Protein SIC Secreted from Streptococcus pyogenes Forms Complexes with Extracellular Histones That Boost Cytokine Production
Bibliographic record
Abstract
Innate immunity relies on an effective recognition of the pathogenic microorganism as well as on endogenous danger signals. While bacteria in concert with their secreted virulence factors can cause a number of inflammatory reactions, danger signals released at the site of infection may in addition determine the amplitude of such responses, and influence the outcome of the disease. Here we report that protein SIC, Streptococcal Inhibitor of Complement, an abundant secreted protein from Streptococcus pyogenes, binds to extracellular histones, a group of danger signals released during necrotizing tissue damage. This interaction leads to the formation of large aggregates in vitro. Extracellular histones and SIC are abundantly expressed and seen co-localized in biopsies from patients with necrotizing soft tissue infections caused by S. pyogenes. In addition, binding of SIC to histones neutralized their antimicrobial activity. Likewise, the ability of histones to induce hemolysis was inhibited in the presence of SIC. However, when added to whole blood, SIC was not able to block the pro-inflammatory effect of histones. Instead SIC boosted the histone-triggered release of a broad range of cytokines and chemokines, including IL-6, TNF-α, IL-8, IL-1β, IL-1ra, G-CSF and IFN-γ. These results demonstrate that the interaction between SIC and histones have multiple effects on the host response to S. pyogenes infection.
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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) | 0.001 | 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".