On the Utility of Genomics-Based Methods for Surveillance of Antimicrobial-Resistant Bacteria in the Food Production Continuum
Bibliographic record
Abstract
Overuse of antimicrobials in medicine and agriculture are believed to be drivers of the spread of AMR among pathogenic bacteria.Antimicrobial use in agriculture and food-producing animals may facilitate spread of resistant bacteria to food products as well as dissemination into the environment.Although regulatory practices currently monitor food, feed, and fertilizer products for presence of pathogenic organisms, they do not test for AMR genes (ARGs).My Ph.D. thesis focuses on filling current knowledge gaps for transmission and detection of AMR in food production by evaluating methodology to monitor transmission of resistance genes throughout the food production continuum.There are three main objectives of my thesis: (i) to evaluate the accuracy of genotype in predicting resistance phenotypes; (ii) to investigate the presence and mechanisms of resistance in food production; (iii) to determine whether short-read metagenomics is a suitable highthroughput method for surveillance of resistance in agri-food products.For the first objective, concordance of AMR phenotypes with whole genome sequencing (WGS) predictions of ARGs were determined in two separate studies.Requirements for sequence coverage suggest genotypic predictions of AMR are highly concordant when using a target-gene identity cutoff of >80%, assuming sufficient genome coverage.To investigate pathogen persistence in food production, analyses were conducted using WGS data for 1279 L. monocytogenes isolated from food products.The most frequently isolated clonal complexes were significantly associated with carriage of plasmid-borne quaternary ammonium compound resistance.Additionally, conjugation iii frequencies of plasmid-borne AMR between commensal food bacteria and a foodborne pathogen were determined.High rates of both pathogen persistence and plasmid transfer in foodborne bacteria emphasize the importance of monitoring resistance determinants in commensal food bacteria as well as pathogens.Finally, to determine the utility of shotgun metagenomics for monitoring AMR in agri-food production I conducted a study on LOD of ARGs in metagenomic sequences.This study highlights that as the number of sequence reads decreases, target organisms must comprise a larger proportion for ARG detection.This research emphasizes the importance of monitoring resistance dissemination throughout agri-food production and has provided guidance to aid in selection of appropriate methods for surveillance of AMR in food products.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".