SLIDE 1 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
Metabolomics: Background and Applications
SLIDE 2
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
SLIDE 3
What Are Metabolites?: Feedforward and Feedback
IDH Mutation Mutant transcript Mutant enzyme 2-HG “mutant” Metabolite Inhibition of demethylation enzymes Chromatin remodeling
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Challenges: metabolites in the central dogma, revised
SLIDE 5
Lipids & Hydrophobic Metabolites Hydrophilic Metabolites Compartmentalized Metabolites Secreted Metabolites
Challenges: Spatial localization
SLIDE 6
Challenges: Total number of molecules
96,892 in Current HMDB
SLIDE 7 Challenges: Total number of molecules
96,892 in Current HMDB
1) How many metabolites are there? 2) How many could there be?
SLIDE 8 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? 1060 possible organic molecules <1000da
SLIDE 9
Challenges: Dynamic Range 12 orders of magnitude in concentration
SLIDE 10
Challenges: Chemical Diversity
Glucose LysoPC 18:0
SLIDE 11 Challenges: State-Dependent Profile
Thyroid hormone AMP Cortisol Succinate Amino acids …
https://link.springer.com/article/10.1007/s11306-017-1205-z
2-hydroxybutyrate Alanine Hypoxanthine Lactate Pyruvate
https://www.ncbi.nlm.nih.gov/pubmed/27686013
SLIDE 12 Challenges: Abstraction of raw data
A, T, C, G
Glucose
S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 175 176 177 178 179 180 181 182 183 184 185 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance
179.0552 180.0584
MS1 Scan
SLIDE 13 Analytical Approaches: 2 major tools
Gas Chromatography Mass Spectrometry Mass Spectrometry Liquid Chromatography Nuclear Magnetic Resonance
SLIDE 14
Analytical Approaches: NMR & MS Advantages and Limitations
Sensitivity Specificity Throughput Deconvolution Reproducibility Destructive
SLIDE 15 Analytical Approaches: Many Flavors of Analysis
“Hydrophobic” Platform - Phenyl “Polar” Platform - ZIC
Mass Spectrometry Liquid Chromatography
SLIDE 16 Metabolomics: How do we know what metabolite we are measuring?
Glucose [C6H11O6]-
S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 175 176 177 178 179 180 181 182 183 184 185 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance
179.0552 180.0584
MS1 Scan
SLIDE 17 Metabolomics: How do we know what metabolite we are measuring?
S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 175 176 177 178 179 180 181 182 183 184 185 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance
179.0552 180.0584
MS1 Scan Glucose [C6H11O6]-
SLIDE 18 Metabolomics: How do we know what metabolite we are measuring?
S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 175 176 177 178 179 180 181 182 183 184 185 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance
179.0552 180.0584
MS1 Scan
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] 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance 59.0127 71.0127 89.0232 101.0228 113.0228 193.7188 149.1310 68.1723 84.5057
MS2 Scan Glucose [C6H11O6]-
SLIDE 19 Metabolomics: How do we know what metabolite we are measuring?
S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 175 176 177 178 179 180 181 182 183 184 185 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance
179.0552 180.0584
MS1 Scan
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] 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance 59.0127 71.0127 89.0232 101.0228 113.0228 193.7188 149.1310 68.1723 84.5057
MS2 Scan
S01420#1308 RT: 10. L-Sorbose Head to Tail MF=874 RMF 60 80 100 120 140 160 180 200 50 100 50 100 54 59 59 71 71 75 85 89 89 95 101 113 113 149 194
Glucose [C6H11O6]-
SLIDE 20 Metabolomics: How do we know what metabolite we are measuring?
S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 175 176 177 178 179 180 181 182 183 184 185 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance
179.0552 180.0584
MS1 Scan
Time (min)
Glucose [C6H11O6]-
SLIDE 21 Metabolomics: How do we know what metabolite we are measuring?
S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 175 176 177 178 179 180 181 182 183 184 185 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance
179.0552 180.0584
MS1 Scan
Time (min)
Glucose [C6H11O6]- D-glucose L-glucose (R or S)
SLIDE 22 Metabolomics: How do we know what metabolite we are measuring?
S01420 #1599 RT:12.88 AV:1 NL:4.67E6 T:FTMS - p ESI Full ms [67.0000-1000.0000] 175 176 177 178 179 180 181 182 183 184 185 m/z 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Relative Abundance
179.0552 180.0584
MS1 Scan
Time (min)
Glucose [C6H11O6]- D-glucose L-glucose (R or S)
SLIDE 23 Metabolomics: How helpful is accurate mass?
https://www.biorxiv.org/content/biorxiv/early/2016/11/26/089904.full.pdf
SLIDE 24
Challenges: Metabolites vs Peptides
SLIDE 25 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
Metabolomics: Background and Applications
SLIDE 26 Bioinformatic Approaches
Global
Small Molecules and Amino Acids Phospholipids Sterols & Fatty Acids
Targeted
1 × 1
1 × 1
1 × 1
1 × 1
1 × 1
1
4
1
5
1
6
1
7
[ M e t a b
] M
r r
2
= . 9 9 5
SLIDE 27
Untargeted Metabolomics: What is a feature?
Intensity
SLIDE 28
Bioinformatic Approaches
SLIDE 29
Bioinformatic Approaches
SLIDE 30
Metabolomics: Widely used resources
SLIDE 31 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
Metabolomics: Background and Applications
SLIDE 32
Global Metabolome Analysis in Cancer Cells and the Tumor Microenvironment
Example Applications
SLIDE 33 Effect of TAM Ornithine on Pancreas Cancer Cells
George Miller et. al.
Tumor Tumor associated macrophages + Ornithine 30 min Cancer Res 2017 J Immunol. 1990 J Immunol. 1992
SLIDE 34 Effect of TAM Ornithine on Pancreas Cancer Cells
George Miller et. al.
Tumor + Ornithine 0, 1, 3, 10, 30 min Experimental questions:
- 1. What does ornithine do to the cells?
- 2. Do any metabolites correlate in time?
- 3. What happens to the ornithine?
SLIDE 35 Effect of TAM Ornithine on Pancreas Cancer Cells
George Miller et. al. Mass Spectrometry Liquid Chromatography
SLIDE 36 Effect of TAM Ornithine on Pancreas Cancer Cells
George Miller et. al.
SLIDE 37 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.
SLIDE 38 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.
SLIDE 39 Effect of Ornithine on pancreas tumor metabolism
George Miller et. al.
SLIDE 40 Effect of Ornithine on pancreas tumor metabolism (global metabolomics analysis)
George Miller et. al. 10 min
c
SLIDE 41 Effect of Ornithine on pancreas tumor metabolism (global metabolomics analysis)
George Miller et. al.
- Acetylcitrulline – Arginine
biosynthesis intermediate, found in deficiency of arginine succinate synthase
- Glycerophosphocholine – Lipid
precursor, reported biomarker in breast cancer; choline storage,
- smolyte, counteracts urea
- Hypoxanthine-guanine
phosphoribosyltransferase (HGPRT) – Converts guanine & hypoxanthine to IMP & GMP (purine salvage pathway)
Associated with protein glycosylation, immune evasion?
10 min
c
SLIDE 42
Metabolomics: Widely used resources
SLIDE 43 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
Metabolomics: Background and Applications
SLIDE 44
Broader coverage of metabolite pathways Better tools for interpreting pathways New tools for discovering unrecognized pathways New methods for identifying unknown structures (no candidates, unknowns) New algorithms and tools for integrating metabolomics and other omics data Making data and results easier to digest and communicate
Metabolomics: Future Challenges
SLIDE 45 How to Work with the NYU Metabolomics Core
GC QExactive x 1 QExactive HF x 2 CAD x 1
SLIDE 46
Targeted Metabolomics (~$50/sample) Global Lipidomics ($150/sample) Global Metabolomics ($250/sample)
Services Offered:
Hybrid Metabolomics (~$50/sample)
SLIDE 47 Metabolomics: Experimental Workflow and Example Data Set/Interpretation
Mass Spectrometry Liquid Chromatography
SLIDE 48
Metabolomics: Experimental Workflow and Example Data Set/Interpretation
Sequence Generator.py Raw2SQL.py SQ321.sqlite3 Groups.tsv Skeleton.py Input.tsv Output.tsv
SLIDE 49
Metabolomics: Experimental Workflow and Example Data Set/Interpretation
Groups.tsv Output.tsv Metabolyze.py
