Characterization of Non-O157 Escherichia coli from Cattle Faecal Samples in the North-West Province of South Africa
Bibliographic record
Abstract
Escherichia coli are commensal bacteria in the gastrointestinal tract of mammals, but some strains have acquired Shiga-toxins and can cause enterohemorrhagic diarrhoea and kidney failure in humans. Shiga-toxigenic E. coli (STEC) strains such as E. coli O157:H7 and some non-O157 strains also contain other virulence traits, some of which contribute to their ability to form biofilms. This study characterized non-O157 E. coli from South African cattle faecal samples for their virulence potential, antimicrobial resistance (AMR), biofilm-forming ability, and genetic relatedness using culture-based methods, pulsed-field gel electrophoresis (PFGE), and whole genome sequencing (WGS). Of 80 isolates screened, 77.5% (62/80) possessed Shiga-toxins genes. Of 18 antimicrobials tested, phenotypic resistance was detected against seven antimicrobials. Resistance ranged from 1.3% (1/80) for ampicillin-sulbactam to 20% (16/80) for tetracycline. Antimicrobial resistance genes were infrequently detected except for tetA, which was found in 31.3% (25/80) and tetB detected in 11.3% (9/80) of isolates. Eight biofilm-forming associated genes were detected in STEC isolates (n = 62) and two non-STEC strains. Prevalence of biofilm genes ranged from 31.3% (20/64) for ehaAβ passenger to 100% for curli structural subunit (csgA) and curli regulators (csgA and crl). Of the 64 STEC and multi-drug resistant isolates, 70.3% (45/64) and 37.5% (24/64) formed strong biofilms on polystyrene at 22 and 37 °C, respectively. Of 59 isolates screened by PFGE, 37 showed unique patterns and the remaining isolates were grouped into five clusters with a ≥90% relatedness. In silico serotyping following WGS on a subset of 24 non-O157 STEC isolates predicted 20 serotypes comprising three novel serotypes, indicating their diversity as potential pathogens. These findings show that North West South African cattle harbour genetically diverse, virulent, antimicrobial-resistant and biofilm-forming non-O157 E. coli. Biofilm-forming ability may increase the likelihood of persistence of these pathogens in the environment and facilitate their dissemination, increasing the risk of cross contamination or establishment of infections in hosts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".