IL-10 Plays Opposing Roles during <i>Staphylococcus aureus</i> Systemic and Localized Infections
Bibliographic record
Abstract
Abstract IL-10 is a potent anti-inflammatory mediator that plays a crucial role in limiting host immunopathology during bacterial infections by controlling effector T cell activation. Staphylococcus aureus has previously been shown to manipulate the IL-10 response as a mechanism of immune evasion during chronic systemic and biofilm models of infection. In the present study, we demonstrate divergent roles for IL-10 depending on the site of infection. During acute systemic S. aureus infection, IL-10 plays an important protective role and is required to prevent bacterial dissemination and host morbidity by controlling effector T cells and the associated downstream hyperactivation of inflammatory phagocytes, which are capable of host tissue damage. CD19+CD11b+CD5+ B1a regulatory cells were shown to rapidly express IL-10 in a TLR2-dependent manner in response to S. aureus, and adoptive transfer of B1a cells was protective during acute systemic infection in IL-10–deficient hosts. In contrast, during localized s.c. infection, IL-10 production plays a detrimental role by facilitating bacterial persistence via the same mechanism of controlling proinflammatory T cell responses. Our findings demonstrate that induction of IL-10 has a major influence on disease outcome during acute S. aureus infection. Too much IL-10 at one end of the scale may suppress otherwise protective T cell responses, thus facilitating persistence of the bacteria, and at the other end, too little IL-10 may tend toward fatal host-mediated pathology through excessive activation of T cells and associated phagocyte-mediated damage.
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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 | 0.001 |
| 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".