Antigen-Specific versus Total Immunoglobulin Synthesis: Total IgE and IgG1, but Not IgG2a Levels Predict Murine Antigen-Specific Responses
Bibliographic record
Abstract
BACKGROUND: Induction of an effective antibody (Ab) response requires delivery of multiple signals to B cells. Cross-linking of the B cell antigen receptor (BCR), signaling through CD40 and CD80/86 and cytokine signals combine to induce class switching and expression of specific isotypes. These signals are principally derived from activated, antigen (Ag)-specific T cells. In contrast, IFNgamma, the only cytokine known to induce class switch to IgG2a, can be produced systemically by activated NK or NKT cells, suggesting that Ag-nonspecific signals may also regulate IgG2a production. METHODS: Given the potential differences in regulation between IgE/IgG1 versus IgG2a, we immunized mice on day 0 with ovalbumin (OVA) in the presence of strong type-1- or type-2-immunity-inducing adjuvants and boosted mice 4 weeks later. Mice were bled during the primary immune response and after boost to assess primary and recall Ab responses. RESULTS: Regardless of strain of mice used, phenotype (type 1 versus type 2 dominated) or nature of the immune response induced (primary versus recall), strong correlations between OVA-specific and total IgE and IgG1 were demonstrated. In contrast, a consistent lack of correlation between OVA-specific and total IgG2a levels was observed in all but BALB/c mice. CONCLUSION: These data indicate that the increase in total levels of IgE/IgG1 isotypes is primarily a result of increased levels of OVA-specific Ab. In contrast, the lack of correlation between total and OVA-specific IgG2a suggests broader activation of IgG2a-producing B cells routinely occurs following exogenous Ag immunization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".