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BioMed Research International
Volume 2015, Article ID 279823, 12 pages
http://dx.doi.org/10.1155/2015/279823
Research Article

Systematic Analysis and Prediction of In Situ Cross Talk of O-GlcNAcylation and Phosphorylation

1School of Life Science, University of Science and Technology of China, Hefei 230027, China
2School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China
3Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027, China

Received 2 August 2015; Revised 1 October 2015; Accepted 4 October 2015

Academic Editor: Cheng-Xin Gong

Copyright © 2015 Heming Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Mann and O. N. Jensen, “Proteomic analysis of post-translational modifications,” Nature Biotechnology, vol. 21, no. 3, pp. 255–261, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. T. Hunter, “The age of crosstalk: phosphorylation, ubiquitination, and beyond,” Molecular Cell, vol. 28, no. 5, pp. 730–738, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. X.-J. Yang, “Multisite protein modification and intramolecular signaling,” Oncogene, vol. 24, no. 10, pp. 1653–1662, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. X.-J. Yang and S. Grégoire, “A recurrent phospho-sumoyl switch in transcriptional repression and beyond,” Molecular Cell, vol. 23, no. 6, pp. 779–786, 2006. View at Publisher · View at Google Scholar
  5. R. J. Sims III and D. Reinberg, “Is there a code embedded in proteins that is based on post-translational modifications?” Nature Reviews Molecular Cell Biology, vol. 9, no. 10, pp. 815–820, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. L. S. Griffith and B. Schmitz, “O-linked N-acetylglucosamine levels in cerebellar neurons respond reciprocally to pertubations of phosphorylation,” European Journal of Biochemistry, vol. 262, no. 3, pp. 824–831, 1999. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Lefebvre, C. Alonso, S. Mahboub et al., “Effect of okadaic acid on O-linked N-acetylglucosamine levels in a neuroblastoma cell line,” Biochimica et Biophysica Acta—General Subjects, vol. 1472, no. 1-2, pp. 71–81, 1999. View at Publisher · View at Google Scholar · View at Scopus
  8. G. W. Hart, C. Slawson, G. Ramirez-Correa, and O. Lagerlof, “Cross talk between O-GlcNAcylation and phosphorylation: roles in signaling, transcription, and chronic disease,” Annual Review of Biochemistry, vol. 80, pp. 825–858, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Butkinaree, K. Park, and G. W. Hart, “O-linked β-N-acetylglucosamine (O-GlcNAc): extensive crosstalk with phosphorylation to regulate signaling and transcription in response to nutrients and stress,” Biochimica et Biophysica Acta, vol. 1800, no. 2, pp. 96–106, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. Z. Wang, N. D. Udeshi, C. Slawson et al., “Extensive crosstalk between O-GlcNAcylation and phosphorylation regulates cytokinesis,” Science Signaling, vol. 3, no. 104, p. ra2, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Hu, S. Shimoji, and G. W. Hart, “Site-specific interplay between O-GlcNAcylation and phosphorylation in cellular regulation,” FEBS Letters, vol. 584, no. 12, pp. 2526–2538, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. J. C. Trinidad, D. T. Barkan, B. F. Gulledge et al., “Global identification and characterization of both O-GlcNAcylation and phosphorylation at the murine synapse,” Molecular & Cellular Proteomics, vol. 11, no. 8, pp. 215–229, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Peng, A. Scholten, A. J. R. Heck, and B. van Breukelen, “Identification of enriched PTM crosstalk motifs from large-scale experimental data sets,” Journal of Proteome Research, vol. 13, no. 1, pp. 249–259, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. P. Minguez, L. Parca, F. Diella et al., “Deciphering a global network of functionally associated post-translational modifications,” Molecular Systems Biology, vol. 8, article 599, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Gupta and S. Brunak, “Prediction of glycosylation across the human proteome and the correlation to protein function,” Pacific Symposium on Biocomputing, pp. 310–322, 2002. View at Google Scholar · View at Scopus
  16. Y. Huang, B. Xu, X. Zhou et al., “Systematic characterization and prediction of post-translational modification cross-talk,” Molecular & Cellular Proteomics, vol. 14, no. 3, pp. 761–770, 2015. View at Publisher · View at Google Scholar
  17. H. Dinkel, C. Chica, A. Via et al., “Phospho.ELM: a database of phosphorylation sites—update 2011,” Nucleic Acids Research, vol. 39, no. 1, pp. D261–D267, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. P. V. Hornbeck, B. Zhang, B. Murray, J. M. Kornhauser, V. Latham, and E. Skrzypek, “PhosphoSitePlus, 2014: mutations, PTMs and recalibrations,” Nucleic Acids Research, vol. 43, no. 1, pp. D512–D520, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Wang, M. Torii, H. Liu, G. W. Hart, and Z.-Z. Hu, “dbOGAP—an integrated bioinformatics resource for protein O-GlcNAcylation,” BMC Bioinformatics, vol. 12, article 91, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. C.-T. Lu, K.-Y. Huang, M.-G. Su et al., “DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications,” Nucleic Acids Research, vol. 41, no. 1, pp. D295–D305, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Cummings, L. Riley, L. Black et al., “Genomic BLAST: custom-defined virtual databases for complete and unfinished genomes,” FEMS Microbiology Letters, vol. 216, no. 2, pp. 133–138, 2002. View at Publisher · View at Google Scholar · View at Scopus
  22. H. Mi, A. Muruganujan, and P. D. Thomas, “PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees,” Nucleic Acids Research, vol. 41, no. 1, pp. D377–D386, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. UniProt Consortium, “UniProt: a hub for protein information,” Nucleic Acids Research, vol. 43, pp. D204–D212, 2015. View at Google Scholar
  24. R. L. Tatusov, E. V. Koonin, and D. J. Lipman, “A genomic perspective on protein families,” Science, vol. 278, no. 5338, pp. 631–637, 1997. View at Publisher · View at Google Scholar · View at Scopus
  25. R. C. Edgar, “MUSCLE: multiple sequence alignment with high accuracy and high throughput,” Nucleic Acids Research, vol. 32, no. 5, pp. 1792–1797, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. M. F. Chou and D. Schwartz, “UNIT 13.15 biological sequence motif discovery using motif-x,” in Current Protocols in Bioinformatics, chapter 13, pp. 15–24, John Wiley & Sons, 2011. View at Publisher · View at Google Scholar
  27. S. Mostafavi, D. Ray, D. Warde-Farley, C. Grouios, and Q. Morris, “GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function,” Genome Biology, vol. 9, supplement 1, article S4, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. H. C. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. Z. Pan, Z. Liu, H. Cheng et al., “Systematic analysis of the in situ crosstalk of tyrosine modifications reveals no additional natural selection on multiply modified residues,” Scientific Reports, vol. 4, article 7331, 2014. View at Publisher · View at Google Scholar
  30. J. R. Woodgett, “Judging a protein by more than its name: GSK-3,” Science's STKE, vol. 2001, no. 100, p. re12, 2001. View at Google Scholar · View at Scopus
  31. P. Cohen and S. Frame, “The renaissance of GSK3,” Nature Reviews Molecular Cell Biology, vol. 2, no. 10, pp. 769–776, 2001. View at Publisher · View at Google Scholar
  32. X. Deupi, M. Olivella, C. Govaerts, J. A. Ballesteros, M. Campillo, and L. Pardo, “Ser and Thr residues modulate the conformation of pro-kinked transmembrane α-helices,” Biophysical Journal, vol. 86, no. 1, pp. 105–115, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. R. Jochmann, P. Holz, H. Sticht, and M. Stürzl, “Validation of the reliability of computational O-GlcNAc prediction,” Biochimica et Biophysica Acta, vol. 1844, no. 2, pp. 416–421, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. T. H. Dang, K. Van Leemput, A. Verschoren, and K. Laukens, “Prediction of kinase-specific phosphorylation sites using conditional random fields,” Bioinformatics, vol. 24, no. 24, pp. 2857–2864, 2008. View at Publisher · View at Google Scholar · View at Scopus
  35. Y. Wan, D. Cripps, S. Thomas et al., “PhosphoScan: a probability-based method for phosphorylation site prediction using MS2/MS3 pair information,” Journal of Proteome Research, vol. 7, no. 7, pp. 2803–2811, 2008. View at Publisher · View at Google Scholar · View at Scopus
  36. Y. Xue, A. Li, L. Wang, H. Feng, and X. Yao, “PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory,” BMC Bioinformatics, vol. 7, article 163, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Leutz, O. Pless, M. Lappe, G. Dittmar, and E. Kowenz-Leutz, “Crosstalk between phosphorylation and multi-site arginine/lysine methylation in C/EBPs,” Transcription, vol. 2, no. 1, pp. 3–8, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. J.-M. Hsu, C.-T. Chen, C.-K. Chou et al., “Crosstalk between Arg 1175 methylation and Tyr 1173 phosphorylation negatively modulates EGFR-mediated ERK activation,” Nature Cell Biology, vol. 13, no. 2, pp. 174–181, 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. R. Schweiger and M. Linial, “Cooperativity within proximal phosphorylation sites is revealed from large-scale proteomics data,” Biology Direct, vol. 5, article 6, 2010. View at Publisher · View at Google Scholar · View at Scopus
  40. P. Minguez, I. Letunic, L. Parca, and P. Bork, “PTMcode: a database of known and predicted functional associations between post-translational modifications in proteins,” Nucleic Acids Research, vol. 41, no. 1, pp. D306–D311, 2013. View at Publisher · View at Google Scholar · View at Scopus
  41. S. Olivier-Van Stichelen, V. Dehennaut, A. Buzy et al., “O-GlcNAcylation stabilizes β-catenin through direct competition with phosphorylation at threonine 41,” The FASEB Journal, vol. 28, no. 8, pp. 3325–3328, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. Q. Zeidan and G. W. Hart, “The intersections between O-GlcNAcylation and phosphorylation: implications for multiple signaling pathways,” Journal of Cell Science, vol. 123, no. 1, pp. 13–22, 2010. View at Publisher · View at Google Scholar · View at Scopus
  43. Z. Wang, M. Gucek, and G. W. Hart, “Cross-talk between GlcNAcylation and phosphorylation: site-specific phosphorylation dynamics in response to globally elevated O-GlcNAc,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 37, pp. 13793–13798, 2008. View at Publisher · View at Google Scholar · View at Scopus