Seasonal H1N1 Influenza Virus Infection Induces Cross-Protective Pandemic H1N1 Virus Immunity through a CD8-Independent, B Cell-Dependent Mechanism
Bibliographic record
Abstract
During the 2009 H1N1 influenza virus pandemic (pdmH1N1) outbreak, it was found that most individuals lacked antibodies against the new pdmH1N1 virus, and only the elderly showed anti-hemagglutinin (anti-HA) antibodies that were cross-reactive with the new strains. Different studies have demonstrated that prior contact with the virus can confer protection against strains with some degree of dissimilarity; however, this has not been sufficiently explored within the context of a pdmH1N1 virus infection. In this study, we have found that a first infection with the A/Brisbane/59/2007 virus strain confers heterologous protection in ferrets and mice against a subsequent pdmH1N1 (A/Mexico/4108/2009) virus infection through a cross-reactive but non-neutralizing antibody mechanism. Heterologous immunity is abrogated in B cell-deficient mice but maintained in CD8(-/-) and perforin-1(-/-) mice. We identified cross-reactive antibodies from A/Brisbane/59/2007 sera that recognize non-HA epitopes in pdmH1N1 virus. Passive serum transfer showed that cross-reactive sH1N1-induced antibodies conferred protection in naive recipient mice during pdmH1N1 virus challenge. The presence or absence of anti-HA antibodies, therefore, is not the sole indicator of the effectiveness of protective cross-reactive antibody immunity. Measurement of additional antibody repertoires targeting the non-HA antigens of influenza virus should be taken into consideration in assessing protection and immunization strategies. We propose that preexisting cross-protective non-HA antibody immunity may have had an overall protective effect during the 2009 pdmH1N1 outbreak, thereby reducing disease severity in human infections.
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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 | 0.001 |
| 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".