Metagenomic Analysis Reveals the Transfer of Antibiotic Resistance from Manure-Fertilized Soil to Carrot Microbiome
Bibliographic record
Abstract
Abstract Crops grown in manure-fertilized soil are more likely to be contaminated with antibiotic resistance genes (ARGs) than when grown in unmanured soil, and therefore more likely to represent a route of ARG exposure to consumers. Existing studies scarcely focused on the residual ARGs on the carrot peel growing in the manured soil after the careful washing. In the present study, residual microbiome and resistome on the carrots surface ( Daucus carota subsp. sativus ) at harvest after careful washing was investigated to reveal the impact of manure fertilization. Residual surface bacteria were recovered from the peel, and total bacterial DNA was extracted for the high-throughput sequencing-based metagenomic analysis. Despite the overall unaltered level of alpha diversity in both soil and carrot peel samples, manuring increased the resistome in soil significantly, but not on the carrot peel. The largely overlapped resistome detected on carrots grown with and without manuring, plus the pattern revealed by source tracking analysis indicated a soil-source of ARGs on carrots, whereas the beta-lactamases CTX-M-84 , OXY-4 , and CTX-M-122 only detected in the manured soil and on the carrot peel harvested from manured soil indicated that beta-lactamases appear to be transferred from manure to the carrot. The evident impact of soil resistome and community on carrot peel microbiome, plus the limited level of plasmid and integron mediated ARGs transfer suggested the main ARGs transfer pathway from manured soil to carrot peel was via the colonization of rhizosphere soil microbes. To further elucidate the ARG propagation within the soil-carrot system, a network was constructed to explore the pattern of nine types of concurrent ARGs genotypes carried by ten different host populations, emphasizing that the residual antibiotic resistance’s transfer via raw carrot represents a risk to human health even after extensive washing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".