Toxin expression during Staphylococcus aureus infection imprints host immunity to inhibit vaccine efficacy
Bibliographic record
Abstract
Abstract Staphylococcus aureus infections are a major public health issue, and a vaccine is urgently needed. Despite a considerable promise in preclinical models, all vaccines tested thus far have failed to protect humans against S. aureus . Unlike laboratory mice, humans are exposed to S. aureus throughout life. In the current study, we hypothesized that prior exposure to S. aureus “imprints” the immune response to inhibit vaccine-mediated protection. We established a mouse model in which S. aureus skin and soft tissue infection (SSTI) is followed by vaccination and secondary SSTI. Unlike naïve mice, S. aureus -sensitized mice were incompletely protected against secondary SSTI by vaccination with the inactivated α-hemolysin (Hla) mutant Hla H35L . Inhibition of protection was specific for the Hla H35L vaccine and required hla expression during primary SSTI. Surprisingly, inhibition occurred at the level of vaccine-elicited effector T cells; hla expression during primary infection limited the expansion of T cells and dendritic cells and impaired vaccine-specific T cell responses. Importantly, the T cell-stimulating adjuvant CAF01 rescued inhibition and restored vaccine-mediated protection. Together, these findings identify a potential mechanism for the failure of translation of promising S. aureus vaccines from mouse models to clinical practice and suggest a path forward to prevent these devastating 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".