Metagenomic Analysis of Ready-to-Eat Foods on Retail Sale in the UK Identifies Diverse Genes Related to Antimicrobial Resistance
Bibliographic record
Abstract
Antimicrobial Resistance (AMR), i.e., the evolution of microbes to become resistant to chemicals used to control them, is a global public health concern that can make bacterial diseases untreatable. Inputs including antibiotics, metals, and biocides can create an environment in the agrifood chain that selects for AMR. Consumption of food represents a potential exposure route to AMR microbes and AMR genes (ARGs), which may be present in viable bacteria or on free DNA. Ready-to-eat (RTE) foods are of particular interest because they are eaten without further cooking, so AMR bacteria or ARGs that are present may be consumed intact. They also represent varied production systems (fresh produce, cooked meat, dairy, etc.). An evidence gap exists regarding the diversity and consumption of ARGs in RTE food, which this study begins to address. We sampled 1001 RTE products at retail sale in the UK, in proportion to their consumption by the UK population, using National Diet and Nutrition Survey data. Bacterial DNA content of sample extracts was assessed by 16S metabarcoding, and 256 samples were selected for metagenomic sequencing for identification of ARGs based on consumption and likely bacterial DNA content. A total of 477 unique ARGs were identified in the samples, including ARGs that may be involved in resistance to important antibiotics, such as colistin, fluoroquinolones, and carbapenems, although phenotypic AMR was not measured. Based on the incidence of ARGs in food types, ARGs are estimated to be present in a high proportion of average diets. ARGs were detected on almost all RTE food types tested (48 of 52), and some efflux pump genes are consumed in 97% of UK diets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".