Use of a nematode infection model for virulence factor screening and polymicrobial virulence testing
Bibliographic record
Abstract
Introduction: Cystic Fibrosis (CF) is a debilitating genetic condition characterised by a chronic, polymicrobial biofilm infections. Pseudomonas aeruginosa is a primary pathogen in the CF lung but many other organisms are present . High throughput infection models are useful for the identification of virulence factors in a given pathogen, or for the virulence testing of polymicrobial combinations. Approach: Caenorhabditis elegans is a nematode worm that feeds on bacteria and is commonly used as an infection host. Previously, 2500 strains from a mini-Tn5-lux mutant library were screened in a nematode feeding behaviour assay to identify mutants that acted as preferred food sources. Here we tested our hypothesis that preferred food sources were less virulent. We also tested the virulence of a large panel of bacterial isolates recovered from CF patients, many of which are considered oropharyngeal (OF), non-pathogenic isolates. Results: Many of the preferred food sources had decreased virulence compared to the wild type control in the “slow killing” infection model. Preferred bacterial food sources were not due to nematode attraction or aversion of bacterial secreted products. Many OF isolates were pathogenic to C. elegans, either individually or when in combination with P. aeruginosa. Furthermore, combinations of 2-3 isolates were often more virulent than combinations of 4-6 unique isolates. Conclusions: We developed a worm feeding behaviour and high-throughput killing assay that is amenable to testing large numbers of isolates individually and in combination. The polymicrobial infections suggest that bacteria normally considered non-pathogenic may be virulent when in combination. Future use of the nematode infection model will help examine the polymicrobial nature of many real world infections.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".