Applying Nanopore sequencing to a One-Health scenario for colistin resistance transmission among pigs, cows and the farmer
Vines, J.; Cusco, A.; Napp, S.; Gonzalez, J.; Perez de Rozas, A.; Francino, O.; Migura-Garcia, L.
Show abstract
One-Health studies applying massive-parallel and single-molecule sequencing are a suitable approximation to try to understand how antibiotic resistances flow between the human-animal-environment scenario. Colistin has been withdrawn in human medicine due to its toxicity, limiting its usage as a last-resort treatment option for multidrug-resistant Gram-negative bacteria. However, it is still used orally to treat Enterobacteriaceae infections in veterinary medicine. Since 2015, colistin resistance appeared to be located in mobile genetic elements, raising the concern of the likelihood of transmission by horizontal gene transfer between animals and humans. In this study, 202 faecal samples were collected in a mixed farm from pigs, calves, and the farmer. PCR for the mcr-1 gene was positive for 18 of the isolates, and Nanopore sequencing allowed us to determine the location of the gene, either on the chromosome or in plasmids. Three types of replicons were found within the positive isolates harbouring the mcr-1: IncX4, IncI2, and IncHI2. Four different genetic contexts probably indicate different stages of gene stabilization, either in the chromosome or plasmid, with ISApl1 as the main insertion element flanking the gene. Moreover, 43 other resistance genes were found in our samples, related to more than six different antibiotic families (e.g. aminoglycosides, lincosamides, beta-lactams, macrolides, trimethoprim, phenicols, and sulphonamides). We found resistance genes against colistin and that six antibiotic families together in at least one of the isolates from human, swine, and bovine. Isolate 15B-22 harboured one plasmid with seven resistance genes related to four families of antibiotics other than polymyxins, meaning that there are more chances to maintain colistin resistance even with the withdrawn of colistin. Nanopore long reads allowed us to assemble the DNA elements within the isolates easily and determine the genetic context of the mcr-1 gene. Furthermore, they allowed us to describe and locate more antimicrobial resistance genes to other antibiotic families and antiseptic compounds.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Characterisation of AmpC Hyper-Producing Escherichia coli from Humans and Dairy Farms Collected in Parallel in the Same Geographical Region 95%
- Limited Phylogenetic Overlap Between Fluoroquinolone-Resistant Escherichia coli Isolated on Dairy Farms and those Causing Bacteriuria in Humans Living in the Same Geographical Region 95%
- Robotic Antimicrobial Susceptibility Platform (RASP): A Next Generation Approach to One-Health Surveillance of Antimicrobial Resistance. 94%
Similar papers in this journal
- The darkest place is under the candlestick - healthy urogenital tract as a source of UTI-related Escherichia coli lineages 96%
- Analysis of 56K genomes identifies the relationship between antibiotic and metal resistance co-Occurrence and the spread of multidrug-resistant non-typhoidal Salmonella 95%
- Limited genetic diversity of blaCMY-2-containing IncI1-pST12 plasmids from Enterobacteriaceae of human and broiler chicken origin in the Netherlands 95%
Similar papers in this journal
- Transmissible antimicrobial resistance in Escherichia coli isolated from household drinking water in Ibadan, Nigeria 96%
- Genomic surveillance of antimicrobial resistance shows cattle are a moderate source of multi-drug resistant non-typhoidal Salmonella in Mexico 96%
- Whole genome sequences of multi-drug resistant Escherichia coli isolated in a Pastoralist Community of Western Uganda: Phylogenomic changes, virulence and resistant genes 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.