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

Abstract

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:
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 Org.:
AWS Cloud Credits for Research Program
OSTI Identifier:
1371697
Alternate Identifier(s):
OSTI ID: 1389296
Grant/Contract Number:  
FG02-02ER63445
Resource Type:
Journal Article: 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
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

Citation Formats

Kuznetsov, Gleb, Goodman, Daniel B., Filsinger, Gabriel T., Landon, Matthieu, Rohland, Nadin, Aach, John, Lajoie, Marc J., and Church, George M.. Optimizing complex phenotypes through model-guided multiplex genome engineering. United States: N. p., 2017. Web. doi:10.1186/s13059-017-1217-z.
Kuznetsov, Gleb, Goodman, Daniel B., Filsinger, Gabriel T., Landon, Matthieu, Rohland, Nadin, Aach, John, Lajoie, Marc J., & Church, George M.. Optimizing complex phenotypes through model-guided multiplex genome engineering. United States. doi:10.1186/s13059-017-1217-z.
Kuznetsov, Gleb, Goodman, Daniel B., Filsinger, Gabriel T., Landon, Matthieu, Rohland, Nadin, Aach, John, Lajoie, Marc J., and Church, George M.. Thu . "Optimizing complex phenotypes through model-guided multiplex genome engineering". United States. doi:10.1186/s13059-017-1217-z. https://www.osti.gov/servlets/purl/1371697.
@article{osti_1371697,
title = {Optimizing complex phenotypes through model-guided multiplex genome engineering},
author = {Kuznetsov, Gleb and Goodman, Daniel B. and Filsinger, Gabriel T. and Landon, Matthieu and Rohland, Nadin and Aach, John and Lajoie, Marc J. and Church, George M.},
abstractNote = {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.},
doi = {10.1186/s13059-017-1217-z},
journal = {Genome Biology (Online)},
number = 1,
volume = 18,
place = {United States},
year = {Thu May 25 00:00:00 EDT 2017},
month = {Thu May 25 00:00:00 EDT 2017}
}

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Cited by: 3 works
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