Download Metabolomics - Horticultural Sciences at University of Florida

Metabolomics
PCB 5530
Tom Niehaus
Fall 2012
Definitions and Background
Metabolome = the total metabolite pool
- All low molecular weight (MW < 1000 Da) organic molecules in a
sample such as a leaf, fruit, or tuber.
Peptides
Oligonucleotides
Sugars
Nucleosides
Organic acids
Ketones
Aldehydes
Amines
Amino acids
Lipids
Steroids
Alkaloids
Drugs (xenobiotics)
Definitions and Background
Metabolomics = high-throughput analysis of metabolites
Metabolomics is the simultaneous ('multiparallel') measurement of the
levels of a large number of cellular metabolites (typically several hundred).
Many of these are not identified (i.e. are just peaks in a profile).
Definitions and Background
Metabolomics = high-throughput analysis of metabolites
Metabolomics analysis is like a snapshot, showing which compounds are
present and at what relative levels at a specific time point.
More generally, metabolomics refers to a holistic analytical approach to
metabolism that is not guided by specific hypotheses. Instead, metabolomics
sets out to determine how (in principle, all) metabolite levels respond to
genetic or environmental changes and, from the data, to generate new
hypotheses.
Definitions and Background
Definitions and Background
Fluxomics = A branch of metabolomics that measures the
turnover of metabolites in pathways using labeled
isotopes such as 13C.
- New technology, just beginning to be utilized
- Instead of being a snapshot of metabolism, it is like a movie
Definitions and Background
History and Development
Metabolic profiling is not new. Profiling for clinical detection of human disease using
blood and urine samples has been carried out for Centuries.
This urine wheel was
published in 1506 by
Ullrich Pinder, in his book
Epiphanie Medicorum.
The wheel describes the
possible colors, smells
and tastes of urine, and
uses them to diagnose
disease.
Nicholson, J. K. & Lindon, J. C. Nature
455, 1054–1056 (2008).
Definitions and Background
History and Development
Advanced chromatographic separation techniques were developed in the late
1960’s.
Linus Pauling published “Quantitative Analysis of Urine Vapor and Breath by GasLiquid Partition Chromatography” in 1971
Chuck Sweeley at MSU helped pioneer metabolic profiling using gas chromatography/
mass spectrometry (GC-MS)
Gates SC, Sweeley CC (1978) Quantitative metabolic profiling based on
gas chromatography. Clin Chem 24:1663-73. Quantitative metabolic
profiles of volatilizable components of human biological fluids, e.g.
urinary organic acids, were established using GC/MS. Data were
processed by computer and statistical methods for analyzing metabolic
profiles were developed. [Note that all the elements of metabolic
profiling are here.]
Definitions and Background
History and Development
Plant metabolic biochemists (e.g. Lothar Willmitzer) were among other early
leaders in the field.
Metabolomics is expanding to catch up with other multiparallel analytical
techniques (transcriptomics, proteomics) but remains far less developed and less
accessible.
Definitions and Background
Plant Metabolome Size
It is estimated that all plant species contain 90,000 - 200,000 compounds.
Each individual plant species contains about 5,000 – 30,000 compounds.
e.g. ~ 5,000 in Arabidopsis
The plant metabolome is much larger than that of yeast, where there are far
fewer metabolites than genes or proteins (<600 metabolites vs. 6000 genes).
The size of the plant metabolome reflects the vast array of plant secondary
compounds. This makes metabolic profiling in plants much harder than in other
organisms.
Definitions and Background
Metabolomics compared to Genomics, Transcriptomics, and Proteomics
Differences between metabolomics and the other multiparallel approaches:
(a) Conceptual:
1 GENE → 1 mRNA → 1 Protein → Many Metabolites
(and conversely:
Many proteins → 1 Metabolite)
There is no direct relationship between metabolite and gene in the way there is
between genes and mRNAs and proteins. A single gene does not specify the level
of a single metabolite, i.e. its pool size (although it may determine whether the
metabolite is present or absent).
Rather, as MCA teaches, the level of a metabolite is determined by the activities of
all the enzymes of all the pathways that involve that metabolite, and by effectors
that act on these enzymes. In practice, therefore, metabolite levels change
according to developmental, physiological, and pathological states.
Biological variance in metabolite levels (i.e., the variation between genetically
identical plants grown in the same conditions) is accordingly large – about 10× the
analytical variability – and limits the resolution of metabolomics.
Definitions and Background
Metabolomics compared to Genomics, Transcriptomics, and Proteomics
Differences between metabolomics and the other multiparallel approaches:
(b) Chemical:
Unlike nucleic acids and proteins, metabolites have a vast range of chemical
structures and properties. Their molecular weights span two orders of magnitude
(30–3000 Da).
Therefore no single extraction or analysis method works for all metabolites.
(Unlike DNA sequencing, microarrays, MS analysis of proteins – all are general
methods.)
(c) Dyamic:
Many metabolite levels change with half times of minutes or seconds – far
faster than nucleic acids or proteins. Thus valuable information is lost if
sampling times are too far apart. Also drastic artifactual changes can occur in
short intervals between harvest and extraction; this adds to biological variance.
Definitions and Background
The Power of Metabolomics
Metabolomics analysis can powerfully complement transcriptomics and proteomics.
Metabolomes are a step nearer actual function.
Transcriptomes or proteomes are very inadequate monitors of cell function because
there is no simple relationship between mRNA or protein levels and metabolism.
Thus changes in mRNA level or protein
level in mutants or transgenics are
usually not closely linked to changes in
metabolic function or phenotype as a
whole.
Part of the reason for this is the nonlinear relation between mRNA and
protein levels (see graph) and the
typically hyperbolic relation between
enzyme level and in vivo flux rate (see
MCA class). Another cause is the high
level of functional redundancy in plant
metabolism – i.e. parallel or alternative
pathways for the same process.
The dependence of protein expression
on mRNA levels, in linear coordinates.
PMID: 1718905
Definitions and Background
The Power of Metabolomics
Silent Knockout Mutations.
~90% of Arabidopsis knockout mutations are silent – i.e. have no visible phenotype
and so provide no clues to gene function. (The search for some sort of visible
phenotype therefore often becomes desperate.) The situation in yeast is similar –
up to 85% of yeast genes are not needed for survival.
When there is little or no change in growth rate (visible phenotype) of a knockout
mutant, the pool sizes of metabolites have altered so as to compensate for the
effect of the mutation, leaving metabolic fluxes are unchanged. Thus – intuitively –
mutations that are silent when scored for metabolic fluxes or growth rate (growth
rate is the sum of all metabolic fluxes) should have obvious effects on metabolite
levels. There is a firm theoretical basis for this in MCA.
Definitions and Background
The Power of Metabolomics
Example.
In the Chloroplast 2010 project
(phenotype analysis of knockouts of
Arabidopsis genes encoding predicted
chloroplast proteins):
Various knockouts
showed essentially
normal growth and
color but highly
abnormal free
amino acid profiles,
e.g. At1g50770
(‘Aminotransferaselike’)
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.
Metabolic Profiling Methods
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.
Metabolic Profiling Methods
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.
Metabolic Profiling Methods
Main Analytical Techniques
Gas Chromatography/Mass-Spectrometry (GC/MS)
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
Metabolic Profiling Methods
Main Analytical Techniques
Gas Chromatography/Mass-Spectrometry (GC/MS)
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
Metabolic Profiling Methods
Main Analytical Techniques
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.
Metabolic Profiling Methods
Main Analytical Techniques
Liquid Chromatography/Mass-Spectrometry (LC/MS)
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.
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)−].
PMID: 11553758
Metabolic Profiling Methods
Main Analytical Techniques
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.
Metabolic Profiling Methods
Main Analytical Techniques
Nuclear Magnetic Resonance (NMR) Spectroscopy
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.
Metabolic Profiling Methods
Main Analytical Techniques
Nuclear Magnetic Resonance (NMR) Spectroscopy
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
Metabolic Profiling Methods
Main Analytical Techniques
Nuclear Magnetic Resonance (NMR) Spectroscopy
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/
Metabolic Profiling Methods
Main Analytical Techniques
Nuclear Magnetic Resonance (NMR) Spectroscopy
HSQC used to select for protons
directly bonded to 13C.
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
Metabolic Profiling Methods
Main Analytical Techniques
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.
Caution: This approach may not be so useful for dissecting metabolic responses to normal
environmental variations (e.g. in nutrient level, soil aeration, salinity, water supply). There
is good reason from MCA theory and from observation to expect such variations to cause
relatively little change in metabolite levels. This is because all enzymes in affected
pathways tend to be up- or down-regulated together (Fell, 2005).
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.
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.
Transgenic
lines
Dendogram obtained after CA of the metabolic profiles of genetically and
environmentally modified potato tuber tissue. PMID: 11158526
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:
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.
PCA of the metabolic profiles of genetically and
environmentally modified potato tuber tissue.
PMID: 11158526
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
metabolite 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:
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 nearequilibrium 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.
Correlation between metabolite levels of the
transgenic potato tissues.
PMID: 11158526
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.