A study of the correlation between phenotypic antimicrobial susceptibility testing methods and the associated genotypes determined by whole genome sequencing for a collection of <i>Escherichia coli</i> of bovine origin
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) has increased at an alarming pace in the recent years. Molecular-based methods such as whole genome sequencing (WGS) offer a potential alternative to the conventional labour-intensive methods traditionally used to characterise AMR phenotypes. The aim of this study was to investigate whether WGS could be used as a predictor of AMR in Escherichia coli isolates of bovine origin. Genomes of 143 E. coli cultured from cattle presenting with diarrhoea or mastitis were sequenced on an Illumina MiSeq platform. AMR genes were identified using the ResFinder and AMRFinder databases. Antimicrobial susceptibility testing by disk diffusion was performed on a panel of 10 antibiotics, covering 7 antimicrobial classes. Minimum inhibitory concentration (MIC) measurements were made using the Sensititre plate with 6 antibiotics, covering 5 antimicrobial classes. Correlation between genotype and phenotype was assessed statistically by means of a two-by-two table analysis and Cohen’s kappa (κ) test. The overall κ correlation between WGS and disk diffusion was 0.81, indicating a near perfect agreement, and the average positive predicted value was 77.4 %. Correlation for individual antimicrobial compounds varied, with five yielding near perfect agreement (κ = 0.81–1.00; amoxicillin, florfenicol, gentamicin, tetracycline and trimethoprim-sulfamethoxazole), one showing substantial agreement (κ = 0.65; nalidixic acid), and four showing moderate agreement (κ = 0.41– 0.60). The overall κ correlation between WGS and MIC was 0.55 indicating moderate agreement, and the average positive predicted value was 68.6 %. Three antibiotics yielded near perfect agreement (gentamicin, tetracycline and trimethoprim-sulfamethoxazole) and a further three showed fair agreement (κ = 0.21–0.40). WGS is a useful tool that can be used for the prediction of AMR phenotypes, and correlates well with disk diffusion results. MIC measurements may be necessary for antimicrobial compounds with a high proportion of intermediately resistant isolates recorded, such as cephalothin. Highlights Culture based antimicrobial susceptibility testing is used to identify therapeutics in the treatment of clinical veterinary isolates Whole genome sequencing is increasingly adopted for surveillance, epidemiological traceback investigations, and detection of antimicrobial resistance genes Little is known in correlations between antimicrobial resistance genotypes and disk diffusion antimicrobial susceptibility testing This study finds that whole genome sequencing is a useful predictor for antimicrobial susceptibility however, minimum inhibitory concentration measurements may still be needed for intermediately resistant isolates
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".