METABOLOMICS AS A TOOL TO STUDY DISEASE PROGRESSION

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1 METABOLOMICS AS A TOOL TO STUDY DISEASE PROGRESSION Nagireddy Putluri, Ph.D. Associate Professor Director Metabolomics core Department of Molecular and Cellular Biology Alkek Center for Molecular Discovery Baylor College of Medicine Houston, TX

2 Out Line Basics of Metabolomics LC-Mass Spectrometry Sample Preparation Techniques Targeted Metabolomics Metabolic Flux Lipidomics

3 Why Metabolomics? DNA Genomics 25,000 Genes RNA Transcriptomics 100,000 Transcripts Studying the Whole human Metabolome Generate metabolic signatures Monitor metabolic flux Monitor enzyme/pathway kinetics Protein Biochemicals (Metabolites) Proteomics > 1,000,000 Proteins O OH C CH NH 2 H 2 C CH 3 CH CH 3 HO HO H OH H O Metabolomics > 3,000 Compound H H OH OH H N N H NH 2 N N

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5 What is Metabolomics? Metabolomics is the comparative analysis of endogenous metabolites found in biological samples. Metabolites are the by-products of metabolism Small molecules (Amino acids, Sugars, organic acids, fatty acids, polyamines, etc chemical differences

6 Human Samples Mouse Samples Cell lines Treatments KD of specific gene Brain Kidney Non-Invasive Sample Collection Liver Muscle Urine Blood Plasma Pancreas

7 Abundance What steps are involved? Derivatization Sample preparation Sample extraction Protein Removal Chromatography Data analysis Detection m/z estrone minor_ri estrone major_ri

8 Sample Preparation Growth/Sample Size Grow cells under identical conditions Quickly freeze sample in liquid nitrogen, store samples at -80 C Be consistent - similar tissue - time of day (avoid freeze thaw) Randomize the samples Run order Reproducibility of Metabolomic Profiling Platform QC Samples Mice liver tissue pool Pool samples number of replicates In general, four replicates for each treatment -due to high biological variability) CV in Log-Scale Min 1st Qu Median Mean 3rd Qu Max Reverse phase positive ionization Reverse phase negative ionization

9 hoosing an extraction method Sample Extraction No universal extraction method exists Non polarity Polarity Water Water: methanol ACN: Chloroform Ethyl acetate Chloroform Sugars Amino Acids Amino sugars Nucleotides Nucleic Acids Polyamines TCA metabolites CoA s Carnitines Neurotransmitters Bile Acid analysis Vitamins Water: Tryptophan metabolites Lipids Prostaglandins Short chain Fatty acid analysis Some solvents may degrade certain compounds Some metabolites need Basic and Acidic extraction Reduced and oxidized glutathione (GSH and GSSG) Its good to have some idea of what metabolites you want to extract

10 Separation of Metabolites. Reverse Phase HILIC/Normal Phase Reverse Phase- Flow ramp from low organic to high organic HILIC/ Normal Phase- Flow ramp from high organic to low organic

11 What we can obtain from a Mass Spectrometer? Molecular weight Nature of elements present Structural characteristics (structure elucidation) Elemental composition of molecular ion and fragment ions Optimized >600 metabolites involved in various pathways

12 Analytical Platforms Triple Quadrupole mass spectrometry (3) Targeted Metabolomics Time of flight mass spectrometry Untargeted Metabolomics/lipidomic

13 Quadrupole mass spectrometry for targeted metabolomics AC/DC =K= mass/ charge

14 Time-of-flight (TOF) Accelerated ions Field-free drift zone Detector

15 low resolution high resolution (Reflectron TOF) Reflectron T O F Q-TOF ultima

16 Typical Workflow for Targeted Metabolomics Question: Levels of metabolites in specific pathway Standard Metabolite LC/MS of Standard metabolite Single Reaction Monitoring (SRM) Separation and standard curve Steady state metabolites Many enzymes involved in one pathway >600 Standards (Metabolites) Metabolites Identity confirmed by 1. Retention Time (RT) check using appropriate standards 2. Spiking labeled standards 3. Compare the with authentic STD

17 >600 Metabolites Targeted Metabolomics QQQ Mass Spectrometry 6490/6495 QQQ Mass Spectrometry 1290 Series HPLC >500 compounds Different LC MS Method Amino acids Different extraction Protocol Amino Sugars Lipids Different Internal Standard Prostaglandins Carnitines Polyamines TCA metabolites CoA's Carnitines Sugars Nucleotides / Nucleic acid Vitamins Steroids 2-HG - D/L isomer Bile acids Mevalonate Pathway Neurotransmitters Short chain fatty acid analysis Tryptophan Metabolites Vitamins Methylated Metabolites Polycyclic aromatic hydrocarbons

18 Targeted Metabolomics Relative Measurements 600 Metabolites Class specific Isotopic labelled internal standards List of metabolites Control KD Absolute Quantification Few Metabolites Individual metabolites using calibration curve Concentration in ng/mg tissues Concentration in um in cells

19 Metabolic Pathways Covered

20 Out Line Basics of Metabolomics LC-Mass Spectrometry Sample Preparation Techniques Targeted Metabolomics Metabolic Flux Lipidomics

21 Tobacco-specific carcinogens induces hyper methylation in Bladder Cancer of smokers Harmful Chemicals in Tobacco Products known to cause Cancer Measured ~350 metabolites

22 Altered metabolism in Bladder cancer smokers vs never smokers 90 out of 350 metabolites are differentially expressed Methylated Metabolites PAH Tissues Jin F, and Thaiparambil J. et. al Cancer Prev Res (Phila) Oct;10(10):

23 Bladder Cancer Transcriptomics data sets Bladder Data mrna data no of sets sets Patients TCGA BLCA 370 Smoking Riester GSE Smoking Grade JNCI 37 Grade Kim GSE Grade Sanchez- Carbayo 102 Grade Lindgren GSE Grade Smoker Non-smoker High Grade Low grade Generate Signature Generate Signature Association with methylation?

24 Smoking Signature vs high Grade Signature

25 High Methylations DNMT1, 3A and 3B TGGA DNA methylation for representation (Top 1000 highly variable CpG sites)

26 DNMT1 expression in smokers mrna survival DNMT1 (Choi -GSE48075) COXPH p_value = Low sig_dnmt1_gene 80% (n = 58) High sig_dnmt1_gene 20% (n = 15) Time (Months) Cancer Prev Res (Phila) Oct;10(10):

27 Methionine Pathway and role of DNMT1 Aza cytidine NNK M e th io n in e S A M S A H R elative abundance p < R e la tiv e a b u n d a n c e p = R e la tiv e a b u n d a n c e p < C o n tr o l N N K N N K + A Z A C o n tr o l N N K N N K + A Z A C o n tr o l N N K N N K + A Z A

28 Conclusion/Working Model Jin F, and Thaiparambil J..et.al Cancer Prev Res Oct;10(10):

29 Out Line Basics of Metabolomics LC-Mass Spectrometry Sample Preparation Techniques Targeted Metabolomics Metabolic Flux Lipidomics Glucose Flux Glycolysis /PPP/TCA Glutamine Flux Glutaminolysis/TCA/Fatty acids

30 Typical Workflow for Metabolic flux Step 1 Design the isotopic labeling experiment s Purchase the Metabolite free media Purchase the appropriate tracer (Do NOT Use D ) Prepare the media with Isotopic labelled compounds Fix the time point (Glycolysis/TCA ~3-6hrs in cancer) Piolet/test experiments Step 2 Culture the cells with Isotopic tracers/4-5 biological replicates (Starvation) In vitro model Step 3 Extract the metabolites Similar to Targeted metabolomics with out IST Step 4 Measure the incorporations Cambridge Isotope Laboratories, Inc. Contact -Randal A. Hall

31 Unlabeled and Labelled MS Spectra and MRM 12C Glucose C 6 H 12 O 6 13C6 Glucose C 6 H 8 O 7

32 Glucose Metabolic flux - in the TCA cycle Pyruvate

33 Glucose Metabolic flux - in the TCA cycle Pyruvate

34 Glucose Metabolic flux - in the TCA cycle Pyruvate

35 Metabolic flux - in the TCA cycle

36 Reductive carboxylation of mitochondrial glutamine (Metallo et al., Nature 2012, 481: )

37 Absorbance (% of Control) SRC-2 regulates total lipid content of PC cells Performed both Glucose and Glutamine Flux ** ** NT sh18 sh19

38 Fatty Acid Biosynthetic Pathways Glucose Glycolysis Pyruvate Acetyl-CoA OAA Citrate Lipids Acetyl-CoA Fumarate/ Malate TCA Isocitrate Succinate α-kg Glutamate Glutamine Dasgupta S, et. Al. J Clin Invest Mar 2;125(3):

39 Fatty Acid Biosynthetic Pathways [ 13 C 6 U] Glucose Pyruvate Glycolysis X Acetyl-CoA Lipids Acetyl-CoA OAA Citrate Fumarate/ Malate TCA Isocitrate Succinate α-kg Glutamate Glutamine

40 Fatty Acid Biosynthetic Pathways Glucose Glycolysis Pyruvate Acetyl-CoA OAA Citrate sh18 Lipids α-kg Acetyl-CoA Citrate Fumarate/ Malate TCA Isocitrate Succinate α-kg Glm [C 13 U] Glutamine

41 Glucose Pyruvate m+4 Fumarate/ Malate Succinate Fatty Acid Biosynthetic Pathways OAA m+4 m+4 Glycolysis TCA Acetyl-CoA m+5 α-kg m+4 m+5 Citrate ACO Isocitrate IDH Acetyl-CoA Glm Lipids α-kg 2HN O O [C 13 U] Glutamine CO 2

42 Out Line Basics of Metabolomics LC-Mass Spectrometry Sample Preparation Techniques Targeted Metabolomics Metabolic Flux Lipidomics

43 Lipidomics Lipids are hydrophobic and amphiphilic small molecules participate in diverse functions in cell highly dynamic: changes constantly with physiological, environmental and pathological conditions

44 Lipid classification Fatty acyls (FA) Glycerolipids (GL eg MAG, DAG, TAG) Glycerophospholipids (GP eg PC, PI, PE, PS) Sphingolipids (SL) Sterol lipids (ST eg cholesterol, bile acids, estrogens, vitamin D) Prenol lipids (PR eg farnesol, vitamin E & K) Saccharolipids (SL eg UDP-3-O- (3R-OHtetradecanoyl)- ad-nacetylglucosamine) Polyketides (PK eg afflatoxin B1)

45 Lipidomics Traditional approaches - limited for total lipids (eg- TLC for Phospho lipids or staining) The modern mass spectrometry, allowed the ability to detect, intact individual lipid species

46 Workflow Sample Preparation MS data Acquisition Bligh/Dyer extraction (Methanol: dichloromethane: water)-spike mixture of IST (Tissues, Cell pellet, serum & Plasma) Completely different extraction from metabolomics AB SCIEX TripleTOF 5600 High Resolution Mass Spectrometry LC- Separation -ESI Positive and Negative ionization IDA MS/MS Data Processing Lipid Annotation via Lipid Blast (13 data bases) Lipid Quantificatio n with MQ Imputing the data for missing values Running Data QC Bioinformat ics /Data analysis Normalization Differential Lipid Analysis Lipid-Gene Integration Pathway analysis Survival Analysis Correlation study

47 AB SCIEX TripleTOF 5600 high resolution Instruments Untargeted Lipidomics ~800 lipids (18 classes of lipids) in 40 mins -ESI Positive and Negative ionization IDA MS/MS Class Specific Internal standards (Avanti Polar Lipids) Internal standards RT ESI 1 17:0 Lyso PC 1.9 Positive 2 17:0 PC Positive :0 PE Positive :0 PG 6.4 Negative 5 17:0 Ceramide 7.1 Positive 6 17:0 SM 6.1 Positive 7 17:0 PS 6.3 Negative 8 17:0 TG 10.9 Positive 9 17:0 MG :0-20:4 PI 5.6 Negative Sample Spectra Library Spectra

48 Differentially expressed lipids in Bladder Tissues Lipid class Number of lipids Direction PE 63 Up in UCB TG 56 Down in UCB PC 41 Up in UCB plasmenyl-pe 31 Up in UCB PG 13 Up in UCB CL 10 Up in UCB PS 8 Up in UCB DG 5 Up in UCB SM 4 Up in UCB lysope 2 Up in UCB CE 1 Up in UCB PA 1 Up in UCB PI 1 Up in UCB >100 Cancer Patients, and 39 benign adjacent tissues Identified altered Lipidomics profiles in urothelial cancer of the bladder (UCB) and associated with its clinical stages Piyarathna DWB et..al. Eur Urol Focus (17)

49 Class enrichment analysis and lipid correlation network Altered lipids were mapped to their corresponding genes List of genes with fold change and significance in TCGA bladder cancer transcriptome data Piyarathna DWB et.al Eur Urol Focus (17)

50 Down regulation of TGs

51 Targeted Free Fatty acid Measurments Sum of mono-unsaturated Sum of poly-unsaturated Sum of saturated Piyarathna DWB et.al Eur Urol Focus (17)

52 Integration of the Data Bladder Cancer Vs Normal /Benign Lipid Signature (230 lipids) Associated gene TCGA Bladder Cohort (Bladder Cancer Vs Normal /Benign Logrank p = integrated gene Signature low, bottom 50% (n = 83) ntegrated gene Signature high, top 50% (n = 82) 0 Piyarathna DWB et.al Eur Urol Focus (17) Time (Months)

53 Altered lipids (class specific) with tumor stage in Bladder cancer Phosphatidylserine synthase (PTDSS1) Altered Kennedy pathway Ta (n=16), T1 (n=30), T2 (n=43), T3 (n=27), and T4 (n=9)

54 Acknowledgements Academic Director Arun Sreekumar, Ph.D Core Director Nagireddy Putluri, Ph.D Staff Vasanta Putluri Sri Ramya Donepudi Vivekananda Shetty, Ph,D Chandraskekar Ambati Bioinformatics Cristian Coarfa, Ph.D Kimal Rajapakshe, Ph.D Staff Matthew Robertson CPRIT Core support award (Dean Edwards) RP NCI Cancer center support award (P30 CA125123) Dan L Duncan Comprehensive Cancer Center, BCM Advanced Technology Core (ATC)

55 Questions?