journal article·2025-10Open access

Functional and Structural Analyses of Diverse G‐Quadruplex and Non‐G‐Quadruplex Structures Formed by Guanine‐Rich Nucleic Acids: A Study on the Insulin Aptamer

Naoya Iwano, Taiji Oyama, Mizuki Tomizawa, Shintaro Inaba, Ryuma Mori, Yasumoto Nakazawa, Koji Sode, Kaori Tsukakoshi, Takayuki Nakano, Kazunori Ikebukuro

Small· Volume 21· Issue 43

Abstract

Abstract Guanine‐rich sequences in nucleic acids play a critical role in various biological processes owing to their ability to form G‐quadruplexes. In addition, biotechnology tools exploiting the unique functionalities of G‐quadruplex structures have garnered considerable attention. However, the potential roles of non‐G‐quadruplex structures have not been explored. Herein, a guanine‐rich oligonucleotide, which forms non‐G‐quadruplexes, is found to still demonstrate an affinity for proteins, specifically in the case of an aptamer. The insulin‐binding aptamer IGA3, known for its guanine‐rich sequence, forms various structures depending on the environmental conditions. At low cation concentrations, IGA3 adopts a distinct monomeric structure that is not G‐quadruplex but retains its insulin‐binding capacity. These findings suggest an interaction mechanism that extends beyond G‐quadruplex structures. To accurately characterize the non‐G‐quadruplex structures of a guanine‐rich oligonucleotide, this study integrates nuclear magnetic resonance, thermal difference spectroscopy, and circular dichroism (CD) spectroscopy with principal component analysis. In addition, the multivariate curve resolution of the CD spectra is used to analyze the structural mixture and identify the specific chemical species responsible for protein affinity. Results indicate that guanine‐rich sequences bind to proteins through non‐G‐quadruplex formation, offering new insights into the functionality of guanine‐rich DNA fragments.
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Research context

OpenAlex topics: DNA and Nucleic Acid Chemistry · Advanced biosensing and bioanalysis techniques · RNA Interference and Gene Delivery

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Toray Research Center, Inc. 10‐1 Tebiro 6‐chome Kamakura Kanagawa 248‐0036 Japan

Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology 2‐24‐16 Naka‐cho Koganei Tokyo 184–8588 Japan

JASCO Corporation 2967‐5, Ishikawamachi Hachioji Tokyo 192–8537 Japan

Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill and North Carolina State University Chapel Hill North Carolina 27599 USA

Funding

Japan Society for the Promotion of Science

References (70)

Show source-provided references
1. 10.1093/nar/gkx94210.1093/nar/gkx942 ↗
2. 10.1093/nar/gkac33910.1093/nar/gkac339 ↗
3. 10.1093/nar/gkz04610.1093/nar/gkz046 ↗
4. 10.1016/j.chembiol.2016.09.01110.1016/j.chembiol.2016.09.011 ↗
5. 10.1016/j.trechm.2019.07.00210.1016/j.trechm.2019.07.002 ↗
6. 10.1111/febs.1418710.1111/febs.14187 ↗
7. 10.1038/s41556-022-00941-910.1038/s41556-022-00941-9 ↗
8. 10.1038/s41557-018-0046-310.1038/s41557-018-0046-3 ↗
9. 10.1093/nar/gkab38810.1093/nar/gkab388 ↗
10. 10.1039/D2CC04366A10.1039/D2CC04366A ↗
11. 10.1038/s41580-020-0236-x10.1038/s41580-020-0236-x ↗
12. 10.1021/acs.analchem.2c0114210.1021/acs.analchem.2c01142 ↗
13. 10.1039/D2CS00317A10.1039/D2CS00317A ↗
14. 10.1038/334364a010.1038/334364a0 ↗
15. 10.1038/nbt.329510.1038/nbt.3295 ↗
16. 10.1016/j.bios.2021.11303010.1016/j.bios.2021.113030 ↗
17. 10.1093/nar/gku49910.1093/nar/gku499 ↗
18. 10.1093/nar/gkn40210.1093/nar/gkn402 ↗
19. 10.1093/nar/gkp56310.1093/nar/gkp563 ↗
20. 10.1093/nar/gky75710.1093/nar/gky757 ↗
21. 10.1093/nar/gkl65510.1093/nar/gkl655 ↗
22. 10.3389/fchem.2016.0003810.3389/fchem.2016.00038 ↗
23. 10.1093/nar/gkm52210.1093/nar/gkm522 ↗
24. 10.1093/nar/gkw97010.1093/nar/gkw970 ↗
25. 10.1093/nar/gkz123510.1093/nar/gkz1235 ↗
26. 10.1016/j.jmb.2014.01.00910.1016/j.jmb.2014.01.009 ↗
27. 10.1002/anie.20150228610.1002/anie.201502286 ↗
28. 10.1002/anie.20230162410.1002/anie.202301624 ↗
29. 10.3390/molecules2405094110.3390/molecules24050941 ↗
30. 10.1038/355564a010.1038/355564a0 ↗
31. 10.1006/jmbi.1997.127510.1006/jmbi.1997.1275 ↗
32. 10.1016/j.chembiol.2004.10.01710.1016/j.chembiol.2004.10.017 ↗
33. 10.3390/molecules1501021510.3390/molecules15010215 ↗
34. 10.1016/j.bios.2016.11.04210.1016/j.bios.2016.11.042 ↗
35. 10.1016/j.bios.2008.06.01610.1016/j.bios.2008.06.016 ↗
36. 10.1021/ja056097c10.1021/ja056097c ↗
37. 10.1002/jssc.20090006010.1002/jssc.200900060 ↗
38. 10.1016/j.snr.2021.10002810.1016/j.snr.2021.100028 ↗
39. 10.1016/j.bios.2012.10.04610.1016/j.bios.2012.10.046 ↗
40. 10.1021/acssensors.8b0157310.1021/acssensors.8b01573 ↗
41. 10.1016/j.bios.2021.11312410.1016/j.bios.2021.113124 ↗
42. 10.1016/j.bioelechem.2021.10799510.1016/j.bioelechem.2021.107995 ↗
43. 10.1016/j.jcis.2022.07.05110.1016/j.jcis.2022.07.051 ↗
44. 10.1002/anie.20170918410.1002/anie.201709184 ↗
45. 10.1016/j.ymeth.2012.03.01110.1016/j.ymeth.2012.03.011 ↗
46. 10.1016/j.ymeth.2012.05.00310.1016/j.ymeth.2012.05.003 ↗
47. 10.1016/j.chemolab.2004.12.00710.1016/j.chemolab.2004.12.007 ↗
48. 10.1093/nar/gnf09110.1093/nar/gnf091 ↗
49. 10.1021/bi048242p10.1021/bi048242p ↗
50. 10.1021/ja048805k10.1021/ja048805k ↗
51. 10.1371/journal.pone.020558410.1371/journal.pone.0205584 ↗
52. 10.1021/acschembio.1c0090410.1021/acschembio.1c00904 ↗
53. 10.1093/nar/gni13410.1093/nar/gni134 ↗
54. 10.1021/jacs.9b1277010.1021/jacs.9b12770 ↗
55. 10.1021/jacs.1c0620010.1021/jacs.1c06200 ↗
56. 10.1002/anie.20140053110.1002/anie.201400531 ↗
57. 10.1002/pro.280910.1002/pro.2809 ↗
58. 10.1002/chem.20200298510.1002/chem.202002985 ↗
59. 10.1038/s41467-022-28726-610.1038/s41467-022-28726-6 ↗
60. 10.1021/jp411293r10.1021/jp411293r ↗
61. 10.3390/molecules2621659510.3390/molecules26216595 ↗
62. 10.1002/anie.20231322610.1002/anie.202313226 ↗
63. 10.1093/nar/gkp02610.1093/nar/gkp026 ↗
64. 10.1093/nar/gki89810.1093/nar/gki898 ↗
65. 10.1021/acs.analchem.3c0095410.1021/acs.analchem.3c00954 ↗
66. 10.1159/00044626810.1159/000446268 ↗
67. 10.1038/srep0476310.1038/srep04763 ↗
68. T.Oyama S.Suzuki H.Masago JASCO Corporation Japan patent. 7320894 2023.
69. M.Newville T.Stensitzki D. B.Allen M.Rawlik A.Ingargiola A.Nelson LMFIT: Non‐linear least‐square minimization and curve‐fitting for Python Version 1.2.2 2014 https://doi.org/10.5281/zenodo.8145703.
70. 10.6028/jres.124.01810.6028/jres.124.018 ↗