Metabonomics and MRS
BCMB/CHEM 8190
Metabolomics, Metabonomics, Metabolic Profiling
• Definition: “The quantitative measurement of the dynamic multi parametric
metabolic response of living systems to physiological stimuli or genetic
modification.” (J.K. Nicholson, J.C. Lindon, E. Holmes, Xenobiotica 29, 11811189, 1999)
• What do you observe? Metabolites – the small molecule (< 1500 Da) substrates
and products of the complex enzyme networks that support life
• Why study metabolomics? Metabolite variations are the end product of the action
of proteins, which are the end product of gene expression, they correlate more
directly with disease, toxicological effects, and environmental variations.
• References:
• NMR in Metabolomics and Natural Products Research: Two Sides of the Same
Coin, Robinette, SL., Bruschweiler, R., Schroeder, FC., Edison, AS. (2012)
Accounts of Chemical Research 45:288-297.
• NMR-based metabolomics in human disease diagnosis: applications, limitations,
and recommendations, Emwas, AM., Salek, RM., Griffin, JL., Merzaban, J. (2013)
Metabolomics 9:1048-1072.
• Multidimensional Approaches to NMR-Based Metabolomics, Bingol, K and
Bruschweiler, R. (2014), Analytical Chemistry 86:47-57.
Observation of Metabolites
Mass Spectrometry (MS) Nuclear Magnetic Resonance (NMR)
Advantages
• High Sensitivity (pico grams)
• Observe a diverse number of
molecular species
• Simple relationship between
observable and molecule: a direct
measure of molecular weight
Limitations
• Limited molecular structure detail
• Requires pre-analysis separation
• Quantitation is relative and usually
requires isotopic labeling
• Some metabolites are difficult to
detect because of ionization
difficulties
Advantages
• Real-time application to many
systems without pre-treatment
• Quantitative response to
concentrations of metabolites
• Near universal detection
• Rich in structural information
Limitations
• Not nearly as sensitive as mass
spectrometry (micro grams)
• Structural analysis is more
complex
• Information content may be
overwhelming without isotopic
labeling
Some Common Metabolic Cycles
Sigma-Aldrich
Wikipedia
NMR spectra of metabolite mixtures can be complex
• Samples: lysates, extracts
from tissues, bio-fluids
• Problem 1 – identifying
biomarkers – correlating
with disease
• Problem 2 – identifying
molecules
From Bingol and Bruschweiler, Analytical Chemistry 2013
Metabolites at mM Conc. Can be Observed
From: Drost, Riddle and Clarke, Med. Phys. (2002) 2177-2197
Example of a Typical Metabolomics Application:
1H NMR spectra for urine from three different mouse strains
Don’t actually need assignments to see differences
Cloarec et al. (2005) Anal. Chem. 77:1282-1289
Principle Component Analysis or PLS Can be
Used to Distinguish Mouse Strains
Principle component analysis can be applied: D = A-1 x C x A
C is a matrix of a large number of spectra (lines binned)
D is diagonal with Eigen values – largest is highest content
Vectors in A weight spectra to give the least correlated representations
Can also apply methods such as PLS (projection on latent structure) to
pick variables giving highest differentiation of sets
O-PLS cross-validated
scores for the
discrimination among 1H
NMR urine spectra of three
mouse strains.
Separating and Assigning Spectra in Mixtures
Increases Information Content
Statistical Total Correlation Spectroscopy: STOCSY
•
•
•
•
•
•
•
Collect 100 1D spectra of samples that vary in composition
Reference each to the average of all spectra
Order in a matrix, M; of n spectra and v spectral points
Construct covariance matrix, C = (1/(n-1)) Mt x M
If peaks are correlated in amplitude get a cross-peak
Numeric example: two line spectra, compounds a and b
Ref: Cloarec et al. (2005). Analytical Chemistry 77, 1282-1289.
a
-2.0 -1.0
-0.5
2.0
1.0
1.0 -1.0
0.5
-2.0 -1.0
-1.0
2.0
1.0
2.0 -2.0
1.0
b
a
b
-2.0 -0.5 -2.0 -1.0
X
-1.0
1.0 -1.0
2.0
2.0 -1.0
2.0 -2.0
1.0
1.0
0.5
1.0
=
9.3
1.5
1.5
2.5 -1.5
9.9 -1.5
-1.0
9.9 -3.0
5.0
9.9 -3.0
5.0 -3.0
9.9
A Simple Example: Correlating Peaks
in a Mixture of Two Sugar Molecules
Lactose
Glc- Glucose
Gal- Galactose
6.0
5.0
PPM
4.0
Coupling Constants Distinguish  and  Anomers
Also Numbers of Protons on Adjacent Sites
Sucrose
Glc- Glucose
F - Fructose
6.0
5.0
PPM
4.0
STOCSY 2D Spectrum:
150 samples of mixed lactose and sucrose
L
S
L
S
L
L
L
S
L
S S
Mixture of lactose and sucrose
S
STOCY Spectrum of Mouse Urine
Spectrum generated by looking at correlated variations in
hundreds of spectra. Coupling between methylenes is shown
STOCSY Shows Connections Even
When no Coupling Exists
Couplings between methylene and aromatic protons shown
Application to a Time-Evolving Metabolic Sub-System:
The Golgi: a biological factory for glycan synthesis
NDP-[13C]sugar1
Transferase 1
NDP-[13C]sugar2
NDP-[13C]sugar3
Transferase 2
Transferase 3
OH
OH
HO
H
H
OH OH
OH
Acceptor
O
HO
HN
H
O
O
HO
HO
H
O
OH
O
OH
O
O
O
HO
HN
O
NHAc
O
H
OH
O
O
Protein
OH
HO
HO
O
HO
H
H
H
H
HO HO
Golgi
H
polysaccharides
and glycoproteins
OH
Kinetics of Subset of the Glycan Synthesis System:
UDP-apiose/UDP-xylose synthase
Guyett, P., Glushka, J., Gu, X. G. & Bar-Peled, M. (2009).
Carbohydrate Research 344, 1072-1078.
Following the UDP-apiose/UDP-xylose synthase
reaction by 1D 1H NMR.
Time=16hrs
0 hrs
This spectrum is particularly well resolved: Can mathematical /
statistical methods improve our ability to deconvolute overlapping
peaks? Can we fit these time courses to kinetic models in which
many enzymes, substrates and products participate?
STOCSY:
Positive and Negative Correlations Indicate
Substrate – Product Relationships

Metabolic Changes Can Also be Followed in 31P NMR
Magic Angle Spinning of tissue Samples Removes Bulk
Susceptibility Effects
From: Lindon, Holmes and Nicholson, Prog. NMR Spec. (2004) 45:109-143
Fig. 7. (a) The 600 MHz 1H MAS NMR
CPMG spectrum of intact control liver
tissue; (b) 600 MHz 1H NMR spectrum
of a control lipid-soluble liver tissue
extract; (c) 600 MHz solvent presaturation 1H NMR spectrum of a
control aqueous-soluble liver tissue
extract; 3HB, 3-D-hydroxybutyrate; Ala,
alanine; Cho, choline; Chol, cholesterol;
EDTA, ethylenediaminetetraacetic acid;
Glu, glucose; Gln,
glutamine; Glu, glutamate; GPC,
glycerophosphorylcholine; Gly, glycerol;
Ile, isoleucine; LDL, low-density
lipoprotein; Leu, leucine; Lys, lysine;
PCho,
phosphocholine; Phe, phenylalanine;
TMAO, trimethylamine-N-oxide; Val,
valine; VLDL, very low-density
lipoprotein.
MRS in Monitoring Disease Progression:
a Mouse Model of Alzheimer’s Disease
Figure 1. Changes in brain metabolites as
a function of age. 9.4T study on 18L voxels.
Myoinositol is also seen in APP-PS1 mice.
(Marjanska,…, Ugurbil, Garwood, (2005) PNAS
102, 11906-11910.
Example of Isotope Editing Using 13C
Acetyl-CoA Synthetase (ACS) Reaction
In Vivo MRS/MRI
Further transfers of acetyl from Co-A to carnitine occur
DNP Enhanced 13C-Acetate:
IV Injection in a Mouse
Magnus Karlsson, René Zandt, Pernille R. Jensen, Georg Hansson, Sven
Månsson, Anna Gisselsson and Mathilde H. Lerche (2008) Experimental
NMR Conference, Asilomar CA