Roles for CD22 in regulating the germinal center 4572
Bibliographic record
Abstract
Abstract Description Humoral immunity generates protective antibodies. The selection of germinal center (GC) B cells is key to generating high-affinity antibodies in the GC. Interactions between GC B cells and follicular helper T (TfH) cells are crucial for guiding B cell differentiation and determining their fate. The regulation of the strength of GC B cell-TfH cell requires further exploration to clarify how these signals drive the formation of memory B cells and plasma cells. CD22, an inhibitory receptor expressed primarily on B cells, plays a significant role in modulating B cell activation. We have established an intrinsic role of CD22 on GC B cells, but ongoing work in our laboratory suggests an extrinsic role of CD22 on GC B cells in regulating the GC. We hypothesize that CD22 modulates B-T cell interactions within the GC through interactions between CD22 and CD22 ligands (CD22L) on TfH cells. To test this hypothesis, we developed models to manipulate the expression of CD22 on GC B cells, CD22L on GC B cells, or CD22L on TfH cells. We will present numerous lines of evidence demonstrating that B-T cell interactions are modulated by CD22-CD22L interaction, which impacts GC output and, in particular, memory B cell formation. Moreover, our results also suggest that natural heterogeneity within CD22L levels on GC B cells regulates the ability of CD22 to bind in trans to TfH cells and, hence, the strength of B-T cells interactions within the GC. Funding Sources Supported by the Canadian Institutes for Health Research (CIHR) Topic Categories Immune Response Regulation: Cellular Mechanisms (IRC)
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".