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Title: Optimizing complex phenotypes through model-guided multiplex genome engineering

Here, 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.ΔA. 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.
Authors:
ORCiD logo [1] ;  [2] ;  [2] ;  [3] ;  [4] ;  [4] ;  [2] ;  [2]
  1. Harvard Univ., Boston, MA (United States). Harvard Medical School, Dept. of Genetics; Harvard Univ., Boston, MA (United States). Harvard Medical School, Wyss Inst. for Biologically Inspired Engineering; Harvard Univ., Boston, MA (United States). Program in Biophysics
  2. Harvard Univ., Boston, MA (United States). Harvard Medical School, Dept. of Genetics; Harvard Univ., Boston, MA (United States). Harvard Medical School, Wyss Inst. for Biologically Inspired Engineering
  3. Harvard Univ., Boston, MA (United States). Harvard Medical School, Dept. of Genetics; Harvard Univ., Boston, MA (United States). Harvard Medical School, Systems Biology Graduate Program; Ecole des Mines de Paris, Mines Paristech, Paris (France)
  4. Harvard Univ., Boston, MA (United States). Harvard Medical School, Dept. of Genetics
Publication Date:
Grant/Contract Number:
FG02-02ER63445
Type:
Accepted Manuscript
Journal Name:
Genome Biology (Online)
Additional Journal Information:
Journal Name: Genome Biology (Online); Journal Volume: 18; Journal Issue: 1; Related Information: Analysis and simulation code is available at https://github.com/churchlab/optimizing-complex-phenotypes; Journal ID: ISSN 1474-760X
Publisher:
BioMed Central
Research Org:
Harvard Univ., Boston, MA (United States). Harvard Medical School; Harvard Univ., Cambridge, MA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Contributing Orgs:
AWS Cloud Credits for Research Program
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; Genome engineering; Predictive modeling; Synthetic organisms; Genome engineering, Predictive modeling, Synthetic organisms
OSTI Identifier:
1371697
Alternate Identifier(s):
OSTI ID: 1389296