Comparative virulome analysis of four Staphylococcus epidermidis strains from human skin and platelet concentrates using whole genome sequencing
Bibliographic record
Abstract
Staphylococcus epidermidis is one of the predominant bacterial contaminants in platelet concentrates (PCs), a blood component used to treat bleeding disorders. PCs are a unique niche that triggers biofilm formation, the main contributor to S. epidermidis infections. We performed whole genome sequencing of four S. epidermidis strains isolated from the skin of healthy human volunteers (AZ22 and AZ39) and contaminated PCs (ST10002 and ST11003) to unravel phylogenetic relationships and decipher virulence mechanisms compared to 25 complete S. epidermidis genomes in GenBank. AZ39 and ST11003 formed a separate unique lineage with 14.1.R1 and SE95 strains, while AZ22 formed a cluster with 1457 and ST10002 closely grouped with FDAAGOS_161. The four isolates were assigned to sequence types ST1175, ST1174, ST73, and ST16, respectively. All four genomes exhibited biofilm-associated genes ebh, ebp, sdrG, sdrH, and atl. Additionally, AZ22 had sdrF and aap, whereas ST10002 had aap and icaABCDR. Notably, AZ39 possesses truncated ebh and sdrG and harbors a toxin encoding gene. All isolates carry multiple antibiotic resistance genes conferring resistance to fosfomycin (fosB), β-lactams (blaZ) and fluoroquinolones (norA). This study revealed a unique lineage for S. epidermidis and provided insight into the genetic basis of virulence and antibiotic resistance in transfusion-associated S. epidermidis strains.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".