A simple mechanistic explanation for original antigenic sin and its alleviation by adjuvants
Bibliographic record
Abstract
A large number of published studies have shown that adaptive immunity to a particular antigen, including pathogen-derived, can be boosted by another, cross-reacting antigen while inducing suboptimal immunity to the latter. Although this phenomenon, called original antigenic sin (OAS), was first reported approximately 70 years ago (Francis et al. 1947 Am. J. Public Health 37, 1013-1016 (doi:10.2105/AJPH.37.8.1013)), its underlying biological mechanisms are still inadequately understood (Kim et al. Proc. Natl Acad. Sci. USA 109, 13 751-13 756 (doi:10.1073/pnas.0912458109)). Here, focusing on the humoral aspects of adaptive immunity, I propose a simple and testable mechanism: that OAS occurs when T regulatory cells induced by the first antigen decrease the dose of the second antigen that is loaded by dendritic cells and available to activate naive lymphocytes. I use both a parsimonious mathematical model and experimental data to confirm the deductive validity of this proposal. This model also explains the puzzling experimental observation that administering certain dendritic cell-activating adjuvants during antigen exposure alleviates OAS. Specifically, the model predicts that such adjuvants will attenuate T regulatory suppression of naive lymphocyte activation. Together, these results suggest additional strategies for redeeming adaptive immunity from the destructive consequences of antigenic 'sin'.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".