OBJECTIVES: Reliable methods for monitoring antimicrobial resistance (AMR) in livestock and other reservoirs are essential to understand the trends, transmission and importance of agricultural resistance. Quantification of AMR is mostly done using culture-based techniques, but metagenomic read mapping shows promise for quantitative resistance monitoring. METHODS: We evaluated the ability of: (i) MIC determination for Escherichia coli; (ii) cfu counting of E. coli; (iii) cfu counting of aerobic bacteria; and (iv) metagenomic shotgun sequencing to predict expected tetracycline resistance based on known antimicrobial consumption in 10 Danish integrated slaughter pig herds. In addition, we evaluated whether fresh or manure floor samples constitute suitable proxies for intestinal sampling, using cfu counting, qPCR and metagenomic shotgun sequencing. RESULTS: Metagenomic read-mapping outperformed cultivation-based techniques in terms of predicting expected tetracycline resistance based on antimicrobial consumption. Our metagenomic approach had sufficient resolution to detect antimicrobial-induced changes to individual resistance gene abundances. Pen floor manure samples were found to represent rectal samples well when analysed using metagenomics, as they contain the same DNA with the exception of a few contaminating taxa that proliferate in the extraintestinal environment. CONCLUSIONS: We present a workflow, from sampling to interpretation, showing how resistance monitoring can be carried out in swine herds using a metagenomic approach. We propose metagenomic sequencing should be part of routine livestock resistance monitoring programmes and potentially of integrated One Health monitoring in all reservoirs.
OBJECTIVES: Reliable methods for monitoring antimicrobial resistance (AMR) in livestock and other reservoirs are essential to understand the trends, transmission and importance of agricultural resistance. Quantification of AMR is mostly done using culture-based techniques, but metagenomic read mapping shows promise for quantitative resistance monitoring. METHODS: We evaluated the ability of: (i) MIC determination for Escherichia coli; (ii) cfu counting of E. coli; (iii) cfu counting of aerobic bacteria; and (iv) metagenomic shotgun sequencing to predict expected tetracycline resistance based on known antimicrobial consumption in 10 Danish integrated slaughter pig herds. In addition, we evaluated whether fresh or manure floor samples constitute suitable proxies for intestinal sampling, using cfu counting, qPCR and metagenomic shotgun sequencing. RESULTS: Metagenomic read-mapping outperformed cultivation-based techniques in terms of predicting expected tetracycline resistance based on antimicrobial consumption. Our metagenomic approach had sufficient resolution to detect antimicrobial-induced changes to individual resistance gene abundances. Pen floor manure samples were found to represent rectal samples well when analysed using metagenomics, as they contain the same DNA with the exception of a few contaminating taxa that proliferate in the extraintestinal environment. CONCLUSIONS: We present a workflow, from sampling to interpretation, showing how resistance monitoring can be carried out in swine herds using a metagenomic approach. We propose metagenomic sequencing should be part of routine livestock resistance monitoring programmes and potentially of integrated One Health monitoring in all reservoirs.
Authors: Jilei Zhang; Jiawei Wang; Li Chen; Afrah Kamal Yassin; Patrick Kelly; Patrick Butaye; Jing Li; Jiansen Gong; Russell Cattley; Kezong Qi; Chengming Wang Journal: Appl Environ Microbiol Date: 2017-12-15 Impact factor: 4.792
Authors: Adair L Borges; Yue Clare Lou; Rohan Sachdeva; Basem Al-Shayeb; Petar I Penev; Alexander L Jaffe; Shufei Lei; Joanne M Santini; Jillian F Banfield Journal: Nat Microbiol Date: 2022-05-26 Impact factor: 30.964
Authors: H Soon Gweon; Liam P Shaw; Jeremy Swann; Nicola De Maio; Manal AbuOun; Rene Niehus; Alasdair T M Hubbard; Mike J Bowes; Mark J Bailey; Tim E A Peto; Sarah J Hoosdally; A Sarah Walker; Robert P Sebra; Derrick W Crook; Muna F Anjum; Daniel S Read; Nicole Stoesser Journal: Environ Microbiome Date: 2019-10-24
Authors: V D Andersen; L V DE Knegt; P Munk; M S Jensen; Y Agersø; F M Aarestrup; H Vigre Journal: Epidemiol Infect Date: 2017-06-27 Impact factor: 4.434
Authors: Richard C Allen; Jan Engelstädter; Sebastian Bonhoeffer; Bruce A McDonald; Alex R Hall Journal: Proc Biol Sci Date: 2017-09-27 Impact factor: 5.349
Authors: Timothy A Johnson; Torey Looft; Andrew J Severin; Darrell O Bayles; Daniel J Nasko; K Eric Wommack; Adina Howe; Heather K Allen Journal: mBio Date: 2017-08-08 Impact factor: 7.867