Multivalent insulin receptor activation using insulin–DNA origami nanostructures
Source abstract
Abstract
Abstract Insulin binds the insulin receptor (IR) and regulates anabolic processes in target tissues. Impaired IR signalling is associated with multiple diseases, including diabetes, cancer and neurodegenerative disorders. IRs have been reported to form nanoclusters at the cell membrane in several cell types, even in the absence of insulin binding. Here we exploit the nanoscale spatial organization of the IR to achieve controlled multivalent receptor activation. To control insulin nanoscale spatial organization and valency, we developed rod-like insulin–DNA origami nanostructures carrying different numbers of insulin molecules with defined spacings. Increasing the insulin valency per nanostructure markedly extended the residence time of insulin–DNA origami nanostructures at the receptors. Both insulin valency and spacing affected the levels of IR activation in adipocytes. Moreover, the multivalent insulin design associated with the highest levels of IR activation also induced insulin-mediated transcriptional responses more effectively than the corresponding monovalent insulin nanostructures. In an in vivo zebrafish model of diabetes, treatment with multivalent—but not monovalent—insulin nanostructures elicited a reduction in glucose levels. Our results show that the control of insulin multivalency and spatial organization with nanoscale precision modulates the IR responses, independent of the insulin concentration. Therefore, we propose insulin nanoscale organization as a design parameter in developing new insulin therapies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced biosensing and bioanalysis techniques · RNA Interference and Gene Delivery · Lipid Membrane Structure and Behavior
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Sims, E. K., Carr, A. L. J., Oram, R. A., DiMeglio, L. A. & Evans-Molina, C. 100 years of insulin: celebrating the past, present and future of diabetes therapy. Nat. Med. 27, 1154–1164 (2021).10.1038/s41591-021-01418-2 ↗
2. James, D. E., Stöckli, J. & Birnbaum, M. J. The aetiology and molecular landscape of insulin resistance. Nat. Rev. Mol. Cell Biol. 22, 751–771 (2021).10.1038/s41580-021-00390-6 ↗
3. Li, J. et al. Synergistic activation of the insulin receptor via two distinct sites. Nat. Struct. Mol. Biol. 29, 357–368 (2022).10.1038/s41594-022-00750-6 ↗
4. Menting, J. G. et al. How insulin engages its primary binding site on the insulin receptor. Nature 493, 241–245 (2013).10.1038/nature11781 ↗
5. Scapin, G. et al. Structure of the insulin receptor-insulin complex by single-particle cryo-EM analysis. Nature 556, 122–125 (2018).10.1038/nature26153 ↗
6. Jarett, L. & Smith, R. M. The natural occurrence of insulin receptors in groups on adipocyte plasma membranes as demonstrated with monomeric ferritin-insulin. J. Supramol. Struct. 6, 45–59 (1977).10.1002/jss.400060104 ↗
7. Gustavsson, J. et al. Localization of the insulin receptor in caveolae of adipocyte plasma membrane. FASEB J. 13, 1961–1971 (1999).10.1096/fasebj.13.14.1961 ↗
8. Boothe, T. et al. Inter-domain tagging implicates caveolin-1 in insulin receptor trafficking and Erk signaling bias in pancreatic beta-cells. Mol. Metab. 5, 366–378 (2016).10.1016/j.molmet.2016.01.009 ↗
9. Li, H. et al. Mechanism of INSR clustering with insulin activation and resistance revealed by super-resolution imaging. Nanoscale 14, 7747–7755 (2022).10.1039/D2NR01051H ↗
10. Shaw, A. et al. Spatial control of membrane receptor function using ligand nanocalipers. Nat. Methods 11, 841–846 (2014).10.1038/nmeth.3025 ↗
11. Verheyen, T. et al. Spatial organization-dependent EphA2 transcriptional responses revealed by ligand nanocalipers. Nucleic Acids Res. 48, 5777–5787 (2020).10.1093/nar/gkaa274 ↗
12. Fang, T. et al. Spatial regulation of T-cell signaling by programmed death-ligand 1 on wireframe DNA origami flat sheets. ACS Nano 15, 3441–3452 (2021).10.1021/acsnano.0c10632 ↗
13. Wang, M. et al. Spatially reprogramed receptor organization to switch cell behavior using a DNA origami-templated aptamer nanoarray. Nano Lett. 22, 8445–8454 (2022).10.1021/acs.nanolett.2c02489 ↗
14. Du, R. R. et al. Innate immune stimulation using 3D wireframe DNA origami. ACS Nano 16, 20340–20352 (2022).10.1021/acsnano.2c06275 ↗
15. Kern, N., Dong, R., Douglas, S. M., Vale, R. D. & Morrissey, M. A. Tight nanoscale clustering of Fcγ receptors using DNA origami promotes phagocytosis. eLife 10, e68311 (2021).10.7554/eLife.68311 ↗
16. Veneziano, R. et al. Role of nanoscale antigen organization on B-cell activation probed using DNA origami. Nat. Nanotechnol. 15, 716–723 (2020).10.1038/s41565-020-0719-0 ↗
17. Dong, R. et al. DNA origami patterning of synthetic T cell receptors reveals spatial control of the sensitivity and kinetics of signal activation. Proc. Natl Acad. Sci. USA 118, e2109057118 (2021).10.1073/pnas.2109057118 ↗
18. Hellmeier, J. et al. Strategies for the site-specific decoration of DNA origami nanostructures with functionally intact proteins. ACS Nano 15, 15057–15068 (2021).10.1021/acsnano.1c05411 ↗
19. Uldry, M. et al. Complementary action of the PGC-1 coactivators in mitochondrial biogenesis and brown fat differentiation. Cell Metab. 3, 333–341 (2006).10.1016/j.cmet.2006.04.002 ↗
20. Jungmann, R. et al. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat. Methods 11, 313–318 (2014).10.1038/nmeth.2835 ↗
21. Simoncelli, S. et al. Multi-color molecular visualization of signaling proteins reveals how C-terminal Src kinase nanoclusters regulate T cell receptor activation. Cell Rep. 33, 108523 (2020).10.1016/j.celrep.2020.108523 ↗
22. Rothemund, P. W. K. Folding DNA to create nanoscale shapes and patterns. Nature 440, 297–302 (2006).10.1038/nature04586 ↗
23. Fu, J. et al. Assembly of multienzyme complexes on DNA nanostructures. Nat. Protoc. 11, 2243–2273 (2016).10.1038/nprot.2016.139 ↗
24. Derr, N. D. et al. Tug-of-war in motor protein ensembles revealed with a programmable DNA origami scaffold. Science 338, 662–665 (2012).10.1126/science.1226734 ↗
25. Cremers, G. A. O. et al. Determinants of ligand-functionalized DNA nanostructure-cell interactions. J. Am. Chem. Soc. 143, 10131–10142 (2021).10.1021/jacs.1c02298 ↗
26. Wang, Y., Baars, I., Fördös, F. & Högberg, B. Clustering of death receptor for apoptosis using nanoscale patterns of peptides. ACS Nano 15, 9614–9626 (2021).10.1021/acsnano.0c10104 ↗
27. Curado, S. et al. Conditional targeted cell ablation in zebrafish: a new tool for regeneration studies. Dev. Dyn. 236, 1025–1035 (2007).10.1002/dvdy.21100 ↗
28. Andersson, O. et al. Adenosine signaling promotes regeneration of pancreatic β cells in vivo. Cell Metab. 15, 885–894 (2012).10.1016/j.cmet.2012.04.018 ↗
29. Chandrasekaran, A. R. Nuclease resistance of DNA nanostructures. Nat. Rev. Chem. 5, 225–239 (2021).10.1038/s41570-021-00251-y ↗
30. Ponnuswamy, N. et al. Oligolysine-based coating protects DNA nanostructures from low-salt denaturation and nuclease degradation. Nat. Commun. 8, 15654 (2017).10.1038/ncomms15654 ↗
31. Kahn, C. R., Baird, K. L., Jarrett, D. B. & Flier, J. S. Direct demonstration that receptor crosslinking or aggregation is important in insulin action. Proc. Natl Acad. Sci. USA 75, 4209–4213 (1978).10.1073/pnas.75.9.4209 ↗
32. Østergaard, M., Mishra, N. K. & Jensen, K. J. The ABC of insulin: the organic chemistry of a small protein. Chemistry 26, 8341–8357 (2020).10.1002/chem.202000337 ↗
33. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).10.1038/nmeth.4197 ↗
34. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4, 1521 (2015).10.12688/f1000research.7563.1 ↗
35. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
36. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).10.1093/bioinformatics/btw313 ↗
37. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innov. 2, 100141 (2021).
38. Marín-Juez, R., Jong-Raadsen, S., Yang, S. & Spaink, H. P. Hyperinsulinemia induces insulin resistance and immune suppression via Ptpn6/Shp1 in zebrafish. J. Endocrinol. 222, 229–241 (2014).10.1530/JOE-14-0178 ↗
39. Craig, M. P., Gilday, S. D., Dabiri, D. & Hove, J. R. An optimized method for delivering flow tracer particles to intravital fluid environments in the developing zebrafish. Zebrafish 9, 108–119 (2012).10.1089/zeb.2012.0740 ↗
40. Benard, E. L. et al. Infection of zebrafish embryos with intracellular bacterial pathogens. J. Vis. Exp. 61, e3781 (2012).10.3791/3781 ↗
41. Jurczyk, A. et al. Dynamic glucoregulation and mammalian-like responses to metabolic and developmental disruption in zebrafish. Gen. Comp. Endocrinol. 170, 334–345 (2011).10.1016/j.ygcen.2010.10.010 ↗
42. Wu, M. et al. Functionally selective signaling and broad metabolic benefits by novel insulin receptor partial agonists. Nat. Commun. 13, 942 (2022).