Skip to main content

Sugar Code (Glycocode)

Abstract

The vast majority of signal transduction processes in most living organisms are caused by four sorts of biomolecules: nucleic acids, proteins, glycoconjugates and lipids. The sequence and structure of nucleic acids, as well as, peptides and proteins have been extensively studied, including different sorts of interactions and functions. Genomics, proteomics, glycomics and lipomics represent four logically, chemically and biologically interconnected areas of research approaches to living organisms. The development in glycomics, in comparison with genomics and proteomics, was more demanding owing to the monumental growth of possible isomers and structural variations. Carbohydrates are unbeatable in information potential, compared with proteins and nucleic acids. This sort of coding (language) has been named glycocode resp. sugar code. It represents the complex information pool that carbohydrate structures are able to express. Monosaccharides as building blocks for oligo- and polysaccharide synthesis, represent therefore high-capacity information-storing and coding units, creating the third alphabet of life. The amount of information carried by glycopeptide dendrimers or glycodendrimers, in comparison with peptide dendrimers, is therefore much higher in all parameters, including structural variability, complexity, spectrum of biological activities, etc. The topics of sugar code, glycodendrimers and different sorts of nanoparticles partly overlap. Especially, carbohydrate-mediated molecular recognitions using nano-vehicles have a deep impact on medicine and open a new area of biomedical applications both in vitro and in vivo.

Keywords

  • Nucleic Acid
  • Living Organism
  • Biomedical Application
  • Carbohydrate Structure
  • Purine Base

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Ambrosi, M., Cameron, N., Davis, B.: Lectins: tools for the molecular understanding of the glycocode. Org. Biomol. Chem. 3(9), 1593–1608 (2005)

    CrossRef  PubMed  CAS  Google Scholar 

  2. Andre, S., Kozar, T., Kojima, S., Unverzagt, C., Gabius, H.J.: From structural to functional glycomics: core substitutions as molecular switches for shape and lectin affinity of N-glycans. Biol. Chem. 390(7), 557–567 (2009)

    CrossRef  PubMed  CAS  Google Scholar 

  3. Andre, S., Renaudet, O., Bossu, I., Dumy, P., Gabius, H.J.: Cyclic neoglycodecapeptides: how to increase their inhibitory activity and selectivity on lectin/toxin binding to a glycoprotein and cells. J. Pept. Sci. 17(6), 427–437 (2011)

    CrossRef  PubMed  CAS  Google Scholar 

  4. Arya, P., Barkley, A., Randell, K.: Automated high-throughput synthesis of artificial glycopeptides. small-molecule probes for chemical glycobiology. J. Comb. Chem. 4(3), 193–198 (2002)

    Google Scholar 

  5. Davis, B.: Synthesis of glycoproteins. Chem. Rev. 102(2), 579–601 (2002)

    CrossRef  PubMed  CAS  Google Scholar 

  6. El-Boubbou, K., Huang, X.: Glyco-nanomaterials: Translating insights from the “sugar-code” to biomedical applications. Curr. Med. Chem. 18(14), 2060–2078 (2011)

    CrossRef  PubMed  CAS  Google Scholar 

  7. Evans, S.J., Prossin, A.R., Harrington, G.J., Kamali, M., Ellingrod, V.L., Burant, C.F., McInnis, M.G.: Fats and factors: lipid profiles associate with personality factors and suicidal history in bipolar subjects. PLoS ONE 7(1), Art. No. e29,297 (2012)

    Google Scholar 

  8. Feizi, T., Mulloy, B.: Carbohydrates and glycoconjugates. Glycomics: the new era of carbohydrate biology. Curr. Opin. Struct. Biol. 13(5), 602–604 (2003)

    CAS  Google Scholar 

  9. Fitzgerald, D.: Lipids plus genomics equals lipomics. Scientist 16(3), 42–42 (2002)

    Google Scholar 

  10. Gabius, H.J.: Glycans: bioactive signals decoded by lectins. Biochem. Soc. Trans. 36(6), 1491–1496 (2008)

    CrossRef  PubMed  CAS  Google Scholar 

  11. Gabius, H.J.: The sugar code. Fundamentals of Glycosciences. Wiley-VCH Verlag GmbH; John Wiley and Sons Ltd (2009)

    Google Scholar 

  12. Gabius, H.J., Andre, S., Kaltner, H., Siebert, H.C.: The sugar code: Functional lectinomics. Biochim. Biophys. Acta 1572(2–3), 165–177 (2002)

    CrossRef  PubMed  CAS  Google Scholar 

  13. Gabius, H.J., Siebert, H.C., Andre, S., Jimenez-Barbero, J., Rudiger, H.: Chemical biology of the sugar code. ChemBioChem. 5(6), 740–764 (2004)

    CrossRef  PubMed  CAS  Google Scholar 

  14. Gabius, H.J., Andre, S., Jimenez-Barbero, J., Romero, A., Solis, D.: From lectin structure to functional glycomics: principles of the sugar code. Trends Biochem. Sci. 36(6), 298–313 (2011)

    CrossRef  PubMed  CAS  Google Scholar 

  15. Graham, D., Elliott, S., Van Eyk, J.: Broad-based proteomic strategies: a practical guide to proteomics and functional screening. J. Physiol. 563(1), 1–9 (2005)

    CrossRef  PubMed  CAS  Google Scholar 

  16. He, Y.: Genomic approach to biomarker identification and its recent applications. Cancer Biomark 2(3–4), 103–133 (2006)

    PubMed  CAS  Google Scholar 

  17. Hirabayashi, J., Kuno, A., Tateno, H.: Lectin-based structural glycomics: a practical approach to complex glycans. Electrophoresis 32(10), 1118–1128 (2011)

    CrossRef  PubMed  CAS  Google Scholar 

  18. Jura, J., Jura, J., Rynska, B., Smorag, Z.: Comparison of transfection methods for rabbit zygotes. Ann. Animal Sci. 10(4), 425–430 (2010)

    Google Scholar 

  19. Kasarskis, A., Yang, X., Schadt, E.: Integrative genomics strategies to elucidate the complexity of drug response. Pharmacogenomics 12(12), 1695–1715 (2011)

    CrossRef  PubMed  CAS  Google Scholar 

  20. Laine, R.: Glycosciences: status and perspectives. The Information-Storing Potential of the Sugar Code, pp. 1–14. Chapman & Hall, London (1997)

    Google Scholar 

  21. Makowski, L., Hotamisligil, G.: The role of fatty acid binding proteins in metabolic syndrome and atherosclerosis. Curr. Opin. Lipidol. 16(5), 543–548 (2005)

    CrossRef  PubMed  CAS  Google Scholar 

  22. Morelle, W., Michalski, J.C.: Glycomics and mass spectrometry. Curr. Pharm. Des. 11(20), 2615–2645 (2005)

    CrossRef  PubMed  CAS  Google Scholar 

  23. Niederhafner, P., Sebestik, J., Jezek, J.: Glycopeptide dendrimers. Part I. J. Pept. Sci. 14(1), 2–43 (2008)

    CAS  Google Scholar 

  24. Patterson, S., Aebersold, R.: Proteomics: The first decade and beyond. Nat. Gen. 33(Suppl.), 311–323 (2003)

    Google Scholar 

  25. Peng, X.: Developing and evaluating genomics- or proteomics-based diagnostic tests: statistical perspectives. Method Mol. Med. 129, 27–39 (2006)

    CAS  Google Scholar 

  26. Raman, R., Raguram, S., Venkataraman, G., Paulson, J., Sasisekharan, R.: Glycomics: an integrated systems approach to structure-function relationships of glycans. Nat. Meth. 2(11), 817–824 (2005)

    CrossRef  CAS  Google Scholar 

  27. Ratner, D., Adams, E., Disney, M., Seeberger, P.: Tools for glycomics: Mapping interactions of carbohydrates in biological systems. ChemBioChem. 5(10), 1375–1383 (2004)

    CrossRef  PubMed  CAS  Google Scholar 

  28. Roemer, T., Davies, J., Giaever, G., Nislow, C.: Bugs, drugs and chemical genomics. Nat. Chem. Biol. 8(1), 46–56 (2012)

    CrossRef  CAS  Google Scholar 

  29. Scatena, R., Bottoni, P., Pontoglio, A., Giardina, B.: The proteomics of cancer stem cells. Potential clinical applications for innovative research in oncology. Proteom. Clin. Appl. 5(11–12), 590–602 (2011)

    CAS  Google Scholar 

  30. Sebestik, J., Niederhafner, P., Jezek, J.: Peptide and glycopeptide dendrimers and analogous dendrimeric structures and their biomedical applications. Amino Acids 40(2), 301–370 (2011)

    CrossRef  PubMed  CAS  Google Scholar 

  31. Seeberger, P.: Automated carbohydrate synthesis to drive chemical glycomics. Chem. Commun. (10), 1115–1121 (2003)

    CrossRef  Google Scholar 

  32. Solis, D., Jimenez-Barbero, J., Kaltner, H., Romero, A., Siebert, H.C., Von Der Lieth, C.W., Gabius, H.J.: Towards defining the role of glycans as hardware in information storage and transfer: basic principles, experimental approaches and recent progress. Cell Tiss. Organ. 168(1–2), 5–23 (2001)

    CAS  Google Scholar 

  33. Song, X., Lasanajak, Y., Xia, B., Heimburg-Molinaro, J., Rhea, J.M., Ju, H., Zhao, C., Molinaro, R.J., Cummings, R.D., Smith, D.F.: Shotgun glycomics: a microarray strategy for functional glycomics. Nat. Meth. 8(1), 85–90 (2011)

    CrossRef  CAS  Google Scholar 

  34. Tao, N., Wu, S., Kim, J., An, H.J., Hinde, K., Power, M.L., Gagneux, P., German, J.B., Lebrilla, C.B.: Evolutionary glycomics: characterization of milk oligosaccharides in primates. J. Proteom. Res. 10(4), 1548–1557 (2011)

    CrossRef  CAS  Google Scholar 

  35. Tran, J.C., Zamdborg, L., Ahlf, D.R., Lee, J.E., Catherman, A.D., Durbin, K.R., Tipton, J.D., Vellaichamy, A., Kellie, J.F., Li, M., Wu, C., Sweet, S.M.M., Early, B.P., Siuti, N., LeDuc, R.D., Compton, P.D., Thomas, P.M., Kelleher, N.L.: Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480(7376), 254–258 (2011)

    CrossRef  PubMed  CAS  Google Scholar 

  36. Watkins, S.: Lipomic profiling in drug discovery, development and clinical trial evaluation. Curr. Opin. Drug Discover. Devel. 7(1), 112–117 (2004)

    CAS  Google Scholar 

  37. Werz, D., Seeberger, P.: Carbohydrates as the next frontier in pharmaceutical research. Chem. Eur. J. 11(11), 3194–3206 (2005)

    CrossRef  PubMed  CAS  Google Scholar 

  38. Wilkins, M., Appel, R., Van Eyk, J., Chung, M., Gorg, A., Hecker, M., Huber, L., Langen, H., Link, A., Paik, Y.K., Patterson, S., Pennington, S., Rabilloud, T., Simpson, R., Weiss, W., Dunn, M.: Guidelines for the next 10 years of proteomics. Proteomics 6(1), 4–8 (2006)

    CrossRef  PubMed  CAS  Google Scholar 

  39. Zhou, J., Bi, D., Lin, Y., Chen, P., Wang, X., Liang, S.: Shotgun proteomics and network analysis of ubiquitin-related proteins from human breast carcinoma epithelial cells. Mol. Cell. Biochem. 359, 375–384 (2012)

    CrossRef  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Wien

About this chapter

Cite this chapter

Šebestík, J., Reiniš, M., Ježek, J. (2012). Sugar Code (Glycocode). In: Biomedical Applications of Peptide-, Glyco- and Glycopeptide Dendrimers, and Analogous Dendrimeric Structures. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1206-9_3

Download citation