<i>Staphylococcus epidermidis</i>undergoes global changes in gene expression during biofilm maturation in platelet concentrates
Bibliographic record
Abstract
BACKGROUND: Staphylococcus epidermidis forms surface-attached aggregates (biofilms) when grown in platelet concentrates (PCs). Comparative transcriptome analyses were undertaken to investigate differential gene expression of S. epidermidis biofilms grown in PCs. STUDY DESIGN AND METHODS: Two S. epidermidis strains isolated from human skin (AZ22 and AZ39) and one strain isolated from contaminated PCs (ST02) were grown in glucose-supplemented Trypticase Soy Broth (TSBg) and PCs. RNA was extracted and sequenced using Illumina HiSeq. Differential expression analysis was done using DESeq, and significantly differentially expressed genes (DEGs) were selected. DEGs were subjected to Kyoto encyclopedia of genes and genomes and Gene Ontology analyses. Differential gene expression was validated with quantitative reverse transcription-PCR. RESULTS: A total of 436, 442, and 384 genes were expressed in AZ22, AZ39, and ST02, respectively. DEG analysis showed that 170, 172, and 117 genes were upregulated in PCs in comparison to TSBg, whereas 120, 135, and 89 genes were downregulated (p < .05) in mature biofilms of AZ22, AZ39, and ST02, respectively. Twenty-seven DEGs were shared by all three strains. While 76 DEGs were shared by AZ22 and AZ39, only 34 and 21 DEGs were common between ST02, and AZ22 and AZ39, respectively. Significant transcriptional expression changes were observed in genes involved in platelet-bacteria interaction, biofilm formation, production of virulence factors, and resistance to antimicrobial peptides and antibiotics. CONCLUSION: Differential gene expression in S. epidermidis is triggered by the stressful PC storage environment. Upregulation of virulence and antimicrobial resistance genes could have clinical implications for transfusion patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".