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Genetic programs can be compressed and autonomously decompressed in live cells

Nature Nanotechnology volume 13, pages 309–315 (2018)Cite this article

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Abstract

Fundamental computer science concepts have inspired novel information-processing molecular systems in test tubes1,2,3,4,5,6,7,8,9,10,11,12,13 and genetically encoded circuits in live cells14,15,16,17,18,19,20,21. Recent research has shown that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity22 of two bits per nucleotide23. In nature, DNA is used to store genetic programs, but the information content of the encoding rarely approaches this maximum24. We hypothesize that the biological function of a genetic program can be preserved while reducing the length of its DNA encoding and increasing the information content per nucleotide. Here we support this hypothesis by describing an experimental procedure for compressing a genetic program and its subsequent autonomous decompression and execution in human cells. As a test-bed we choose an RNAi cell classifier circuit25 that comprises redundant DNA sequences and is therefore amenable for compression, as are many other complex gene circuits15,18,26,27,28. In one example, we implement a compressed encoding of a ten-gene four-input AND gate circuit using only four genetic constructs. The compression principles applied to gene circuits can enable fitting complex genetic programs into DNA delivery vehicles with limited cargo capacity, and storing compressed and biologically inert programs in vivo for on-demand activation.

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Fig. 1: Mechanism of compression and decompression of a multi-input miRNA logic circuit.
Fig. 2: Optimization of the decompression process.
Fig. 3: Compression of three-input AND gate circuits.
Fig. 4: Compression of four-input AND gate circuits.

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References

  1. Adleman, L. M. Molecular computation of solutions to combinatorial problems. Science 266, 1021–1024 (1994).

    Article  Google Scholar 

  2. Mao, C. D., LaBean, T. H., Reif, J. H. & Seeman, N. C. Logical computation using algorithmic self-assembly of DNA triple-crossover molecules. Nature 407, 493–496 (2000).

    Article  Google Scholar 

  3. Benenson, Y. et al. Programmable and autonomous computing machine made of biomolecules. Nature 414, 430–434 (2001).

    Article  Google Scholar 

  4. Qian, L., Winfree, E. & Bruck, J. Neural network computation with DNA strand displacement cascades. Nature 475, 368–372 (2011).

    Article  Google Scholar 

  5. Liu, Q. et al. DNA computing on surfaces. Nature 403, 175–179 (2000).

    Article  Google Scholar 

  6. Rothemund, P. W. K. Folding DNA to create nanoscale shapes and patterns. Nature 440, 297–302 (2006).

    Article  Google Scholar 

  7. Phillips, A. & Cardelli, L. A programming language for composable DNA circuits. J. Royal Soc. Interface 6, S419–S436 (2009).

    Article  Google Scholar 

  8. Gehani, A., LaBean, T. & Reif, J. DNA-based cryptography. Lecture Notes Comp. Sci. 2950, 167–188 (2003).

    Article  Google Scholar 

  9. Chen, Y.-J. et al. Programmable chemical controllers made from DNA. Nat. Nanotech. 8, 755–762 (2013).

    Article  Google Scholar 

  10. Church, G. M., Gao, Y. & Kosuri, S. Next-generation digital information storage in DNA. Science 337, 1628 (2012).

    Article  Google Scholar 

  11. Padirac, A., Fujii, T. & Rondelez, Y. Bottom-up construction of in vitro switchable memories. Proc. Natl Acad. Sci. USA 109, E3212–E3220 (2012).

    Article  Google Scholar 

  12. Weitz, M. et al. Diversity in the dynamical behaviour of a compartmentalized programmable biochemical oscillator. Nat. Chem. 6, 295–302 (2014).

    Article  Google Scholar 

  13. Stojanovic, M. N. & Stefanovic, D. A deoxyribozyme-based molecular automaton. Nat. Biotechnol. 21, 1069–1074 (2003).

    Article  Google Scholar 

  14. Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    Article  Google Scholar 

  15. Rinaudo, K. et al. A universal RNAi-based logic evaluator that operates in mammalian cells. Nat. Biotechnol. 25, 795–801 (2007).

    Article  Google Scholar 

  16. Regot, S. et al. Distributed biological computation with multicellular engineered networks. Nature 469, 207–211 (2011).

    Article  Google Scholar 

  17. Bonnet, J., Yin, P., Ortiz, M. E., Subsoontorn, P. & Endy, D. Amplifying genetic logic gates. Science 340, 599–603 (2013).

    Article  Google Scholar 

  18. Daniel, R., Rubens, J. R., Sarpeshkar, R. & Lu, T. K. Synthetic analog computation in living cells. Nature 497, 619–623 (2013).

    Article  Google Scholar 

  19. Nielsen, A. et al. Genetic circuit design automation. Science 352, aac7341 (2016).

    Article  Google Scholar 

  20. Green, A. A., Silver, Pamela, A., Collins, James, J. & Yin, P. Toehold switches: de-novo-designed regulators of gene expression. Cell 159, 925–939 (2014).

    Article  Google Scholar 

  21. Anderson, J. C., Voigt, C. A. & Arkin, A. P. Environmental signal integration by a modular and gate. Mol. Syst. Biol. 3, 133 (2007).

    Article  Google Scholar 

  22. Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948). 623–656.

    Article  Google Scholar 

  23. Erlich, Y. & Zielinski, D. DNA fountain enables a robust and efficient storage architecture. Science 355, 950–954 (2017).

    Article  Google Scholar 

  24. Kuruppu, S., Puglisi, S. & Zobel, J. Optimized relative Lempel–Ziv compression of genomes. Proc. 34th Australas. Comp. Sci. Conf. 91–98 (2011).

  25. Xie, Z., Wroblewska, L., Prochazka, L., Weiss, R. & Benenson, Y. Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science 333, 1307–1311 (2011).

    Article  Google Scholar 

  26. Xie, M. et al. β-cell-mimetic designer cells provide closed-loop glycemic control. Science 354, 1296–1301 (2016).

    Article  Google Scholar 

  27. Prochazka, L., Angelici, B., Haefliger, B. & Benenson, Y. Highly modular bow-tie gene circuits with programmable dynamic behaviour. Nat. Commun. 5, 4729 (2014).

    Article  Google Scholar 

  28. Schreiber, J., Arter, M., Lapique, N., Haefliger, B. & Benenson, Y. Model-guided combinatorial optimization of complex synthetic gene networks. Mol. Syst. Biol. 12, 899 (2016).

    Article  Google Scholar 

  29. Heagerty, P., Lumley, T. & Pepe, M. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56, 337–344 (2000).

    Article  Google Scholar 

  30. Lapique, N. & Benenson, Y. Digital switching in a biosensor circuit via programmable timing of gene availability. Nat. Chem. Biol. 10, 1020–1027 (2014).

    Article  Google Scholar 

  31. Bhatia, S., LaBoda, C., Yanez, V., Haddock-Angelli, T. & Densmore, D. Permutation machines. ACS Syn. Biol. 5, 827–834 (2016).

    Article  Google Scholar 

  32. Ham, T. S., Lee, S. K., Keasling, J. D. & Arkin, A. P. Design and construction of a double inversion recombination switch for heritable sequential genetic memory. PLoS ONE 3, e2815 (2008).

    Article  Google Scholar 

  33. Haefliger, B., Prochazka, L., Angelici, B. & Benenson, Y. Precision multidimensional assay for high-throughput microRNA drug discovery.Nat. Commun. 7, 10709 (2016).

    Article  Google Scholar 

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Acknowledgements

The research was funded by the National Institutes of Health award 5R01CA155320 and by ETH Zürich. We thank B. Angelici for discussions and E. Shapiro for commenting on the manuscript.

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  1. Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland

    Nicolas Lapique & Yaakov Benenson

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  1. Nicolas Lapique
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N.L. conceived research, performed experiments, analysed data, and wrote the paper. Y.B. conceived research, analysed data, supervised the project, and wrote the paper.

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Correspondence to Yaakov Benenson.

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Competing interests

The original miRNA circuit technology is protected by patents awarded to Y.B. and co-inventors (US patent no. 9458509). The output delay technology is pending, with N.L. and Y.B. listed as co-inventors.

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Lapique, N., Benenson, Y. Genetic programs can be compressed and autonomously decompressed in live cells. Nature Nanotech 13, 309–315 (2018). https://doi.org/10.1038/s41565-017-0004-z

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