Evaluating the Pathogenic Potential of Environmental Escherichia coli by Using the Caenorhabditis elegans Infection Model
Bibliographic record
Abstract
The detection and abundance of Escherichia coli in water is used to monitor and mandate the quality of drinking and recreational water. Distinguishing commensal waterborne E. coli isolates from those that cause diarrhea or extraintestinal disease in humans is important for quantifying human health risk. A DNA microarray was used to evaluate the distribution of virulence genes in 148 E. coli environmental isolates from a watershed in eastern Ontario, Canada, and in eight clinical isolates. Their pathogenic potential was evaluated with Caenorhabditis elegans, and the concordance between the bioassay result and the pathotype deduced by genotyping was explored. Isolates identified as potentially pathogenic on the basis of their complement of virulence genes were significantly more likely to be pathogenic to C. elegans than those determined to be potentially nonpathogenic. A number of isolates that were identified as nonpathogenic on the basis of genotyping were pathogenic in the infection assay, suggesting that genotyping did not capture all potentially pathogenic types. The detection of the adhesin-encoding genes sfaD, focA, and focG, which encode adhesins; of iroN2, which encodes a siderophore receptor; of pic, which encodes an autotransporter protein; and of b1432, which encodes a putative transposase, was significantly associated with pathogenicity in the infection assay. Overall, E. coli isolates predicted to be pathogenic on the basis of genotyping were indeed so in the C. elegans infection assay. Furthermore, the detection of C. elegans-infective environmental isolates predicted to be nonpathogenic on the basis of genotyping suggests that there are hitherto-unrecognized virulence factors or combinations thereof that are important in the establishment of infection.
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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 | 0.000 |
| 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".