Microfluidics‐based liquid chromatography/mass spectrometry multiple reaction monitoring approach for the relative quantification of <i>Burkholderia cenocepacia</i> secreted virulence factors
Bibliographic record
Abstract
Rationale Burkholderia cenocepacia is an opportunistic pathogen that is commonly isolated from patients with cystic fibrosis (CF). Quorum sensing has been suggested to play a role in the activity of type II and type VI secretion systems and the release of virulence factors. Apart from the classical acyl homoserine lactone quorum sensing, B. cenocepacia also uses the diffusible signal factor system (DSF). Quantitative information on the true impact of DSF molecules on the release of ZmpA and other virulence factors is lacking. Methods Based on results of a label‐free proteomics analysis addressing changes in the secretome in response to DSFs, a panel of peptides was selected to develop a microfluidics liquid chromatography/mass spectrometry (LC/MS) method implementing single reaction monitoring (SRM) to quantify B. cenocepacia virulence factors. Results Increase in secretion of virulence factors upon treatment with BDSF was observed for ZmpA and Aida, but not for ZmpB. Type VI secretion system dependent Hcp1 and TecA were decreased. However, non‐physiological amounts of BDSF were needed to provoke the effect. DSFs from P. aeruginosa and S. maltophilia were also affecting virulence factor secretion, but the effect was smaller than for the endogenous BDSF. Conclusions Microfluidics‐based SRM is a useful tool to quantitatively assess the impact of quorum sensing on the release of virulence factors by (opportunistic) pathogens.
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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 | 0.000 |
| 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".