About OMICS Group OMICS Group International is an

About OMICS Group OMICS Group International is an

About OMICS Group OMICS Group International is an amalgamation of Open Access publications and worldwide international science conferences and events. Established in the year 2007 with the sole aim of making the information on Sciences and technology Open Access, OMICS Group publishes 500 online open access scholarly journals in all aspects of Science, Engineering, Management and Technology journals. OMICS Group has been instrumental in taking the knowledge on Science & technology to the doorsteps of ordinary men and women. Research Scholars, Students, Libraries, Educational Institutions, Research centers and the industry are main stakeholders that benefitted greatly from this knowledge dissemination. OMICS International also organizes 500 International conferences annually across the globe, where knowledge transfer takes place through debates, round table discussions, poster presentations, workshops, symposia and exhibitions. About OMICS Group About OMICS International Conferences

OMICS International is a pioneer and leading science event organizer, which publishes around 500 open access journals and conducts over 300 Medical, Clinical, Engineering, Life Sciences, Pharma scientific conferences all over the globe annually with the support of more than 1000 scientific associations and 30,000 editorial board members and 3.5 million followers to its credit. OMICS International has organized 500 conferences, workshops and national symposiums across the major cities including San Francisco, Las Vegas, San Antonio, Omaha, Orlando, Raleigh, Santa Clara, Chicago, Philadelphia, Baltimore, United Kingdom, Valencia, Dubai, Beijing, Hyderabad, Bengaluru and Mumbai. Urinary Metabolome Jayoung Kim, PhD Associate Professor, Cedars-Sinai Medical Center UCLA Harvard Medical School

Two Grand Challenges in Omics Hanahan and Weinberg. Cell 2011, 144: 646-74. Biomarker discovery Signaling networks Non-invasive Biomarker to monitor disease progression and drug responses Waste? Valuable as a diagnostic biofluid? Active role by regulating bladder biology? Urine is an ideal bio-medium to

monitor bladder condition Readily obtained and available with no required preparation by the patient The ease of collection allows for serial sampling to monitor disease and therapeutic response. Less complex than other body fluids. Body fluids that are most proximal to a disease site can often provide a source of informative biomarkers; therefore, urine-based monitoring for bladder condition is the most attractive strategy among other biofluids-based methods. Why do we measure the metabolome? Genotype x Environment mRNA expression protein expression metabolite levels & fluxes

temporal x spatial resolution Phenotype What is metabolomics? 2. Metabolite profiling someselectedmetaboli tes 3. Metabolomics all metabolites Metabolicfingerprintin g classifyingsamples scope accuracy 1. Target analysis fewmetabolites

Metabolites. Mar 2014; 4(1): 7197. APPLICATION OF METABOLOMICS IN URINE BIOLOGY RESEARCH Techniques and Data Analysis of Metabolomics Data Analytical techniques NMR or MS?: advantages and limitations NMR: minimum sample requirement, quantitative ability, and safe metabolite identification that provides detailed information on structure MS: sensitivity Targeted or nontargeted?: APPLICATION OF METABOLOMICS IN URINE BIOLOGY RESEARCH Data processing and metabolite identification: Databases: HMDB (http://www.hmdb.ca/), METLIN (

http://metlin.scripps.edu/), Massbank (http://www.massbank.jp), PubChem (http://ncbi.nim.nih.gov/), KEGG (http://www.kegg.com/), MetaCyc, ChEBI, PDB, UniProt, and GenBank as well as to GeneCard IDs, GeneAtlas IDs and HGNC IDs Pre-processing: Peak detection Deconvolution Pattern recognition PCA PLS-DA OPLS-DA Database search: Metabolite identification IC biomarkers Sampling Analytical

techniques NMR GC-MS LC-MS FT-IR Data processing Sample preparation ID LC-MS: Liquid chromatography-mass spectrometry; GC-MS: Gas chromatographymass spectrometry; NMR: Nuclear magnetic resonance; PCA: Principal component analysis; OPLS-DA: Orthogonal partial least squares discriminant analysis; PLS-DA: Partial least squares discriminant analysis.

Controls Biological interpretation A workflow for metabolic profiling. IC BUTanalytical challenges Wide variations in the ionic strength, pH, and osmolarity, particularly under conditions of physiological stress, diet, medications, environmental conditions. STUDY EXAMPLES: How can we apply metabolomics analysis to understand of urine biology? Nature Protocols 6, 14831499 (2011)

Omics Approaches Q1. Q1. WHATs WHATs the the difference? difference? Signature Signature Profiling: Profiling: Omics Omics Objective Objective diagnostic diagnostic tool

tool Q2. Q2. WHY WHY different? different? Biology/Mechanism Biology/Mechanism Therapeutic Therapeutic targeting targeting Urinary Metabolite Profiling Combined with Computational Analysis Suggest Interstitial Cystitis-Associated Candidate Biomarkers Interstitial Cystitis

A chronic syndrome of unknown etiology Very common bladder disease among old generation (more than one out of 77 people in USA) Affects quality of life, productivity and work performancePublic health burden Elmiron, the first FDA-approved oral drug for IC, shows unfavorable side effects Need for new medication for IC Need for objective and clinically relevant indicators IC-Associated Mechanistic Signaling Network 1: The Frizzled 8-Associated Antiproliferative Factor Enhances p53 Stability Through USP2a and MDM2 APF USP2a MDM2

p53 Interstitial Cystitis IC-Associated Mechanistic Signaling Network 2: SILAC ratio Direct activation Direct repression Indirect activation Physical interaction Mock APF 0.69 CTNNB1

0.70 J UP 0.77 EGFR 0.69 CAPN2 0.56 STAT3 1.40 ITGB1

1.42 F3 1.79 PTGS2 1.86 NDRG1 1.02 ACTB IC-Associated Mechanistic Signaling Network 3: Integration Analysis of Quantitative Proteomics and Transcriptomics Data Identifies Potential Targets of Frizzled-8 Protein-related

Antiproliferative Factor In Vivo A C 3256 5050 probe sets Up:2636 Down:2414 1794 164 1957 probe sets Up:1188 Down:769 Our method:FDR0.01, Fold>1.40 Gampers method:FDR0.01, Fold>2.00 Gamper method

Our method Inflammation pathw 1.TCR signaling pathway 2.BCR signaling pathway 3.Fc RI signaling pathwa 4.TLR signaling pathway 5.Antigen processing an 6.Leukocyte transendoth B Ulcer Non- 10 11 2 5 7 9 12 14 13 15 4 Healthy

Non-ulcer tissue OMICS Approaches to Understand Intersitital Cystitis More OMICS Profiles using the Cutting-Edge Technology are needed Urinary Metabolite Profiling Combined with Computational Analysis The goals of this study are to identify non-invasive biomarker candidates for IC and to gain new insight into disease mechanisms suggesting objective, clinically relevant indicators of the disease that might be employed clinically. Representative 1H Nuclear Magnetic Resonance (NMR) spectra of urine from IC and matched controls A Ctrl

B IC H-NMR Spectra Could Segregate IC Patients from Controls A PCA Ctrl IC Component 3 1 p m o Component 1 C

B 50 0 40 0 en n o t2 OPLS-DA Ctrl IC 30 0 20

0 10 0 0 10 -200 0 20 0 - -100 0 100 200 Identification of NMR Peaks Perturbed in Specimens from IC Patients

Increased Subjects Decreased 140 NMR peaks 3D Ctrl IC 3.24 B 85 4.35 05

3.24 3 2.92 4 3.25 04 3.01 57 3.02 12 2.96 25 4.44 22 0.70 17 4.35 23 4.34 32

9.27 18 3.01 02 ppm 75 80 85 90 100 Coefficients 95 IC B A

Ctrl NMR Spectra Segregating IC from Controls Upregulated metabolites that could be used to segregating IC patients from normal subjects A B ppm Assignment 3.2485 Tyramine

3.243 Tyramine 2.9606 n.a. 2.924 Tyramine 3.2504 n.a. 3.0157 2-oxoglutarate

3.0212 2-oxoglutarate C Tyramin e Tyramine 3.2485* Relative Abundance 3 2 2oxoglutarate

2-oxoglutarate 2.924* 3.243* 3 3 2 2 3.0157* 4 2 3

1 2 1 1 1 1 0 0 0 0 3.0212*

0 1 -1 -1 1 2 2 2 2 Ctrl

IC Ctrl IC Ctrl IC Ctrl IC -1 2 Ctrl

IC Summary o Three IC-related signaling networks were suggested. In vitro culture system: USP2a-MDM2-p53 pathway A quantitative proteomics analysis: -catenin-COX2-PGE2 pathway Computational analysis of publicly available IC data sets: Chronic inflammation, immune responses o In the recent metabolomics study, we identify non-invasive classifiers that can discriminate IC patients from controls. This finding can be the basis for one or more prospective clinical trials and thus has direct relevance to human health and patient care. www.mappnetwork.org Chronic Pelvic Pain (MAPP) Research Network

Cooperative Agreement (U01) funded by NIDDK, NIH MAPP Research Network Sites Acknowledgements NIDDK/NIH 1R01DK100974 NIDDK/NIH 1UO1 DK103260 Steven Spielberg Discovery Fund Research Career Development Award U24 DK097154 UCLA CTSI UL1TR000124 Interstitial Cystitis Association (ICA) Pilot grant Fishbein Family IC Research Foundation New York Academy of Medicine Childrens Hospital Boston Faculty Development J.K. is an IMAGINE NO IC Scholar, American Urological Association Foundation Research Scholar and an Eleanor and Miles Shore Scholar of Harvard Medical School.

Let Us Meet Again We welcome you all to our future conferences of OMICS International Please Visit: www.metabolomicsconference.com www.conferenceseries.com http://www.conferenceseries.com/clinical-research-co nferences.php

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