Periodic Reporting for period 1 - EvoSpa (Structured, not stirred: experimental evolution in programmable antibiotic landscapes)
Reporting period: 2021-07-01 to 2023-06-30
Our discovery pipeline for new antibiotics is drying and resistance is strongly on the rise. How do we make the most use of our existing antibiotics whilst minimizing resistance evolution? In order to optimize therapy, we need to be able to understand and predict the different factors influencing resistance evolution. We can actually directly study this directly by using experimental evolution. Here, bacteria are exposed to antibiotics in a lab setting and resistance evolves within days to weeks - enabling the tests of the predictive value of evolutionary models. However, these experiments are typically performed in well-mixed environments, whereas antibiotics are typically highly inhomogeneously distributed inside the body. The objective of this project was to develop and exploit a new model system to study the effect of spatial inhomogeneity on antibiotic resistance evolution.
We have developed programmable antibiotic landscapes in which bacteria could migrate, grow and evolve. We also developed an imaging system including analysis to quantitatively track these processes. Importantly, tens of plates can be measured simultaneously, allowing to systematically study evolution despite its stochastic nature. Furthermore, complex, two-dimensional landscapes can be programmed consisting of hundreds of microenvironments for which the antibiotic concentration can be locally controlled (the attached image shows a programmed 'smiley face' to showcase its versatality). We have shown that smooth drug gradients lead to rapid drug resistance and have developed mathematical models to further understand this resistance evolution. We are currently finalizing the project and will disseminate the results in open access publications.
This progress clearly goes beyond the state of the art, as we have developed the first experimental method in which the effect of spatial inhomogeneity on resistance evolution can be directly studied with high throughput and fine detail. We expect that this assay can be widely used as it is not specific to one drug or even drug family. Furthermore, it works for any pathogen capable of migrating. Therefore, we expect that this approach will in the long run contribute to the optimization of less resistance-prone antibiotic treatment.