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EMBO reports 4, 10, 989–993 (2003)
doi:10.1038/sj.embor.embor944
AOP Published online: 12 September 2003
Parallel analysis of transcript and metabolic profiles: a new approach in systems biology
Ewa Urbanczyk-Wochniak, Alexander Luedemann, Joachim Kopka, Joachim Selbig, Ute Roessner-Tunali, Lothar Willmitzer & Alisdair R. Fernie
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Max-Planck-Institut für Molekulare
Pflanzenphysiologie, Am Mühlenberg 1, 14476
Golm, Germany
To whom correspondence should be addressed
Alisdair R. Fernie Tel: + 49 331 567 8221; Fax: + 49 331 567 8408;
fernie@mpimp-golm.mpg.de
Received 6 May 2003; Accepted 20 August 2003; Published online 12 September 2003.
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Abstract
The past few years in the medical and biological sciences have been
characterized by the advent of systems biology. However, despite the well-known
connectivity between the molecules described by transcriptomic, proteomic and
metabolomic approaches, few studies have tried to correlate parameters across
the various levels of systemic description. When comparing the discriminatory
power of metabolic and RNA profiling to distinguish between different potato
tuber systems, using the techniques described here suggests that metabolic
profiling has a higher resolution than expression profiling. When applying
pairwise transcript–metabolite correlation analyses, 571 of the 26,616
possible pairs showed significant correlation, most of which was novel and
included several strong correlations to nutritionally important metabolites. We
believe this approach to be of high potential value in the identification of
candidate genes for modifying the metabolite content of biological systems.
EMBO reports 4, 10, 989–993 (2003)
doi:10.1038/sj.embor.embor944
AOP Published online: 12 September 2003
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Introduction
Information flow in biological systems follows the sequence DNA to RNA
to protein, with gene function generally described by the latter. Most current
genomic research is carried out at the DNA and RNA levels; however, it is
increasingly clear that a wide range of post-translational factors have
functional importance in the cell. The current focus on the nucleic acid level
is in part due to the fact that mature technologies are available that allow
the sequencing (The Arabidopsis Genome Initiative,
2000; International Human Genome Sequencing
Consortium, 2001) and analysis of the expression levels of complete
genomes (or large proportions thereof) at the transcript level (Lockhart et al., 1996), whereas strategies to allow
similar comprehensive studies of the protein and metabolite complements of the
cell are still at relatively early stages in their development. However, rapid
progress has been made in proteomic (Shevchenko et
al., 1996) and metabolomic (Fiehn et
al., 2000; Roessner et al.,
2001) analyses in recent years, and combined studies of the
transciptome and a subset of the proteome have been carried out in
Saccharomyces cerevisiae (Futcher et al.,
1999; Gygi et al., 1999;
Ideker et al., 2001a), facilitating the use
of systems-biology approaches (Ideker et al.,
2001b; Kitano 2002; Oltvai & Barabasi, 2002).
Here, we describe the parallel analysis of potato tuber systems using
a recently established platform for metabolic profiling based on gas
chromatography–mass spectrometry (GC–MS) analysis in combination
with the parallel analysis of gene-expression data using classical array
technology. In carrying out these experiments, we set out to answer two
questions: first, what is the relative power of the two phenotyping
technologies to discriminate biological systems that either differ in
developmental state or show well-characterized changes in response to the
expression of transgenes? Second, can the combined analysis of transcript and
metabolite profiling data present a new and meaningful approach in the
identification of candidate genes for changing the metabolic composition of a
biological system?
Results and Disscusion
When we designed these experiments, we decided to use a crop-plant
system, the potato tuber system, to answer the above questions. The reasons for
this were twofold: first, the potato tuber has well-defined but nevertheless
highly related developmental stages and allows the assessment of several
well-characterized transgenic situations; second, our group is experienced in
the analysis of the potato tuber on both the biochemical and molecular levels
(Fernie et al., 2002). As a first step, the
expression of 1,000 genes (represented by at least two expressed sequence tags
(ESTs), representing many transcription factors and a wide variety of
biosynthetic genes) was analysed across development and in two transgenic
situations using a custom array. After analysing the data sets obtained, we
selected 279 ESTs that gave highly reproducible results with respect to the
consensus sequence from which they were derived. We next performed a
principal-component analysis (PCA) to determine whether various developmental
stages could be discriminated from one another and from the transcript profiles
of potato tubers that ectopically express a more efficient pathway of sucrose
mobilization (Roessner et al., 2001). As
shown in Fig. 1A, some of the developmental stages can be
readily distinguished from each other on the basis of their transcript
profiles. Most notable in this respect is the clear differentiation of tubers
harvested ten weeks after transfer to the greenhouse from the other
developmental stages. The exact reason for this discrimination is unclear.
Examination of the upregulated clones suggests a higher metabolic activity at
this stage; however, this finding is not consistent with changes in the
metabolite levels documented in identical samples (see below). Despite the fact
that these results are surprising, they are consistent for all three replicates
harvested at this time point.
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Figure 1
Principal-component analysis of gene-expression and metabolite
profiles of genetically and temporally distinct systems. (A)
Gene-expression profiles. (B) Metabolite profiles. The distances between
these populations were calculated as described in Roessner
et al. (2001). The percentage of variance explained by each
component is shown in parentheses. The transgenic lines INV2-30 (INV) and SP 29
(SP) are represented by black circles, and wild-type samples harvested 8, 9,
10, 13 and 14 weeks after transfer to the greenhouse are represented by
coloured circles. Each data point represents an independent sample.
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We also obtained the surprising result that the transcript levels of
the transgenic systems could not be discriminated either from each other or
from the corresponding wild-type tuber samples. It is important to note here
that a similar situation was seen after PCA of the entire transcript data set
(2,200 ESTs). Although it is clear that the tuber samples harvested ten weeks
after transfer to the greenhouse were markedly different from those harvested
at other developmental stages, the fact remains that transcriptional variation
during development is greater than that after a relatively severe genetic
perturbation of primary metabolism (Roessner et al.,
2001).
Metabolic profiling was then carried out on identical samples to
determine the levels of the major metabolites of primary metabolism, including
sugars, sugar alcohols, organic acids, amino acids, as well as the
nutritionally important ascorbate and tocopherol. When a PCA was carried out on
the data set obtained from the metabolic profiling studies (Fig.
1B), a different situation was observed from that seen on analysis of
the transcript data. In this case, the transgenic events clustered completely
independently (both from the wild-type control and from each other);
furthermore, on the basis of their metabolite complement, samples taken after
ten weeks of growth were similar to those taken at other time points. As with
the results presented for transcript levels, we believe that these data have
important ramifications for the potential risks associated with transgenic
organisms and theories of substantial equivalence (Kuiper
et al., 2001; Trewavas & Leaver,
1999).
As stated above, this study was started with two main objectives.
First, we wanted to compare the discriminatory power of both profiling
approaches as described above. Second, we were interested in using the combined
evaluation of both analyses as a new approach in experimental systems biology
(for a definition, see Sweetlove et al.,
2003). As a first step, we decided to run all data points through
pairwise correlation analysis, determining for each transcript whether it is
correlated to any of the metabolites. Of the 26,616 pairs analysed, 363
positive and 208 negative correlations were seen, the total number of 571
correlations being well above the number of correlations that might be expected
by chance (266 at p < 0.01). Several representative examples of
transcript–metabolite correlations are shown in Fig.
2 and are discussed in detail below, and the entire list is available in
the supplementary information
online.
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Figure 2
Correlation between metabolite and transcript levels of the analysed
systems. All correlations between the relative levels of chemically defined
metabolites and those of each selected clone were assessed. All correlations at
p = 0.01 when assessed by Spearman's rank-order correlation are given in
the supplementary information
online. Several representative examples are given here (all are plotted using
the logarithmic scale). rs-values are given in parentheses.
(A) Sucrose transporter versus sucrose (rs =
-0.52). (B) glutamate decarboxylase versus 4-aminobutric acid
(rs = 0.55). (C) Tryptophan synthase  -chain 1
versus tryptophan (rs = 0.61). (D) Tryptophan synthase
-chain 1 versus tyrosine (rs = 0.65). (E)
Ornithine carbamoyltransferase versus serine (rs = 0.53).
(F) Ornithine carbamoyltransferase versus cysteine (rs
= 0.54). (G) Aminotransferase-like protein versus fructose-6-phosphate
(rs = 0.85). (H) Aminotransferase-like protein versus
glucose-6-phosphate (rs = 0.85). (I) Glutamate
decarboxylase versus spermidine (rs = 0.64). (J)
Glutamate decarboxylase versus tyrosine (rs = 0.63).
(K) CONSTANS-like protein versus ascorbate (rs =
-0.72). (L) Succinyl-co-enzyme-A synthetase  -subunit versus
tocopherol (rs = -0.64). (M) Transcription
factor WRKY6 versus lysine (rs = 0.51). (N)
S-adenosyl-L-methionine synthetase versus lysine (rs =
0.6). (O) Ornithine carboxyltransferase versus lysine
(rs = 0.54). (P) Caffeoyl-co-enzyme-A
O-methyltransferase versus lysine (rs =
-0.52).
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First, as with any new approach, it is important to determine whether
the data obtained are in agreement with observations made using different
experimental strategies. This is clearly the case, with a strong negative
correlation between sucrose and sucrose transporter expression (Fig. 2A), and a strong positive correlation between
4-aminobutyric acid and glutamate decarboxylase isoform I (Fig.
2B). As both of these relationships have been reported previously
(Vaughn et al., 2002; Facchini et al., 2000), these examples provide
confirmation of the validity of our approach. Second, many further correlations
seem to have a functional basis that can be explained retrospectively. The
positive correlations of both tryptophan and tyrosine with the 2-chain of
tryptophan synthase (Fig. 2C,D), and of ornithine
carbamoyltransferase with serine and cysteine (Fig.
2E,F), are two such examples. Third, although several of the
correlations, such as those described above, were predictable, most of the
correlations obtained using this approach were novel and not directly related
to the biochemical pathway in which the gene products participate. Although it
is clear that many of these correlations are due to chance, the fact that
several correlations occurred between transcripts and metabolites from the same
or related pathways might strengthen the interpretation of the linkages. An
example of this includes aminotransferase, which correlates with both
fructose-6-phosphate and glucose-6-phosphate (Fig. 2G,H).
Such comparisons may offer hints as to the functions of the genes involved (the
elucidation of which is a subject that requires a huge amount of research
effort). It is also interesting to note that several transcripts correlate with
more than one metabolite, as highlighted above for the aminotransferase. Other
examples of this include glutamate decarboxylase isoform I, which correlates
with both spermidine (Fig. 2I) and tyrosine (Fig. 2J), and the same is also true for 4-aminobutyric acid and
tryptophan, and various transcription factors. Finally, it is exciting to see
that nutritionally important metabolites, such as ascorbate, tocopherol and
lysine, were closely correlated to the expression levels of various genes or
transcription factors: ascorbate was negatively correlated with a homologue of
the clock gene CONSTANS (Fig. 2K); tocopherol was
negatively correlated with succinyl-co-enzyme-A synthetase (Fig.
2L); lysine was positively regulated by the transcription factor WRKY6
(Fig. 2M), S-adenosyl-L-methionine synthetase
(Fig. 2N) and ornithine carbamoyltransferase (Fig. 2O), and was negatively regulated by caffeoyl-co-enzyme A
O-methyltransferase (Fig. 2P). These unexpected
results only reveal correlated parameters and do not allow the identification
of causality between the transcript and metabolite. Although many possible
explanations for such metabolite linkages can be postulated, including unknown
metabolic links between pathways (such as, for example, the sharing of a common
cofactor; or, in the case of transcription factors, interactions with
previously undescribed regulatory genes), further, more direct experimentation
is required to elucidate the mechanisms that underlie the correlations
presented here. Nevertheless, we believe these essentially unexpected
correlations to be of great potential use for biotechnological applications in
which the goal is the modification of metabolite compositions through genetic
means. Although these data do not, in any way, prove causal or even mechanistic
links between molecular entities, the approach of linking transcript and
metabolite data through pairwise correlation analysis provides a powerful tool
for the rapid identification of candidate genes.
In conclusion, the discriminatory powers of transcript and metabolite
profiling approaches are different, with metabolite profiling allowing greater
resolution of the different systems studied here. Whether this reflects the
fact that changes at the transcript level are less pronounced as compared with
changes at the metabolite level or merely highlights limitations of the
profiling technologies used, remains an open question. It is possible that this
result merely reflects low sensitivity of ESTs as probes and that profiling
using full-length complementary DNAs (Seki et al.,
2001), or oligonucleotide-specific probes would allow greater
descrimination. However, whatever the reason, these results suggest that the
discrimination of biological systems should be performed at more than one
level, something that is of particular importance in the establishment of
substantial equivalence between transgenic and conventional crops. The second
main conclusion of our work is that although the number of strong
metabolite–transcript correlations was relatively small, as would be
expected from previous experimental work aimed at comparing transcript and
protein levels (Futcher et al., 1999;
Gygi et al., 1999; Ideker et al.,
2001) and mathematical studies (ter Kuile & Westerhoff,
2001), we believe they allow the generation of clear, testable
hypotheses. Of particular interest in our data set was the correlation of genes
with the essential amino acid lysine and with the vitamins ascorbate and
tocopherol, as these linkages define candidate genes for the manipulation of
the content of these nutritionally important compounds in plants. To our
knowledge, only the combination of the two platforms presented here is able to
provide such novel hypotheses; therefore, the pairwise correlation between
transcripts and metabolites is a potentially powerful new tool for hypothesis
creation in systems biology.
Methods
Materials.
Solanum tubersosum L. cv. Desiree was obtained from Saatzucht
Lange AG. The generation of the transgenic plants used in this study has been
described previously (Sonnewald et al.,
1997; Trethewey et al., 2001).
Plants were handled as described in the literature (Tauberger et al., 2000; Regierer
et al., 2002) and harvested at time points described in the
figure legends.
Gene-expression analysis.
Microarrays were constructed on nylon filters as described
previously (Thimm et al., 2001;
Colebatch et al., 2002). More than 2,200
tomato clones were collected from cDNAs constructed in the laboratories of S.
Tanksley, G. Martin and J. Giovannoni and provided by The Institute for Genomic
Research. These ESTs correspond to approximately 1,000 tomato genes, which are
highly homologous to those from potato (Fulton et al.,
2002). Microarray analysis was carried out as described prevously
(Thimm et al., 2001; Colebatch et al., 2002), and full details are given
at
http://www.mpimp-golm.mpg.de/willmitzer/zusaetzlich/index-e.html.
Processing of array data.
The 'raw' data sets obtained from microarray analysis were submitted
to our in-house database
(http://www.mpimp-golm.mpg.de/haruspex/index-e.html). In this
database, the raw data were annotated, rearranged and normalized, as described
previously (Thimm et al., 2001;
Colebatch et al., 2002). The normalized data
were then subjected to Grubbs' test (Grubbs, 1969;
Stefansky, 1972) to detect outliers in the data
set.
Metabolite analysis.
The preparation and derivation of samples for metabolite analysis,
the operation of the GC–MS, the evaluation of the resultant chromatograms
and the normalization of the results was carried out exactly as described in
Roessner et al. (2001).
Pre-selection of relevant genes.
As there are no EST/gene libraries publically available for
crop-plant systems, including the potato tuber system, the relationships
between ESTs and potential genes were checked. Relevant genes for a meaningful
co-response analysis were required to show significant changes in gene
expression under the experimental conditions analysed, and ESTs representing
the same gene had to show the same hybridization behaviour. We applied
non-parametric Spearman's rank-order correlation and parametric Pearson's
linear-correlation analyses to those EST pairs representing the same gene.
Those genes showing positive correlation at significance threshold p =
0.001 in both analyses were used for further analysis.
Discriminant analysis.
We tested several data transformations, distance measures and
clustering methods on our data set before choosing PCA. PCA was performed
independently on the data sets obtained from transcript and metabolite
profiling with the software package S-Plus 2000 (Insightfull) using the default
weighted covariance-estimation function. PCA is a dimensionality-reduction
method that attempts to compress the original features into the smallest
possible number of components. This is necessary because in analysing
transcript and/or metabolite profiles, dimensionality is often a problem: in
most cases, the number of features (genes and/or metabolites) exceeds the
number of samples considerably. Each principal component is associated with an
eigenvalue, which corresponds to the amount of variability explained by
the corresponding principal component. PCAs are presented as three-dimensional
plots that comprise 86.9% and 79.2% of the metabolite and transcript variances,
respectively.
The data was log10-transformed before further analysis,
and metabolites or ESTs that were not common to all samples were removed. This
transformation reduces the influence of rare high-measurement values, but does
not change the order of the data and, therefore, does not influence
non-parametric analysis methods such as the statistical tests described
below.
Co-response analysis.
Correlated expression of the set of pre-selected as well as
non-selected genes and correlation of changes in metabolite and messenger RNA
levels were detected by non-parametric Spearman's rank-order correlation
analysis, with the significance threshold set at p = 0.01. Rank-order
correlation was chosen because mRNA and metabolite levels can be correlated in
a nonlinear manner. Non-parametric methods for correlation analysis are also
appropriate in our case, because from relatively few data points it cannot be
estimated whether they follow a normal distribution, an assumption required for
application parametric methods such as Pearson's linear-correlation analysis.
Spearman's rank-order method was preferred to Kendall test statistics because
the latter requires more data points.
Supplementary
information is available at EMBO reports online
(http://www.emboreports.org).
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Acknowledgements
We thank K. Koehl for excellent supervision of the plants and T.
Altmann, S. Fischer and S. Kloska for consultancy on array preparation and
analysis. E.U.-W. thanks the Max Planck Gesellschaft for a fellowship.
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