PNAG exopolysaccharide eradication gives neutrophils access to <i>Staphylococcus aureus</i> biofilm infections
Bibliographic record
Abstract
Abstract Staphylococcus aureus ( S. aureus ) can form biofilms on biotic or abiotic surfaces making biofilm infections a relevant clinical problem. Biofilms can evade immunity and resist antimicrobial treatment, and as such an understanding of biofilm infection in vivo is necessary to inform new therapeutics. Using a mouse model of S. aureus foreign-body skin infection and intravital microscopy, we imaged the interactions between neutrophils and S. aureus biofilm. We observed that neutrophils were separated from bacteria by a biofilm matrix composed of the polysaccharide intercellular adhesin (PIA), an exopolysaccharide chemically designated as poly-N-acetylglucosamine (PNAG) that is produced by enzymatic machinery encoded by the icaADBC operon. Infection with icaADBC-deficient S. aureus strains led to increased neutrophil infiltration and access to bacteria and resulted in full clearance of infection by 7 days. Moreover, enzymatic treatment with PgaB, which hydrolyzes partially deacetylated PNAG, was shown to disaggregate the biofilm giving neutrophils access into the infection site to improve clearance. Taken together, our results show that PNAG shelters S. aureus biofilms from innate host defense, and that targeting the biofilm matrix with glycoside hydrolases is a promising therapeutic avenue to treat S. aureus biofilm infections. Author Summary Staphylococcus aureus is a major cause of biofilm-associated infections, which pose a major threat to human health. A biofilm is difficult to treat since bacteria are protected from antimicrobials within an extracellular matrix. This study is the first to show that the PgaB enzyme, a glycoside hydrolase, can disrupt the S. aureus biofilm matrix in vivo. Disrupting the biofilm matrix with PgaB gives neutrophils access to bacteria for elimination.
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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 | 0.001 |
| 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".