We will integrate leading expertise from bacteriology, -omics and mathematical biology in the development of an integrated theoretical-empirical framework of plasmid borne transmission of AMR cassettes. We will employ massive-scale experimental evolution of Escherichia coli and Salmonella enterica gene deletion and overexpression collections, where adaptation requires transfer of AMR carrying conjugative plasmids. In addition, we will select for, identify and functionally dissect de novo mutations that promote horizontal transmission during long-term experimental evolution. Both approaches will disclose cellular functions controlling horizontal AMR transmission that are candidate targets for helper drugs delaying AMR development and spread.
Second, we will sequence vast swaths of the genotype space inhabited by clinical bacterial isolates and disclose variants likely to alter transmission properties. DNA sequence data will be complemented by data on transcriptome, proteome and antibiotics resistance, allowing causally cohesive reconstruction of the history of antibiotics resistance. Third, we will integrate the omics data into a mathematical framework capable of predicting AMR transmission in clinical isolates, thereby laying the foundations for a future personalized medicine that tailors antibiotic choice to infection.
- Jonas Warringer, University of Gothenburg, Sweden (Coordinator)
- Edward Moore, University of Gothenburg, Sweden
- Gianni Liti, University of Nice, France
- Danesh Moradigaravand, The Wellcome Trust Sanger Institute, United Kingdom
- Jan Michiels, University of Leuven, Belgium
- Anne Farewell, University of Gothenburg, Sweden
- Ville Mustonen, The Wellcome Trust Sanger Institute, United Kingdom
Resistance to antibiotics, particularly in Gram-negative bacteria, is an accelerating health crisis. Only few new antibiotics against Gram-negatives are in clinical trials and resistance also to new antibiotics is predicted to spread rapidly upon clinical introduction. Horizontal transmission of antibiotic resistance factors within pathogenic species, combined with the selective pressure imposed by extensive antibiotic use that favours resistant strains, explains much of the accelerating antibiotics resistance crisis.
We propose to disclose candidate drug targets controlling the horizontal cell-cell transmission of anti-microbial resistance and to predict AMR and its transmission dynamics from bacterial genome composition. We employ experimental evolution of Escherichia coli, one of the most problematic species, to experimentally identify genes controlling the plasmid transmission rates. The hope is that these will be good targets
for developing drugs that slows antibiotics resistance development. We also sequence the genomes of many clinical bacterial isolates and
mathematically and computationally disclose natural variants likely to affect plasmid transmission properties. We hope that this will lay the
foundations for a future personalized medicine that tailors antibiotic choice to infection such that resistance development within each
patient is delayed or avoided completely.
- Studies on Symbiotic and pathogenic interactions from Jan Michiels group
- CARe – Centre for Antibiotic Resistance Research at University of Gothenburg
- The Problem of Antibiotic Resistance: An Open Education Resource
- bioRxiv, 2020. Genomic epidemiology and evolution of Escherichia coli in wild animals
- mSystems, December 2020. A High-Throughput Method for Screening for Genes Controlling Bacterial Conjugation of Antibiotic Resistance.
- Scientific Reports, 2020. Complete genome sequences of Streptococcus pyogenes NCTC 8198T and CCUG 4207T, the type strain of the type species of the genus Streptococcus: 100% match in length and sequence identity between PacBio solo and Illumina plus Oxford Nanopore hybrid assemblies
- bioRxiv, 2018. Conjugation factors controlling F-plasmid antibiotic resistance transmissio
- Drug Development Research, 2018. Inhibiting conjugation as a tool in the fight against antibiotic resistance
- Plos Computational Biology, 2018. Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data