Automatic annotation of N-glycans in MALDI-TOF spectra for rapid glycan profiling and comparison

1. Indiana University Bloomington School of Informatics and Computing  Automatic annotation of N-glycans in MALDI-TOF spectra for rapid glycan profiling and comparison Chuan-Yih, Yu 2010.05.14 Capstone Presentation Advisor: Prof. Haixu Tang

2. Outline • Background • Problem definition and goals • Implementation of Multi N-Glycan • Results • Future work

3. Background • Post-Translation Modification (PTM) • Enzyme-catalyzed protein modification after protein synthesized • Acetylation, Glycosylation, Methylation, Phosphorylation, Prenylation, and etc. • >50% of all eukaryotic proteins are glycosylated1 [Apweiler, et al.] 1.Apweiler, R., H. Hermjakob, and N. Sharon, On the frequency of protein glycosylation, as deduced from analysis of the SWISS-PROT database.BiochimBiophysActa, 1999. 1473(1): p. 4-8 http://yahoo.brand.edgar-online.com/EFX_dll/EDGARpro.dll?FetchFilingHTML1?SessionID=WD8AC7y2l3h1FMr&ID=5101862

4. Glycosylation • Attachment of a glycan(sugar) to the peptide chain • N-linked glycosylation • Nitrogen link to Asn • Asn-X-Ser(NXS) or Asn-X-Thr(NXT), X can be any but Pro (glycosylation  sequon) • Core structure – 2 GlcNac + 3 Man • Glycosylation while folding • O-linked glycosylation • Many different core structures • Serine or Threonine • Glycosylation after folding

5. N-linked glycosylation • Tree structure • Monosaccharides- building blocks of polysaccharide chain • Diverse linkage – at most four branches • Three types of N-linked glycan tree • High mannose • Complex • Hybrid Graphs: Varki, A., Essentials of glycobiology. 2nd ed. 2009, Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press. xxix, 784 p

6. Analytical strategies for analyzing glycans

7. Mass Spectrometry • Wright scale of molecular • High throughput, High accuracy, High sensitivity • Ion Source • Electrospray ionization (ESI) • Matrix-assisted laser desorption/ionization (MALDI) • Mass Analyzer • Time of flight (TOF) • Quadrupole • Fourier transform mass spectrometry (FTMS) • Detector • Charge induced or the current produced

8. Mass Spectrometry Spectrum Isotopic envelope

9. N-Glycan Profiling • Given a MS spectrum screen which glycans present in this spectrum(annotation) and how abundance it is (quantification)

10. Problem Definition • Glycan isotope envelope • Isotope present in the natural world • different numbers of neutrons http://en.wikipedia.org/wiki/Carbon Graphs: Isotope Pattern Calculator v4.0 http://yanjunhua.tripod.com/pattern.htm

11. Problem Definition 2 GlcNac + 9 Man = 2374.5960 7 GlcNac + 3 Man = 2375.63 2 GlcNac + 9 Man = 2374.5960 ？ 2 GlcNac + 9 Man = 2374.5960 Unknown

12. Goals • Annotation of N-glycan • Decompose observed isotopic envelopes into non-overlapping and overlapping isotopic envelopes of glycan • Quantify the relative abundance of glycan • Glycan profile comparison • Report glycans that show significant different abundance between groups of samples • Discover glycan biomarkers

13. Glycans Annotation • For each glycan( i.e. monosaccharides composition) • 412 different glycans [Krambeck, et al. ]1 • Generate a theoretical isotope envelope • Calculate the correlation between the theoretical and observed isotope envelopes for each of following scenarios • Glycans • Glycans + Glycans, linear fitting applied • Glycans + Unknown, linear fitting applied • Mercury algorithm2 - generate the unknown isotope envelopes 1.Krambeck, F.J. and M.J. Betenbaugh, A mathematical model of N-linked glycosylation.BiotechnolBioeng, 2005. 92(6): p. 711-28. 2.Rockwood, A., S. Van Orden, and R. Smith, Rapid Calculation of Isotope Distributions. Analytical Chemistry, 1995. 67: p. 2699-2704.

14. Three scenarios Correlation Score Glycan Theoretical isotope envelope 0.6 Experimental isotope envelope Glycan α β 0.8 Unknown α β 0.2

15. Glycan Profiles • Decompose the abundance for two glycans with overlapping isotopic envelopes Experimental isotope envelope Glycans Glycans α β

16. Glycan Profile Comparison • Comparison of glycan abundances in multiple samples • Biomarker discovery • Given glycan spectra from multiple samples under different (e.g. disease vs. health) conditions • Goal: To find glycans with distinct abundances between samples Z Kyselova, Y. Mechref, M. M. Al Bataineh, L. E. Dobrolecki, R. J. Hickey, J. Vinson, C. J. Sweeney, and M. V. Novotny. Alterations in the serum glycome due to metastatic prostate cancer. Journal of Proteome Research, 6:18221832, 2007.

17. Approach Remove the least significant component. Repeat until all the score above threshold. 70% identical with a cutoff at 0.5 Health spectra (H1, H2, H3…Hk) Disease spectra (D1, D2, D3…Dk) 1.Hastie, T., et al., 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol, 2000. 1(2): p. RESEARCH0003

18. Implementation of Multi N-Glycan • Software Requirements • .net framework 2.0 using C# • C++ runtime • [R] for PCA analysis • Thermo Scientific Xcalibur • Input • Spectrum • File format: Plain text (Peak list), mzXML1,RAW file (Thermo Scientific raw file) • N-Glycans list • CSV file (User-defined); default define by [Krambeck, et al. ] • Output • List of glycans with scores 1.Pedrioli, P., et al., A Common Open Representation of Mass Spectrometry Data and its Application in a Proteomics Research Environment. Nature Biotechnology, 2004. 22(11): p. 1459-1466.

19. Software Interface

20. Software features • Signal preprocessing provided • Subtracting background • Smoothing and picking peaks • Tolerating mass accuracy • Flexible parameters incorporate actual experiment • Isotope envelopes generator • Content rich output, supporting multiple formats • csv, text, html

21. Software screenshot Html result export

22. Software screenshot

23. Result • Data set[ Zhiqun T., et al] • Liver Cancer : 73 individuals • Health: 78 individuals • 412 N-glycanare used • Parameters • Correlation score < 0.5 will be discarded. • Present in >30% of all samples 1.Zhiqun T., et al., Identification of N-Glycan Serum Markers Associated with Hepatocellular Carcinoma from Mass Spectrometry Data. J Proteome Res, 2009

24. Result Derived from The Paper Identified Low correlation score Overlap with 2192 Filtered out Can’t find the glycan structure neither in my list nor CFG database Zhiqun T., et al., Identification of N-Glycan Serum Markers Associated with Hepatocellular Carcinoma from Mass Spectrometry Data. J Proteome Res, 2009

25. Result Derived from Multi N-Glycan Confirmed result Distinct glycan

26. Future Work • Test on more clinical samples • Extend to O-glycan profiling • Apply de novo glycan sequencing on reported glycan (ongoing) • Connect reported glycans to glycan research literatures

27. Acknowledge • Advisor: Prof. Haixu Tang • Co-worker: AnoopMayampurath • Collaborator: YehiaMechref, Department of Chemistry • COL Lab members • This work will be presented on May 26th 2010, 58th ASMS Conference Salt Lake City, Utah; and will be submitted to the Bioinformatics. • This work is funded by NCI/NIH grant number 1 U01 A128535-01.

28. Thank You