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Multiplexed tracking of combinatorial genomic mutations in engineered cell populations

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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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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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.

Corresponding author

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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