Metabolomics: Background and Applications Todays Outline What are - PowerPoint PPT Presentation

Metabolomics: Background and Applications Today’s Outline What are Metabolites? Challenges in Metabolomics Analytical Approaches Bioinformatic Approaches Example Applications The Future (Jones Lab Development) Drew Jones, PhD Assistant Professor, Dept of Biochemistry Director, Metabolomics Core Resource Laboratory New York University Langone Health Drew.Jones@nyumc.org 2019 Guest Lecture – Proteomics Informatics April 15, 2019

What Are Metabolites?: Role in central dogma 1) End of line for gene expression 2) Starting point for environment 3) Building blocks for all macromolecules

What Are Metabolites?: Feedforward and Feedback 2-HG “mutant” IDH Mutation Mutant transcript Mutant enzyme Metabolite Inhibition of Chromatin demethylation remodeling enzymes

Challenges: metabolites in the central dogma, revised

Challenges: Spatial localization Secreted Metabolites Hydrophilic Metabolites Lipids & Hydrophobic Metabolites Compartmentalized Metabolites

Challenges: Total number of molecules 96,892 in Current HMDB

Challenges: Total number of molecules 96,892 in Current HMDB 1) How many metabolites are there? 2) How many could there be?

Challenges: Total number of molecules 96,892 in Current HMDB 1) How many metabolites are there? ~65 million in pubchem 2) How many could there be? 10 60 possible organic molecules <1000da

Challenges: Dynamic Range 12 orders of magnitude in concentration

Challenges: Chemical Diversity Glucose LysoPC 18:0

Challenges: State-Dependent Profile 2-hydroxybutyrate Thyroid hormone Alanine AMP Hypoxanthine Cortisol Lactate Succinate Pyruvate Amino acids … https://link.springer.com/article/10.1007/s11306-017-1205-z https://www.ncbi.nlm.nih.gov/pubmed/27686013

Challenges: Abstraction of raw data A, T, C, G 179.0552 MS1 Scan S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 100 95 90 85 80 75 70 65 60 Relative Abundance 55 50 45 40 35 30 25 20 Glucose 15 180.0584 10 5 0 175 176 177 178 179 180 181 182 183 184 185 m/z

Analytical Approaches: 2 major tools Liquid Chromatography Mass Spectrometry Nuclear Magnetic Resonance Gas Chromatography Mass Spectrometry

Analytical Approaches: NMR & MS Advantages and Limitations Sensitivity Specificity Throughput Deconvolution Reproducibility Destructive

Analytical Approaches: Many Flavors of Analysis “Polar” Platform - ZIC Liquid Chromatography Mass Spectrometry “Hydrophobic” Platform - Phenyl

Metabolomics: How do we know what metabolite we are measuring? 179.0552 S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 100 MS1 Scan 95 90 85 80 75 70 65 60 Relative Abundance 55 50 45 40 35 30 25 20 Glucose 15 180.0584 10 5 [C 6 H 11 O 6 ] - 0 175 176 177 178 179 180 181 182 183 184 185 m/z

Metabolomics: How do we know what metabolite we are measuring? 179.0552 S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 100 MS1 Scan 95 90 85 80 75 70 65 60 Relative Abundance 55 50 45 40 35 30 25 20 Glucose 15 180.0584 10 5 [C 6 H 11 O 6 ] - 0 175 176 177 178 179 180 181 182 183 184 185 m/z

Metabolomics: How do we know what metabolite we are measuring? 179.0552 S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 100 MS1 Scan 95 90 85 80 75 70 65 60 Relative Abundance 55 50 45 40 35 30 25 20 Glucose 15 180.0584 10 5 [C 6 H 11 O 6 ] - 0 175 176 177 178 179 180 181 182 183 184 185 m/z S01420 #1308 RT: 10.65 AV: 1 NL: 1.99E5 F: FTMS - p ESI d Full ms2 179.0551@hcd40.00 [50.0000-200.0000] MS2 Scan 59.0127 100 95 90 85 80 71.0127 75 70 65 60 55 Relative Abundance 50 45 89.0232 40 35 30 25 20 15 101.0228 10 113.0228 5 193.7188 68.1723 149.1310 84.5057 0 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 m/z

Metabolomics: How do we know what metabolite we are measuring? 179.0552 S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 100 MS1 Scan 95 90 85 80 75 70 65 60 Relative Abundance 55 50 45 40 35 30 25 20 Glucose 15 180.0584 10 5 [C 6 H 11 O 6 ] - 0 175 176 177 178 179 180 181 182 183 184 185 m/z S01420 #1308 RT: 10.65 AV: 1 NL: 1.99E5 F: FTMS - p ESI d Full ms2 179.0551@hcd40.00 [50.0000-200.0000] MS2 Scan 59.0127 100 95 90 85 59 100 80 71 71.0127 75 70 89 50 65 101 113 60 54 85 149 194 0 55 Relative Abundance 75 95 113 50 89 45 89.0232 50 40 71 35 100 30 59 25 20 60 80 100 120 140 160 180 200 15 101.0228 S01420#1308 RT: 10. Head to Tail MF=874 RMF L-Sorbose 10 113.0228 5 193.7188 68.1723 149.1310 84.5057 0 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 m/z

Metabolomics: How do we know what metabolite we are measuring? 179.0552 S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 100 MS1 Scan 95 90 85 80 75 70 65 60 Relative Abundance 55 50 45 40 35 30 25 20 Glucose 15 180.0584 10 5 [C 6 H 11 O 6 ] - 0 175 176 177 178 179 180 181 182 183 184 185 m/z Time (min)

Metabolomics: How do we know what metabolite we are measuring? 179.0552 S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 100 MS1 Scan 95 90 85 80 75 70 65 60 Relative Abundance 55 50 45 40 35 30 25 20 Glucose 15 180.0584 10 5 [C 6 H 11 O 6 ] - D-glucose 0 175 176 177 178 179 180 181 182 183 184 185 m/z L-glucose (R or S) Time (min)

Metabolomics: How do we know what metabolite we are measuring? 179.0552 S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 100 MS1 Scan 95 90 85 80 75 70 65 60 Relative Abundance 55 50 45 40 35 30 25 20 Glucose 15 180.0584 10 5 [C 6 H 11 O 6 ] - D-glucose 0 175 176 177 178 179 180 181 182 183 184 185 m/z L-glucose (R or S) Time (min)

Metabolomics: How helpful is accurate mass? https://www.biorxiv.org/content/biorxiv/early/2016/11/26/089904.full.pdf

Challenges: Metabolites vs Peptides

Metabolomics: Background and Applications Today’s Outline What are Metabolites? Challenges in Metabolomics Analytical Approaches Bioinformatic Approaches Example Applications The Future (Jones Lab Development) Drew Jones, PhD Assistant Professor, Dept of Biochemistry Director, Metabolomics Core Resource Laboratory New York University Langone Health Drew.Jones@nyumc.org 2019 Guest Lecture – Proteomics Informatics April 15, 2019

Bioinformatic Approaches Targeted 7 1 0 2 r = 0 . 9 9 5 6 1 0 5 1 0 4 1 0 1 × 1 1 × 1 1 × 1 1 × 1 1 × 1 - 8 - 7 - 6 - 5 - 4 0 0 0 0 0 [ M e t a b o l i t e ] M o l a r Global Small Molecules and Amino Acids Phospholipids Sterols & Fatty Acids

Untargeted Metabolomics: What is a feature? Intensity

Bioinformatic Approaches

Bioinformatic Approaches

Metabolomics: Widely used resources

Metabolomics: Background and Applications Today’s Outline What are Metabolites? Challenges in Metabolomics Analytical Approaches Bioinformatic Approaches Example Applications The Future (Jones Lab Development) Drew Jones, PhD Assistant Professor, Dept of Biochemistry Director, Metabolomics Core Resource Laboratory New York University Langone Health Drew.Jones@nyumc.org 2019 Guest Lecture – Proteomics Informatics April 15, 2019

Example Applications Global Metabolome Analysis in Cancer Cells and the Tumor Microenvironment

Effect of TAM Ornithine on Pancreas Cancer Cells Tumor Tumor associated macrophages + Ornithine 30 min Cancer Res 2017 J Immunol. 1990 J Immunol. 1992 George Miller et. al.

Effect of TAM Ornithine on Pancreas Cancer Cells Experimental questions: 1. What does ornithine do to the cells? 2. Do any metabolites correlate in time? Tumor + Ornithine 3. What happens to the ornithine? 0, 1, 3, 10, 30 min George Miller et. al.

Effect of TAM Ornithine on Pancreas Cancer Cells Liquid Chromatography Mass Spectrometry George Miller et. al.

Effect of TAM Ornithine on Pancreas Cancer Cells George Miller et. al.

Untargeted Pathway Analysis: Effect of ornithine supplementation on urea cycle NH3 + CO2 + aspartate + 3 ATP + 2 H2O → urea + fumarate + 2 ADP + 2 Pi + AMP + PPi George Miller et. al.

Untargeted Pathway Analysis: Effect of ornithine supplementation on urea cycle (30 min time point) NH3 + CO2 + aspartate + 3 ATP + 2 H2O → urea + fumarate + 2 ADP + 2 Pi + AMP + PPi George Miller et. al.

Effect of Ornithine on pancreas tumor metabolism George Miller et. al.

Effect of Ornithine on pancreas tumor metabolism (global metabolomics analysis) 10 min c George Miller et. al.