metabolomics-Overview.pdf

Metabolomics
Metabolism (from Greek:metabolē, "change") is the set of
Life sustaining chemical transformations within the cells of
living organisms.
• The three main purposes of metabolism are the
conversion of food/fuel to energy to run cellular
processes, the conversion of food/fuel to building blocks
for proteins, lipids, nucleic acids, and some carbohydrates,
and the elimination of nitrogenous wastes.
These Enzyme catalyzed reactions allow organisms to grow
and reproduce, maintain their structures, and respond to
their environments.
Dr. Shiny C Thomas, Department of Biosciences, ADBU

• The word metabolism can also refer to:
• the sum of all chemical reactions that occur in
living organisms, including digestion and the transport
of substances into and between different cells
• in which case the set of reactions within the cells is
called intermediary metabolism or intermediate
metabolism.

What Are Metabolic Pathways?
Many of the molecular transformations that occur within
cells require multiple steps to accomplish.
• For instance, that cells split one glucose molecule into
two pyruvate molecules by way of a ten step process
called glycolysis.
• This coordinated series of chemical reactions is an
example of a metabolic pathway in which the product of
one reaction becomes the substrate for the next reaction.
• Consequently, the intermediate products of a metabolic
pathway may be short lived.

• The breaking down of complex organic molecules
occurs via catabolic pathways and usually involves the
release of energy.
• Through catabolic pathways, polymers such as proteins,
nucleic acids, and polysaccharides are reduced to their
constituent parts: amino acids, nucleotides, and sugars,
respectively.
• In contrast, the synthesis of new macromolecules
occurs via anabolic pathways that require energy input.
This macro or micro molecules are called metabolites.

Scientific Considerations of Properties
Metabolites are the products of enzyme-catalyzed
reactions that occur naturally within cells.
To be classified as a metabolite a compound must meet
certain criteria. Below is a summary of the major factors to
consider in designating a substance a metabolite.
1. Metabolites are compounds found inside cells
2. Metabolites are recognized and acted upon by enzymes
3. The product of a metabolite must be able to enter into
subsequent reactions
4. Metabolites have a finite half-life;, they do not
accumulate in cells

5. Many metabolites are regulators that control the pace
of metabolism
6. Metabolites must serve some useful biological functions
in the cell

Rationale for Metabolic Pathways and Metabolites
The intermediates that were formed in the pathway were
referred to as "metabolites".
Metabolites were thus compounds that intervened between
the start and end of a pathway.
• The pathways begin with defined compounds either
derived directly from the blood stream or from an
adjoining pathway.
• A metabolite is either a building block of a larger structure
or a degradative product of macromolecules for example,
during oxidation reactions where the carbon appears in
smaller size molecules and ultimately as carbon dioxide.

Metabolites are the intermediates and products of
metabolism. The term metabolite is usually restricted to
small molecules.
• Metabolites have various functions, including fuel,
structure, signaling, stimulatory and inhibitory effects on
enzymes, catalytic activity of their own (usually as a
cofactor to an enzyme), defense, and interactions with
other organisms (e.g. pigments, odorants, and
pheromones).
• A primary metabolite is directly involved in normal
"growth", development, and reproduction.
• A secondary metabolite is not directly involved in those
processes, but usually has an important ecological
function. Examples include antibiotics and pigments such
as resins and terpenes etc.

• The metabolome forms a large network of metabolic
reactions, where outputs from one enzymatic chemical
reaction are inputs to other chemical reactions.
Metabolites from chemical compounds, whether inherent or
pharmaceutical, are formed as part of the natural
biochemical process of degrading and eliminating the
compounds.
• The rate of degradation of a compound is an important
determinant of the duration and intensity of its action.
• Profiling metabolites of pharmaceutical compounds, drug
metabolism, is an important part of drug discovery,
leading to an understanding of any undesirable side
effects.

• Technically speaking, a compound outside the cell is not
considered a metabolite.
• One definition would hold that metabolites arise by
enzyme-catalyzed chemical changes within a cell. This is
not a hard-fast rule.
• Glucose, for example, when in the blood is considered a
metabolic product excreted from the cell but when inside
the cell, glucose is a metabolite because of its
vulnerability to chemical change.

• Metabolites are the byproducts of metabolism, they
represent defined chemical intermediates in a pathway.
• A metabolite owes its instability to the enzyme that will
use the metabolite as a substrate for a subsequent step in
the pathway.
• Metabolites have a finite existence in a cell and generally
are not allowed to accumulate.
• Metabolic turnover is a principle of life and the synthesis
and degradation of metabolites is one of the ways
turnover is accomplished.

Metabolomics
Metabolomics, the study of global metabolite profiles in a
system (cell, tissue, or organism) under a given set of
conditions.
• Metabolome analysis, to identify and quantify the entire
collection of intracellular and extracellular metabolites.
• The metabolome comprises the complete set of
metabolites, the non-genetically encoded substrates,
intermediates, and products of metabolic pathways,
associated to a cell.
• An integrative information of functional levels of
metabolite by linking DNA encoded processes with the
environment, the metabolome helps to map the genes
responsible for different phenotypes.
.

• In metabolic engineering the identification of metabolome
through quantification and the understanding of trafficking of
metabolites through the metabolic network impact cellular
behavior.
• Metabolomics has emerged as an important complementary
technology to the cell wide measurements of mRNA, proteins,
fluxes, and interactions (e.g. protein- DNA).
Metabolomics is already a powerful tool in drug discovery and
development.

There are two basic analytical methodologies used in
metabolomics
1. Metabolite profiling strategies investigate qualitative
scanning of a large number of metabolites (i.e. more than
100).
Here, the pattern of metabolites (or even spectra from
chromatography or mass spectrometry) is used to find
discriminatory elements via high-throughput detection
followed by data deconvolution methods.
Metabolite profiling comprises of metabolic fingerprinting,
which covers the endometabolome (intracellular
metabolites), and metabolic foot printing, which covers the
exometabolome (metabolites in the growth media or
extracellular fluid).

Metabolic Profiling Methods
Sample Preparation
• Metabolites are typically extracted in aqueous or methanolic media,
then fractionated into lipophilic and polar phases that are then
analyzed separately. Further fractionation of each phase may follow
to split metabolites into classes prior to analysis.
No single extraction procedure works for all metabolites because
conditions that stabilize one type of compound will destroy other types
or interfere with their analysis. Therefore the extraction protocol has to
be tailored to the metabolites to be profiled.

Sample Preparation
In practice, these considerations mean that metabolic
profiling is often confined to fairly stable compounds that
can be extracted together. These include major primary
metabolites (sugars, sugar phosphates, amino acids, and
organic acids) and certain secondary metabolites (e.g.,
phenylpropanoids, alkaloids).
The most comprehensive profiling can cover several hundred
such compounds, many of which are unidentified. Many
crucial metabolites, particularly minor or unstable ones, are
currently being missed in metabolomics analyses.

Main Analytical Techniques
Gas Chromatography/Mass-Spectrometry (GC/MS)
In GC/MS, it may be necessary to first
derivatize the sample to increase metabolite
stability and volatility. The derivatized mix is
then fractionated by a gas chromatograph that
is coupled to a mass spectrometer.
The mass spectrometer scans the peaks
emerging from the GC column at frequent
intervals (~1 sec) and so acquires the mass
spectrum of each peak, from which peaks can
be identified and quantified. Mass
spectrometry ‘weighs’ ionized individual
molecules and their fragments. Molecules are
identified from their fragmentation pattern
and ‘weights’ (mass/charge ratios – m/z
values), with the help of mass spectra libraries,
and can be quantified from peak size.

Overlapping peaks can be
deconvoluted because the
spectra of their
constituents are distinct
Target metabolites are
identified by exact retention
times and their corresponding
mass spectra (B) as shown for
the co-eluting peaks of malate,
gamma-aminobutyric acid
(GABA), and an unidentified
compound. m/z, Ratio of mass
to charge.
PMID: 11062433

Unfortunately, knowing only the exact masses of molecules and their fragments is not
enough to identify them. Huge number of chemical structures can have the same exact
mass. This is why libraries of retention times and mass spectra, determined for standard
compounds, are critical.
The major challenge for metabolomics is identification of unknown peaks. Basically,
standards are essential to the process. If there is no standard, a compound cannot be
identified with certainty. Thus, the more novel the compound, the less powerful
metabolomics becomes.
Mass spectrometry (MS) metabolomic datasets provide relative quantification of cellular
metabolites (i.e. –fold changes in levels between different samples. Absolute
quantification (i.e. moles per weight of tissue) is possible with MS methods but requires
an authentic standard for each metabolite to be quantified.
Animated explanation of GC/MS:
http://www.shsu.edu/~chm_tgc/sounds/flashfiles/GC-MS.swf
Tutorial on MS: http://www.asms.org/whatisms/page_index.html

Liquid Chromatography/Mass-Spectrometry (LC/MS)
In LC/MS (also termed high performance liquid chromatography, HPLC/MS) the samples
are not derivatized before analysis and an HPLC instrument is used for separation.
LC/MS is more suitable than GC/MS for labile compounds, for those that are hard to
derivatize, or hard to render volatile. LC/MS is less developed than GC/MS. A closely
related method is capillary electrophoresis (CE)/MS.

Liquid Chromatography/Mass-Spectrometry (LC/MS)
LC-MS analysis of endogenous pools of prenyl
diphosphates in isolated peppermint oil gland secretory
cells.
A, Total ion chromatogram (TIC; m/z 50–350)
B, detection of endogenous GPP in the m/z 313 [(M −
H)−] extracted ion chromatogram (EIC)
C, detection of endogenous DMAPP and IPP in the m/z
245 [(M − H)−] EIC
D, EIC of a mixture of authentic DMAPP and IPP standards
at m/z 245 [(M − H)−].
Profiling example: Metabolites related to plant
isoprenoid biosynthesis. The total ion chromatogram
(TIC) is the total output of the ion detector; the
extracted ion chromatograms (EICs) are the outputs
for particular ions characteristic of isoprenoid
synthesis intermediates.
PMID: 11553758

Nuclear Magnetic Resonance (NMR) Spectroscopy
Advantages of NMR over MS:
- NMR does not destroy the sample
- NMR can detect and quantify metabolite because the signal intensity is only
determined by the molar concentration
- NMR can provide comprehensive structural information, including stereochemistry
Many atoms have nuclei that are NMR active, but most
NMR data are collected for 1H and 13C since these are
present in all organic molecules.
The main weakness of NMR is low sensitivity relative to MS.
It is therefore less suited for analysis of trace compounds.
As the natural abundance of 13C is only 1.1%, 13C-NMR is
less sensitive than 1H-NMR. Recent developments have
considerably increased sensitivity, making it less of a
problem.

NMR uses radio-frequency (RF) radiation and magnetic fields. RF radiation is used to
stimulate nuclei present within molecules. The information obtained is displayed as a
spectrum. The horizontal axis is the chemical shift (delta, in units of ppm), which is a
measure of the position at which RF absorption occurs relative to an internal standard
(tetramethylsilane, TMS). The vertical axis is the intensity of the absorption. As with
other spectral techniques, compounds have characteristic spectra. More than 100
metabolites occur in plants at levels high enough for analysis by NMR, so NMR spectra
of mixtures contain many peaks.

Profiling example: 1H-NMR spectra of extracts of leaves of various Verbascum species
(medicinal plants)
600 MHz 1H NMR spectra of
extracts of Verbascum leaves.
From bottom to top:
V. xanthophoeniceum, V.
nigrum, V. phlomoides, V.
phoeniceum, V. phlomoides, V.
densiflorum.
PMID: 21807390

Signal overlap is a problem in the complex spectra of plant extracts. Signal overlap
hampers metabolite identification and quantification. Better signal resolution can be
obtained using various types of 2D NMR spectroscopy. These approaches cut signal
overlap by spreading the resonances in a second dimension.
Example: Heteronuclear single quantum coherence (HSQC) spectroscopy. The 2D
spectrum has one axis for 1H and the other for a heteronucleus (an atomic nucleus
other than a proton), usually 13C or 15N. The spectrum contains a peak for each unique
proton attached to the heteronucleus being considered.
NMR tutorial: http://www.cis.rit.edu/htbooks/nmr/

Use of HSQC spectroscopy for analysis of
common metabolites. In 1D spectra,
overlapped signals hamper identification of
individual metabolites, whereas in 2D
correlation, spots are easily visible.
(a) 1D 1H NMR spectrum of an equimolar
mixture of the 26 standards.
(b) 2D 1H–13C HSQC NMR spectra of the
same synthetic mixture (red) overlaid onto a
spectrum of aqueous whole-plant extract
from Arabidopsis (blue).
PMID: 21435731
HSQC used to select for protons
directly bonded to 13C.

How can one decide which analytical platform should be used?
- Should be rapid, reproducible, with easy sample preparation.
- Selection based on objectives, target metabolites, availability, etc.
Scale from - to +++ for major disadvantages to major advantages
Phytochem Rev (2008) 7:525–537

Data Analysis
The avalanche of metabolome data presents great
difficulties to analyze. There are also challenges in
archiving such data; a standard framework for this is in
place.
• The problems in extracting meaning from large data
sets are similar for all forms of profiling.
• The goal is to recognize patterns for further
exploration.
• Various data mining tools are used for this. These
statistical tools reduce data complexity by focusing on
the information content of a given data set, i.e. they
try to ‘tame’ the wild profusion of profiling data.

• Unlike many other statistical procedures, these
methods are mostly applied when there are no a priori
hypotheses.
• Data mining tools include cluster analysis (CA) and
principal components analysis (PCA). The metabolite
data can be known or unidentified peaks.
• CA and PCA can establish ‘guilt by association’ – they
can point to where in metabolism mutations act from
the similarity of their metabolite profiles to those of
known mutations.
• External factors (e.g. toxins, herbicides, environmental
insults) can be studied in an analogous way.

Data Analysis
• Thus, in principle, the function of an unknown gene
can be determined by comparing the metabolic profile
of a mutant in that gene with a library of such profiles
generated by deleting individual genes of known
function.
Two key drawbacks of clustering and other current data
mining methods are:
-
-

• Typically, they detect only simple, one-to-one linear
relationships. They do not detect non-linear or multi-
input relationships, which are common in biology.
• They do not assign confidence levels, so it is not clear
which clusters are trustworthy when the input data are
not well separated.

Data Analysis
Cluster Analysis (CA)
• CA is a set of statistical methods that group similar data
together.
• The group (‘cluster’) members have certain properties in
common and the resultant classification can yield new
insights.
• The classification reduces the dimensionality of a data
set.
• Data are presented in dendrograms that emphasize
natural groupings.

Dendogram obtained after CA of the metabolic profiles of genetically and
environmentally modified potato tuber tissue. PMID: 11158526
Transgenic
lines
Data Analysis
Cluster Analysis (CA)
Profiling example: Dendrogram of the metabolic profiles of transgenic potato tubers and
tubers incubated in a range of glucose concentrations (0 to 500 mM). Note that:
1) The glucose-fed samples form a
cluster that is nearer the cluster of
wild-type samples than any of the
transgenics.
2) That independent
transgenic lines
carrying the same
transgene (e.g., the
four ‘SP’ lines) tend to
cluster together (the
principle of ‘guilt by
association’).

Data Analysis
Principal Component Analysis (PCA)
PCA uses all the metabolite data from a sample to compute an
individual metabolic profile that is then compared to all the other
profiles. In essence, PCA takes the resulting cloud of data points and
rotates it such that the maximum variability is visible – i.e. the
extraction of principal components amounts to a variance maximizing
rotation of the original variable space. PCA finds the vectors
(‘principal components’) that give the best overall sample separation.
The data can be represented as two- or three-dimensional
plots in which the axes (principal components or vectors)
are those that include as much as possible of the total
information derived from metabolic variances.

Data Analysis
Principal Component Analysis (PCA)
Profiling example: Clusters found after PCA analysis of the same data
set for potato tubers as above. Note that:
PCA of the metabolic profiles of genetically and
environmentally modified potato tuber tissue.
PMID: 11158526
1) The two components chosen
account together for 69% of the total
metabolic variance, i.e. only 1/3 of
the original variation has been lost
during data reduction.
2) As before, the glucose-fed samples
form a cluster that is nearer the
cluster of wild-type samples than any
of the transgenics.
3) Again, independent transgenic
lines carrying the same transgene
(e.g., the four ‘SP’ lines) tend to
cluster together.

Data Analysis
Simple Correlations
• Computer-generated pairwise plots of every
metabolite in the data set against every other meta-
bolite can be informative.
• But when hundreds of metabolites are analyzed the
potential number of such plots is very large – many
thousands – and most of them will show no
relationship.

Data Analysis
Simple Correlations
Profiling examples: correlations between pairs of metabolites among transgenic potato
tubers. Note:
Correlation between metabolite levels of the
transgenic potato tissues.
PMID: 11158526
1) The linear correlation (Frame A) between
glucose-6-phosphate and fructose-6-phosphate
levels. These metabolites are interconvertible by
phosphoglucose isomerase, which catalyzes a near-
equilibrium reaction. A linear relation is thus
predicted.
2) The non-linear correlation between methionine
and lysine levels (Frame C), in which lysine
accumulates continuously but methionine reaches
a plateau. This is expected because methionine
synthesis is under tighter feedback and
feedforward control than lysine.

Metabolomics Resources
http://fiehnlab.ucdavis.edu/ Oliver Fiehn’s group at UC Davis. Includes databases.
http://www.noble.org/plantbio/MS/metabolomics.html Lloyd Sumner’s group at the
Noble Foundation. Useful short summary of analytical approaches and bioinformatics
involved in metabolomics.
http://dbkgroup.org/default.htm Douglas Kell’s group at University of Manchester – a
gateway site with explanations of metabolic profiling technologies and links to other useful
sites.

Useful Values
(for interpreting metabolite concentration data)
- In typical plant tissues, dry weight is ~10% of fresh weight (so that there is ~ 0.9 ml of
water per gram fresh weight)
- In very rough terms, the cytoplasmic volume is 10% of the total tissue water volume.
(‘Cytoplasm’ includes mitochondria, plastids, peroxisomes, nucleus, and cytosol). The
vacuolar volume is 70% of total water, and extracellular water is 20% . The extracellular
water compartment is also termed the apoplast; the cytoplasmic + vacuole (i.e.
intracellular) water compartment is also termed the symplast.
- Plant leaves typically have a protein content of ~20% of dry weight. N content × 6.25 =
protein content (i.e. protein is ~16% N). The free amino acid content of plant tissues is
usually only a few percent of the protein-bound amino acid content.
- The osmotic potential of a typical plant cell is ~ -10 bars. A 1 molar solution of a sugar or
other non-dissociating solute has an osmotic potential of ~ -25 bars; that of a 1 molar
solution of a salt such as NaCl is ~ -45 bars. Thus the intracellular accumulation of high
concentrations of small molecules or salts has osmotic implications.

2. The other general method used in metabolomics is target analysis.
Here, absolute, or at least semi-quantification and unambiguous
detection of metabolites are achieved.
Target analysis has been reserved for interrogating relatively small
numbers of metabolites (e.g. less than 20), new developments enable
quantitative analysis of more expanded metabolome coverage

Strategies for metabolome analysis. (Figure)
The metabolome is comprised of two parts, the endometabolome
(intracellular metabolites) and the exometabolome (extracellular
metabolites).
Metabolome analysis seeks to identify cellular metabolites through
targeted analysis (identification and quantification of pre-defined
metabolites) or metabolite profiling (scanning of all metabolites
identified by a specific analytical technique).
Extracellular metabolites: A*, B*, and C*. Intracellular metabolites: A,
B, C, !, ?. Note: ! and ? are unidentified metabolites.

A variety of analytical platforms have been utilized for metabolite
detection. While most quantitative strategies couple a separation
technique (e.g. capillary electrophoresis (CE), liquid chromatography
(LC), and gas chromatography (GC)) with mass spectrometry (MS) or
NMR based detection.
Applications of metabolomics
• Metabolite profiling has been used for medical and diagnostic
purposes as well as strain classification and characterization
As an example, detection and quantification of mycotoxins from
fungi has been a focal point for characterization studies.
• Metabolome analysis is also an important tool in functional
genomics, revealing the roles of genes from comprehensive
analysis of the metabolome
For example, metabolite profiling and target analysis
have been effectively used to classify molecular signatures
responsible for the phenotype of silent and unknown mutations

• Hierarchical metabolomics is also well suited to guide targeted
analysis of metabolism
Eg: metabolome coverage of conventional and genetically modified
(GM) potato crops to reveal that, apart from anticipated engineered
differences, metabolic compositions were comparable among several
types of cultivars.
The role of metabolomics in systems biology
Metabolomics is emerging as a powerful tool in systems biology.
Systems biology is the quantitative study of an organism, viewed as a
complex web of interacting and interchanging molecular participants
(DNA, mRNA, proteins, and metabolites) and their environment.
Here, studying defined biological systems as a whole, through the
combination of mathematical modeling and experimental biology, will
provide insights into cellular behaviour that are not apparent from
investigating the components alone.

It enhances to study the relationships among active
molecular players of the cell for describing and predicting
cellular behaviour.
It promises to transform the practice of medicine and our
ability to engineer living organisms by facilitating drug
discovery, treating disease, and improving bioprocesses

Metabolon
A metabolon is a temporary structural functional complex formed
between sequential enzymes of a metabolic pathway, held together
both by noncovalent interactions and by structural elements of the
cell, such as integral membrane proteins and proteins of the
cytoskeleton.
The formation of metabolons allows the intermediate product from
one enzyme to be passed (channelling) directly into the active site of
the next consecutive enzyme of the metabolic pathway. The citric acid
cycle is an example of a metabolon that facilitates substrate
channeling

Flux, or metabolic flux is the rate of turnover of molecules through a
metabolic pathway. Flux is regulated by the enzymes involved in a
pathway.
Fluxomics = A branch of metabolomics that measures the turnover of
metabolites in pathways using labeled isotopes such as 13C.
Within cells, regulation of flux is vital for all metabolic pathways to
regulate the pathway's activity under different conditions.
Flux is therefore of great interest in metabolic network modelling,
where it is analysed via flux balance analysis.

Flux: is a term used in metabolic analysis to indicate the
rate of a multi-component system (metabolic pathway),
while “rate” is reserved for individual components (enzyme)
• In this manner, flux is the movement of matter through
metabolic networks that are connected by metabolites
and cofactors, and is therefore a way of describing the
activity of the metabolic network as a whole using a
single characteristic.

Metabolic channelling
The association of various enzymes in large complexes
(supramolecular organization) allows the direct transfer of their
common intermediate metabolite, (metabolic channelling) i.e.
without releasing it to the bulk solvent. This will result in the
existence of microcompartments within the soluble phases of cells.
The multienzyme complexes can be divided in two groups:
1. static, if the complex can exist in the absence of the intermediate
metabolite.
2. dynamic, if the complex can only exist when the intermediate
metabolite is also bound.

A metabolic network is the complete set of metabolic and physical
processes that determine the physiological and biochemical properties
of a cell. As such, these networks comprise the chemical reactions of
metabolism, the metabolic pathways, as well as the regulatory
interactions that guide these reactions.
With the sequencing of complete genomes, it is now possible to
reconstruct the network of biochemical reactions in many organisms,
from bacteria to human. Several of these networks are available
online:
Kyoto Encyclopedia of Genes and Genomes (KEGG)[1]
(http://www.genome.ad.jp), EcoCyc [2]
(http://www.ecocyc.org), BioCyc [3] (
http://biocyc.org) and
metaTIGER [4]
(http://www.bioinformatics.leeds.ac.uk/metatiger/).
Metabolic networks are powerful tools for studying and modelling
metabolism.

Major metabolic pathways in metrostyle map

metabolomics-Overview.pdf

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