1. 日期時間授 課 內 容授課教師 7/2 ( 一 ) 10:10-12:00 Introduction to the course and Introduction to Nomics 張玉生 7/3( 二 ) 10:10-12:00Proteomics and human diseases 余兆松 7/3( 二 ) 14:10-16:00Biotechnology in Genomics 王子豪 7/4( 三 ) 10:10-12:00Application of Genomics in diseases 王馨世 7/4( 三 ) 14:10-16:00Biotechniques in proteomics 游佳融 7/5( 四 ) 10:10-12:00 Introduction to metabolomics and chemical Library 蕭明熙 7/5( 四 ) 14:10-16:00Biotechniques in metabolomics 駱碧秀 7/6( 五 ) 10:10-12:00Bioinformatics in the Nomic Era 林文昌 7/7( 六 ) 10:10-12:00Examination (Take Home Exam) ( 全體教師 ) Biotechnology in the “Nomic Era” ( 生物技術在 “ 体學 ” 時代 )

2. Jau-Song Yu ( 余兆松 ) Department of Cell and Molecular Biology, Institute of Basic Medical Sciences, Medical College of Chang Gung University ( 長庚大學基礎醫學所分子生物學科 ) Proteomics and human diseases

3. Genomics Proteomics Bioinformatics Genomics: Identification and characterization of genes (gene expression) and their arrangement in chromosomes Proteomics (Functional Genomics): Functional analysis of gene products (proteins) --- Global analysis of hundreds to thousands of proteins in cells or tissues simultaneously Bioinformatics: Storage, analysis and manipulation of the information from genomics and proteomics

4. Research Description: Interactome research, proteomics, bioinformatics for proteomics and its application to biomedical research. The University of New South Wales (UNSW), Sydney, Australia The term proteome, refers to proteins that are encoded and expressed by a genome, and was first suggested in 1994 by Marc Wilkins. Wilkins defines proteomics as " the study of proteins, how they're modified, when and where they're expressed, how they're involved in the metabolic pathways and how they interact with each other." What is “proteomics”? (“ 蛋白質體學 ”) “the PROTEin complement of the genOME”

5. Medical research Changes of physiological functions Global changes of DNA, RNA and protein Alterations of functional molecules Diseases 99% sequence of human genome published 16 February 2001 The Human Genome 15 February 2001

6. PNAS USA 98, 10869–10874 (2001) Global gene expression analysis --- cDNA microarray Breast cancer samples vs. normal tissues

7. The extent of gene expression (i.e. the amount of mRNA) is only one of the many factors determining the protein function in cells C 2 H 5 PO 4 mRNA stability, alternative splicing, etc. Post-translational Modification of proteins (covalent modification, proteolytic cleavage, activator, inhibitor, etc)

8. How to analyze hundreds to thousands of proteins in cells or tissues simultaneously? Separation of proteins on one or more matrixes --- 2D-gel MDLC Identification and/or quantitation of separated proteins in a high-throughput way --- mass spectrometry MS * *

9. sample pH 9 - pH 3 + Isoelectric focusing (1 st dimension) General principle and protocol of 2-dimension gel electrophoresis MW pH gradient SDS-PAGE Ampholytes polyacrylamide 2nd dimension

10. Traditional equipment for isoelectric focusing (IEF) Ampholytes polyacrylamide Cathode (-) electrode solution Anode (+) electrode solution

11. Immobilized pH Gradient (IPG) Polyacrylamide gel Acidic buffering group: Basic buffering group: CH2 - CH-C-NH-R O COO - NH 3 + Acrylamide monomer

12. Gradient maker plastic support film Production of Immobilized pH Gradient (IPG) strip A C B F E D acidic basic pH 3 pH 10

13. IPG strip rehydration and sample loading Strip holder Cathode (-) electrode Anode (+) electrode 30 voltage 12hr

14. First dimension: Isoelectric focusing 1. Place electrode pads (?) 2. 200 V step-n-hold 1.5hr 3. 500 V step-n-hold 1.5hr 4. 1000 V gradient 1500vhr 5. 8000 V gradient (?) 36000vhr Time Voltage Holder cover IPG strip Electrode pads

15. Second dimension: SDS-PAGE SDS equilibration SDS-PAGE SDS equilibration buffer 50 mM Tris-HCl 6 M Urea 30% Glycerol 2% SDS Trace Bromophenol SDS SDS-PAGE 0.5% agarose in running buffer SDS-PAGE Marker in paper IPG strip

16. Detection of proteins separated on gels --- Protocol of silver stain: 50% methanol 25% acetic acid 4hr ddH 2 O x 3 times 30min/time 0.004% DTT solution 30min 0.1% AgNO 3 30min ddH 2 O 30 sec 3% Na 2 CO 3 0.0185% formaldehyde 2.3M citric acid 5% acetic acid 25% methanol Fluorescent dyes: Sypro Ruby, Cy3, Cy5, Cy2 etc.

17. pH310 200 116 97 66 55 36 31 20 14 kDa

18. DeCyder and ImageMaster software

19. How to analyze hundreds to thousands of proteins in cells or tissues simultaneously? ● Separation of proteins on one or more matrixes --- 2D-gel MDLC ● Identification and/or quantitation of separated proteins in a high-throughput way --- mass spectrometry MS

20. What is a mass spectrometer and what does it do? Gary Siuzdak (1996) Mass Spectrometry for Biotechnology, Academic Press

21. Components of a mass spectrometer

22. Mass spectrometers used in proteome research NATURE, 422, 198-207, (2003) Electrospray ionization (ESI)MALDI Two ionization methods NATURE REVIEWS MOLECULAR CELL BIOLOGY, 5, 699-711 (2004)

23. NATURE, 422, 198-207, (2003)

24. MALDI-TOF MS (Matrix-assisted laser desorption/ionization-Time of flight ) ( 基質輔助雷射脫附游離 - 飛行時間質譜儀 ) Target plate Time of Flight Target plate M/Z

25. Mass Analyzer-Time of Flight (TOF) Kinetic Energy = ½ mv 2 v = (2KE/m) m/z

26. Sensitivity of MALDI-TOF MS ~10 fg 1347.7 g/mole x 5 x 10 -18 mole = 6.74 x 10 –15 g

27. (?????) MALDI-TOF MS analysis Digested by trypsin (Lys, Arg) Database search/mapping Protein identified (100%?) (621, 754, 778, 835, 1204,, 1398, 1476, 1582) (664, 711, 735, 904, 1079, 1188, 1438) (602, 755, 974, 1166, 1244, 1374) (854, 931, 935, 1021, 1067, 1184, 1386, 1438) (Masses of tryptic peptides are predictable from gene sequence databases) (621, 778, 835, 1204,, 1398, 1582) (735, 904, 1079, 1188, 1438) (755, 974, 1244, 1374) (854, 935, 1021, 1067, 1184, 1386, 1438) (M/Z) How to identify proteins by MALDI-TOF MS? Linking between genomics/bioinformatics/proteomics

28. 170 116.3 66.3 55.4 29 21.5 pH 310 4 3 12 (1) (2) (3) (4)

29. Bruker’s movie for MALDI-TOF Mass Spectrometry

30. Direct identification of the amino acid sequence of peptides by tandem mass spectrometry

31. Cell. Mol. Life Sci. 62 (2005) 848–869 LC-MS/MS

32. Nature, 422, 198-207, 2003 Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein–protein interactions via affinity- based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine. How useful is the mass spectrometry-based proteomics?

33. Paper No. in PubMed Year “Proteomics” and “Genomics” as the key words Genomics (since 1988) Total: 11102 Year Proteomics (since 1998) Total: 20795

34. The Nobel Prize in Chemistry 2002 The Nobel Prize in Chemistry for 2002 is to be shared between scientists working on two very important methods of chemical analysis applied to biological macromolecules: mass spectrometry (MS) and nuclear magnetic resonance (NMR). Laureates John B. Fenn, Koichi Tanaka (MS) and Kurt Wuthrich (NMR) have pioneered the successful application of their techniques to biological macromolecules. Biological macromolecules are the main actors in the makeup of life whether expressed in prospering diversity or in threatening disease. To understand biology and medicine at molecular level where the identity, functional characteristics, structural architecture and specific interactions of biomolecules are the basis of life, we need to visualize the activity and interplay of large macromolecules such as proteins. To study, or analyse, the protein molecules, principles for their separation and determination of their individual characteristics had to be developed. Two of the most important chemical techniques used today for the analysis of biomolecules are mass spectrometry (MS) and nuclear magnetic resonance (NMR), the subjects of this year’s Nobel Prize award.

38. A high throughput process including subcellular fractionation and multiple protein separation and identification technology allowed us to establish the protein expression profile of human fetal liver, which was composed of at least 2,495 distinct proteins and 568 non-isoform groups identified from 64,960 peptides and 24,454 distinct peptides. In addition to the basic protein identification mentioned above, the MS data were used for complementary identification and novel protein mining. By doing the analysis with integrated protein, expressed sequence tag, and genome datasets, 223 proteins and 15 peptides were complementarily identified with high quality MS/MS data. Molecular & Cellular Proteomics 5:1703–1707, 2006.

41. It has long been thought that blood plasma could serve as a window into the state of one’s organs in health and disease because tissue-derived proteins represent a significant fraction of the plasma proteome. Although substantial technical progress has been made toward the goal of comprehensively analyzing the blood plasma proteome, the basic assumption that proteins derived from a variety of tissues could indeed be detectable in plasma using current proteomics technologies has not been rigorously tested. Here we provide evidence that such tissue-derived proteins are both present and detectable in plasma via direct mass spectrometric analysis of captured glycopeptides and thus provide a conceptual basis for plasma protein biomarker discovery and analysis. Molecular & Cellular Proteomics 6:64–71, 2007.

43. Antibody-based proteomics provides a powerful approach for the functional study of the human proteome involving the systematic generation of protein-specific affinity reagents. We used this strategy to construct a comprehensive, antibody-based protein atlas for expression and localization profiles in 48 normal human tissues and 20 different cancers. Here we report a new publicly available database containing, in the first version, 400,000 high resolution images corresponding to more than 700 antibodies toward human proteins. Each image has been annotated by a certified pathologist to provide a knowledge base for functional studies and to allow queries about protein profiles in normal and disease tissues. Our results suggest it should be possible to extend this analysis to the majority of all human proteins thus providing a valuable tool for medical and biological research. et al. Molecular & Cellular Proteomics 4:1920–1932, 2005. From the ‡Department of Biotechnology, AlbaNova University Center, Royal Institute of Technology (KTH), SE-106 91 Stockholm, Sweden and the ¶Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden

46. Other Proteome Databases/Datasets Human natural killer cell secretory lysosome --- 222 proteins --- MCP 2007 Human Jurkat T lymphoma cells protein kinases --- 140 kinases --- MCP 2006 Human amniotic fluid proteome --- 69 proteins --- Electrophoresis 2006 Human platelet proteome --- 641 proteins --- Proteomics 2005 Human salivary proteome --- 309 & 1381 proteins --- Proteomics 2005/J Proteome Res 2006 Human breast tumor interstitial fluid proteome --- 267 proteins --- MCP 2004 Human pituitary adenoma proteome --- 111 proteins --- Proteomics 2003 Human cell line (6) proteomes --- 2341 proteins --- MCP 2003 Human stomach tissue --- 136 proteins --- Electrophoresis 2002 Human colon cancer cell line membrane proteome --- 284 proteins --- Electrophoresis 2000 Human centrosome proteome --- 64 proteins --- Nature 2003 Human pleural effusion proteome --- 1415 proteins --- J Proteome Res 2005 Rat liver rough ER, smooth ER, and Golgi apparatus proteomes - >1400 proteins -Cell 2006 Mouse mitochondria proteome --- 591 proteins --- Cell 2003 Mouse cortical neuron proteome --- 3590 proteins --- MCP 2004 Plasma proteome of lymphoma-bearing SJL mice --- 1079 proteins --- J Proteome Res 2005 Bovine proteome database --- 534 proteins --- J Chromatography B 2005 Drosophila phosphoproteome --- 887 phosphopeptides --- Nat Methods 2007 C. elegans proteome --- 1616 proteins --- J Proteome Res 2003 Snake venom proteome --- 42 proteins --- Toxicon 2006 Malaria parasite Plasmodium falciparum proteome --- 2415 & 1289 proteins --- Nature 2002 Yeast proteome --- 2003 proteins --- Genome Biology 2006 Oral microorganisms proteomes --- 330 proteins --- Oral Microbiol Immunol. 2005 Bacillus subtilis phosphoproteome --- 78 phosphorylation sites --- MCP 2007 Rice proteome database --- 11941 proteins --- Nucleic Acids Res 2004 HMDB: the Human Metabolome Database --- >2180 metabolites --- Nucleic Acids Res 2007

48. Mass spectrometry (MS)-based proteomics has become a powerful technology to map the protein composition of organelles, cell types and tissues. In our department, a large-scale effort to map these proteomes is complemented by the Max-Planck Unified (MAPU) proteome database. MAPU contains several body fluid proteomes; including plasma, urine, and cerebrospinal fluid. Cell lines have been mapped to a depth of several thousand proteins and the red blood cell proteome has also been analyzed in depth. The liver proteome is represented with 3200 proteins. By employing high resolution MS and stringent validation criteria, false positive identification rates in MAPU are lower than 1:1000. Thus MAPU datasets can serve as reference proteomes in biomarker discovery. MAPU contains the peptides identifying each protein, measured masses, scores and intensities and is freely available at http://www.mapuproteome.com using a clickable interface of cell or body parts. Proteome data can be queried across proteomes by protein name, accession number, sequence similarity, peptide sequence and annotation information. More than 4500 mouse and 2500 human proteins have already been identified in at least one proteome. Basic annotation information and links to other public databases are provided in MAPU and we plan to add further analysis tools.

49. DATA GENERATION AND VALIDATION Figure 1. Workflow for protein identification and validation. trypsin or endoproteinase Lys-C 75  m chromatography column and eluted using a 2 h gradient. LTQ-FTICR MS or LTQ-Orbitrap MS

50. Genome Biology 2006, 7:R72 (doi:10.1186/gb-2006-7-8-r72) Results: In this study, we employ state-of-the-art mass spectrometric identification, using both a hybrid linear ion trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) mass spectrometer, and high confidence identification by two consecutive stages of peptide fragmentation (MS/MS/MS or MS3), to characterize the protein content of the tear fluid. Low microliter amounts of tear fluid samples were either pre-fractionated with one-dimensional SDSPAGE and digested in situ with trypsin, or digested in solution. Five times more proteins were detected after gel electrophoresis compared to in solution digestion (320 versus 63 proteins). Ontology classification revealed that 64 of the identified proteins are proteases or protease inhibitors. Of these, only 24 have previously been described as components of the tear fluid. We also identified 18 anti-oxidant enzymes, which protect the eye from harmful consequences of its exposure to oxygen. Only two proteins with this activity have been previously described in the literature. Conclusion: Interplay between proteases and protease inhibitors, and between oxidative reactions, is an important feature of the ocular environment. Identification of a large set of proteins participating in these reactions may allow discovery of molecular markers of disease conditions of the eye.

51. Specific “disease pattern” of proteins exists in clinical specimens ?

52. PNAS November 11, 2003 vol. 100 no. 23 13537–13542 A total of 682 individual protein spots were quantified in 90 lung adenocarcinomas by using quantitative two-dimensional polyacrylamide gel electrophoresis (2-DE) analysis. A leave-one- out cross-validation procedure using the top 20 survival- associated proteins identified by Cox modeling indicated that protein profiles as a whole can predict survival in stage I tumor patients (P<0.01)

53. proteinWB

54. Protein Expression Profiles Predict Survival in Stage I. Univariate Cox proportional hazards regression analysis using all 90 samples and 682 protein spots indicated 46 proteins were associated with patient survival (P<0.05, Table 1).

55. Fig. 2. Protein expression profiles and patient survival (A) Kaplan–Meier survival plots showing the relationship between patient survival and the risk index based on the leave-one- out cross-validation procedure using the top 20 survival-associated proteins among all 682 proteins using all 90 tumors. The high- and low-risk groups differ significantly (P 0.005). (B) Relationship between patient survival and the risk index based on the leave-one- out cross-validation procedure using the top 20 survival-associated proteins among the 62 stage I tumors. The high- and low-risk groups differ significantly (P 0.01). (C) Relationship between patient survival and PGK1 protein expression in an independent validation set of 90 lung adenocarcinomas. PGK1 immunohistochemical analysis of a tissue array indicates that increased PGK1 is associated with a reduced survival (P 0.04). (D) Relationship between patient survival and serum PGK1 levels (ratio of PGK1total serum protein) by using ELISA analysis with 107 lung adenocarcinomas (P 0.004).

56. THE LANCET Vol 359 February 16, 2002 Use of proteomic patterns in serum to identify ovarian cancer Chips for binding proteins from clinical samples Specific “disease pattern” of proteins exists in serum samples ?

57. SELDI-TOF MS (Surface-enhanced laser desorption ionization)

58. Mass spectra from serum of normal controls

60. This result yielded 100% sensitivity (95% CI 93–100) and 95% specificity (87–99). The positive predictive value for this sample set was 94% (84–99), compared with 35% for CA125 for the same samples. Total 116 cases 63/66 Total 66 Total 50 50/50

61. Disease-related functional proteomics / www.sciencexpress.org / 26 September 2002 / Page 1/ 10. Contribution of Human α-Defensin-1, -2, and -3 to the Anti-HIV-1 Activity of CD8 Antiviral Factor (by David D. Ho’s group) It is known since 1986 that CD8 T lymphocytes from certain HIV-1- infected individuals who are immunologically stable secrete a soluble factor, termed CAF, that suppresses HIV-1 replication. However, the identity of CAF remained elusive despite an extensive search. By means of a protein-chip technology, we identified a cluster of proteins that were secreted when CD8 T cells from long- term non-progressors with HIV-1 infection were stimulated. These proteins were identified as α-defensins-1, -2, and -3.

62. SELDI-TOF mass spectra of secrectory proteins of CD8 T cells from different groups LTNP ( 感染但長期未發病 ) Progressor ( 感染且已發病 )

64. α-defensins stain in green, CD8 proteins in red, and nuclei in blue. (Human neutrophil as positive control)

65. Tea Break

66. NATURE|VOL 429 | 3 JUNE 2004 |www.nature.com/nature Erika Check is Nature’s Washington biomedical correspondent.

67. The first criticisms of OvaCheck Published: 9 June 2003 BMC Bioinformatics 2003, 4:24 Received: 28 March 2003 Accepted: 9 June 2003

68. 0 20,000 0 1,000 p-value M/Z Diagnostic value of Low M/Z values 435.46 2.7921

69. Emanuel Petricoin (above) holds a protein pattern generated by the blood test he believes can reliably diagnose cancer.

70. “Whether or not OvaCheck works, we will learn from this experience what rules of evidence we might apply in the future to find useful results more efficiently.”--- Erika Check

72. Featured Article Identification of Serum Amyloid A Protein As a Potentially Useful Biomarker to Monitor Relapse of Nasopharyngeal Cancer by Serum Proteomic Profiling William C. S. Cho,1 Timothy T. C. Yip,1 Christine Yip,2 Victor Yip,2 Vanitha Thulasiraman,2 Roger K. C. Ngan,1 Tai-Tung Yip,2 Wai-Hon Lau,1 Joseph S. K. Au,1 Stephen C. K. Law,1 Wai-Wai Cheng,1 Victor W. S. Ma,1 and Cadmon K. P. Lim1 1Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong Special Administrative Region, The People’s Republic of China and 2Ciphergen Biosystems Inc., Fremont, California Vol. 10, 43–52, January 1, 2004 Clinical Cancer Research

73. Serum samples were thawed, and 20 ul of each serum were denatured byadding 30 ul of 50 mM Tris-HCl buffer containing 9 M urea and 2% 3-[(3- cholamidopropyl)dimethylammonio]-1- propanesulfonic acid (pH 9). The proteins were fractionated in an anion exchange Q HyperD F 96-well filter plate (Ciphergen Biosystems, Fremont, CA). Six fractions (namely fractions from the flow through pH 7, pH 5, pH 4, and pH 3 and organic eluant fractions) were collected by stepwise decrease in pH. The fractions were diluted and profiled on a Cu (II) Immobilized Metal Affinity Capture (IMAC3) Protein Chip Array (Ciphergen Biosystems, Fremont, CA; see Ref. 11). All fractionation and profiling steps were performed on a Biomek 2000 Robotic Station (Beckman Coulter). Fig. 1 Identification of serum biomarkers associated with relapse of NPC

74. Fig. 2. Distribution of the peak intensities of the two protein-chip-identified biomarkers (11.6 and 11.8 kDa) in nasopharyngeal carcinoma (NPC) patients, lung cancer patients, patients with benign metabolic disease (thyrotoxicosis), and normal individuals.

75. Table 1 Clinical parameters and peak intensities of the 11.6- and 11.8-kDa biomarkers in the relapse group of nasopharyngeal cancer patients under study

76. Table 2 Clinical parameters and peak intensities of the 11.6- and 11.8-kDa biomarkers in the remission group of nasopharyngeal cancer patients under study

77. Fig. 3 Protein identification. A, peptide mapping of the two relapse-associated biomarkers by tryptic digestion. B, tandem mass spectrometry (MS/MS) fragmentation analysis of 2177.9-Da peptide generated from tryptic digest of the two biomarkers.

78. A, B, and C, three NPC patients in relapse were monitored by SAA protein chip (SAA Protein Chip), SAA enzyme immunoassay (SAA EIA), and EBV DNA by Q-PCR (EBV DNA Q-PCR). D, 11, 5, and 8 patients in remission were also monitored by the three techniques, respectively. and E, follow-up profiling curves of 11.6 and 11.8 kDa SAA isoforms by protein chip; ‚, follow-up curves of SAA protein by immunoassay; f (EBER1), follow-up of serum EBV DNA encoding EBV small RNA-1 by Q-PCR; BN 2o, bone metastasis; CR, complete response to chemotherapy; CT, salvage chemotherapy; DLN 2o, distant lymph node metastasis; DX, histopathological diagnosis of NPC; Groin LN 2o, metastasis in lymph nodes of the groin; LV 2o, liver metastasis; LV 2oPR, partial response to chemotherapy in tumor lesion in metastatic liver; DLN 2oCR, complete response to chemotherapy in tumor lesion in metastatic distant lymph node; PG, progression of disease; PR, partial response; RT, radiation therapy; SAA, immunoassay curves for SAA protein; SP 2o, spleen metastasis. Fig. 4. Longitudinal monitoring of SAA protein level by protein chip profiling and immunoassay and circulating serum EBV DNA by real-time Q-PCR in NPC patients.

79. NATURE REVIEWS MOLECULAR CELL BIOLOGY, 5, 699-711 (2004)

80. What is “proteomics”? ( 何謂 “ 蛋白質體學 ”) Proteomics is “the study of protein properties (expression level, post-translational modification, interactions, etc.) on a large-scale that results in a global integrated view of disease processes, cellular programs and networks at protein level” “Proteome”, defined as “the PROTEin complement of the genOME”, was first coined by Wilkins working as part of a collaborative team at Macquarie (Australia) and Sydney Universities (Australia) in 1995.