Role of Virulence Factors in Resistance of Avian Pathogenic <i>Escherichia coli</i> to Serum and in Pathogenicity
Bibliographic record
Abstract
In chickens, colibacillosis is caused by avian pathogenic Escherichia coli (APEC) via respiratory tract infection. Many virulence factors, including type 1 (F1A) and P (F11) fimbriae, curli, aerobactin, K1 capsule, and temperature-sensitive hemagglutinin (Tsh) and plasmid DNA regions have been associated with APEC. A strong correlation between serum resistance and virulence has been demonstrated, but roles of virulence factors in serum resistance have not been well elucidated. By using mutants of APEC strains TK3, MT78, and chi7122, which belong to serogroups O1, O2, and O78, respectively, we investigated the role of virulence factors in resistance to serum and pathogenicity in chickens. Our results showed that serum resistance is one of the pathogenicity mechanisms of APEC strains. Virulence factors that increased bacterial resistance to serum and colonization of internal organs of infected chickens were O78 lipopolysaccharide of E. coli chi7122 and the K1 capsule of E. coli MT78. In contrast, curli, type 1, and P fimbriae did not appear to contribute to serum resistance. We also showed that the iss gene, which was previously demonstrated to increase resistance to serum in certain E. coli strains, is located on plasmid pAPEC-1 of E. coli chi7122 but does not play a major role in resistance to serum for strain chi7122.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".