Optimizing complex phenotypes through model-guided multiplex genome engineering

Kuznetsov, Gleb; Goodman, Daniel B.; Filsinger, Gabriel T.; Landon, Matthieu; Rohland, Nadin; Aach, John; Lajoie, Marc J.; Church, George M.

doi:10.1186/s13059-017-1217-z

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

Abstract

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:

; Goodman, Daniel B.; Filsinger, Gabriel T.; Landon, Matthieu; Rohland, Nadin; Aach, John; Lajoie, Marc J.; Church, George M.

Publication Date:: Thu May 25 00:00:00 EDT 2017

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)

Contributing Org.:: AWS Cloud Credits for Research Program

OSTI Identifier:: 1618923

Alternate Identifier(s):: OSTI ID: 1371697; OSTI ID: 1389296

Grant/Contract Number:: FG02-02ER63445

Resource Type:: Published Article

Journal Name:: Genome Biology (Online)

Additional Journal Information:: Journal Name: Genome Biology (Online) Journal Volume: 18 Journal Issue: 1; Journal ID: ISSN 1474-760X

Publisher:: Springer Science + Business Media

Country of Publication:: United Kingdom

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 Kingdom: N. p., 2017. 
Web.  doi:10.1186/s13059-017-1217-z.

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                    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 Kingdom.  https://doi.org/10.1186/s13059-017-1217-z

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                    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 Kingdom.  https://doi.org/10.1186/s13059-017-1217-z.

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@article{osti_1618923,

  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 = {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 Kingdom},

  year         = {Thu May 25 00:00:00 EDT 2017},

  month        = {Thu May 25 00:00:00 EDT 2017}

}

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https://doi.org/10.1186/s13059-017-1217-z

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Cited by: 15 works

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Figures / Tables:

Fig. 1: Workflow for improving phenotypes through model-guided multiplex genome editing. First, an initial set of target alleles (hundreds to thousands) is chosen for testing based on starting hypotheses. These targets may be designed based on differences from a reference strain, synthesis or design errors, or biophysical modeling. Multiplex genomemore »

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

Abstract

Citation Formats

Figures / Tables:

Design, synthesis, and testing toward a 57-codon genome journal, August 2016

Genomic Recoding Broadly Obstructs the Propagation of Horizontally Transferred Genetic Elements journal, August 2016

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Programming cells by multiplex genome engineering and accelerated evolution journal, July 2009

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Design, synthesis, and testing toward a 57-codon genome
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Genomic Recoding Broadly Obstructs the Propagation of Horizontally Transferred Genetic Elements
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Diminishing Returns Epistasis Among Beneficial Mutations Decelerates Adaptation
journal, June 2011

Multiplexed tracking of combinatorial genomic mutations in engineered cell populations
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Enhanced levels of λ Red-mediated recombinants in mismatch repair mutants
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Biocontainment of genetically modified organisms by synthetic protein design
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Rational optimization of tolC as a powerful dual selectable marker for genome engineering
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Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants the Keio collection
journal, February 2006

Precise Manipulation of Chromosomes in Vivo Enables Genome-Wide Codon Replacement
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Programming cells by multiplex genome engineering and accelerated evolution
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EcoCyc: a comprehensive database of Escherichia coli biology
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Adaptive laboratory evolution – principles and applications for biotechnology
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Negative Epistasis Between Beneficial Mutations in an Evolving Bacterial Population
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Trackable Multiplex Recombineering for Gene-Trait Mapping in E. coli
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Gene Ontology: tool for the unification of biology
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Codon Bias as a Means to Fine-Tune Gene Expression
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Use of Adaptive Laboratory Evolution To Discover Key Mutations Enabling Rapid Growth of Escherichia coli K-12 MG1655 on Glucose Minimal Medium
journal, October 2014

Genome engineering Escherichia coli for L-DOPA overproduction from glucose
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Emergent rules for codon choice elucidated by editing rare arginine codons in Escherichia coli
journal, September 2016

Adenylate Cyclase and the Cyclic AMP Receptor Protein Modulate Stress Resistance and Virulence Capacity of Uropathogenic Escherichia coli
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journal, May 2015

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journal, December 2014

Design and synthesis of a minimal bacterial genome
journal, March 2016

Evolution of Escherichia coli to 42 °C and Subsequent Genetic Engineering Reveals Adaptive Mechanisms and Novel Mutations
journal, July 2014

Emergent Properties of Reduced-Genome Escherichia coli
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Millstone: software for multiplex microbial genome analysis and engineering
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Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements
journal, March 1997

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journal, January 2012

Refactoring bacteriophage T7
journal, January 2005

Genetic Screens in Human Cells Using the CRISPR-Cas9 System
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