Using Quantitative Spectrometry to Understand the Influence of Genetics and Nutritional Perturbations On the Virulence Potential of Staphylococcus aureus
Bibliographic record
Abstract
Staphylococcus aureus ( Sa ) is the leading cause of a variety of bacterial infections ranging from superficial skin infections to invasive and life threatening diseases such as septic bacteremia, necrotizing pneumonia, and endocarditis. The success of Sa as a human pathogen is contributed to its ability to adapt to different environments by changing expression, production, or secretion of virulence factors. Although Sa immune evasion is well-studied, the regulation of virulence factors under different nutrient and growth conditions is still not well understood. Here, we used label-free quantitative mass spectrometry to quantify and compare the Sa exoproteins ( i.e. exoproteomes) of master regulator mutants or established reference strains. Different environmental conditions were addressed by growing the bacteria in rich or minimal media at different phases of growth. We observed clear differences in the composition of the exoproteomes depending on the genetic background or growth conditions. The relative abundance of cytotoxins determined in our study correlated well with differences in cytotoxicity measured by lysis of human neutrophils. Our findings demonstrate that label-free quantitative mass spectrometry is a versatile tool for predicting the virulence of bacterial strains and highlights the importance of the experimental design for in vitro studies. Furthermore, the results indicate that label-free proteomics can be used to cluster isolates into groups with similar virulence properties, highlighting the power of label-free quantitative mass spectrometry to distinguish Sa 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".