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NMR & Metabolomics The Possibilities & The Limitations. David Wishart Depts. Comp. Sci and Bio. Sci. University of Alberta & NINT david.wishart@ualberta.ca. The (Human) Pyramid of Life. Metabolomics Proteomics Genomics. 1400 Chemicals. 3000 Enzymes. 30,000 Genes.

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Nmr metabolomics the possibilities the limitations l.jpg

NMR & MetabolomicsThe Possibilities & The Limitations

David Wishart

Depts. Comp. Sci and Bio. Sci.

University of Alberta & NINT

david.wishart@ualberta.ca


The human pyramid of life l.jpg
The (Human) Pyramid of Life

Metabolomics

Proteomics

Genomics

1400

Chemicals

3000 Enzymes

30,000 Genes


The bacterial pyramid of life l.jpg
The (Bacterial) Pyramid of Life

Metabolomics

Proteomics

Genomics

761

Chemicals

1152 Enzymes

4269 Genes


Why measure metabolites l.jpg
Why Measure Metabolites?

Metabolites are the Canaries of the Genome


Metabonomics metabolomics l.jpg
Metabonomics & Metabolomics

  • Metabonomics:The quantitative measurement of the time-related “total” metabolic response of vertebrates to pathophysiological (nutritional, xenobiotic or toxic) stimuli

  • Metabolomics:The quantitative measurement of invertebrate metabolic profiles to characterize their phenotype or phenotypic response to genetic or nutritional perturbations

MetaboXomics


Metabolomics allows one to l.jpg
Metabolomics Allows One to..

  • Generate metabolic “signatures”

  • Monitor/measure metabolite flux

  • Monitor enzyme/pathway kinetics

  • Assess/identify phenotypes

  • Monitor gene/environment interactions

  • Track effects from toxins/perturbants

  • Monitor consequences from gene KOs

  • Identify functions of unknown genes



Problems with traditional methods l.jpg
Problems with Traditional Methods

  • Requires separation followed by identification (coupled methodology)

  • Requires optimization of separation conditions each time

  • Often requires multiple separations

  • Slow (up to 72 hours per sample)

  • Manually intensive (constant supervision, high skill, tedious)



Advantages l.jpg
Advantages

  • Measure multiple (10’s to 100’s) of metabolites at once – no separation!!

  • Allows metabolic profiles or “fingerprints” to be generated

  • Mostly automated, relatively little sample preparation or derivitization

  • Can be quantitative (esp. NMR)

  • Analysis & results in < 60 s


Why nmr l.jpg
Why NMR?

Mixture separation

by HPLC (followed

by ID via Mass Spec)

Mixture separation

by NMR (simultaneous

separation & ID)

Chemical Shift

Chromatography


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Why NMR?

  • 1H NMR

    • Rapid metabolite identification and quantification

    • Monitoring flux/kinetics in real time

  • 13C NMR

    • Metabolite sources/sinks, chemistry

  • 31P NMR

    • ATP/ADP ratios, energy balance, cAMP


Metabolomics and nmr l.jpg

25

PC2

20

15

pet191D

10

5

0

-5

WT

-10

-15

pfk27D

-20

PC1

-25

-30

-20

-10

0

10

pfk27D

pet191D

Wildtype

Metabolomics and NMR

Principle Component Analysis


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Functional Proteomics via Metabolic Profiling

Forster, J. et al., (2002) Biotechnol. Bioeng. Vol. 79, 703-712


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Detecting Silent Mutations in Yeast

Nature Biotechnology, vol. 19, pg. 45-50 (2001)


Is there a better way l.jpg
Is There A Better Way?

  • Why not try to identify the individual peaks in an NMR spectrum automatically?

  • Use software to deconvolute individual spectral signatures using a database of known compounds

  • Use relative peak intensity to quantify

  • Gives identification and quantitation

  • Possibility of chemical shift microscopy


Slide31 l.jpg

Mixture

Compound A

Compound B

Compound C

Spectral Deconvolution of a Mixture

Containing Compounds A, B and C


Slide32 l.jpg

Data Analysis (Principles)

Glucose Fit

Extract Spectrum

Fructose Fit


Slide33 l.jpg

Data Analysis (Principles)

Fructose Fit

Extract Spectrum

Glucose Fit


Slide34 l.jpg

Data Analysis (Principles)

Glucose and

Fructose Fit

Extract Spectrum

Fructose + Glucose

Glucose Fit


Data analysis l.jpg
Data Analysis

  • Fitting 5-10 rounded peaks is trivial, fitting 1000+ sharp peaks is not, i.e. dense matrix problem with very high probability of cumulative rounding errors and singularities(LLSOL - Stanford)

  • Peak positions & shapes dependent on salt, pH, temperature, ligands, ligand/ion interactions, shimming, signal-to-noise digital resolution, phasing, field strength, etc. etc.

  • Requires special databases - key innovation

  • Requires “intelligent” data preprocessing - (ditto)

  • Partnered with Chenomx/Varian  Eclipse



Current compound list l.jpg

(+)-(-)-Methylsuccinic Acid

2,5-Dihydroxyphenylacetic Acid

2-hydroxy-3-methylbutyric acid

2-Oxoglutaric acid

3-Hydroxy-3-methylglutaric acid

3-Indoxyl Sulfate

5-Hydroxyindole-3-acetic Acid

Acetamide

Acetic Acid

Acetoacetic Acid

Acetone

Acetyl-L-carnitine

Alpha-Glucose

Alpha-ketoisocaproic acid

Benzoic Acid

Betaine

Beta-Lactose

Citric Acid

Creatine

Creatinine

D(-)Fructose

D-(+)-Glyceric Acid

D(+)-Xylose

Dimethylamine

DL-B-Aminoisobutyric Acid

Current Compound List

  • L-Isoleucine

  • L-Lactic Acid

  • L-Lysine

  • L-Methionine

  • L-phenylalanine

  • L-Serine

  • L-Threonine

  • L-Valine

  • Malonic Acid

  • Methylamine

  • Mono-methylmalonate

  • N,N-dimethylglycine

  • N-Butyric Acid

  • Pimelic Acid

  • Propionic Acid

  • Pyruvic Acid

  • Salicylic acid

  • Sarcosine

  • Succinic Acid

  • Sucrose

  • Taurine

  • trans-4-hydroxy-L-Proline

  • Trimethylamine

  • Trimethylamine-N-Oxide

  • Urea

  • DL-Carnitine

  • DL-Citrulline

  • DL-Malic Acid

  • Ethanol

  • Formic Acid

  • Fumaric Acid

  • Gamma-Amino-N-Butyric Acid

  • Gamma-Hydroxybutyric Acid

  • Gentisic Acid

  • Glutaric acid

  • Glycerol

  • Glycine

  • Glycolic Acid

  • Hippuric acid

  • Homovanillic acid

  • Hypoxanthine

  • Imidazole

  • Inositol

  • isovaleric acid

  • L(-) Fucose

  • L-alanine

  • L-asparagine

  • L-aspartic acid

  • L-Histidine

  • L-homocitrulline


Why not apply it to bacterial id l.jpg
Why Not Apply It to Bacterial ID?


Metabolomics e coli l.jpg
Metabolomics & E. coli

  • 25-50 mL shake flasks or large 0.5L flask

  • Remove aliquots at regular intervals

  • Analyze cells or media or both?

  • Cells

    • Lysis protocol, sonication? freeze-thaw? soluble fraction? lipid fraction? protein rmvl

  • Media

    • Rich (LB)? Defined? MOPS? M9? glucose?



A toy problem l.jpg
A “Toy” Problem

  • Succinate Dehydrogenase is a key enzyme in the aerobic TCA cycle (converts succinate to fumarate)

  • Fumarate Reductase is responsible for converting fumarate to succinate under anaerobic conditions

  • Fumarate Reductase & Succinate Dehydrogenase share ~60% sequence identity


Slide42 l.jpg

Fumarate Reductase

The TCA Cycle

Acetate

Acetyl-CoA

Glycerol

Pyruvate

Oxaloacetate

Citrate

Isocitrate

L-Malate

-Ketoglutarate

Fumarate

2

1

Succinate dehydrogenase

Succinate

Succinyl-CoA


Questions l.jpg
Questions

  • Can Fumarate Reductase (FumR) substitute for Succinate Dehydrogenase (SucD)?

  • Can we detect any phenotypic differences between WT vs. SucD- vs. SucD-/FumR-?

  • Can NMR-based metabolomics be used to detect mutations/characterize phenotypes?


Methods l.jpg

StrainsGenotypePhenotype

Succingate dehydrogenase

Fumarate Reductase.

MC4100

Rapid Growth

DW35

Slow Growth

Double knockout

Slow Growth

DW35/pH3

Fumarate Reductase.

Methods

  • Obtain 3 E. coli strains as shown below


Methods45 l.jpg
Methods

  • Grow Cells on Glycerol Minimal Media at 37 degrees

  • Collect 3 mL aliquots every hour for 30+ hours or until cells die

  • Monitor pH and OD600

  • Spin down cells, retain supernatant

  • Lyse cells with chloroform/water, spin down

  • Analyze cell extracts & supernatant by NMR


Typical eclipse readout concentration in mm l.jpg
Typical Eclipse Readout (Concentration in mM)





Slide50 l.jpg

Metabolic Responses

Acetate

Glycerol

Pyruvate

Acetate

Glycerol

Pyruvate

Succinate

Succinate


Results interpretation l.jpg
Results & Interpretation

  • DW35 (the double knockout) demonstrates a clear increase in succinate over time

  • DW35 complemented with FumR (on the PH3 plasmid) appears to be capable of metabolizing succinate

  • Analysis of growth media via NMR was sufficient to distinguish the two strains and to ID the gene knockout



Cellular automata l.jpg
Cellular Automata

  • Computer modelling method that uses lattices and discrete state “rules” to model time dependent processes – a way to animate things

  • No differential equations to solve, easy to calculate, more phenomenological

  • Simple unit behavior -> complex group behavior

  • Used to model fluid flow, percolation, reaction + diffusion, traffic flow, pheromone tracking, predator-prey models, ecology, social nets

  • Scales from 10-12 to 10+12


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Cellular Automata

Can be extended to 3D lattice


Succinate production l.jpg
Succinate Production

Observed Predicted (SimCell)


Glycerol consumption l.jpg
Glycerol Consumption

Observed Predicted (SimCell)


Metabolic profiling the possibilities l.jpg

Genetic Disease Tests

Nutritional Analysis

Clinical Blood Analysis

Clinical Urinalysis

Cholesterol Testing

Drug Compliance

Dialysis Monitoring

MRS and fMRI

Toxicology Testing

Clinical Trial Testing

Fermentation Monitoring

Food & Beverage Tests

Nutraceutical Analysis

Drug Phenotyping

Water Quality Testing

Petrochemical Analysis

Metabolic Profiling: The Possibilities


Metabolic profiling and drug toxicology l.jpg

25

PC2

20

15

ANIT

10

5

0

-5

Control

-10

-15

PAP

-20

PC1

-25

-30

-20

-10

0

10

PAP

ANIT

Control

Metabolic Profiling and Drug Toxicology

Principal Component Analysis



140 detectable conditions l.jpg

Adenine Phosphoribosyltransferase Deficency

Adenylosuccinase Deficiency

Alcaptonuria

a-Aminoadipic Aciduria

b-Aminoisobutyric Aciduria

a-AminoketoadipicAciduria

Anorexia Nervosa

Argininemia

Argininosuccinic Aciduria

Aspartylglycosaminuria

Asphyxia

Biopterin Disorders

Biotin-responsive Multiple Carboxylase Deficiency

Canavan’s Disease

Carcinoid Syndrome

Carnosinemia

Cerebrotendinous Xanthomatosis/sterol 27-hydroxylaseDeficiency

Citrullinemia

Cystathioninemia

Cystinosis

Cystinuria (Hypercystinuria)

Diabetes

Dibasic Aminoaciduria

140+ Detectable Conditions

  • Dicarboxylic Aminoaciduria

  • Dichloromethane Ingestion

  • Dihydrolipoyl Dehydrogenase Deficiency

  • Dihydropyrimidine Dehydrogenase Deficiency

  • Dimethylglycine Dehydrogenase Deficiency

  • Essential Fructosuria

  • Ethanolaminosis

  • Ethylmalonic Aciduria

  • Familial Iminoglycinuria

  • Fanconi’s Syndrome

  • Folate Disorder

  • Fructose Intolerance

  • Fulminant Hepatitis

  • Fumarase Deficiency

  • Galactosemia

  • Glucoglycinuria

  • Glutaric Aciduria Types 1 & 2

  • Glutathionuria

  • Glyceroluria (GKD)

  • D-Glyceric Aciduria

  • Guanidinoacetate-Methyltransferase Deficiency

  • Hartnup Disorder

  • Hawkinsinuria

  • Histidinemia

  • Histidinuria

  • Homocystinsufonuria

  • Homocystinuria

  • 4-Hydroxybutyric Aciduria

  • 2-Hydroxyglutaric Aciduria

  • Hydroxykynureninuria

  • Hydroxylysinemia

  • Hydroxylysinuria

  • 3-Hydroxy-3-methylglutaric Aciduria

  • 3-Hydroxy-3-methylglutaryl-Co A Lyase Deficiency

  • Hydroxyprolinemia

  • Hyperalaninemia

  • Hyperargininemia (Argininemia)

  • Hyperglycinuria

  • Hyperleucine-Isoleucinemia

  • Hyperlysinemia

  • Hyperornithinemia

  • Hyperornithinemia-Hyperammonemia-Homocitrullinuria Syndrome (HHH)

  • Hyperoxaluria Types I & 2

  • Hyperphenylalaninemia

  • Hyperprolinemia

  • Hyperthreoninemia


Applications in clinical analysis l.jpg

14 propionic acidemia

11 methylmalonic aciduria

11 cystinuria

6 alkaptonuria

4 glutaric aciduria I

3 pyruvate decarboxylase deficiency

3 ketosis

3 Hartnup disorder

3 cystinosis

3 neuroblastoma

3 phenylketonuria

3 ethanol toxicity

3 glycerol kinase deficiency

3 HMG CoA lyase deficiency

2 carbamoyl PO4 synthetase deficiency

96% sensitivity and 100% specificity in ID of abnormal from normal by metabolite concentrations

95.5% sensitivity and 92.4% specificity in ID of disease or condition by characteristic metabolite concentrations

120 sec per sample

Applications in Clinical Analysis

Clinical Chemistry 47, 1918-1921 (2001).


Applications in cancer l.jpg

Acetic Acid

Betaine

Carnitine

Citric Acid

Creatinine

Dimethylglycine

Dimethylamine

Hippulric Acid

Lactic Acid

Succinic Acid

Trimethylamine

Trimn-N-Oxide

Urea

Lactose

Suberic Acid

Sebacic Acid

Homovanillic Acid

Threonine

Alanine

Glycine

Glucose

Applications in Cancer

Normal

Below Normal

Above Norrmal

Absent

Patient 1

Patient 2

Patient 3

Patient 4

Patient 5

Patient 6

Patient 7

Patient 8

Patient 9

Patient 10

Patient 11

Patient 12

Patient 13

Patient 14

Patient 15

Metabolic Microarray - 35 min.



Nmr metabolomics l.jpg
NMR & Metabolomics

  • Rapid, robust, largely automatic

  • Allows real time monitoring of metabolite fluxes as low as 1 uM

  • Allows rapid ID of common and unusual metabolites

  • Can be applied to chemical shift imaging

  • But…

    • Is it sensitive enough?

    • Need to expand metabolite database


Improving sensitivity l.jpg
Improving Sensitivity

  • Higher fields (2x)

  • Selective NOE enhancement (1.5x)

  • Selective decoupling (~3x)

  • 2-3X longer acquisition (~1.6x)

  • 5-10X larger volume (~5x)

  • Chili-probe technology (~3x)

  • Advanced signal processing (2x?)


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Expanding the Metabolomic Database

  • Human Metabolome Project

  • $7.25 million Genome Canada project officially launched Dec. 1

  • 2 Key outputs:

    • Electronic database (HMD) of metabolites, chem. properties, spectra and pathways

    • Freezer full of ~1400 metabolites that are either isolated, synthesized or purchased


Future challenges l.jpg
Future Challenges

  • Completing the human metabolome and expanding the library of cmpds so that the spectral ID software (Eclipse) is more robust and more widely applicable

  • Developing improved software or techniques to interpret both NMR and MS (or MS/MS) data for metabolite ID and classification


Future challenges68 l.jpg
Future Challenges

  • Developing software tools to interpret, visualize and predict metabolic outcomes due to genetic perturbations

  • Developing more robust spectral deconvolution software that handles baseline distortion and peak position variability

  • Developing software to interpret “metabolic microarray” data in terms of disease or phenotype identification