NMR - PowerPoint PPT Presentation

Loading...

PPT – NMR PowerPoint presentation | free to view - id: 10ecc2-ZDc1Z



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

NMR

Description:

Requires separation followed by identification (coupled methodology) ... Lysis protocol, sonication? freeze-thaw? soluble fraction? lipid fraction? protein rmvl ... – PowerPoint PPT presentation

Number of Views:183
Avg rating:3.0/5.0
Slides: 70
Provided by: DSW47
Category:
Tags: nmr | lysis

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: NMR


1
NMR MetabolomicsThe Possibilities The
Limitations
  • David Wishart
  • Depts. Comp. Sci and Bio. Sci.
  • University of Alberta NINT
  • david.wishart_at_ualberta.ca

2
The (Human) Pyramid of Life
Metabolomics Proteomics Genomics
1400 Chemicals
3000 Enzymes
30,000 Genes
3
The (Bacterial) Pyramid of Life
Metabolomics Proteomics Genomics
761 Chemicals
1152 Enzymes
4269 Genes
4
Why Measure Metabolites?
Metabolites are the Canaries of the Genome
5
Metabonomics Metabolomics
  • MetabonomicsThe quantitative measurement of the
    time-related total metabolic response of
    vertebrates to pathophysiological (nutritional,
    xenobiotic or toxic) stimuli
  • MetabolomicsThe quantitative measurement of
    invertebrate metabolic profiles to characterize
    their phenotype or phenotypic response to genetic
    or nutritional perturbations

MetaboXomics
6
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

7
Traditional Metabolite Analysis
HPLC, GC, CE, MS
8
(No Transcript)
9
(No Transcript)
10
(No Transcript)
11
(No Transcript)
12
(No Transcript)
13
(No Transcript)
14
(No Transcript)
15
(No Transcript)
16
(No Transcript)
17
(No Transcript)
18
(No Transcript)
19
(No Transcript)
20
(No Transcript)
21
(No Transcript)
22
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)

23
New Approaches
24
Advantages
  • Measure multiple (10s to 100s) 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 lt 60 s

25
Why NMR?
Mixture separation by HPLC (followed by ID via
Mass Spec)
Mixture separation by NMR (simultaneous separation
ID)
Chemical Shift Chromatography
26
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

27
Metabolomics and NMR
Principle Component Analysis
28
Functional Proteomics via Metabolic Profiling
Forster, J. et al., (2002) Biotechnol. Bioeng.
Vol. 79, 703-712
29
Detecting Silent Mutations in Yeast
Nature Biotechnology, vol. 19, pg. 45-50 (2001)
30
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

31
Spectral Deconvolution of a Mixture Containing
Compounds A, B and C
32
Data Analysis (Principles)
Glucose Fit
Extract Spectrum
Fructose Fit
33
Data Analysis (Principles)
Fructose Fit
Extract Spectrum
Glucose Fit
34
Data Analysis (Principles)
Glucose and Fructose Fit
Extract Spectrum
Fructose Glucose
Glucose Fit
35
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

36
Fitting NMR Spectra with Eclipse
37
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
  • ()-(-)-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
  • 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

38
Why Not Apply It to Bacterial ID?
39
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?

40
M9-Glucose
MOPS
41
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

42
The TCA Cycle
Acetate
Acetyl-CoA
Glycerol
Pyruvate
Oxaloacetate
Citrate
Isocitrate
L-Malate
?-Ketoglutarate
Fumarate
2
1
Succinate dehydrogenase
Succinate
Succinyl-CoA
43
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?

44
Methods
  • Obtain 3 E. coli strains as shown below

45
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

46
Typical Eclipse Readout (Concentration in mM)
47
Glycerol Consumption
48
Acetate Production
49
Succinate Production
50
Metabolic Responses
Acetate Glycerol Pyruvate
Acetate Glycerol Pyruvate
Succinate
Succinate
51
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

52
Interpretation via SimCell
53
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 -gt 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 1012

54
Cellular Automata
Can be extended to 3D lattice
55
Succinate Production
Observed Predicted (SimCell)
56
Glycerol Consumption
Observed Predicted (SimCell)
57
Metabolic Profiling The Possibilities
  • 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

58
Metabolic Profiling and Drug Toxicology
Principal Component Analysis
59
Genetic Disease Testing
60
140 Detectable Conditions
  • Adenine Phosphoribosyltransferase Deficency
  • Adenylosuccinase Deficiency
  • Alcaptonuria
  • a-Aminoadipic Aciduria
  • b-Aminoisobutyric Aciduria
  • a-Aminoketoadipic Aciduria
  • Anorexia Nervosa
  • Argininemia
  • Argininosuccinic Aciduria
  • Aspartylglycosaminuria
  • Asphyxia
  • Biopterin Disorders
  • Biotin-responsive Multiple Carboxylase Deficiency
  • Canavans Disease
  • Carcinoid Syndrome
  • Carnosinemia
  • Cerebrotendinous Xanthomatosis/sterol
    27-hydroxylaseDeficiency
  • Citrullinemia
  • Cystathioninemia
  • Dicarboxylic Aminoaciduria
  • Dichloromethane Ingestion
  • Dihydrolipoyl Dehydrogenase Deficiency
  • Dihydropyrimidine Dehydrogenase Deficiency
  • Dimethylglycine Dehydrogenase Deficiency
  • Essential Fructosuria
  • Ethanolaminosis
  • Ethylmalonic Aciduria
  • Familial Iminoglycinuria
  • Fanconis Syndrome
  • Folate Disorder
  • Fructose Intolerance
  • Fulminant Hepatitis
  • Fumarase Deficiency
  • Galactosemia
  • Glucoglycinuria
  • Glutaric Aciduria Types 1 2
  • Glutathionuria
  • Glyceroluria (GKD)
  • 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)

61
Applications in Clinical Analysis
  • 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
  • 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

Clinical Chemistry 47, 1918-1921 (2001).
62
Applications in Cancer
Acetic Acid Betaine Carnitine Citric
Acid Creatinine Dimethylglycine Dimethylamine Hipp
ulric Acid Lactic Acid Succinic
Acid Trimethylamine Trimn-N-Oxide Urea Lactose Sub
eric Acid Sebacic Acid Homovanillic
Acid Threonine Alanine Glycine Glucose
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.
63
Applications in Screening Plant Metabolism
AgriGenomics
64
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

65
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?)

66
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

67
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

68
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

69
Thanks to...
About PowerShow.com