1. High throughput urine biomarker discovery and integrative analysis for translational medicine High throughput urine biomarker discovery and integrative analysis for translational medicine Bruce Ling, Ph.D.

2. A molecular indicator of a specific biological property; a biochemical feature or facet that can be used to measure the progress of disease or the effects of treatment (NIH, 2002) Biomarker

3. Small molecules Glucose (diabetes) Serum cholesterol (cardiovascular disease) Proteins PSA (prostate cancer) HER2 (IHC) (breast cancer Herceptin Therapy) hCG (pregnancy test) RNA/DNA HER2 (FISH) (breast cancer) OncoDX (Genomic Health, breast cancer) Biomarker examples

4. Pediatric Diseases Kidney transplant Acute Rejection Kawasaki Disease Systemic Juvenile Idiopathic Arthritis Necrotizing Enterocolitis Inflammatory Bowel Disease Glioblastoma multiforme Preterm Labor

5. Where to look for biomarkers –Disease tissue –Proximal/distal fluids Plasma/serum, urine, amniotic, synovial fluid, CSF, saliva, tears, etc.

6. Why Urine? Patient consenting Non-invasive Easy to collect for time course analysis Abundant and stable

7. Urine is a rich resource for biomarker discovery Filtration of plasma 900 liters daily Urine proteome > 1500 proteins, ~30 mg/day 30% from circulation 70% from urogenital tract Urine peptidome > 100, 000 naturally occurring peptide, ~20 mg/day

8. 1)Equal mass of protein and peptide in urine translates into at least a ten-fold greater molar abundance of peptides than proteins 2)Urine peptide analysis is not hampered by highly abundant protein issues 3)One hour one dimensional HPLC separation is sufficient for the analysis of greater than 100,000 urine peptides, allowing a high throughput biomarker discovery Urine Peptidome: a fertile ground for biomarker discovery

9. Challenges of Urine Analysis Dilution factor causing concentration variations –Solution: content normalization Creatinine; house keeping urine abundant peptides; equal peptide mass Peptide content can be complicated by –Diet, exercise, circadian rhythm, circulatory levels of hormones –Solution: careful experimental design to avoid these confounding issues, e.g., Cohorts of patients of similar demographics Multi-center sample collection and validation

10. Urine Peptidome Profiling by Mass Spectrometry

11. Biomarker HTS Flows Sample peptides: -Class 1:1,2,3… -Class 2:1,2,3… -Class 3:1,2,3… RP-HPLC Collect 120 fractions on MALDI plates MALDI-TOF MS on each fraction MASS-Conductor ® Machine learning feature discovery and classification Candidate Biomarkers 987.62 1027.51 1098.55 etc.

12. Biomarker Confirmation/Validation Identify Differentiating Markers New sample Sets Validation New Center sample sets Higher throughput Quantitative methods Quantitative MS Immunoassay Testing New Longitudinal sample sets Exploration Protein ID MS/MS

13. Data Challenges in Urine Peptide Biomarker Discovery Data tracking and storage –Patient demographics –Peptide profiles in various fractions/samples Dimension reduction and data reduction –Multi-dimensional data sets –Huge data sets and lots of noise A project of 40 samples produced 241.5 GB raw data in MYSQL database HPLC fraction Peptide mass Patient ID Patient demographics Peptide signal

14. Decode the Urine Peptidome Patient 1Patient 2Patient 3Patient 4… peptide 1 signal … peptide 2 signal … peptide 3…………… peptide 4…………… peptide 5…………… ……………… peptide 100,000 …………… ???

15. Decode the Urine Peptidome Peak finding in each fraction for each sample Align the peaks across the samples Create common peak index

16. Data mining issues in Biomarker Discovery Peak number >> sample number False discovery in multiple hypothesis testing Multi-class classification and validation Discovery of biomarker signature

17. Robustly loading and tracking of high volume proteomic data Robust reduction of raw data sets and enabling of efficient and accurate peak finding, alignment and indexing Robust and automatic high throughput computing for expensive algorithms Integration of FDR analysis and multi-class classification algorithms to obtain statistically differentiating feature panels Automatic generation of data reports with graphics MASS-Conductor® Platform Support Urine Peptide Biomarker Discovery

18. MASS-Conductor® Platform High Throughput Computing

19. Urine Biomarker Discovery: Case Study

20. Kidney Transplant Rejection Most effective treatment for end stage renal disease 16,000 per year in US Grafts monitored by biopsy Unmet needs: –Less invasive and more frequent monitoring –Acute rejection vs. stable graft –Acute rejection vs. BK virus

21. Allograft Acute Rejection Urine Biomarker Discovery Peak finding Peak alignment Peak indexing Supervised Data mining Feature selection Training Testing LCMS Data reduction Unsupervised Data mining 2D - Clustering QuantitativeLCMS Validation 1234

22. Biomarker Panel: Supervised Analysis

23. Biomarker Panel: Unsupervised Analysis

24. NH 2 ZP-domain EGF-like Domain I EGF-like Domain II EGF-like Domain III COOH 28 64 65 107 108 149 334 585 Urine THP Peptide Biomarkers Fall into a Tight Cluster in C-Terminus 1. R.VLNLGPITR.K 2. G.SVIDQSRVLNLGPI.T 3. I.DQSRVLNLGPITR.K 4. R.SGSVIDQSRVLNLGPI.T 5. S.VIDQSRVLNLGPITR.K 6.R.SGSVIDQSRVLNLGPIT.R 7. G.SVIDQSRVLNLGPITR.K 8.R.SGSVIDQSRVLNLGPITR.K

25. MRM: Multiplexed Quantitative Biomarker Validation

27. 0.0 0.2 0.4 0.6 0.8 1.0 SAMPLE: URINE PEPTIDES THP1680.98 VIDQSRVLNLGPITR THP1912.07 SGSVIDQSRVLNLGPITR THP1680.98 VIDQSRVLNLGPITR THP1912.07 SGSVIDQSRVLNLGPITR AR versus STA AR versus BK Sensitivity 1- Specificity 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 AUC: 0.83 AUC: 0.74 AUC: 0.92 AUC: 0.83 ROC Analysis of THP Peptide Biomarkers Quantified by MRM

28. 1. COL1A11235.56 APGDRGEPGPPGP 2. COL1A11251.55 APGDRGEPGPPGP 3. COL1A11322.57 APGDRGEPGPPGPA 4. COL1A11316.59 DAGPVGPPGPPGPPG 5. COL1A11409.66 GPPGPPGPPGPPGPPS 6. COL1A12048.92 NGDDGEAGKPGRPGERGPPGP 7. COL1A12064.91 NGDDGEAGKPGRPGERGPPGP 8. COL1A12192.97 NGDDGEAGKPGRPGERGPPGPQ 9. COL1A12362.12 GKNGDDGEAGKPGRPGERGPPGPQ 10. COL1A12378.10 GKNGDDGEAGKPGRPGERGPPGPQ 11. COL1A12645.24 GPPGKNGDDGEAGKPGRPGERGPPGPQ 12. COL1A11709.79 PPGEAGKPGEQGVPGDLG 13. COL1A12031.95 PPGEAGKPGEQGVPGDLGAPGP 14. COL1A12221.97 ADGQPGAKGEPGDAGAKGDAGPPGP 15. COL1A12205.99 ADGQPGAKGEPGDAGAKGDAGPPGP 16. COL1A12277.01 ADGQPGAKGEPGDAGAKGDAGPPGPA 17. COL1A12293.01 ADGQPGAKGEPGDAGAKGDAGPPGPA 18. COL1A12617.15 GPPGADGQPGAKGEPGDAGAKGDAGPPGPA 19. COL1A12086.93 EGSPGRDGSPGAKGDRGETGPA 20. COL1A12157.96 AEGSPGRDGSPGAKGDRGETGPA 21. COL1A13014.41 ESGREGAPGAEGSPGRDGSPGAKGDRGETGPA 22. COL1A11266.58 SPGPDGKTGPPGPA 23. COL1A12129.99 DGKTGPPGPAGQDGRPGPPGPPG 24. COL1A12017.93 GRPGEVGPPGPPGPAGEKGSPG 25. COL1A22081.94 DGPPGRDGQPGHKGERGYPG 26. COL1A22195.99 NDGPPGRDGQPGHKGERGYPG 27. COL2A11861.85 SNGNPGPPGPPGPSGKDGPK 28. COL3A11738.76 NDGAPGKNGERGGPGGPGP 29. COL3A12008.93 DGESGRPGRPGERGLPGPPG 30. COL3A12079.92 DAGAPGAPGGKGDAGAPGERGPPG 31. COL3A12565.18 GAPGQNGEPGGKGERGAPGEKGEGGPPG 32. COL3A12743.24 KNGETGPQGPPGPTGPGGDKGDTGPPGPQG 33. COL4A11424.66 PGQQGNPGAQGLPGP 34. COL4A21126.51 GLPGLPGPKGFA 35. COL4A31161.52 GEPGPPGPPGNLG 36. COL4A41218.55 GLPGPPGPKGPRG 37. COL4A51144.52 GPPGPPGPLGPLG 38. COL4A51269.53 PGLDGMKGDPGLP 39. COL4A51733.76 GIKGEKGNPGQPGLPGLP 40. COL4A61158.52 GLPGPPGPPGPPS 41. COL5A11748.82 KGPQGKPGLAGMPGANGPP 42. COL7A11690.80 PGLPGQVGETGKPGAPGR 43. COL9A11732.84 KRPDSGATGLPGRPGPPG 44. COL11A11441.64 GPPGPPGLPGPQGPKG 45. COL11A11828.84 DGPPGPPGERGPQGPQGPV 46. COL17A11368.62 LPGPPGPPGSFLSN 47. COL18A11142.51 GPPGPPGPPGPPS 1. THP 982.59 VLNLGPITR 2. THP1047.48 SGSVIDQSRV 3. THP1211.66 DQSRVLNLGPI 4. THP1225.69 SRVLNLGPITR 5. THP1324.76 IDQSRVLNLGPI 6. THP1423.83 VIDQSRVLNLGPI 7. THP1468.82 DQSRVLNLGPITR 8. THP1510.87 SVIDQSRVLNLGPI 9. THP1567.91 GSVIDQSRVLNLGPI 10. THP1581.91 IDQSRVLNLGPITR 11. THP1654.91 SGSVIDQSRVLNLGPI 12. THP1680.98 VIDQSRVLNLGPITR 13. THP1755.96 SGSVIDQSRVLNLGPIT 14. THP1768.01 SVIDQSRVLNLGPITR 15. THP1912.07 SGSVIDQSRVLNLGPITR 16. THP2040.16 SGSVIDQSRVLNLGPITRK AB AR Urine Biomarkers are Collagen and THP Peptides Collagen peptide biomarkers THP peptide biomarkers

29. Hypothesis 1 Gene expression alteration in AR Hypothesis 2 Protease expression alteration in AR Hypothesis 3 Protease inhibitor expression alteration in AR Hypothesis of Molecular Mechanisms for AR Urine Biomarkers

30. Exploration data set 6 (TGCG) 1 Affymetirics HG-U95Av2 (AR: PBL, n=6; BX, n=7) (STA: PBL, n=9; BX, n=10) (NR: PBL, n=8; BX, n=5) (HC: PBL, n=8; BX, n=9) Exploration Analysis Confirmation 2 Affymetirics HU-133 (AR: BX, n=37) (HC: BX, n=23) Confirmation Analysis Validation 3 Quantitative RT-PCR (AR: BX, n=14) (STA: BX, n=10) (HC: BX, n=10) Validation Analysis Expression analysis of peptide biomarkers’ corresponding precursor genes Expression analysis of metzincin superfamily genes Expression analysis of protease inhibitor genes Discovery  mechanism biomarkers Confirmation data set (Stanford ) Validation data set (Stanford ) Transcriptome Analysis of Allograft Biopsies

31. Parental Protein Expression Analysis of Allograft Biopsies Contrasting Urine Peptide Biomarker Changes

33. Genome-wide Protease and Protease Inhibitor Expression Analysis of Allograft Biopsies Revealed Up Regulation of MMP7, SERPING1, TIMP1

34. AR STA HC Signal Intensity 0 10 20 30 40 50 TIMP1COL1A2UMODSERPING1MMP7COL3A1 0.0 0.2 0.4 0.6 0.8 1.0 1- Specificity Mean ( AUC): 0.98 Sensitivity 0.0 0.2 0.4 0.6 0.8 1.0 Allograft Biopsies Expression Biomarkers Effectively Classified AR

35. Proposed Underlying Mechanisms for the AR Urine Peptide Biomarkers

36. Hypothesis: Collagen Breakdown and Deposition in AR Decreased Collagen Peptides In AR Increased TIMP1 (Collagenase Inhibitor) in AR Increased Collagen Deposition in AR More Graft Fibrosis After an AR episode? Biopsy Gene Expression GSE 14328 Increased MMP7 in AR Decreased Collagen Breakdown in AR Decreased Collagenase Activity In AR tissue Increased Collagen Expression in AR Integrated Analysis Urine Peptidomics Urine Renal Biopsy Urine Peptide Analysis by MS

37. Urine Biomarker Discovery: Case Study

38. Unmet Medical Needs in Necrotizing Entrocolitis Necrotizing enterocolitis (NEC) is a medical condition primarily seen in premature infants, where portions of the bowel undergo necrosis (tissue death). Despite decades of research the pathogenesis of NEC remains obscure, the diagnostic parameters unclear, and both treatment and prevention strategies remain inadequate and dated. There is the real need for better molecular identification of NEC in order to assist in altering its onset and progression.

39. Clinical parameters do not adequately predict outcome in Necrotizing Enterocolitis

40. Low Risk Group Intermediate Risk Group High Risk Group Rate of NEC-S occurrence (% patients) NEC score -10010203040 0 10 20 30 M: n = 2 S: n = 15 M: n = 16 S: n = 10 M: n = 26 S: n = 0 MS NEC Clinical Parameters Based Model stratifies Necrotizing Enterocolitis Patients

41. NEC Urine Naturally Occurring Peptide Biomarker Discovery Peak finding Peak alignment Peak indexing Supervised Data mining Feature selection Training Testing LCMS Data reduction Unsupervised Data mining 2D - Clustering 123

42. Biomarker Panel: Supervised Analysis (Training and Testing)

43. Biomarker Panel: Unsupervised Analysis

44. Biomarker Panel: Combined data set and ROC analysis

45. Permutation based FDR analysis of the biomarker signature

46. Discovery set n = 34 17 Clinical Diagnosis Medical NEC Scoring Percent Agreement with clinical diagnosis MS NEC 70 Urine peptide based Classification MS Low n=7 Classified as M Classified as S 70 00 NEC Risk Groups 96 MS Intermediate n=15 81 15 09 MS High n=9 00 09 100 % +- +- 88.9 %83.3 % +- 86.1 % Diagnosed as M Diagnosed as S 70 00 43 53 01 08 P = 0.01 Clinical Diagnosis N/A n=3 Proposed Ensemble Approach to Diagnose Necrotizing Enterocolitis Patients NEC Patients Clinical Model NEC Risk Urine Biomarkers NEC Diagnosis

47. TABLE 2 ClusterProteinLocationMH+Sequence Relative Abundance U test P value MS 1 COL1A1220-2492924.41 RGppGPPGKNGDDGEAGKPGRPGERGPpGp 0.2562-0.25624.25E-03 COL1A1220-2492940.36 RGPPGppGKNGDDGEAGKpGRpGERGpPGP 0.2541-0.25416.80E-03 2 COL1A2485-5142889.36 ARGEPGNIGFPGPKGPTGDPGKNGDKGHAG 0.2265-0.22658.93E-05 3 COL1A2925-9522865.31 GRDGNpGNDGpPGRDGQpGHKGERGYpG 0.2919-0.29191.99E-03 COL1A2933-9522081.94 DGpPGRDGQpGHKGERGYpG 0.2655-0.26555.39E-03 4 COL1A2135-1572229.06 AGpPGKAGEDGHpGKPGRpGERG 0.2732-0.27321.45E-02 COL1A2131-1572626.27 ARGpAGpPGKAGEDGHpGKPGRpGERG 0.223-0.2232.16E-02 COL1A2131-1572642.28 ARGpAGpPGKAGEDGHpGKpGRpGERG 0.2016-0.20163.14E-02 COL1A2137-1572142.05 GpPGKAGEDGHPGKPGRpGERG 0.2624-0.26241.06E-02 COL1A2131-1572158.03 GPpGKAGEDGHpGKPGRpGERG 0.3038-0.30382.16E-02 5 COL3A1813-8402565.18 GApGQNGEPGGKGERGApGEKGEGGpPG 0.2623-0.26232.58E-03 6 COL3A11168-11942680.19 NRGERGSEGSPGHPGQpGppGppGAPGP -0.23820.23821.06E-02 COL3A11168-11942696.22 NRGERGSEGSpGHpGQpGPPGPpGApGp 0.1893-0.18931.96E-02 Overlapping Urine Peptide Biomarkers for NEC

48. Proposed Underlying Mechanisms of Urine Naturally Occurring Peptide Biomarkers

49. PR Enbrel CR Anakinra CRPR CR EnbrelAnakinra A B C Prediction of drug response in SJIA

50. Urine peptide biomarkers: the discovery process Sample peptides: -Class 1:1,2,3… -Class 2:1,2,3… -Class 3:1,2,3… SCX/RP-HPLC Collect 100 fractions on MALDI plates MALDI-TOF MS for each sample LC fraction -- m/.z --abundance MASS-Conductor ® Machine learning feature discovery and classification Biomarker panels MSMS protein ID Prospective validation with quantitative mass spec (MRM)

51. Interdisciplinary Skills for Biomarker Discovery Biology Analytic biochemistry Biostatistics Computer Science Medicine

52. Q & A

53. Genome vs. Proteome

54. The Isotope Envelope

55. Predictor discovery in training set 2 Training set (10 AR, 10 STA, 6 BK) 1 LCMS raw spectra Peak finding peak alignment feature extraction 20937 unique features Classifier training Six-fold Cross-validation Classify AR, STA, BK MASS-ConductorUrine biomarker discovery and testing Predictor confirmation in testing set 3 Testing set (10 AR, 10 STA, 4 BK) Predictor sets Linear discriminant analysis (LDA) Calculate estimates of predicted class probabilities Analysis of goodness of class separation Pattern analysis in all set 4 Cluster analysis All set (20 AR, 20 STA, 10 BK, 10 NS, 10 HC) Predictors of 40 peptides 2d hierarchical clustering heatmap plotting Remove background signals Normalization Platform Validation 5 Correlation Analysis 2 peptide biomarkers MRM assay development MRM assay AR, STA, BK, NS, HC Training + Testing Samples LC-MALDI  MRM Allograft Acute Rejection Urine Biomarker Discovery

56. Correlation Studies Between LCMS and MRM Platforms

58. Analytical Challenges High complexity and wide dynamic range

62. Tirumalai, R. S. (2003) Mol. Cell. Proteomics 2: 1096-1103 Plasma Proteins Big Trees

63. Tirumalai, R. S. (2003) Mol. Cell. Proteomics 2: 1096-1103 Plasma Proteins Big Trees Bushes

64. Tirumalai, R. S. (2003) Mol. Cell. Proteomics 2: 1096-1103 Plasma Proteins Big Trees Bushes Grass + Bugs

65. www.genwaybio.com Analytical Challenges Detect low abundance proteins Big Trees = HAP Bushes = MAP Grass + Bugs = LAP

66. Bottom up LCMS Biomarker Discovery Sample preparation Digestion Peptide purification SCXRP Protein mixtureDigested peptides Mass-spec Spectra Data Analysis Multi-dimensional chromatography MS/MS Protein ID

67. Mass Spectrometry In A Nutshell time hνhν F=ma Ion source detector m/z MS Spectrum Mass Analyzer

68. MS/MS Peptide Sequencing hνhν source detector Fragment ions gate Collision cell MS/MS Spectrum 1 st Mass Analyzer 2 nd Mass Analyzer

69. Differential Expression Analysis in Quantitative LCMS Peptide 1: M/Z Peptide 2: M/Z’ Peptide 3: M/Z’’ Peptide 1: protein ID Peptide 2: protein ID’ Peptide 3: protein ID’’ MS basedMS/MS based MASS-Conductor® Exhaustive MS comparison Spectrum counting Labeling, e.g. iTRAQ

70. Qualitative Comparative Analysis – Spectrum Counting PROTEIN X Sample A Sample B MS/MS Number of Detected Peptides Number of Detected Peptides [PROTEIN X] IF THEN PROTEIN IDENTIFICATION

71. - Peptide fragments EQUAL MS/MS b y b y b y b y MS Mix -N H 114 31 -N H 115 30 -N H 116 29 -N H 117 28 + + + + - PRG 114 31 - PRG 115 30 - PRG 116 29 - PRG 117 28 S1 S2 S3 S4 Parallel Denature & Digest -Reporter-Balance-Peptide INTACT - 4 samples identical m/z 114 115 116 117 - Reporter ions DIFFERENT -Chemically identical -Migrate together in HPLC MSMS Based Comparative Analysis – iTRAQ (isobaric tag) Reporter Ions 114, 115, 116, 117

72. More abundant proteins tends to get more sequence coverage in MS/MS, masking away the MSMS opportunities for the peptides coming from the low abundant proteins Spectrum counting is semi-quantitative iTRAQ is not scalable for a moderate throughput biomarker discovery iTRAQ cost iTRAQ tag number Issues in MS/MS Based Analysis

73. MS Based Comparative Analysis – Targeted MASS-Conductor® Approach 1. ALL peptide MS signals will be exhaustively compared leading to the discovery of statistically differential signals 2. ONLY peptides of interest, usually a very small number, will be tried with full attention for the MS/MS ID. If necessary, MS/MS signals can be enhanced by more loading or fraction enrichment before MS

74. Robustly handling of high volume proteomic data –e.g. One SCX fraction and 120 RP fractions 40 sample project MYSQL data storage –raw data is 241.5 GB –Peak data is 4.4 GB Robust and automatic high throughput computing Robust reduction of raw data sets and enabling of efficient and accurate feature discovery Sophisticated data mining approaches to obtain statistically differentiating features Graphic data analysis MASS-Conductor® Platform Data Mining Requirements

75. “MASS-Conductor ®” An in house software platform, including JAVA, PERL, R, RUBY and MYSQL implementations Interface with AB and Thermo mass specs –Convert LC-MALDI T2D files in a batch manner to text files Extract mono-isotopic LC-MALDI peaks Track multiple scans of the same MALDI plate and HPLC SCX/RP fractions where each peak resides Cluster mono-isotopic peaks across categorical samples for comparative analysis Interface and integrate SAM, PAM, 1d classifiers, 2d classifiers, margin tree, CART algorithm packages for differential feature selection and classification

76. Common Feature Alignment/Extraction Spectrum Raw datasets Peak datasets Feature datasets Indexed datasets Mass-Conductor Database Binary/Multi-class Classification False Discovery Rate Analysis Biomarker Discovery Potential Biomarkers Web-Service Collaboration Peak Extraction Feature indexing Patient datasets “MASS-Conductor ®”

77. DATA REDUCTION in “MASS-Conductor ®” Peak Extraction from Spectra Raw Data Patient sample LC-MALDI Spot/fraction 13. m/z 900 – 4000: 118142 raw data points  1690 peak data points 62 peaks 2530 data points m/z 1200 – 1250

78. Before data reductionAfter data reduction Class A Class B Class C fractions MS signal DATA REDUCTION – One Peptide Example Peak Extraction from Spectra Raw Data

79. SEQUENCE 640 AA; 69761 MW 001 MGQPSLTWML MVVVASWFIT TAATDTSEAR WCSECHSNAT CTEDEAVTTC TCQEGFTGDG 061 LTCVDLDECA IPGAHNCSAN SSCVNTPGSF SCVCPEGFRL SPGLGCTDVD ECAEPGLSHC 121 HALATCVNVV GSYLCVCPAG YRGDGWHCEC SPGSCGPGLD CVPEGDALVC ADPCQAHRTL 181 DEYWRSTEYG EGYACDTDLR GWYRFVGQGG ARMAETCVPV LRCNTAAPMW LNGTHPSSDE 241 GIVSRKACAH WSGHCCLWDA SVQVKACAGG YYVYNLTAPP ECHLAYCTDP SSVEGTCEEC 301 SIDEDCKSNN GRWHCQCKQD FNITDISLLE HRLECGANDM KVSLGKCQLK SLGFDKVFMY 361 LSDSRCSGFN DRDNRDWVSV VTPARDGPCG TVLTRNETHA TYSNTLYLAD EIIIRDLNIK 421 INFACSYPLD MKVSLKTALQ PMVSALNIRV GGTGMFTVRM ALFQTPSYTQ PYQGSSVTLS 481 TEAFLYVGTM LDGGDLSRFA LLMTNCYATP SSNATDPLKY FIIQDRCPHT RDSTIQVVEN 541 GESSQGRFSV QMFRFAGNYD LVYLHCEVYL CDTMNEKCKP TCSGTR F R SG SVIDQSRVLN 601 LGPITRK GVQ ATVSRAFSSL GLLKVWLPLL LSATLTLTFQ Human THP precursor, Swiss-Prot: P07911 Urine THP Peptide Biomarkers Fall into Tight Clusters in C-Terminus