Inhibitory Receptor Paired Ig-like Receptor B Is Exploited by <i>Staphylococcus aureus</i> for Virulence
Bibliographic record
Abstract
The innate immune system has developed to acquire a wide variety of pattern-recognition receptors (PRRs) to identify potential pathogens, whereas pathogens have also developed to escape host innate immune responses. ITIM-bearing receptors are attractive targets for pathogens to attenuate immune responses against them; however, the in vivo role of the inhibitory PRRs in host-bacteria interactions remains unknown. We demonstrate in this article that Staphylococcus aureus, a major Gram-positive bacteria, exploits inhibitory PRR paired Ig-like receptor (PIR)-B on macrophages to suppress ERK1/2 and inflammasome activation, and subsequent IL-6 and IL-1β secretion. Consequently, Pirb(-/-) mice infected with S. aureus showed enhanced inflammation and more effective bacterial clearance, resulting in resistance to the sepsis. Screening of S. aureus mutants identified lipoteichoic acid (LTA) as an essential bacterial cell wall component required for binding to PIR-B and modulating inflammatory responses. In vivo, however, an LTA-deficient S. aureus mutant was highly virulent and poorly recognized by macrophages in both wild-type and Pirb(-/-) mice, demonstrating that LTA recognition by PRRs other than PIR-B mediates effective bacterial elimination. These results provide direct evidence that bacteria exploit the inhibitory receptor for virulence, and host immune system counterbalances the infection.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".