skip to main content

DOE PAGESDOE PAGES

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

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., 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.. 2017. "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 = {2017},
month = {5}
}

Works referenced in this record:

Multiplexed tracking of combinatorial genomic mutations in engineered cell populations
journal, March 2015
  • Zeitoun, Ramsey I.; Garst, Andrew D.; Degen, George D.
  • Nature Biotechnology, Vol. 33, Issue 6, p. 631-637
  • DOI: 10.1038/nbt.3177

Enhanced levels of λ Red-mediated recombinants in mismatch repair mutants
journal, December 2003
  • Costantino, N.; Court, D. L.
  • Proceedings of the National Academy of Sciences, Vol. 100, Issue 26, p. 15748-15753
  • DOI: 10.1073/pnas.2434959100

Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants the Keio collection
journal, February 2006
  • Baba, Tomoya; Ara, Takeshi; Hasegawa, Miki
  • Molecular Systems Biology, Vol. 2, Article No. 2006.0008
  • DOI: 10.1038/msb4100050

Precise Manipulation of Chromosomes in Vivo Enables Genome-Wide Codon Replacement
journal, July 2011
  • Isaacs, Farren J.; Carr, Peter A.; Wang, Harris H.
  • Science, Vol. 333, Issue 6040, p. 348-353
  • DOI: 10.1126/science.1205822

Programming cells by multiplex genome engineering and accelerated evolution
journal, July 2009
  • Wang, Harris H.; Isaacs, Farren J.; Carr, Peter A.
  • Nature, Vol. 460, Issue 7257, p. 894-898
  • DOI: 10.1038/nature08187

Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells
journal, December 2013
  • Shalem, Ophir; Sanjana, Neville E.; Hartenian, Ella
  • Science, Vol. 343, Issue 6166, p. 84-87
  • DOI: 10.1126/science.1247005

Evolution-guided optimization of biosynthetic pathways
journal, December 2014
  • Raman, Srivatsan; Rogers, Jameson K.; Taylor, Noah D.
  • Proceedings of the National Academy of Sciences, Vol. 111, Issue 50, p. 17803-17808
  • DOI: 10.1073/pnas.1409523111

Emergent Properties of Reduced-Genome Escherichia coli
journal, May 2006
  • Posfai, G.; Plunkett III, Guy; Fehér, Tamás
  • Science, Vol. 312, Issue 5776, p. 1044-1046
  • DOI: 10.1126/science.1126439

Genomically Recoded Organisms Expand Biological Functions
journal, October 2013
  • Lajoie, M. J.; Rovner, A. J.; Goodman, D. B.
  • Science, Vol. 342, Issue 6156, p. 357-360
  • DOI: 10.1126/science.1241459