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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)
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}
}

Journal Article:
Free Publicly Available Full Text
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  • 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.
  • Inexpensive DNA sequencing and advances in genome editing have made computational analysis a major rate-limiting step in adaptive laboratory evolution and microbial genome engineering. Here, we describe Millstone, a web-based platform that automates genotype comparison and visualization for projects with up to hundreds of genomic samples. To enable iterative genome engineering, Millstone allows users to design oligonucleotide libraries and create successive versions of reference genomes. Millstone is open source and easily deployable to a cloud platform, local cluster, or desktop, making it a scalable solution for any lab.
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  • Self-organized anisotropic strain engineering guided on shallow- and deep-patterned GaAs (311)B substrates is exploited for formation of complex laterally ordered architectures of connected InGaAs quantum dot (QD) arrays and isolated InAs QD groups by molecular beam epitaxy. The combination of strain and step engineerings on shallow stripe-patterned substrates transforms the periodic spotlike arrangement of the InGaAs QD arrays and InAs QD groups (on planar substrates) into a zigzag arrangement of periodic stripes which are well ordered over macroscopic areas on zigzag mesa-patterned substrates. In contrast, the formation of slow-growing facets on deep-patterned substrates produces QD-free mesa sidewalls, while InGaAs QDmore » arrays and InAs QD groups form on the GaAs (311)B top and bottom planes with arrangements modified only close to the sidewalls depending on the sidewall orientation. The QDs on the shallow- and deep-patterned substrates exhibit excellent optical properties up to room temperature. Therefore, the concept of guided self-organization demonstrated on shallow-patterned (due to steps) and deep-patterned (due to facets) substrates is highlighted for creation of complex architectures of laterally ordered QDs for future quantum functional devices.« less