Optimizing complex phenotypes through model-guided multiplex genome engineering.

Area of Science:

• Synthetic biology
• Genomics
• Microbial engineering

Background:

• Complex phenotypes are challenging to optimize genetically.
• Previous methods struggle with multifactorial genetic interactions and editing biases.

Purpose of the Study:

• To present a novel method for identifying genomic modifications that optimize complex phenotypes.
• To apply this method to enhance the fitness of a genetically modified E. coli strain.

Main Methods:

• Multiplex genome engineering was employed to introduce multiple genetic changes simultaneously.
• Predictive modeling and regularized multivariate linear regression were used to analyze genotype-phenotype relationships.
• Whole-genome sequencing and doubling time measurements characterized engineered clones.

Main Results:

• Six single nucleotide mutations were identified that significantly improved E. coli C321.∆A strain fitness.
• These mutations recovered 59% of the fitness defect in the engineered strain.
• The regression model accurately quantified individual mutation effects, mitigating bias.

Conclusions:

• The developed method is effective for identifying beneficial genomic modifications in complex biological systems.
• This approach enables precise optimization of microbial phenotypes through targeted genetic engineering.
• Understanding allele effects in combination is crucial for successful strain improvement.