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Multiplexed tracking of combinatorial genomic mutations in engineered cell populations

Nature Biotechnology volume 33, pages 631–637 (2015)Cite this article

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Abstract

Multiplexed genome engineering approaches can be used to generate targeted genetic diversity in cell populations on laboratory timescales, but methods to track mutations and link them to phenotypes have been lacking. We present an approach for tracking combinatorial engineered libraries (TRACE) through the simultaneous mapping of millions of combinatorially engineered genomes at single-cell resolution. Distal genomic sites are assembled into individual DNA constructs that are compatible with next-generation sequencing strategies. We used TRACE to map growth selection dynamics for Escherichia coli combinatorial libraries created by recursive multiplex recombineering at a depth 104-fold greater than before. TRACE was used to identify genotype-to-phenotype correlations and to map the evolutionary trajectory of two individual combinatorial mutants in E. coli. Combinatorial mutations in the human ES2 ovarian carcinoma cell line were also assessed with TRACE. TRACE completes the combinatorial engineering cycle and enables more sophisticated approaches to genome engineering in both bacteria and eukaryotic cells than are currently possible.

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Figure 1: By condensing genotype information into a single construct compatible with high-throughput sequencing technology, we can track libraries containing >105 members.
Figure 2: Mathematical modeling of the multiplexed assembly kinetics occurring when condensing information directly from the genome in TRACE.
Figure 3: Generalizability and demonstration of TRACE assembly approach on single genotypes.
Figure 4: Tracking an artificial combinatorial population using next-generation sequencing.
Figure 5: Assessment of a hydrolysate tolerance library generated by recursive multiplexed recombineering.
Figure 6: Assessment of a 6-site RBS library targeting membrane genes found to be influential to isobutanol tolerance.

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References

  1. Wang, H.H. et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898 (2009).

    Article  CAS  Google Scholar 

  2. Wang, H.H. et al. Genome-scale promoter engineering by coselection MAGE. Nat. Methods 9, 591–593 (2012).

    Article  Google Scholar 

  3. Alper, H., Miyaoku, K. & Stephanopoulos, G. Construction of lycopene-overproducing E. coli strains by combining systematic and combinatorial gene knockout targets. Nat. Biotechnol. 23, 612–616 (2005).

    Article  CAS  Google Scholar 

  4. Lee, J.W. et al. Systems metabolic engineering of microorganisms for natural and non-natural chemicals. Nat. Chem. Biol. 8, 536–546 (2012).

    Article  CAS  Google Scholar 

  5. Wang, B.L. et al. Microfluidic high-throughput culturing of single cells for selection based on extracellular metabolite production or consumption. Nat. Biotechnol. 32, 473–478 (2014).

    Article  CAS  Google Scholar 

  6. Pál, C., Papp, B. & Pósfai, G. The dawn of evolutionary genome engineering. Nat. Rev. Genet. 15, 504–512 (2014).

    Article  Google Scholar 

  7. Romero, P.A. & Arnold, F.H. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell Biol. 10, 866–876 (2009).

    Article  CAS  Google Scholar 

  8. Wang, H.H. & Church, G.M. Multiplexed genome engineering and genotyping methods applications for synthetic biology and metabolic engineering. Methods Enzymol. 498, 409–426 (2011).

    Article  CAS  Google Scholar 

  9. Barrick, J.E. & Lenski, R.E. Genome dynamics during experimental evolution. Nat. Rev. Genet. 14, 827–839 (2013).

    Article  CAS  Google Scholar 

  10. Sandoval, N.R. et al. Strategy for directing combinatorial genome engineering in Escherichia coli. Proc. Natl. Acad. Sci. USA 109, 10540–10545 (2012).

    Article  CAS  Google Scholar 

  11. Woodruff, L.B. et al. Genome-scale identification and characterization of ethanol tolerance genes in Escherichia coli. Metab. Eng. 15, 124–133 (2013).

    Article  CAS  Google Scholar 

  12. Warner, J.R., Reeder, P.J., Karimpour-Fard, A., Woodruff, L.B. & Gill, R.T. Rapid profiling of a microbial genome using mixtures of barcoded oligonucleotides. Nat. Biotechnol. 28, 856–862 (2010).

    Article  CAS  Google Scholar 

  13. Kosuri, S. & Church, G.M. Large-scale de novo DNA synthesis: technologies and applications. Nat. Methods 11, 499–507 (2014).

    Article  CAS  Google Scholar 

  14. Wang, X. et al. Engineering furfural tolerance in Escherichia coli improves the fermentation of lignocellulosic sugars into renewable chemicals. Proc. Natl. Acad. Sci. USA 110, 4021–4026 (2013).

    Article  CAS  Google Scholar 

  15. Phillips, P.C. Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems. Nat. Rev. Genet. 9, 855–867 (2008).

    Article  CAS  Google Scholar 

  16. Khan, A.I. et al. Negative epistasis between beneficial mutations in an evolving bacterial population. Science 332, 1193–1196 (2011).

    Article  CAS  Google Scholar 

  17. Craig, D.W. et al. Identification of genetic variants using bar-coded multiplexed sequencing. Nat. Methods 5, 887–893 (2008).

    Article  CAS  Google Scholar 

  18. Tewhey, R. et al. Microdroplet-based PCR enrichment for large-scale targeted sequencing. Nat. Biotechnol. 27, 1025–1031 (2009).

    Article  CAS  Google Scholar 

  19. Mamanova, L. et al. Target-enrichment strategies for next-generation sequencing. Nat. Methods 7, 111–118 (2010).

    Article  CAS  Google Scholar 

  20. Metzker, M.L. Sequencing technologies—the next generation. Nat. Rev. Genet. 11, 31–46 (2010).

    Article  CAS  Google Scholar 

  21. Casini, A. et al. One-pot DNA construction for synthetic biology: the Modular Overlap-Directed Assembly with Linkers (MODAL) strategy. Nucleic Acids Res. 42, e7 (2014).

    Article  CAS  Google Scholar 

  22. Wetmur, J.G. et al. Molecular haplotyping by linking emulsion PCR: analysis of paraoxonase 1 haplotypes and phenotypes. Nucleic Acids Res. 33, 2615–2619 (2005).

    Article  CAS  Google Scholar 

  23. Estep, A.L., Palmer, C., McCormick, F. & Rauen, K.A. Mutation analysis of BRAF, MEK1 and MEK2 in 15 ovarian cancer cell lines: implications for therapy. PLoS ONE 2, e1279 (2007).

    Article  Google Scholar 

  24. Williams, R. et al. Amplification of complex gene libraries by emulsion PCR. Nat. Methods 3, 545–550 (2006).

    Article  CAS  Google Scholar 

  25. Kanagawa, T. Bias and artifacts in multitemplate polymerase chain reactions (PCR). J. Biosci. Bioeng. 96, 317–323 (2003).

    Article  CAS  Google Scholar 

  26. Pääbo, S., Irwin, D.M. & Wilson, A.C. DNA damage promotes jumping between templates during enzymatic amplification. J. Biol. Chem. 265, 4718–4721 (1990).

    PubMed  Google Scholar 

  27. Volkmer, B. & Heinemann, M. Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling. PLoS ONE 6, e23126 (2011).

    Article  CAS  Google Scholar 

  28. Sawitzke, J.A. et al. Probing cellular processes with oligo-mediated recombination and using the knowledge gained to optimize recombineering. J. Mol. Biol. 407, 45–59 (2011).

    Article  CAS  Google Scholar 

  29. Farasat, I. et al. Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria. Mol. Syst. Biol. 10, 731 (2014).

    Article  Google Scholar 

  30. Minty, J.J. et al. Evolution combined with genomic study elucidates genetic bases of isobutanol tolerance in Escherichia coli. Microb. Cell Fact. 10, 18 (2011).

    Article  CAS  Google Scholar 

  31. Goodarzi, H. et al. Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli. Mol. Syst. Biol. 6, 378 (2010).

    Article  Google Scholar 

  32. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    Article  CAS  Google Scholar 

  33. Na, D. et al. Metabolic engineering of Escherichia coli using synthetic small regulatory RNAs. Nat. Biotechnol. 31, 170–174 (2013).

    Article  CAS  Google Scholar 

  34. Yoo, S.M., Na, D. & Lee, S.Y. Design and use of synthetic regulatory small RNAs to control gene expression in Escherichia coli. Nat. Protoc. 8, 1694–1707 (2013).

    Article  CAS  Google Scholar 

  35. Jiang, W. et al. RNA-guided editing of bacterial genomes using CRISPR-Cas systems. Nat. Biotechnol. 31, 233–239 (2013).

    Article  CAS  Google Scholar 

  36. Datta, S., Costantino, N., Zhou, X. & Court, D.L. Identification and analysis of recombineering functions from Gram-negative and Gram-positive bacteria and their phages. Proc. Natl. Acad. Sci. USA 105, 1626–1631 (2008).

    Article  CAS  Google Scholar 

  37. Gevertz, J.L., Dunn, S.M. & Roth, C.M. Mathematical model of real-time PCR kinetics. Biotechnol. Bioeng. 92, 346–355 (2005).

    Article  CAS  Google Scholar 

  38. Eklund, A.C., Friis, P., Wernersson, R. & Szallasi, Z. Optimization of the BLASTN substitution matrix for prediction of non-specific DNA microarray hybridization. Nucleic Acids Res. 38, e27 (2010).

    Article  Google Scholar 

  39. Miura, F., Uematsu, C., Sakaki, Y. & Ito, T. A novel strategy to design highly specific PCR primers based on the stability and uniqueness of 3′-end subsequences. Bioinformatics 21, 4363–4370 (2005).

    Article  CAS  Google Scholar 

  40. Caporaso, J.G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 108, 4516–4522 (2011).

    Article  CAS  Google Scholar 

  41. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    Article  CAS  Google Scholar 

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Acknowledgements

Special thanks to S.A. Lynch and W.C. Grau for support and advice on this work. We would like to thank J. Huntley and S. Gao at the Colorado Biofrontiers Advanced Sequencing Core Facility for assistance with high-throughput sequencing. We would like to thank J. Liddle for generously sharing the ES2 cancer cell line. This work was funded by DOE BER Genomic Sciences Program Award Number DE-SC0008812. We would also like to acknowledge the C2B2/NSF REU Program (NSF#1261303) for funding G.D.D.

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Authors and Affiliations

  1. Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado, USA

    Ramsey I Zeitoun, Andrew D Garst, Gur Pines, Thomas J Mansell, Tirzah Y Glebes & Ryan T Gill

  2. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    George D Degen

  3. Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, Colorado, USA

    Nanette R Boyle

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  1. Ramsey I Zeitoun
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Contributions

R.I.Z., A.D.G. and R.T.G. conceived this idea. R.I.Z., A.D.G., G.P., T.J.M. and R.T.G. designed experiments. G.D.D. and R.I.Z. performed kinetic modeling. R.I.Z. performed experiments with assistance from G.P., T.Y.G. and N.R.B. R.I.Z. and R.T.G. wrote the manuscript.

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Correspondence to Ryan T Gill.

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The authors declare no competing financial interests.

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Zeitoun, R., Garst, A., Degen, G. et al. Multiplexed tracking of combinatorial genomic mutations in engineered cell populations. Nat Biotechnol 33, 631–637 (2015). https://doi.org/10.1038/nbt.3177

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