Evolutionary Engineering is a Versatile Strain Optimization Approach for Sustainable Bioproduction
Adam M. Feist1,2,3* (email@example.com), and Jay D. Keasling 2,3,4,5
1University of California–San Diego; 2Technical University of Denmark; 3Joint Bioenergy Institute; 4Lawrence Berkeley National Laboratory; and 5University of California–Berkeley
The vision of Joint Bioenergy Institute (JBEI) is that bioenergy crops can be converted into economically viable, carbon-neutral, biofuels and renewable chemicals currently derived from petroleum, and many other bioproducts that cannot be efficiently produced from petroleum.
Strain engineering of microbes for bioproduction is a challenge and requires multiple optimization approaches and engineering cycles to reach commercial viability. An approach that is gaining in utility in the Design-Build-Test-Learn (DBTL) cycle is adaptive laboratory evolution (ALE) as it can uniquely solve problems encountered in the stain engineering process using selection and the natural ability of microbes to adapt. A platform to effectively utilize ALE built around custom automation, process control software, and bioinformatics pipelines was developed and it has been effectively applied to engineer a range of microorganisms. Specific use cases demonstrating how ALE can be used in different implementations of the DBTL cycle will be presented (Sandberg et al. 2019). First, an implementation where ALE is used after the Build step but before the Test step to engineer Pseudomonas putida to utilize non-native hemicellulose monomers is presented (Lim et al. 2021). Second, an implementation where aggregated ALE generated mutations in the ALE database (ALEdb) are utilized in the Design step to introduce novel mutations for enhanced glycerol uptake is described (Phaneuf et al. 2021). The third implementation will describe how ALE can replace the Design and Build steps to generate a strain with enhanced secretion and tolerance to L-serine in one DBTL cycle to contribute to a commercially viable production strain (Mundhada et al. 2017). Finally, what is likely achievable for ALE and laboratory automation in the short term and how it can be broadly applied to solve more problems in industrial bioproduction is presented.
Sandberg, T. E., et al. 2019. “The Emergence of Adaptive Laboratory Evolution as an Efficient Tool for Biological Discovery and Industrial Biotechnology.” Metabolic Engineering 56, 1–16. DOI: 10.1016/j.ymben.2019.08.004.
Lim, H. G., et al. 2021. “Generation of Pseudomonas putida KT2440 Strains with Efficient Utilization of Xylose and Galactose via Adaptive Laboratory Evolution.” ACS Sustainable Chemistry & Engineering 9(34), 11512–23.
Phaneuf, P. V., et al. 2021. “Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data.” ACS Synthetic Biology 10(12), 3379–95. DOI: 10.1021/acssynbio.1c00337.
Mundhada H., et al. 2017. “Increased Production of L-serine in Escherichia coli through Adaptive Laboratory Evolution.” Metabolic Engineering 39, 141–50.
This work was part of the DOE Joint BioEnergy Institute (http://www.jbei.org) supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the U.S. Department of Energy.