resulted in fewer unique CDR3s in the BCRs and reduced Shannon entropy. Ezrin-deficient pro-, pre- and immature B cells utilized similar number of joining (J) genes but significantly fewer variable (V) genes, thereby decreasing V-J combinatorial diversity. V-J junctional diversity, measured by CDR3 length, nucleotide additions and deletions, was also altered in Ezrin-deficient pro-, pre- and immature B cells. Mechanistically, Ezrin-deficient bone marrow B cells showed a marked decrease in RAG1 gene expression, indicating a less efficient DNA recombination machinery. Overall, our results demonstrate a novel role for Ezrin in shaping the BCR repertoire through combinatorial and junctional diversification. Supported by grants from NIH (R01 AR067705) to N.G.
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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".