Abstract
Antimicrobial resistance is one of the biggest challenge to public health globally. The development of new antibiotics has been lagging behind and our best hope is to optimise the use of available compounds which implies the administration of the right drug to the right patient. This, in turn requires effective and fast diagnostics based on genetic typing. This approach is complicated for gram-negative bacteria, which have so called “open genomes” characterised by a highly variable complement of genes. This project will characterise the genetic basis of drug resistance in gram-negative bacteria using large datasets, novel machine learning tools and functional wet-lab validation.
References
- Eldholm V and F Balloux 2016 Antimicrobial resistance in Mycobacterium tuberculosis: the odd one out. Trends in Microbiology. 24:637-648. doi: 10.1016/j.tim.2016.03.007
- Bradley, P., et al. 2015 Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis. Nature Communications 21:10063. doi: 10.1038/ncomms10063
- Earle SG, et al. (2016) Identifying lineage effects when controlling for population structure improves power in bacterial association studies. Nature Microbiology 4: 16041. doi: 10.1038/nmicrobiol.2016.41
- Wang Y, et al. 2017 Comprehensive resistome analysis reveals the prevalence of NDM and MCR-1 in Chinese poultry production. Nature Microbiology 6:16260. doi: 10.1038/nmicrobiol.2016.260.