Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Non-complementary strand commutation as a fundamental alternative for information processing by DNA and gene regulation

Abstract

The discovery of the DNA double helix has revolutionized our understanding of data processing in living systems, with the complementarity of the two DNA strands providing a reliable mechanism for the storage of hereditary information. Here I reveal the ‘strand commutation’ phenomenon—a fundamentally different mechanism of information storage and processing by DNA/RNA based on the reversible low-affinity interactions of essentially non-complementary nucleic acids. I demonstrate this mechanism by constructing a memory circuit, a 5-min square-root circuit for 4-bit inputs comprising only nine processing ssDNAs, simulating a 572-input AND gate (surpassing the bitness of current electronic computers), and elementary algebra systems with continuously changing variables. Most importantly, I show potential pathways of gene regulation with strands of maximum non-complementarity to the gene sequence that may be key to the reduction of off-target therapeutic effects. This Article uncovers the information-processing power of the low-affinity interactions that may underlie major processes in an organism—from short-term memory to cancer, ageing and evolution.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Signal transduction—conventional complementarity paradigm versus non-complementary strand commutation.
Fig. 2: Design and performance of the basic logic gates.
Fig. 3: Performance aspects of the basic YES/OR logic gates.
Fig. 4: Memory circuit and its design and performance.
Fig. 5: Square-root circuit and its design and performance.
Fig. 6: High bitness systems—500-input and 572-input AND gates.
Fig. 7: Analogue systems solving elementary algebra problems.
Fig. 8: Gene regulation circuits, and the strand commutation mechanism versus the conventional antisense concept.

Similar content being viewed by others

Data availability

The data that support the findings of this study are provided in the Article and its Supplementary Information, and are also available from the author on request. Source data are provided with this paper.

Code availability

The NUPACK (Ubuntu 14.04 BASH) and MATLAB scripts used to design the systems and analyse their performance are too numerous to be readily shared publicly, but can be made available from the corresponding author on reasonable request.

References

  1. Watson, J. D. & Crick, F. H. C. Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid. Nature 171, 737–738 (1953).

    Article  CAS  Google Scholar 

  2. Ceze, L., Nivala, J. & Strauss, K. Molecular digital data storage using DNA. Nat. Rev. Genet. 20, 456–466 (2019).

    Article  CAS  Google Scholar 

  3. Benenson, Y. Biomolecular computing systems: principles, progress and potential. Nat. Rev. Genet. 13, 455–468 (2012).

    Article  CAS  Google Scholar 

  4. Tregubov, A. A., Nikitin, P. I. & Nikitin, M. P. Advanced smart nanomaterials with integrated logic-gating and biocomputing: dawn of theranostic nanorobots. Chem. Rev. 118, 10294–10348 (2018).

    Article  CAS  Google Scholar 

  5. Nikitin, M. P. et al. Enhancement of the blood-circulation time and performance of nanomedicines via the forced clearance of erythrocytes. Nat. Biomed. Eng. 4, 717–731 (2020).

    Article  CAS  Google Scholar 

  6. De Silva, P. A., Gunaratne, N. H. Q. & McCoy, C. P. A molecular photoionic and gate based on fluorescent signalling. Nature 364, 42–44 (1993).

    Article  Google Scholar 

  7. Erbas-Cakmak, S. et al. Molecular logic gates: the past, present and future. Chem. Soc. Rev. 47, 2228–2248 (2018).

    Article  CAS  Google Scholar 

  8. Nikitin, M. P., Shipunova, V. O., Deyev, S. M. & Nikitin, P. I. Biocomputing based on particle disassembly. Nat. Nanotechnol. 9, 716–722 (2014).

    Article  CAS  Google Scholar 

  9. Katz, E. & Privman, V. Enzyme-based logic systems for information processing. Chem. Soc. Rev. 39, 1835–1857 (2010).

    Article  CAS  Google Scholar 

  10. Friedland, A. E. et al. Synthetic gene networks that count. Science 324, 1199–1202 (2009).

    Article  CAS  Google Scholar 

  11. 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  CAS  Google Scholar 

  12. Xie, M. & Fussenegger, M. Designing cell function: assembly of synthetic gene circuits for cell biology applications. Nat. Rev. Mol. Cell Biol. 19, 507–525 (2018).

    Article  CAS  Google Scholar 

  13. Fan, D., Wang, J., Wang, E. & Dong, S. Propelling DNA computing with materials’ power: recent advancements in innovative DNA logic computing systems and smart bio-applications. Adv. Sci. 7, 2001766 (2020).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  15. Qian, L. & Winfree, E. Scaling up digital circuit computation with DNA strand displacement cascades. Science 332, 1196–1201 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  17. Elbaz, J. et al. DNA computing circuits using libraries of DNAzyme subunits. Nat. Nanotechnol. 5, 417–422 (2010).

    Article  CAS  Google Scholar 

  18. Zadeh, J. N. et al. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170–173 (2011).

    Article  CAS  Google Scholar 

  19. Dirks, R. M., Bois, J. S., Schaeffer, J. M., Winfree, E. & Pierce, N. A. Thermodynamic analysis of interacting nucleic acid strands. SIAM Rev. 49, 65–88 (2007).

    Article  Google Scholar 

  20. Bissels, U. et al. Absolute quantification of microRNAs by using a universal reference. RNA 15, 2375–2384 (2009).

    Article  CAS  Google Scholar 

  21. Calabrese, J. M., Seila, A. C., Yeo, G. W. & Sharp, P. A. RNA sequence analysis defines Dicer’s role in mouse embryonic stem cells. Proc. Natl Acad. Sci. USA 104, 18097–18102 (2007).

    Article  CAS  Google Scholar 

  22. Zhang, J. X. et al. Predicting DNA hybridization kinetics from sequence. Nat. Chem. 10, 91–98 (2018).

    Article  Google Scholar 

  23. Bee, C. et al. Molecular-level similarity search brings computing to DNA data storage. Nat. Commun. 12, 4764 (2021).

    Article  CAS  Google Scholar 

  24. Song, T. et al. Fast and compact DNA logic circuits based on single-stranded gates using strand-displacing polymerase. Nat. Nanotechnol. 14, 1075–1081 (2019).

    Article  CAS  Google Scholar 

  25. Camunas-Soler, J., Alemany, A. & Ritort, F. Experimental measurement of binding energy, selectivity and allostery using fluctuation theorems. Science 355, 412–415 (2017).

    Article  CAS  Google Scholar 

  26. Bennett, C. F. & Swayze, E. E. RNA targeting therapeutics: molecular mechanisms of antisense oligonucleotides as a therapeutic platform. Annu. Rev. Pharmacol. Toxicol. 50, 259–293 (2010).

    Article  CAS  Google Scholar 

  27. Modarresi, F. et al. Inhibition of natural antisense transcripts in vivo results in gene-specific transcriptional upregulation. Nat. Biotechnol. 30, 453–459 (2012).

    Article  CAS  Google Scholar 

  28. Viswa Virinchi, M., Behera, A. & Gopalkrishnan, M. A reaction network scheme which implements the EM algorithm. In DNA Computing and Molecular Programming. DNA 2018. Lecture Notes in Computer Science (eds Doty, D. & Dietz, H.) Vol. 11145, 189–207 (Springer, 2018).

  29. Cappelletti, D., Ortiz-Muñoz, A., Anderson, D. F. & Winfree, E. Stochastic chemical reaction networks for robustly approximating arbitrary probability distributions. Theor. Comput. Sci. 801, 64–95 (2020).

    Article  Google Scholar 

  30. Zhang, D. Y. & Winfree, E. Control of DNA strand displacement kinetics using toehold exchange. J. Am. Chem. Soc. 131, 17303–17314 (2009).

    Article  CAS  Google Scholar 

  31. Srinivas, N. et al. On the biophysics and kinetics of toehold-mediated DNA strand displacement. Nucleic Acids Res. 41, 10641–10658 (2013).

    Article  CAS  Google Scholar 

  32. Zhang, J. X. et al. Predicting DNA hybridization kinetics from sequence. Nat. Chem. 10, 91–98 (2017).

    Article  CAS  Google Scholar 

  33. Storz, G. An expanding universe of noncoding RNAs. Science 296, 1260–1263 (2002).

    Article  CAS  Google Scholar 

  34. Janssen, A., Colmenares, S. U. & Karpen, G. H. Heterochromatin: guardian of the genome. Annu. Rev. Cell Dev. Biol. 34, 265–288 (2018).

    Article  CAS  Google Scholar 

  35. Tiwari, V. & Wilson, D. M. DNA damage and associated DNA repair defects in disease and premature aging. Am. J. Human Genet. 105, 237–257 (2019).

    Article  CAS  Google Scholar 

  36. Bédécarrats, A., Chen, S., Pearce, K., Cai, D. & Glanzman, D. L. RNA from trained aplysia can induce an epigenetic engram for long-term sensitization in untrained aplysia. eNeuro 5, 38–56 (2018).

    Article  Google Scholar 

  37. Manghwar, H. et al. CRISPR/Cas systems in genome editing: methodologies and tools for sgRNA design, off-target evaluation, and strategies to mitigate off-target effects. Adv. Sci 7, 1902312 (2020).

    Article  CAS  Google Scholar 

  38. Gong, H., Liu, C.-M., Liu, D.-P. & Liang, C.-C. The role of small RNAs in human diseases: potential troublemaker and therapeutic tools. Med. Res. Rev. 25, 361–381 (2005).

    Article  CAS  Google Scholar 

  39. Nobeli, I., Favia, A. D. & Thornton, J. M. Protein promiscuity and its implications for biotechnology. Nat. Biotechnol. 27, 157–167 (2009).

    Article  CAS  Google Scholar 

  40. Su, C. J. et al. Ligand-receptor promiscuity enables cellular addressing. Cell Syst. 13, 408–425 (2022).

  41. Edelstein, A., Amodaj, N., Hoover, K., Vale, R. & Stuurman, N. Computer control of microscopes using µManager. Curr. Protoc. Mol. Biol. 14, 14.20 (2010).

    Google Scholar 

  42. De Chaumont, F. et al. Icy: an open bioimage informatics platform for extended reproducible research. Nat. Methods 9, 690–696 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

I express deep gratitude to all developers of and contributors to the NUPACK algorithm, without which this study would be far less comprehensive. I thank I. L. Nikitina for assistance with manuscript preparation and the Cell Technologies Center core facility of the Institute of Cytology of the Russian Academy of Sciences for the confocal images in Figs. 4 and 5.

Author information

Authors and Affiliations

Authors

Contributions

M.P.N. conceived the idea, designed and performed the study, and wrote the manuscript.

Corresponding author

Correspondence to Maxim P. Nikitin.

Ethics declarations

Competing interests

M.P.N. has filed patent applications RU2019145384 (granted) and PCT/RU2020/050402 covering aspects of these findings. M.P.N. is the founder of the Abisense company, which manufacturers the LumoTrace bioimaging system.

Peer review

Peer review information

Nature Chemistry thanks Anne Condon, Grigory Tikhomirov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Note 1, Figs. 1–17 and Table 1.

Reporting Summary

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 8

Statistical source data and unprocessed gels.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nikitin, M.P. Non-complementary strand commutation as a fundamental alternative for information processing by DNA and gene regulation. Nat. Chem. 15, 70–82 (2023). https://doi.org/10.1038/s41557-022-01111-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41557-022-01111-y

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing