Cargando…

Optimizing complex phenotypes through model-guided multiplex genome engineering

We present a method for identifying genomic modifications that optimize a complex phenotype through multiplex genome engineering and predictive modeling. We apply our method to identify six single nucleotide mutations that recover 59% of the fitness defect exhibited by the 63-codon E. coli strain C3...

Descripción completa

Detalles Bibliográficos
Autores principales: Kuznetsov, Gleb, Goodman, Daniel B., Filsinger, Gabriel T., Landon, Matthieu, Rohland, Nadin, Aach, John, Lajoie, Marc J., Church, George M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445303/
https://www.ncbi.nlm.nih.gov/pubmed/28545477
http://dx.doi.org/10.1186/s13059-017-1217-z
Descripción
Sumario:We present a method for identifying genomic modifications that optimize a complex phenotype through multiplex genome engineering and predictive modeling. We apply our method to identify six single nucleotide mutations that recover 59% of the fitness defect exhibited by the 63-codon E. coli strain C321.∆A. By introducing targeted combinations of changes in multiplex we generate rich genotypic and phenotypic diversity and characterize clones using whole-genome sequencing and doubling time measurements. Regularized multivariate linear regression accurately quantifies individual allelic effects and overcomes bias from hitchhiking mutations and context-dependence of genome editing efficiency that would confound other strategies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-017-1217-z) contains supplementary material, which is available to authorized users.