Structured, not stirred: experimental evolution in programmable antibiotic landscapes

Informazioni relative al progetto

EvoSpa

ID dell’accordo di sovvenzione: 884163

DOI

10.3030/884163

Progetto concluso il 31 Marzo 2023

Data della firma CE 17 Luglio 2020

Data di avvio 1 Luglio 2021

Data di completamento 30 Giugno 2023

Finanziato da

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Costo totale

€ 174 806,40

Contributo UE

€ 174 806,40

Coordinato da

UNIVERSITAT ZU KOLN
Germany

Periodic Reporting for period 1 - EvoSpa (Structured, not stirred: experimental evolution in programmable antibiotic landscapes)

Periodo di rendicontazione: 2021-07-01 al 2023-06-30

Sintesi del contesto e degli obiettivi generali del progetto

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.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

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.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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.

Programmable antibiotic landscape, transparent areas show inhibited growth due to antibiotic.