A fibronectin-binding protein (FbpA) of <i>Weissella cibaria</i> inhibits colonization and infection of <i>Staphylococcus aureus</i> in mammary glands
Bibliographic record
Abstract
Staphylococcus aureus (S. aureus) is a frequent cause of infections in both humans and animals. Probiotics are known to inhibit colonization of pathogens on host tissues. However, mechanisms for the inhibition are still elusive due to complex host-microbe and microbe-microbe interactions. Here, we show that reduced abilities of S. aureus to infect mammary glands in the presence of Weissella cibaria (W. cibaria) were correlated with its poor adherence to mammary epithelial cells. Such inhibition by W. cibaria isolates was at least partially attributed to a fibronectin-binding protein (FbpA) on this lactic acid bacterium. Three W. cibaria isolates containing fbpA had higher inhibitory abilities than other three LAB isolates without the gene. The fbpA-deficient mutant of W. cibaria isolate LW1, LW1ΔfbpA, lost the inhibitory activity to reduce the adhesion of S. aureus to mammary epithelial cells and was less able to reduce the colonization of S. aureus in mammary glands. Expression of FbpA to the surface of LW1ΔfbpA reversed its inhibitory activities. Furthermore, addition of purified FbpA inhibited S. aureus biofilm formation. Our results suggest that W. cibaria FbpA hinders S. aureus colonization and infection through interfering with the S. aureus invasion pathway mediated by fibronectin-binding proteins and inhibiting biofilm formation of S. aureus.
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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) | 0.001 | 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".