Widespread transfer of mobile antibiotic resistance genes within individual gut microbiomes revealed through bacterial Hi-C.
Alyssa G KentAlbert C VillQiaojuan ShiMichael J SatlinIlana Lauren BritoPublished in: Nature communications (2020)
The gut microbiome harbors a 'silent reservoir' of antibiotic resistance (AR) genes that is thought to contribute to the emergence of multidrug-resistant pathogens through horizontal gene transfer (HGT). To counteract the spread of AR, it is paramount to know which organisms harbor mobile AR genes and which organisms engage in HGT. Despite methods that characterize the overall abundance of AR genes in the gut, technological limitations of short-read sequencing have precluded linking bacterial taxa to specific mobile genetic elements (MGEs) encoding AR genes. Here, we apply Hi-C, a high-throughput, culture-independent method, to surveil the bacterial carriage of MGEs. We compare two healthy individuals with seven neutropenic patients undergoing hematopoietic stem cell transplantation, who receive multiple courses of antibiotics, and are acutely vulnerable to the threat of multidrug-resistant infections. We find distinct networks of HGT across individuals, though AR and mobile genes are associated with more diverse taxa within the neutropenic patients than the healthy subjects. Our data further suggest that HGT occurs frequently over a several-week period in both cohorts. Whereas most efforts to understand the spread of AR genes have focused on pathogenic species, our findings shed light on the role of the human gut microbiome in this process.
Keyphrases
- genome wide
- multidrug resistant
- genome wide identification
- antibiotic resistance genes
- gram negative
- bioinformatics analysis
- patients undergoing
- high throughput
- dna methylation
- genome wide analysis
- end stage renal disease
- clinical trial
- drug resistant
- ejection fraction
- chronic kidney disease
- endothelial cells
- wastewater treatment
- randomized controlled trial
- prognostic factors
- escherichia coli
- newly diagnosed
- single molecule
- machine learning
- big data
- peritoneal dialysis
- pseudomonas aeruginosa
- patient reported outcomes
- deep learning
- data analysis
- cystic fibrosis