Metagenomic tracking of antibiotic resistance genes through a pre-harvest vegetable production system: an integrated lab-, microcosm- and greenhouse-scale analysis.
Ishi KeenumLauren WindPartha RayGiselle GuronChaoqi ChenKatharine KnowltonMonica PonderAmy J PrudenPublished in: Environmental microbiology (2022)
Prior research demonstrated the potential for agricultural production systems to contribute to the environmental spread of antibiotic resistance genes (ARGs). However, there is a need for integrated assessment of critical management points for minimizing this potential. Shotgun metagenomic sequencing data were analysed to comprehensively compare total ARG profiles characteristic of amendments (manure or compost) derived from either beef or dairy cattle (with and without dosing antibiotics according to conventional practice), soil (loamy sand or silty clay loam) and vegetable (lettuce or radish) samples collected across studies carried out at laboratory-, microcosm- and greenhouse-scale. Vegetables carried the greatest diversity of ARGs (n = 838) as well as the most ARG-mobile genetic element co-occurrences (n = 945). Radishes grown in manure- or compost-amended soils harboured a higher relative abundance of total (0.91 and 0.91 ARGs/16S rRNA gene) and clinically relevant ARGs than vegetables from other experimental conditions (average: 0.36 ARGs/16S rRNA gene). Lettuce carried the highest relative abundance of pathogen gene markers among the metagenomes examined. Total ARG relative abundances were highest on vegetables grown in loamy sand receiving antibiotic-treated beef amendments. The findings emphasize that additional barriers, such as post-harvest processes, merit further study to minimize potential exposure to consumers.
Keyphrases
- antibiotic resistance genes
- human health
- microbial community
- wastewater treatment
- anaerobic digestion
- risk assessment
- sewage sludge
- municipal solid waste
- genome wide
- copy number
- heavy metals
- climate change
- health risk
- drinking water
- health risk assessment
- healthcare
- primary care
- genome wide identification
- life cycle
- dna methylation
- quality improvement
- machine learning
- single cell
- transcription factor
- case control