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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]
+ Show Author Affiliations
  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:
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.
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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 States. 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., 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.
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@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 = {2017},
month = {5}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI:  10.1186/s13059-017-1217-z
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Cited by: 6 works
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Figures / Tables:

Fig. 1 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 » editing creates a set of modified clones enriched with combinations of the targeted changes. Clones are screened for genotype and phenotype and predictive modeling is used to quantify allele effects. The workflow is repeated to validate and test new alleles. Beneficial alleles are combined to create an optimized genotype« less
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    Works referencing / citing this record:

    Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    Additional file 10: Table S9. of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    • Kuznetsov, Gleb; Goodman, Daniel; Filsinger, Gabriel
    • Figshare-Supplementary information for journal article at DOI: 10.1186/s13059-017-1217-z, 1 xlsx file (12.48 MB)
    • DOI: 10.6084/m9.figshare.c.3788539_d1

    Additional file 8: Table S7. of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    • Kuznetsov, Gleb; Goodman, Daniel; Filsinger, Gabriel
    • figshare-Supplementary information for journal article at DOI: 10.1186/s13059-017-1217-z, 1 XLSX file (39.89 kB)
    • DOI: 10.6084/m9.figshare.c.3788539_d10

    Additional file 9: Table S8. of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    Additional file 11: Table S10. of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    • Kuznetsov, Gleb; Goodman, Daniel; Filsinger, Gabriel
    • figshare-Supplementary information for journal article at DOI: 10.1186/s13059-017-1217-z, 1 XLSX file (45.85 kB)
    • DOI: 10.6084/m9.figshare.c.3788539_d2

    Additional file 1: of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    • Kuznetsov, Gleb; Goodman, Daniel; Filsinger, Gabriel
    • figshare-Supplementary information for journal article at DOI: 10.1186/s13059-017-1217-z, 1 PDF file (1.12 MB)
    • DOI: 10.6084/m9.figshare.c.3788539_d3

    Additional file 2: Table S1. of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    • Kuznetsov, Gleb; Goodman, Daniel; Filsinger, Gabriel
    • Figshare-Supplementary information for journal article at DOI: 10.1186/s13059-017-1217-z, 1 xlsx file (36.52 kB)
    • DOI: 10.6084/m9.figshare.c.3788539_d4

    Additional file 3: Table S2. of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    Additional file 4: Table S3. of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    • Kuznetsov, Gleb; Goodman, Daniel; Filsinger, Gabriel
    • Figshare-Supplementary information for journal article at DOI: 10.1186/s13059-017-1217-z, 1 xlsx file (37.95 kB)
    • DOI: 10.6084/m9.figshare.c.3788539_d6

    Additional file 5: Table S4. of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    • Kuznetsov, Gleb; Goodman, Daniel; Filsinger, Gabriel
    • figshare-Supplementary information for journal article at DOI: 10.1186/s13059-017-1217-z, 1 XLSX file (9.51 kB)
    • DOI: 10.6084/m9.figshare.c.3788539_d7

    Additional file 6: Table S5. of Optimizing complex phenotypes through model-guided multiplex genome engineering [Supplemental Data]
    dataset, May 2017

    Additional file 7: Table S6. of Optimizing complex phenotypes through model-guided multiplex genome engineering
    dataset, May 2017

    • Kuznetsov, Gleb; Goodman, Daniel; Filsinger, Gabriel
    • figshare-Supplementary information for journal article at DOI: 10.1186/s13059-017-1217-z, 1 XLSX file (35.83 kB)
    • DOI: 10.6084/m9.figshare.c.3788539_d9
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