Feeding behaviour of <i>Caenorhabditis elegans </i>is an indicator of <i>Pseudomonas aeruginosa </i>PAO1<i> </i>virulence
Bibliographic record
Abstract
Caenorhabditis elegans is commonly used as an infection model for pathogenesis studies in Pseudomonas aeruginosa. While the standard virulence assays rely on the slow and fast killing or paralysis of nematodes, here we developed a behaviour assay to monitor the preferred bacterial food sources of C. elegans. The type III secretion system is a well-conserved virulence trait that is not required for slow or fast killing of C. elegans. However, ΔexsE mutants that are competent for hypersecretion of ExoS, ExoT and ExoY effectors were avoided as food sources in binary assays. Conversely, mutants lacking the secretion machinery or type III effectors were preferred food sources for PAO1. In binary feeding assays, both food sources were ingested and observed in the gastrointestinal tract, but non-preferred food sources were ultimately avoided. Next we developed a high throughput feeding behaviour assay to test a library of 2370 transposon mutants in order to identify preferred food sources. After primary and secondary screens, 37 mutants were identified as preferred food sources, which included mutations in many known virulence genes and that showed reduced virulence in the slow killing assay. We propose that C. elegans feeding behaviour can be used as a sensitive indicator of virulence for bacterial strains that have moderate worm killing activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".