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Bacterial motility and chemotaxis as drivers of antimicrobial resistance in biofilms

Investigators
Dr Nuno Miguel Oliveira
Institutions
University of Cambridge
Start date
2020
End date
2023
Objective
Antimicrobial resistance is a growing public concern and is now ranked alongside other major threats such as climate change and terrorism. While antibiotic research is ever-intensifying, most laboratory studies focus on homogenous systems, which sharply contrasts with the antibiotic gradients that bacteria find in environmental and clinical settings. Bacteria often grow as surface-associated communities-biofilms-where they experience steep and stable gradients of nutrients and toxic compounds, including antibiotics. However, we know very little about how biofilm bacteria respond to antibiotic gradients, and that is exactly what I will tackle here. I will study how biofilm cells control their motility in antibiotic gradients. We have a thorough understanding of how swimming bacteria control their flagella to move towards or away from chemicals (chemotaxis), but how biofilm bacteria control their motility remains poorly explored. Using microfluidics and massively-parallel tracking, I showed that biofilm bacteria control type IV pili via the Pil-Chp chemosensory system to climb gradients of nutrients and other canonical chemoattractants. My recent data shows that biofilm bacteria also control their motility when expose to antibiotic gradients: unexpectedly, biofilm cells actively move towards increasing concentrations of antibiotics resulting in extremely high phenotypic resistance within a few hours. I now want to understand the genetic and ecological basis of this this counterintuitive behaviour, and how it affects the evolution of genetic resistance. Towards these aims, I will perform a random genetic screen to identify key genes for directed motion towards antibiotics; I will study how biofilm cells use motility to respond to antibiotic-producing species growing side-by-side; and I will use microbial genomics, coupled with mathematical modelling, to characterize how biased cell motility affect the dynamics of resistance evolution in gradients of antibiotics.
Funding Source
Biotechnology and Biological Sciences Research Council
Project source
View this project
Project number
BB/T009098/1
Categories
Antimicrobial Resistance
Bacterial Pathogens
Predictive Microbiology