**The *Populus* Genome Science Plan**

**Panel on Metabolic Characterization and Metabolomics**

Panel Members: Tim Tschaplinski**,** Thomas Moritz, Andrea Polle, Scott
Harding,

Janice Cooke, and Reinhard Jetter

**BACKGROUND AND** **SCOPE**

The emerging science of metabolomics couples metabolite profiling with
the analysis of mutant and transgenic lines to elucidate protein
function, the structure of metabolic pathways, and offers tremendous
potential to discover and assign function to novel genes. Stated
explicitly, metabolic profiling is the unbiased, relative quantification
of the broad array of cellular metabolites, and their fluxes. As such,
metabolic profiling can provide information on how gene function affects
the complex biochemical network, and the levels of regulation of
biochemical networks that are not revealed by DNA microarray technology.
A comprehensive functional genomics research platform, that links
metabolite profiling to gene expression arrays and protein profiles,
will facilitate the cataloguing of genes. About 500-1000 metabolites may
be expected to accumulate to detectable levels in a typical eukaryotic
genome, which codes for \>10,000 proteins. Therefore, it is unlikely
that a single gene knockout or up-regulation event will often lead to
direct relationships of a single gene completely regulating the
production and accumulation of a single metabolite. Some examples
indicate that genetic mutations can lead to changes that are highly
pleiotropic, depending on where the mutation is operating in the
metabolic networks. However, the ability to detect a wide array of
metabolites (and their fluxes) will permit determination of how
biochemical networks, with their distributed control (regulation), have
been perturbed.

Successful deployment of metabolite profiling requires the development
of rapid, reliable, and efficient assays for detecting phenotypes that
are metabolic variants within natural or mutated populations. Assays
need to be developed which will allow the detection of as many
metabolites as possible and preferably at high-throughput rates.
Although the desire is to have a single analysis that captures all
metabolites in a short time, there is, as yet, no single "silver bullet"
analysis that will be appropriate for all metabolites with a high degree
of sensitivity and resolution. The varying chemical characteristics of
the different classes of compounds will necessitate several analyses,
but it is possible to standardize the use of a limited number of
protocols that rapidly captures the bulk of small molecules. A number of
analytical approaches are currently available that can image a large
number of metabolites, but they need to address the problems of
co-eluting interference, and be able to accurately identify as many of
the peaks as possible. The current status of promising analytical
approaches and what is needed to forward these approaches will be the
focus of this plan. Included are sample preparation needs, description
of the advantages and disadvantages of a given analytical approach and
how a combination of multiple approaches can circumvent limitations.
Overall, the current-best protocols need to be modified for high
throughput, while simultaneously developing the next generation of
high-throughput protocols that are scalable and address
difficult-to-measure metabolites. Classes of challenging metabolites
include intermediate-sized molecules (1000-2500 Da), and charged
molecules, such as phosphorylated compounds. In addition to
determination of the steady-state concentrations of large numbers of
metabolites, in many cases, it is the flux of these metabolites that
will provide key insight in to which gene was perturbed. The
concentrations and fluxes of metabolites will need to be assessed in
biochemical pathway inference models to probe pathway linkages.
Recognizing that the greatest gains in functional genomics analysis will
be derived from the integration of the different data streams, tools and
approaches that combine the different classes of genomic data that are
available, including DNA sequence data, mRNA expression profiles,
protein profiles, and metabolite profiles, need to be developed.

**SCIENTIFIC** **OBJECTIVES**

#### Sampling and Sample Preparation

Given that many compounds are unstable, have very high turnover rates,
or exhibit diurnal variation in concentration, etc., sampling is
important. Sample extraction prior the chemical analysis must be adapted
for each type of sample (e.g., extraction protocols for leaf tissue from
*Populus* might be different from extraction protocols for developing
xylem tissue). The metabolites represent many different classes of
compounds, and therefore the chemical properties of the metabolites are
highly variable. Depending on the extraction protocol, different classes
of compounds show different extraction efficiency under specific
conditions. It is unlikely that a single extraction procedure for plant
tissues allows accurate quantification for all compounds, but the goal
is to capture as many metabolites as possible.

*Short-term goal:* To establish standardized extraction protocols that
are tailored to each tissue-type of *Populus* with high recovery and
reproducibility. The extraction protocols will include addition of
internal standards representing all major classes of compounds (e.g.
carbohydrates, organic acids, steroids, amines, etc.) prior to
extraction to increase accuracy and precision of the analysis.

*Long-term goal:* To establish methods for reproducible fractionation of
extracts using solid phase extraction (SPE) columns or other methods.
The goal of fractionation is to concentrate metabolites, permitting more
of the extract to be analyzed by the gas chromatography (GC)- or liquid
chromatography-mass spectrometry (LC-MS), and lessening the probability
of saturating columns or MS-detectors.

### Derivatization for GC-MS analysis

GC-MS analysis of extracts containing such varied metabolites as organic
acids, sugars, sugar alcohols, amino acids and steroids is complicated.
Many of the metabolites are not volatile and must be derivatized prior
analysis by GC. Methoxymation in combination with trimethylsilylation
(methoxy-TMS) is widely used as the main derivatization protocol. By
first protecting the carbonyl group(s), the coupled derivatizations are
more efficient (than silylation alone) for low molecular weight organic
acids, but the relatively low temperature of the protocol may limit the
derivatization of the more difficult to derivatize metabolites,
including secondary carbon compounds that are typical of *Populus*. The
organic acids that are most vulnerable to TMS derivatization alone can
also be captured in other separations and analyses.

*Short-term goal*: Confirm the method(s) that minimizes sample
preparatory time and maximizes spectral output (i.e., maximum
metabolites observed) of the *Populus* species under investigation. The
protocols must ensure the stability of the derivatized compounds and
reproducibility of the data generated.

*Long-term goal*: Develop standardized extraction and derivatization
protocols that are suitable for complete automation.

# Analytical Techniques for Steady-State Metabolite Analyses

##### High-throughput GC-MS

*Comparison of GC-MS techniques:* The "oldest" and best-established
coupling of methods to MS for metabolite analysis is GC-MS. The
thermo-stable samples are vaporized and then ionized by either
electron-impact (EI) or chemical-ionization (CI). For metabolomics
analyses there are in practice two types of instruments used: 1)
quadrupole (single stage MS and two stage MS/MS (ion trap)) and 2)
time-of-flight (TOF) MS instruments. Both instruments have advantages
and disadvantages, e.g. the quadrupole instruments have large dynamic
range, are robust and easy to use. GC-TOF instruments have the
capability to perform rapid spectral acquisition (up to 4-500 spectra/s
over the full mass range), which results in possibility to speed up the
analyses (high throughput), as narrow, short GC columns are used. The
deconvolution of overlapping peaks is also greatly improved because of
spectral continuity across a peak (no skewing of different masses). A
disadvantage with GC-TOF instruments has been a reduced dynamic range
(in practice) compared to quadrupole instruments, which can be a problem
when analyzing complex samples with high variation in concentrations of
compounds. GC-TOF MS is the approach of choice for high-throughput GC
analyses, but as instrument manufacturers produce new quadrupole
instruments with much higher scan rates, their ease-of-use may make them
more versatile.

##### 1D vs 2D GCxGC-MS

Rapid advancement in two-dimensional (2D) gas chromatography (GCxGC)
makes it a powerful tool when coupled with high-speed TOF-MS for the
deconvolution of metabolites that co-elute in traditional
one-dimensional (1D) GC-MS. The GCxGC-TOF-MS approach couples columns of
different polarity and operated at different temperatures to shift
retention times of co-eluting metabolites. The peak capacity is approx.
equal to the product of the separation capacities of the individual
columns. The eluent from the first column is pulsed into a second
column, generating an array of high-speed secondary chromatograms that
can be detected by the high speed, high capacity time-array detector of
TOF-MS. All of the eluent from the first dimension is subjected to
separation in the second dimension, not just a single congested area of
the chromatogram. The approach can be used for deconvolution to ensure
the proper assignment of fragments to the metabolites being deconvolved,
which can then be incorporated into the data extraction algorithms of
the 1D analyses. As a deconvolution tool, it can be applied with the
introduction of each novel heterogeneous matrix (e.g., a new poplar
species/tissue). Given that the approach requires the second column to
function much faster than the first column, it is unlikely that
GCxGC-TOF-MS will be deployed (in the near-term) as the standard data
acquisition approach for high throughput profiling, until detectors with
even more rapid acquisition rates become available.

##### Library Compilation of Mass Spectral Fragmentation Patterns 

The effective deployment of GC-MS approaches for metabolite profiling
must include the expansion of MS databases to identify unknowns for
establishing data extraction and deconvolution strategies (metabolites
quantified free from co-eluting interference). A greatly enlarged mass
spectral database of EI and CI fragmentation patterns of TMS- and
methoxime/TMS derivatives of metabolites will be required to identify as
many of the large number of metabolites as possible. Up to 70% of the
metabolites in a complex GC-MS analysis are typically unidentified.
Although commercially available GC-MS databases have a large number of
compounds, they have only a small proportion of the large number of
low-molecular weight (\<1200 Da) organic metabolites that need to have
the fragmentation pattern of their methoxime/TMS derivatives
characterized. *Populus* has a large number of unique phenolic
compounds, including salicyl alcohol and higher-order conjugates with
simple phenols (e.g., salicyl alcohol, catechol, 4-ethylphenol),
phenolic acids (e.g., caffeic acid, benzoic acid, salicylic acid), and
their glucosides. Compounds previously reported to be present in *Salix*
sp. have a high likelihood of also being observed in *Populus* sp. Given
that many plant species have some of metabolites in common, the data
emerging from plant species, such as *Arabidopsis* and potato
(*Solanum*), can be exploited to expand the metabolite database of mass
spectra for metabolite profiling that will be an essential tool for
phenotyping. Elucidation of many of the currently unidentified compounds
is imperative and would provide an invaluable resource for metabolite
profiling. Unidentified peaks can be subjected to more detailed
characterization by subjecting the same samples to CI by using methane
plasma to generate a milder ionization source, increasing the
probability of observing the molecular ion ((M+1)^+^, (M-1)^-^). CI is
of great benefit to identify the eluting peaks by confirming molecular
weight and aiding in attempts to deconvolve overlapping peaks by
simplifying the spectra (i.e., minimal fragments).

*Short-term goals*: Compilation of EI and CI mass spectral fragmentation
patterns of *Populus* metabolites, including identification and
localization of 500 of the most abundant metabolites with respect to key
fragment assignments and retention time of their TMS and methoxime/TMS
derivatives by GC-MS.

*Mid-term goal*: Develop of data deconvolution and extraction
algorithms/strategies to accurately quantify metabolite concentrations
free from co-eluting interference.

*Long-term goal*: Develop a centralized, web-based searchable repository
of EI and CI mass spectral fragmentation patterns of TMS and
methoxime/TMS derivatives of known *Populus* metabolites.

**LC-MS**

In recent years, protocols for assessing metabolites using LC coupled to
MS have been established with high analytical precision and sensitivity.
Profiling methods based on this approach have been established for
isoprenes, alkaloids, phenylpropanoids, glucosinolates, flavonoids,
saponins and oxylipins. However, polar, non-volatile samples and LC
conditions require special ionisation techniques:

**ES**. Electrospray ionization (ES) is the optimum method of ionizing
the widest range of polar metabolites. Initially the sample is dissolved
in a solvent where, to a certain extent, it will exist in an ionized
form, e.g. \[*M*­H\]^+^ (\[*M*-H^-^\]). In conventional ES the solution
is then pumped through a thin capillary, which is raised to a high
potential. Small charged droplets are sprayed from the ES capillary into
a bath gas at atmospheric pressure and travel down a pressure and
potential gradient towards an orifice in the MS high-vacuum system. As
the droplets traverse this path they become desolvated and reduced in
size until the point is reached that either an ion desorbs from a
droplet or solvent is completely removed. The result of ES is a beam of
ions, which are sampled by the mass spectrometer. ES is a concentration-
rather than a mass-dependent process, and improved sensitivity is
obtained for high-concentration low-volume samples, which has led to the
development of low-flow-rate ES (e.g. nano-HPLC).

**MS/MS**. MS combined with ES was developed from single analyzer
systems to very complex MS/MS couplings. Principle: In an MS/MS
experiment a precursor ion is mass-selected by mass analyzer 1 (MS^1^)
and focused into a collision region preceding a second mass analyzer
(MS^2^). The process can be continued for several further steps (MS^n^).
The mass analyzers are arranged in series either in space (sector,
triple quadrupole and hybrid instruments) or in time (trapping
instruments). Inert gas is generally introduced into the collision
region and collisions occur between the precursor ion and inert gas
atoms (molecules). In these collisions part of the precursor ion\'s
translational energy can be converted into internal energy, and as a
result of single or multiple collisions an unstable excited state is
populated. Excited precursor ions decompose to product ions in a process
termed "collision-induced dissociation" (CID). Product ions are
mass-analyzed by MS^2^. There are also further alternative methods of
precursor-ion dissociation that include surface-induced dissociation
(SID), black-body infrared radiative dissociation (BIRD) and
electron-capture-induced dissociation (ECD).

**Hybrid MS/MS**. While each type of MS/MS instrument has its own
strengths and weaknesses, by combining analysers in a hybrid
conformation, an attempt was made to accentuate positive features while
canonizing the negative ones. The hybrid MS/MS instruments that use a
quadrupole as MS, and an orthogonally arranged reflectron TOF as MS
(e.g. *Q-TOF*), are also commercially available and holds promise in
metabolomic profiling.

**Direct Infusion (DI)-MS and MS/MS** can be developed as a
next-generation high-throughput screening tool for characterization of
the large number of silent phenotypes, as an initial screen, without the
use of time-demanding chromatographic resolution. A wide array of
metabolites can be directly injected (infused) into a tandem MS that is
operated with multiple ionization modes. Samples can be quickly (30
sec/analysis) subjected to both APCI and ES in the positive and negative
ion modes to ionize different components within the heterogeneous
matrix. The analysis can especially target metabolites that are
otherwise typically difficult to derivatize or analyze by GC-MS (for
example), because of large size (500-1500 Da), too many reactive
functional groups, and low volatility.

**FTICR.** Another direct infusion MS technology is Fourier transform
ion cyclotron MS, wherein extracts are infused into MS instruments using
soft ionization techniques to gain fingerprints of molecular ions
present in an extract. FTICR instruments show promise in that they also
provide exceptional high resolution (HR) and mass accuracy. The power of
this technique relies on the presence of a mass analyzer capable of
generating mass data that is sufficiently accurate for determination of
definitive empirical formulae for several hundred ions.

*Short-term goals*: Establish standardized LC-MS analyses for
metabolomics, progressing towards high throughput rates by developing
direct infusion analyses (e.g., DI-nanoES, FTICR). Although LC-MS
fragmentation patterns are minimal with respect to fragment generation,
compilation of available ES spectra, including retention time and key
fragment assignment (molecular ion in \[*M*­H\]^+^ (\[*M*-H^-^\] forms
most likely) would promote metabolomic analyses by targeting those
classes of compounds that are not readily analyzed by GC-MS approaches.

*Mid-term goal*: Analyses must move beyond simply detecting metabolites,
but approaches and strategies developed in tandem to deconvolute the
complex spectral output. Therefore, data analyses should also approach
the throughput rates at which the analyses are conducted.

*Long-term goal*: Once the protocols are refined, a fully automated
robotic sample preparation and handling systems can be developed for
even greater sample throughput.

**Pressure-assisted chemical electrophoresis-MS** (PACE-ES-MS) is
ideally suited for the efficient separation and analysis of highly
polar, charged metabolites (e.g., polyphosphorylated compounds,
adenylates (e.g., ATP, ADP), nucleotides (e.g., NADP, NAD, FAD), and
coenzymes). The mass detection limit with a sheath-flow interface is ca.
10 to 100 fmol. Sensitivity can be increased by converting to a
sheathless interface between the PACE and the MS ion trap. The approach
will capture the many phosphorylated carbon intermediates, and can
provide assessments of metabolite flux, when coupled with tracing stable
isotope-labeled precursors and intermediates through pathways with MS
and NMR analyses of isotopomers, as discussed below.

*Short-term goals*: Demonstrate utility of PACE-MS to profile
phosphorylated carbon intermediates and coenzyme A (CoA)-bound
intermediates, and trace stable isotope-labeled precursors and
intermediates through pathways over time to determine metabolite flux
rates.

*Long-term goal*: Increase throughput by the development and
incorporation of chip technology and/or instruments with large numbers
of capillaries. Once the technologies are refined, a fully automated
robotic sample preparation and handling system can be incorporated for
even greater sample throughput.

# Transitioning from Steady-State Analysis of Metabolites to Metabolic Dynamics

One of the most exciting but daunting challenges in conducting global
metabolic analyses are to create tools that facilitate the transition
from quantifying static pools of metabolites to analyzing metabolite
dynamics. Four approaches that are essential for analyzing metabolite
dynamics are: 1. temporal quantification of metabolites in living
tissues, 2. spatial analysis of metabolites in tissues, 3. experimental
analysis of flux through one or more metabolic pathways, and 4. modeling
of metabolic flux using bioinformatics tools.

*In vivo* NMR is a non-invasive, non-destructive technique for both
qualitative and quantitative analysis of ^13^C- and ^15^N-labelled
compounds, making it the procedure of choice to quantify metabolites in
live, intact tissues, as well as to trace the metabolic fate of labeled
compounds in real time. Recent studies indicate that it may also be
feasible to modify existing *in vivo* NMR protocols to quantify
metabolites by means of endogenous isotope levels. *In vivo* NMR
technology has been used to study several plant species, including
forest trees. However, there are a plethora of variables that affect the
detection of high-resolution spectra, and there is substantial methods
development to be done, including the lowering of detection limits, in
order to proficiently examine trace regulatory metabolites in different
poplar tissues.

It will also be important to investigate emerging instrumentation and
technologies for *in vivo* spatial and temporal metabolite analyses.
Novel imaging techniques, currently being developed within the medical
bioengineering domain, such as *in vivo* positron emission tomography,
hold considerable promise for quantifying metabolic dynamics in living
cells, tissues, and organs.

*Short-term goals*: Develop standardized protocols for in vivo NMR
analysis of different poplar tissues. Develop freely-available libraries
of peak assignments.

*Mid-term goals*: Develop protocols for improved resolution and improved
detection of metabolites using in vivo NMR.

*Long-term goals*: Exploit emerging imaging instrumentation for enhanced
resolution real-time spatial and temporal metabolite analyses.

In addition to experimental protocols and instrumentation,
bioinformatics tools are needed to model metabolic flux. These tools
would either be selected from pre-existing tools, or created *de novo*.
The tools will need to be parameterized for the pathways of interest,
and then validated with real data. Eventually, it will be advantageous
to be able to overlay transcript and protein profiling data onto
metabolic flux models.

*Short-term goals*: Assess the utility of various existing programs for
modeling metabolic flux.

*Mid-term goals*: Develop new programs or customize existing programs
for modeling metabolic flux. Create a database for publicly available
metabolite flux data.

*Long-term goals*: Integrate metabolic flux modeling program with a
poplar Pathway/Genome Database.

# Data Analysis 

## Chemometrics and Visualization of Metabolite Data

Metabolomics projects generate large sets of data, and the accepted way
of comparing large data sets is to use different multivariate tools,
e.g. principal component analysis (PCA) or partial least squares
projections to latent structures (PLS). PCA is an unsupervised method
where no "a priori" knowledge of the class of samples is needed, and it
is based on calculation of latent variables. The principle components
are linear descriptions of the original descriptors, and are
uncorrelated. The components also describe decreasing amount of data
variance, i.e. P~1~\>P~2~ and more. PCA will show the best
representation of metabolic variation to be described in a limited
number of dimensions. PLS is supervised method so that the class of a
sample from an independent data set can be predicted on the basis of a
series of models that are derived from the original data. This will help
maximize the separation between classes, but also enable the data
validation. Although the use multivariate statistical tools for
evaluation of data are critical to handling all the generated data, it
is also important develop tools for visualization of obtained data. The
possibility to interpret differences between samples is dependent on
describing metabolic differences in visually simple ways, relating
relative differences with known metabolic pathways to pinpoint impacted
steps (reactions).

*Short-term goals:* To establish a common strategy for evaluation of
data by using multivariate statistical tools as PCA and PLS when
appropriate. To establish tools to rapidly visualize metabolic
differences.

*Long-term goals:* To establish a web-based (or software based) system
for automatically compare large series of e.g. GC-MS and/or LC-MS data.
A web-based tool where the [identified]{.underline} metabolic
differences observed can be visualized.

**Systems Integrations Approach to Functional Genomics**

While the focus of this first 5-year Poplar Science Plan is on tool and
resource development, these tools and resources are being developed with
an eye towards functional genomics, which is the projected goal of the
second 5-year Plan. At the heart of functional genomics is delineating
the function of genes identified via sequencing projects. For genes
encoding enzymes, the most basic descriptors of function are enzymatic
activity, substrate(s) and product(s). Thus, together with transcript
profiling and protein analysis, global metabolite analysis represents
one of the three cornerstones to delineating gene function.

It follows that integrating sequence data with transcriptomic,
proteomic, and metabolomic data will be central to assigning function to
genes. Bioinformatics tools such as Pathway/Genome Databases combine
sophisticated database capabilities with gene ontology in order to
permit integration of transcriptomic, proteomic, and metabolomic data
with annotated whole genome sequence data. These tools often use
graphical representations of metabolic networks as a visual, interactive
interface for data integration and data mining. Thus, graphical,
interactive metabolic networks form an important anchor for these
integrative databases. The overall goal for this 5-year Science Plan is
to create an interactive, queriable version of Pathway/Genome Database
for Poplar.

*Short-term goals*: Create PoplaCyc, a Pathway/Genome Database
containing only metabolic and biosynthetic pathways known (or suspected)
to exist in poplar.

*Mid-term goals*: Add interactive functions to PoplaCyc to enable
visualization of genomic, transcriptomic, proteomic, and/or metabolomic
data within the graphical environment of PoplaCyc, and to allow for
integration of datasets. Steps will include: a. Annotate PoplaCyc with
queriable *Populus* locus identifiers such as genomic sequence IDs and
GenBank accession numbers. These locus identifiers will be linked to
databases containing *Populus* gene function information, such as Gene
Ontology. b. Enable links between queriable *Populus* locus identifiers
in PoplaCyc and *Populus* locus identifiers in microarray datasets,
permitting gene expression data to be interactively overlaid onto
PoplaCyc. c. Enable links between metabolite identifiers in PoplaCyc and
metabolite identifiers in metabolite analyses datasets.

*Long-term goals*: 1. Add previously unknown metabolites and
undetermined metabolic pathways to PoplaCyc. 2. Add cellular activities
to PoplaCyc, such as signal transduction, transmembrane molecular
transport, and molecular trafficking. 3. Link PoplaCyc to public
databases of gene expression and metabolite datasets.

Strategies: PoplaCyc could be created using available tools such as
Pathologic, which was used to create MetaCyc, a compendium of metabolic
networks, and the species-specific BioCyc databases, such as AraCyc
(*Arabidopsis*). AraCyc should serve as a model for PoplaCyc. It would
be important to work with the developers of Pathologic and AraCyc. It
will also be necessary to attract the necessary bioinformatics
specialists to achieve these goals in a timely manner.

Potential Future Applications: PoplaCyc has the potential to evolve into
a pivotal, higher-order bioinformatics tool that allows for easy
integration of other diverse metabolic and genomic data to create a
"virtual interactive tree". Such a tool will allow for *in silico*
dissection of complex events such as responses to environmental
perturbation.