Staphylococcal Enterotoxins Enhance Biofilm Formation by Staphylococcus aureus in Platelet Concentrates
Bibliographic record
Abstract
Biofilm formation and slow growth by Staphylococcus aureus in platelet concentrates (PCs) cause missed detection of this bacterium during routine PC screening with automated culture systems. This heightens the chances of false-negative screening transfusions and pre-disposes transfusion patients to an elevated risk of sepsis due to secretion of staphylococcal enterotoxins (SEs) in PCs. A hybrid approach of comparative RNAseq analyses and CRISPR mutagenesis of SE genes was employed to investigate the effect of SEs in S. aureus growth and biofilm formation in PCs. RNAseq data showed no differential expression for key biofilm genes, whereas SE genes were upregulated (>0.5- to 3.6-fold change) in PCs compared to trypticase soy broth (TSB). Remarkably, growth and biofilm formation assays revealed increased growth for the S. aureus SE mutants, while their ability to form biofilms was significantly impaired (−6.8- to −2.4-fold change) in comparison to the wild type strain, in both PCs and TSB. Through the well-established superantigen mechanism of SEs, we propose three roles for SEs during biofilm development in PCs: (1) provide a scaffold for biofilm matrix, (2) mediate cell-to-cell aggregation, and (3) guarantee biofilm survival. Furthermore, SE contribution to both growth and biofilm development seems to be centrally regulated by agr via quorum sensing and by saeSR and sigB. This study reveals new roles for SEs, which enforce their relevance in ensuring PC safety for transfusion patients. It further deciphers the underlying reasons for failed S. aureus detection in PCs during screening with automated culture systems.
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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".