PSM Peptides of <i>Staphylococcus aureus</i> Activate the p38–CREB Pathway in Dendritic Cells, Thereby Modulating Cytokine Production and T Cell Priming
Bibliographic record
Abstract
The challenging human pathogen Staphylococcus aureus has highly efficient immune evasion strategies for causing a wide range of diseases, from skin and soft tissue to life-threatening infections. Phenol-soluble modulin (PSM) peptides are major pathogenicity factors of community-associated methicillin-resistant S. aureus strains. In previous work, we demonstrated that PSMs in combination with TLR2 ligand from S. aureus induce tolerogenic dendritic cells (DCs) characterized by the production of high amounts of IL-10, but no proinflammatory cytokines. This in turn promotes the activation of regulatory T cells while impairing Th1 response; however, the signaling pathways modulated by PSMs remain elusive. In this study, we analyzed the effects of PSMs on signaling pathway modulation downstream of TLR2. TLR2 stimulation in combination with PSMα3 led to increased and prolonged phosphorylation of NF-κB, ERK, p38, and CREB in mouse bone marrow-derived DCs compared with single TLR2 activation. Furthermore, inhibition of p38 and downstream MSK1 prevented IL-10 production, which in turn reduced the capacity of DCs to activate regulatory T cells. Interestingly, the modulation of the signaling pathways by PSMs was independent of the known receptor for PSMs, as shown by experiments with DCs lacking the formyl peptide receptor 2. Instead, PSMs penetrate the cell membrane most likely by transient pore formation. Moreover, colocalization of PSMs and p38 was observed near the plasma membrane in the cytosol, indicating a direct interaction. Thus, PSMs from S. aureus directly modulate the signaling pathway p38-CREB in DCs, thereby impairing cytokine production and in consequence T cell priming to increase the tolerance toward the pathogen.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".