Modeling the Limits of Detection for Antimicrobial Resistance Genes in Agri-Food Metagenomic Samples
Bibliographic record
Abstract
Abstract Background Despite the potential for dissemination of antimicrobial resistance (AMR) through food and food production, there are few studies of the prevalence of AMR organisms (AROs) in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance AROs within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches. Results We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2 and Bracken indicate presence of organisms not included in the synthetic metagenomes. MetaPhlAn3 analysis of community composition was more specific but with lower sensitivity than both Kraken2 and Bracken. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 falsely reported presence of distantly related ARGs at all isolate genome coverage levels. Conclusions Overall, ARGs were accurately detected in the synthetic metagenomes (approx. 40 million paired-end reads) by all methods when the ARO reads constituted > 0.4% of the reads (approximately 5X isolate coverage). Reducing target gene coverage cutoffs allowed detection of ARGs present at lower abundance; however, this reduced cutoff may result in alternative ARG-allele detection. Background flora in metagenomes resulted in differences in detection of ARGs by KMA. Further advancements in sequencing technologies providing increased depth of coverage or longer read length may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking low-abundance AROs in agri-food samples.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".