<i>Staphylococcus aureus</i> Downregulates IP-10 Production and Prevents Th1 Cell Recruitment
Bibliographic record
Abstract
Abstract Staphylococcal superantigens cause toxic shock syndrome, which is characterized by massive T cell activation and a predominant Th1 profile of cytokine production. However, superantigen-producing Staphylococcus aureus strains are often part of the human nasal microbiome, and this carrier state has often been associated with some type 2 immune responses such as chronic sinusitis with polyps and atopic dermatitis. We have previously reported that the S. aureus cell wall downregulates the human T cell response to superantigens through a TLR2-dependent, IL-10–mediated mechanism. In this study, we show that S. aureus also regulates the profile of superantigen-induced T cell recruitment. The staphylococcal superantigen SEE induced the production of Th1 cell–recruiting chemokines, including IP-10, through an IFN-γ–dependent mechanism. Such an induction was suppressed by the concomitant presence of S. aureus. The downregulation of IP-10 by S. aureus was mediated by components of its cell wall, but was not due to peptidoglycan-induced IL-10 production. Instead, S. aureus triggered activation of MAPKs p38 and ERK, as well as inhibition of STAT1 signaling in monocytes, altogether contributing to the downregulation of IP-10 and other Th1 cell–recruiting chemokines (e.g., CXCL9 and CXCL11). These effects translated into inhibition of superantigen-induced Th1 cell recruitment. Taken together, our data may explain why colonization of superantigen-producing S. aureus can induce, under some circumstances, mucosal type 2 immune responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".