An automated DIY framework for experimental evolution of Pseudomonas putida.
David R EspesoPavel DvořákTomás AparicioVictor de LorenzoPublished in: Microbial biotechnology (2020)
Adaptive laboratory evolution (ALE) is a general and effective strategy for optimizing the design of engineered genetic circuits and upgrading metabolic phenotypes. However, the specific characteristics of each microorganism typically ask for exclusive conditions that need to be adjusted to the biological chassis at stake. In this work, we have adopted a do-it-yourself (DIY) approach to implement a flexible and automated framework for performing ALE experiments with the environmental bacterium and metabolic engineering platform Pseudomonas putida. The setup includes a dual-chamber semi-continuous log-phase bioreactor design combined with an anti-biofilm layout to manage specific traits of this bacterium in long-term cultivation experiments. As a way of validation, the prototype was instrumental for selecting fast-growing variants of a P. putida strain engineered to metabolize D-xylose as sole carbon and energy source after running an automated 42 days protocol of iterative regrowth. Several genomic changes were identified in the evolved population that pinpointed the role of RNA polymerase in controlling overall physiological conditions during metabolism of the new carbon source.
Keyphrases
- copy number
- biofilm formation
- genome wide
- high throughput
- pseudomonas aeruginosa
- randomized controlled trial
- staphylococcus aureus
- machine learning
- wastewater treatment
- deep learning
- candida albicans
- high intensity
- magnetic resonance imaging
- computed tomography
- dna methylation
- image quality
- cystic fibrosis
- risk assessment
- dual energy