Update on Integrative Studies
Integrative Approaches to Enhance Understanding of
Plant Metabolic Pathway Structure and Regulation1
Takayuki Tohge*, Federico Scossa, and Alisdair R. Fernie
Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany (T.T., A.R.F.); and
Consiglio per la Ricerca e Analisi dell’Economia Agraria, Centro di Ricerca per la Frutticoltura, 00134 Rome,
Italy (F.S.)
Huge insight into molecular mechanisms and biological network coordination have been achieved following the application of
various profiling technologies. Our knowledge of how the different molecular entities of the cell interact with one another
suggests that, nevertheless, integration of data from different techniques could drive a more comprehensive understanding of
the data emanating from different techniques. Here, we provide an overview of how such data integration is being used to aid
the understanding of metabolic pathway structure and regulation. We choose to focus on the pairwise integration of large-scale
metabolite data with that of the transcriptomic, proteomics, whole-genome sequence, growth- and yield-associated phenotypes,
and archival functional genomic data sets. In doing so, we attempt to provide an update on approaches that integrate data
obtained at different levels to reach a better understanding of either single gene function or metabolic pathway structure and
regulation within the context of a broader biological process.
The diversity of metabolites in the plant kingdom is
staggering: a commonly quoted estimate is that plants
produce somewhere in the order of 200,000 unique
chemical structures (Dixon and Strack, 2003; YonekuraSakakibara and Saito, 2009; Tohge et al., 2014). Of these,
only a relatively small subset will be abundant in any
given tissue or any one species (Fernie, 2007); however,
certain species have evolved a particularly rich metabolic diversity, presumably in response to environmental features of their habitat (for examples, see
Futuyma and Agrawal, 2009; Moore et al., 2014; Li et al.,
2015). Given these facts, it is unsurprising that our
current understanding of the metabolic structure of a
large number of pathways remains fragmentary; not to
mention our current views of regulatory mechanisms
underlying metabolite accumulation, which cover, at
best, a very limited fraction of the metabolic network.
This statement is especially true for the highly specialized pathways of secondary metabolism, although a
number of gaps still remain to be filled also concerning
important sectors of plant primary metabolism. As
detailed in other Update articles within this issue, the
adoption of various broad-scale profiling technologies to assess the gene, transcript, protein, and small
molecule complement of the cell has started to mine
this metabolic complexity. Additionally, the same approaches have also started to shed light on the evolution of gene and metabolite regulatory networks across
the plant kingdom. In addition to large-scale profiling
approaches, classical reductionist biochemistry and
reverse genetic approaches retain, in any case, great
1
This work was supported by the Max Planck Society (to T.T. and
A.R.F.) and an Alexander von Humboldt grant (to T.T.).
* Address correspondence to [email protected].
www.plantphysiol.org/cgi/doi/10.1104/pp.15.01006
utility in enhancing our understanding of enzyme
mechanisms (and their regulation) and about the in
vivo functions of enzymes, respectively. To give just a
couple of recent examples from organic acid metabolism, a detailed study of the effect of phosphorylation of
phosphoenolpyruvate carboxylase reveals an important
anaplerotic control point in developing castor bean
(Ricinus communis) endosperm (Hill et al., 2014), while
the enzyme pyruvate orthophosphate dikinase was
recently demonstrated to represent a second gateway
for organic acids into the gluconeogenic pathway in
Arabidopsis (Arabidopsis thaliana; Eastmond et al., 2015).
We aim to provide examples from both primary and
secondary metabolism and to illustrate the power of
such approaches both in (1) gene functional annotation
and (2) enhancing our understanding of the systemslevel response to cellular circumstances. We will additionally discuss recent studies combining genome
sequence data with metabolomics in order to highlight
the utility of such approaches in metabolic quantitative
loci analyses. Finally, we will detail insight that can be
obtained from fusing archived data that can be downloaded from databases with experimental data generated de novo. Given that, as documented previously
(Fernie and Stitt, 2012), a number of complicating factors still exist when attempting such analyses, we will
discuss these on an approach-by-approach basis.
INTEGRATING METABOLITE AND
TRANSCRIPTOME DATA
The earliest integrative approaches with relevance to
plant metabolism featured the combination of data
from transcript and metabolite profiling (UrbanczykWochniak et al., 2003; Achnine et al., 2005; Tohge
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Tohge et al.
et al., 2005). Such studies were initially restricted to
model species for which ESTs or oligonucleotides were
available; early transcriptomics approaches relied in
fact on differential hybridization of complementary
DNA samples to known sequences immobilized on
solid supports. The advent of next-generation sequencing technologies, however, has removed this
barrier, and far more exotic species are beginning to be
studied using this approach (Góngora-Castillo et al.,
2012; Gechev et al., 2013; Li et al., 2015). Two basic
questions are commonly addressed by combining transcript and metabolite data. The first concerns whether a
gene functions within a given metabolic pathway.
When a better characterization of the pathway is achieved,
it becomes fundamental to investigate also the extent
of transcriptional control (except in some cases, for example, regulation by posttranscriptional modifications
of the enzyme and positive/negative feedback regulation by substrates/products) under various physiological conditions and how it is distributed across the
various enzymatic steps.
Initial observations about the role of differential gene
expression in tuning the synthesis of metabolites date
back to the 1990s. Some specific pathways, such as
hormone, glucosinolate, and flavonoid biosynthesis,
were the initial focus of these investigations. For example, differential mechanisms of gene expression
helped clarify in Arabidopsis the involvement of two
different nitrilase genes in regulating the synthesis of
auxin (Bartling et al., 1994). Similarly, the contributions
of gene duplication and inducible gene expression
(differential activation of subsets of biosynthetic genes)
were shown to impact the amount and the composition
of glucosinolates (Kliebenstein et al., 2001). An additional early evidence of the role of specific transcript
accumulation on a metabolic phenotype came from the
elucidation of the role that different regulation mechanisms affecting Trp synthase a and b had on the
amount of 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3one, a natural pesticide synthesized in maize (Zea mays)
leaves (Melanson et al., 1997). Another example of the
coordination between transcripts and metabolite accumulation came from the analysis of maize anthers,
where a strong correlation was found between the expression of a structural gene (flavanone 3-hydroxylase)
and the appearance of specific flavonols (mainly quercetin and kaempferol; Deboo et al., 1995). These same
approaches have also been used to select a number of
candidate genes involved in the biosynthesis of capsaicinoids, a group of vanillylamides conferring pungency to hot peppers (Capsicum spp.). In this case, the
comparison between sweet and hot pepper varieties
facilitated the identification of some placenta-specific,
differentially expressed genes that were directly correlated with the accumulation of capsaicinoids (Curry
et al., 1999). The examples cited above laid the foundation for large-scale studies using the parallel analysis
of transcripts and metabolites. One of the first examples
of this approach focused on the identification of transcripts strongly correlated with the abundance of given
metabolites across tuber development, irrespective of
whether the transcript was associated with the metabolic pathway under question or not (UrbanczykWochniak et al., 2003).
This approach was indeed able to identify some
transcripts that exhibited very high correlation with the
expression of certain genes and, as such, proved effective in identifying a number of candidate genes for
biofortification. By corollary, the same approach can be,
and indeed has been, used to elucidate the variation in
gene-to-metabolite networks following short- and longterm nutritional stresses in Arabidopsis (Hirai et al.,
2004) or to identify metabolic regulators of gene expression (Hirai et al., 2007). Cryptoxanthin, for example, was identified as highly correlating with a broad
number of genes across diverse environmental conditions in Arabidopsis (Hannah et al., 2010), and the organic acid malate was putatively identified (Carrari
et al., 2006) and subsequently confirmed (Centeno et al.,
2011) to be important in mediating the ripening process
in tomato (Solanum lycopersicum). Such current studies
are all examples of the guilt-by-association approach,
which in essence postulates biological entities as being
functionally related if they exhibit strong correlation or
coresponse across a wide range of cellular circumstances. The power of this approach is that, given that it
does not rely on a priori pathway knowledge, it can
have great utility in identifying novel metabolic integration and/or novel regulatory mechanisms (Hirai
et al., 2007; Tohge et al., 2007; Yonekura-Sakakibara
et al., 2008; Tohge and Fernie, 2010). However, a
drawback of the approach is that, in the absence of
subsequent rounds of experimentation, it is difficult to
gain any insight into the mechanistic links underlying
the observed behavior, given that correlation between
biological entities does not always imply causation or
the existence of functional links (Sweetlove and Fernie,
2005; Sweetlove et al., 2008; Stitt, 2013). In this regard, it
becomes imperative to validate the outputs of coexpression analyses with follow-up approaches in order
to prove the existence of putative functional links. Arguably, the greatest advances made to date following
approaches to integrate transcript and metabolite data
have been achieved in gene annotation and the structural elucidation of plant intermediary and secondary
metabolism.
Two early studies of particular note are those from
the Saito and Dixon laboratories investigating Arabidopsis anthocyanin and Medicago truncatula triterpene
metabolism, respectively (Achnine et al., 2005; Tohge
et al., 2005). In the case of the anthocyanin pathway,
prior to the study of Tohge et al. (2005), no late biosynthetic genes involved in anthocyanin decoration
steps had been identified in Arabidopsis, although all
early biosynthetic genes have been characterized by
visible phenotype screening. A combination of transcript and metabolite profiling on a Production of Anthocyanin Pigment1 activation-tagged line alongside
validatory experiments involving both heterologously
expressed enzymes and knockout mutants resulted in
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the identification of five genes and the identification of
up to 11 anthocyanins. Such confirmatory experiments
are essential in order to unequivocally assign gene
function. The combination of reverse genetic strategies
with the characterization of enzyme activity when the
gene is expressed in a heterologous system remains the
gold standard for the molecular identification of novel
enzyme-catalyzed reactions (Tohge et al., 2005; Luo
et al., 2007; Yonekura-Sakakibara et al., 2012). Subsequent follow-up studies have identified some six genes
associated with flavonol metabolism, and some 24
compounds (among 35 compounds found) of this class
have now been identified in Arabidopsis (Tohge et al.,
2007; Yonekura-Sakakibara et al., 2007, 2008, 2014;
Nakabayashi et al., 2009; Tohge and Fernie, 2010; Saito
et al., 2013; Fig. 1). While the expansion of the characterized triterpenoid metabolism in M. truncatula is not
quite so impressive, the study of Achnine et al. (2005)
allowed the functional annotation of 30 different saponins, and currently, over 70 metabolites of this compound class have been identified in M. truncatula
(Pollier et al., 2011; Gholami et al., 2014; Watson et al.,
2015). The utility of this approach is at its greatest for
the relatively unchartered pathways of specialized
metabolism; however, it is worth noting that slight
variations on this strategy independently identified the
gene encoding plant Thr aldolase (Fernie et al., 2004;
Jander et al., 2004) in Arabidopsis and 2,4-dihydroxy-7methoxy-1,4-benzoxazin-3-one glucoside methyltransferase in maize (Meihls et al., 2013). A decade later, the
number of species and pathways for which this approach has been adopted has expanded massively to
include several crops and medicinal plants. Strategies
combining transcript and metabolite profiling have
proved effective in elucidating the structure of several
metabolic pathways involved in the synthesis of primary metabolites, flavonoids, terpenoids, and alkaloids
(Osorio et al., 2011, 2012; Shelton et al., 2012; Lin et al.,
2015).
On a broader level, the combination of transcript and
metabolite profiling has commonly been used for
multilayered descriptions of plant responses, particularly those to abiotic stress (Gibon et al., 2006;
Maruyama-Nakashita et al., 2006; Kusano et al., 2011;
Gechev et al., 2013; Bielecka et al., 2014; Nakabayashi
et al., 2014). In this vein, a number of studies have been
carried out that assess the combined transcript and
metabolite responses to water stress, temperature
stress, light stress, and limitations of nutrient supply
(Urano et al., 2009; Caldana et al., 2011; Kusano et al.,
2011; Nakabayashi et al., 2014). Such studies, while by
nature descriptive, can afford insight into global metabolic variations under certain conditions as well as
identify which pathways are under tight and which are
under loose transcriptional control. Given the highly
interconnected nature and nonlinearity of metabolic
pathways in the global network structure, and even in
the absence of flux profiling data, the integration of
transcriptomics with wide metabolic profiling can, in
any case, narrow down which metabolic steps could
be active under specific conditions. Occasionally,
however, they can also provide more mechanistic information. One prominent example of this is the detailed analysis of several transgenic Arabidopsis lines
with altered flavonoid levels via transcriptomic and
metabolomics analyses, including hormone analysis,
which revealed that the overaccumulation of flavonoids exhibiting strong oxidative capacity in vitro
also confers oxidative stress and drought tolerance
(Nakabayashi et al., 2014; Nakabayashi and Saito,
2015). In addition, a range of developmental processes
have been followed at high resolution by a combination
of transcript and metabolite profiles. Such studies are
dominated by studies of fruit ripening (Zamboni et al.,
2010; Lin et al., 2015; Vallarino et al., 2015) and leaf
development (Pick et al., 2011; Wang et al., 2014);
however, they are not limited to these processes, with
studies also covering the development of various organs, lignin deposition, and the establishment of
arbuscular mycorrhizal symbiosis (Vanholme et al.,
2013; Laparre et al., 2014; Nakamura et al., 2014; Wang
et al., 2014). In this regard, these approaches prove
informative in clarifying the relative importance of
seemingly redundant pathways of biosynthesis and the
degradation of specific metabolites or may also help
to define the role of those primary metabolites (e.g.
g-aminobutyrate) for which a signaling role was hypothesized (Batushansky et al., 2014). For example,
ascorbate biosynthesis, which is one of the well-studied
metabolisms in several higher plants, especially in
Arabidopsis (Wheeler et al., 1998; Gatzek et al., 2002;
Laing et al., 2004; Conklin et al., 2006; Dowdle et al.,
2007), has been revealed as the dominant route of
ascorbate biosynthesis during ripening in tomato
(Carrari et al., 2006). Another example could be found in
the elucidation of the arogenate pathway as an alternative route for Phe biosynthesis (Dal Cin et al., 2011). A
similar approach in Arabidopsis, based on feeding
studies and coexpression analysis, allowed an alternative pathway to be proposed for Lys degradation in
dark-induced senescent leaves (Araújo et al., 2010).
However, despite the fact that these examples illustrate that combined transcriptome/metabolome studies provide increases in our understanding of the
regulation of metabolic networks, we contend that they
remain at their most powerful in gene functional annotation and in the elucidation of species- and/or
tissue-specific metabolic pathway structures.
INTEGRATING METABOLITE AND PROTEOME/
ENZYME ACTIVITY DATA
Less commonly used to date than combined transcriptome and metabolome analyses are combined
proteome and metabolome analyses. They are additionally largely used in a manner analogous to the more
descriptive studies reviewed above. That said, considerable insight into metabolic network structure as well
as into general aspects of metabolic regulation have
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Tohge et al.
Figure 1. Current model of flavonol/anthocyanin biosynthesis in Arabidopsis. Colors are as follows: blue, early biosynthetic genes;
green, flavonol-specific biosynthetic genes; and purple, anthocyanin-specific biosynthetic genes. CHS, Chalcone synthase, At5g13930; CHI,
chalcone isomerase, At3g55120; CHIL1, At5g05270; F3H, flavanone-3-hydroxylase, At3g51240; F39H, flavonoid 39-hydroxylase, At5g07990;
DFR, dihydroflavonol reductase, At5g42800; ANS, anthocyanidin synthase, At4g22880; F3GlcT, flavonoid-3-O-glucosyltransferase,
UGT78D2, At5g17050; A5GlcT, anthocyanin-5-O-glucosyltransferase, UGT75C1, At4g14090; A3Glc299XylT, anthocyanin-3-Oglucoside-299-O-xylosyltransferase, UGT79B1, At5g54060; A5Glc69999MalT, anthocyanin-5-O-glucoside-69999-O-malonyltransferase,
At3g29590; A3Glc699pCouT, anthocyanin-3-O-glucoside-699-O-p-coumaroyltransferase, At1g03940, At1g03495; A3Glc299XylSinT,
anthocyanin-3-O-(299-O-xylosyl)-glucoside-6999-O-sinapoyltransferase, At2g23000; A3Glc699pCouT, anthocyanin-3-O-(699O-coumaroylglucoside-O-glucosyltransferase, At4g27830; FLS1, flavonol synthase, At5g08640; F3RhaT, flavonol-3-O-rhamnosyltransferase,
UGT78D1, At1g30530; F3AraT, flavonol-3-O-arabinosyltransferase, UGT78D3, At5g17030; F7RhaT, flavonol 7-O-rhamnosyltransferase,
UGT89C1, At1g06000; F7GlcT, flavonol 7-O-glucosyltransferase, UGT73C6, At2g36790; OMT1, O-methyltransferase, At5g54160.
been gained in this manner. Here, we will describe eight
studies that illustrate how the integration of proteomic
and metabolomic data sets has been used to inform
our understanding of systems regulation. In the first
of these examples, metabolite data were studied in
parallel to enzyme data (and transcriptomics data)
across varying diurnal cycles in wild-type and a
starchless mutant of Arabidopsis, revealing that rapid
changes in transcripts are integrated over time to generate essentially stable changes in many sectors of
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metabolism (Gibon et al., 2006). The same group went
on to apply this approach to tomato fruit development
and natural variance in Arabidopsis. In tomato, enzyme
profiles were sufficiently characteristic to allow stages
of development and cultivars and the wild species to be
distinguished, but comparison of enzyme activity and
metabolites revealed remarkably little connectivity
between the developmental changes of enzyme and
metabolite levels, suggesting the operation of posttranslational modification mechanisms (Steinhauser
et al., 2010). In Arabidopsis, they documented highly
coordinated changes between enzyme activities, particularly within those of the Calvin-Benson cycle, as
well as significant correlations in specific metabolite
pairs and between starch and growth. On the other
hand, few correlations, and thus low overall connectivity, were observed between enzyme activities and
metabolite levels (Sulpice et al., 2010), but strong links
were seen between starch levels and growth, which we
describe below. In an alternative approach, proteomic
and metabolic data were used merely to extend the
range of molecular entities in order to demonstrate that
fascicular and extrafascicular phloem are isolated from
one another and divergent in function (Zhang et al.,
2010). A similar approach was taken to identify root as
the major organ involved in alkaloid biosynthesis in
Macleaya spp. (Zeng et al., 2013). Three further studies
of note are more similar to that of Gibon et al. (2006) in
that they use a combination of proteomics and metabolomics as a means to define the complex response of
the cell to varying circumstances, be they iron nutrition
in Arabidopsis (Sudre et al., 2013), the drought response in maize xylem (Alvarez et al., 2008), or heat
stress acclimation in the model alga Chlamydomonas
reinhardtii (Hemme et al., 2014). The fact that many of
these studies were published in the last 2 years reflects
the growing uptake of such strategies. That said, in our
opinion, it remains an underexploited research approach to date.
INTEGRATING METABOLITE AND GENOME DATA
Given that the advent of metabolomics more or less
paralleled the release of the first plant genome, the
integration of metabolomics and whole-genome sequence data is perhaps unsurprising. The true potential
of this approach has been realized only within the last
few years; we will not describe it again in detail, given
that it is discussed in a previous correspondence in
Plant Physiology (Fernie and Stitt, 2012). Suffice it to say,
there are considerable complexities in such combinations; tellingly, early studies aimed at computational
prediction of the size of the Escherichia coli metabolome
estimated a complement of approximately 750 metabolites, while subsequent experimental approaches have
revealed many metabolites that were not computed
from the genome (van der Werf et al., 2007). Several
potential reasons could be put forward to explain this
discrepancy (for review, see Fernie and Stitt, 2012;
Tohge et al., 2014); we contend here that an additional
reason to explain this (partial) lack of concordance in
the integrative approaches involving metabolism could
lie within the incomplete annotation of most genomes,
including those of model organisms. However, we believe the most likely reason to be the lack of linear
relationship between genes, their protein products, and
metabolites and, secondly, the fact that most genomes,
even those of model organisms, remain incompletely
annotated. Despite this serious drawback, we hope
to illustrate in this section that the integration of
metabolomics and genomic data can be incredibly
powerful in understanding natural variation in metabolism and its regulation.
Whole-genome sequences are available for more than
100 plant species (including microalgae; Tohge et al.,
2014); this massive acceleration afforded by nextgeneration technologies cannot currently be matched
by metabolomics, especially if high-quality speciesoptimized approaches are adopted (Fukushima et al.,
2014). The KNApSAcK database, which is one of the
largest curated compendia of phytochemicals, contains
over 700 compounds for early sequenced plants like
Arabidopsis and rice (Oryza sativa) but no entries for
recently sequenced species such as goatgrass (Aegilops
tauschii) and wild tobacco (Nicotiana tomentosiformis).
In this section, we will describe insight gained from
combining metabolomic data with genome sequences
in three different case studies: (1) a simple comparison
of a reference genome with metabolomics data; (2) a
comparison of natural allelic and metabolic variance;
and (3) integrating genome sequence data into quantitative genetics approaches. The first of these has been
covered in considerable detail recently (Fukushima
et al., 2014; Tohge et al., 2014), so we will only briefly
describe it here. The starting point is to perform
genome-wide ortholog searches using functionally annotated genes; best practice is to use cross-species
cluster-based BLAST searches such as those housed in
the PLAZA database (Proost et al., 2009) or, in the case of
photosynthetic microbes, pico-PLAZA (Vandepoele et al.,
2013). Illustrations of how such analyses have been
performed for central, shikimate, phenylpropanoid,
terpenoid, alkaloid, and glucosinolate metabolism have
been presented (Hofberger et al., 2013; Tohge et al.,
2013a, 2013b, 2014; Cavalcanti et al., 2014; Boutanaev
et al., 2015). Thereafter, comparison of these gene inventories with metabolite profiles of the species under
evaluation allows the construction of putative metabolic pathway structures that can be further tested via
reverse genetics or heterologous expression, as described in “Integrating Metabolite and Transcript Data”
above. Important insights into pathway evolution can
be gained from such approaches, as illustrated by the
recent cross-kingdom comparison of ascorbate biosynthesis (Wheeler et al., 2015).
The second case study, that of evaluating allelic and
metabolic variance across natural diversity, is similar in
scope yet far more targeted than genome-wide association studies, which we describe below. The majority of
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recent examples of its utility come from the analysis of
wild species tomato; however, it is important to note
that the approach itself is essentially just a modification
of that adopted over decades in the cloning of natural
color mutants (Fernie and Klee, 2011). In the last few
years, understanding of primary as well as secondary
and cuticular cell wall metabolism has been enhanced
considerably via this approach (Schauer et al., 2005;
Matas et al., 2011; Kim et al., 2012, 2014; Koenig et al.,
2013), albeit the greatest insight into the latter was
ultimately elucidated via the use of an introgression line
population, as described below. In essence, this approach starts with the identification of metabolic variance within a population of ecotypes, cultivars, or
similarly related species and attempts to link this with
allelic diversity or gene duplication, as has been
achieved for acyl-sugar metabolites (Schilmiller et al.,
2015), terpenes (Matsuba et al., 2013), and isoprenoids
(Kang et al., 2014), or even with the presence or absence
of genes, as described recently for methylated flavonoids of glandular trichomes (Kim et al., 2014). The
preceding list documents the success of this approach;
until recently, however, it was constrained by the limits
of our a priori knowledge, which is needed in order to
select the candidate genes in which we search for allelic
variance. The development of RNA sequencing technologies means that we are no longer limited by the
amount of sequence data; a potential hurdle to these
integrative approaches, however, can still be present
when comparing highly genetically divergent individuals, since the number of genetic polymorphisms is
too great to evaluate one by one. For this reason, the
quantitative trait loci approach is a powerful alternative
method of associating phenotypes to their underlying
genetic variance. The use of such approaches in plant
metabolism has been the subject of several recent
comprehensive reviews (Kliebenstein, 2009; Scossa
et al., 2015); however, we will provide a couple of
examples of their utility for advancing the understanding of metabolite accumulation and metabolic
regulation.
The fruit of tomato, as the model species for ripening of fleshy fruits, has been the subject of combined
large-scale genomic, physiological, and metabolic investigations, often making use of specific biparental
populations or large sets of unrelated individuals, in
an attempt to understand the causal variants of the
metabolic variations (Schauer et al., 2005; Lin et al.,
2014; Sauvage et al., 2014). In particular, the use of a
population of introgression lines, obtained from the
cross between tomato and Solanum pennellii (a wild
tomato species), has greatly aided the identification of
quantitative trait loci for a large number of physiological and metabolic traits. Profiling data of primary
and secondary metabolites in this population were
collected over several years (along with some classical yield-related traits), revealing more than 1,500
metabolic quantitative trait loci affecting the levels
of several sugars, amino acids, organic acids, vitamins, phenylpropanoids, and glycoalkaloids. The
availability of the sequences of both parental genomes
(Bolger et al., 2014) narrowed down the origin of the
metabolic variation to specific genetic polymorphisms
in some selected metabolic quantitative trait loci
(Quadrana et al., 2014; Alseekh et al., 2015). The integration between genotypic and metabolic variance can
be, and has actually been applied, also on large collections of unrelated individuals (metabolite-based
genome-wide association studies): as in the case of
biparental populations, also with this strategy, several
cases of polymorphological variants of genomic sequences have been identified and related to metabolic
variation. These two approaches, based either on biparental populations or on large collections of natural
accessions, have been used in Arabidopsis and crop
species (maize, rice, wheat [Triticum aestivum], and fruit
trees; Gong et al., 2013; Li et al., 2013; Wen et al., 2014;
Matsuda et al., 2015; for review, see Luo, 2015). The
boon that new sequences will provide, especially from
wild relatives or locally adapted varieties, will be represented by the possibility to dissect the genetic basis of
metabolite variation, with a view to introgress beneficial traits in crop improvement.
INTEGRATING METABOLITE AND
PHYSIOLOGICAL DATA
While the above examples concentrate on the integration of various types of profiling data with one
another in order to advance our understanding of
metabolic pathway structure and/or metabolic regulation, relatively few studies have attempted to correlate metabolite content with physiological data,
including growth and yield (for review, see Stitt et al.,
2010; Carreno-Quintero et al., 2013). One of the earliest
studies to do so was the above-described metabolic
quantitative trait loci analysis of the S. pennellii introgression lines, in which yield-associated plant traits
were measured alongside primary metabolite content
of the fruit (Schauer et al., 2006). In this study, network
analysis based on cartographic modeling algorithms
developed by Guimerà and Nunes Amaral (2005)
identified that yield-associated traits were positively
correlated to a range of previously defined signal metabolites, compounds that have signaling as well as
metabolic functions, including Suc, hexose, and inositol
phosphates, Pro, and g-aminobutyrate. In addition, this
study indicated that the harvest index (i.e. the ratio of
harvestable product to total biomass) negatively correlated with the content of the vast majority of amino
acids. This relationship was confirmed in an independent population and following experiments that artificially altered the fruit load per truss (Do et al., 2010).
However, as would perhaps be anticipated, subsequent
evaluation of the relationship between growth and
secondary metabolite content revealed far less correlation (Alseekh et al., 2015). Using essentially the same
approach in an Arabidopsis recombinant inbred line
population, Meyer et al. (2007) found that, although no
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single metabolite exhibited a very high correlation with
biomass, canonical correlation analysis in which the
data of a linear combination of metabolites allowed the
improvement of this correlation by a factor of 10, thus
defined a metabolic signature of growth. Intriguingly,
the hexose phosphates Glc-6-P and Fru-6-P as well as
Suc were among the 20 top metabolites contributing
to this signature. When similar approaches were applied to maize, strong genome-wide association links
were found between coumaric and caffeic acids and
cinnamoyl-CoA reductase, while these precursors also
significantly correlated with lignin content plant height
and dry matter yield, presenting another example of the
narrowing of the genotype-phenotype gap of complex
agronomic traits (Riedelsheimer et al., 2012). The same
group revealed that models based on data obtained for
130 metabolites gave highly accurate predictions of
agronomic traits and suggested that combined metabolite, genomic, and agronomic phenotyping represents
an important screening tool for the identification of
parental lines for the creation of superior hybrid crops
(Riedelsheimer et al., 2012).
Returning to Arabidopsis, evaluation of the variation
of growth, metabolite levels, and enzyme activities was
also carried out across 94 accessions, revealing that
biomass correlated negatively with many metabolites,
including starch and protein and to a much lesser extent
Suc (Sulpice et al., 2009). However, further experiments
in which 97 accessions grown in near-optimal carbon
and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and
54 metabolic traits revealed that robust prediction of
phenotypic traits (biomass, starch, and protein) is most
effective (and reliable) when metabolite data (upon
which predictions are based) are collected from the
same growth environments (Meyer et al., 2007; Sulpice
et al., 2009; Korn et al., 2010; Steinfath et al., 2010).
Clearly, attempting to predict biomass, for example,
from metabolic profiles collected in a different growth
environment generally yields fewer (and weaker) correlations (Sulpice et al., 2013). Therefore, the prediction
of biomass across a range of conditions would better require condition-specific measurement of metabolic traits to take account of environment-dependent
changes of the underlying networks (Sulpice et al.,
2013). Data from this study were subsequently analyzed with respect to the tradeoffs between metabolism
and growth, specifically comparing increasing size with
increasing protein concentration, demonstrating that
accessions with high metabolic efficiency lie closer to
the Pareto performance frontier (the optimal solution
for the two contending tasks) and hence exhibit increased metabolic plasticity (Kleessen et al., 2014). A
related study addressing an ecological tradeoff between
secondary metabolism and fitness relates to the accumulation of capsaicinoids in the placenta of pepper
fruits (Capsicum spp.). Capsaicinoids constitute a class
of vanillylamides derived from Phe; they accumulate
in ripening pepper fruit and are responsible for the
pungency sensation occurring upon ingestion. In
natural environments, the accumulation of capsaicinoids
in populations of Capsicum chacoense (a wild pepper
species) is inversely correlated with seed set; these
metabolites, however, have a defensive role in highly
humid environments, where their accumulation deters
the attack of phytopathogenic fungi. Across a geographical gradient of decreasing rainfall (with a gradual decreasing pressure of the pathogens, which thrive
only in humid environments), the accumulation of
capsaicinoids also decreases in Capsicum spp. populations, while seed set, on the other hand, increases. This
study is an example of the combination of targeted
metabolic approaches with population ecology in dissecting the basis of natural polymorphic traits (Haak
et al., 2012). Further studies that address the concept of
metabolism and growth tradeoffs have used reciprocal
crosses to assess the contribution of the organellar genome to the processes and came to the conclusion that
there is far greater diversity in defense chemistry than
primary metabolism (Joseph et al., 2013, 2015). The interrogation of such tradeoffs is only possible via the
integrated approach described here and appears to be
very powerful; as such, we would expect considerable
advances in our understanding of this phenomenon to
be gained following its application. Not just the last
three studies but all of the above studies have been
published within the last 6 years, reflecting the fact
that such analyses are in their infancy. Given the
recognized complexity of the metabolism-to-growth
interactions, a comprehensive understanding of the
intricate networks that coordinate this interface is
likely some time off. That said, as the above examples
illustrate, the integration of growth data into metabolite profiling data as well as that of simpler physiological processes such as photosynthetic or respiratory
rates (Florez-Sarasa et al., 2012) has already presented
a number of key findings.
POSTGENOMIC INTEGRATION OF DATABASEHOUSED RESEARCH WITH NOVEL EXPERIMENTS
The examples described above rely on the integration
of data obtained in parallel using different experimental approaches. While such approaches are ideal for
addressing a number of questions, particularly those
concerning the temporal aspects underlying dynamic
responses to a systems perturbation, the integration
of novel experimental data with different types of
archived data can also prove highly informative, providing an appropriate amount of caution is used in
interpreting the results. Here, we will provide several
examples illustrative of such approaches, which largely
fit into two major types of approaches: (1) those using
correlative approaches and (2) those using genomescale stoichiometric models. The first study we will
describe fits into the former category, being an attempt
to define the storage metabolome of the vacuole (Tohge
et al., 2011; Fig. 2). In this research, a combination of
gas chromatography-mass spectrometry and Fourier
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Tohge et al.
Figure 2. Schematic overview of an integrative approach using metabolite profiling of storage metabolite and membrane proteomic data.
Example of network: barley vacuole network from Tohge et al. (2011).
transform mass spectrometry was used to detect and
quantify some 59 primary metabolites and 200 secondary metabolites (defined on the basis of strong
chemical formulae predictions) in either silicon oilpurified barley (Hordeum vulgare) vacuoles or the protoplasts from which these were derived. Of the 259
putative metabolites, 12 were exclusively detected in
the vacuole, 34 were exclusively in the protoplast, and
213 were common to both samples. At the quantitative
level, the difference between vacuole and protoplast
was yet more striking, with secondary metabolites being differentially abundant between the two sample
types. As a next step to predict the underlying cytosolicvacuolar transporters, tonoplast proteins predicted to
have a transport function were evaluated within the
context of the metabolic profiling data. Specifically, 88
proteins reported to be tonoplast proteins in barley
(Endler et al., 2006) were evaluated after conversion to
Affymetrix probe identifiers and coexpression analysis
of the resultant 128 probe sets was carried out using
PlaNet for barley (Mutwil et al., 2011; http://aranet.
mpimp-golm.mpg.de). Coexpressed networks of these
probes separated into 13 subgroups, with the most
dense cluster being highly correlated with aromatic
amino acid-related genes and the second most dense
cluster including several vacuolar ATP synthase proteins and tricarboxylic acid cycle-related genes. In
addition, clear associations were found between the
expression of transport proteins and that of pathways
of flavonoid and mugineic acid synthesis as well
as storage protein functions (Tohge et al., 2011). This
study was thus able to putatively assign function to
previously described transporter proteins as well as to
highlight the dynamic nature of the storage metabolome.
The coexpression approach has also been combined
with metabolic profiling in the annotation of plasma
membrane lignin and plastidial glycolate/glycerate
and bile acid transporters (Gigolashvili et al., 2009;
Sawada et al., 2009; Alejandro et al., 2012) as well as
a multitude of cell wall-associated proteins (Persson
et al., 2005). Moreover, this approach has also been used
to identify process, as opposed to pathway-specific, proteins, identifying proteins involved in dark-induced
senescence (Araújo et al., 2011) and in the response to
UV-B irradiance (Kusano et al., 2011).
The other type of examples we would like to discuss
are based on the integration of transcriptomic and
metabolomics level genome-scale models (Töpfer et al.,
2014). In the first of these studies, microarray data from
Arabidopsis exposed to eight different light and temperature conditions (data published in Caldana et al.,
2011) were integrated into a genome-scale model
(Mintz-Oron et al., 2012). Before discussing the outcome of this integration, we first digress to provide a
brief description of how genome-scale models are
generated. Essentially, a genome-scale model corresponds metabolic genes with metabolic pathways in a
manner whereby a stoichiometrically balanced metabolic network is generated, which corresponds to all
gene functions annotated for that organism. Such
models were originally published for microbes at the
turn of the century (Edwards and Palsson, 2000), with
many models for plants species being subsequently
generated, including the model species Arabidopsis as
well as crop species such as rice and maize (for review,
see Simons et al., 2014). Returning to the superimposition of experimental data on the model, the addition of
transcriptomic data was able to predict flux capacities
and statistically assess whether these vary under the
experimental conditions tested. Moreover, this study
introduced the concepts of metabolic sustainers and
modulators, with the former being metabolic functions
that are differentially up-regulated with respect to the
null model whereas the latter are differentially downregulated in order to control a certain flux and, therefore, modulate affected processes (Töpfer et al., 2013).
In a follow-up study, predictions made from the integration of transcriptomics were complemented with
metabolomics data from the same experiment. In doing
so, the authors were able to bridge flux-centric and
metabolomics-centric approaches and, in so doing,
demonstrate that, under certain conditions, metabolites
serving as pathway substrates in pathways defined as
either modulators or sustainers display lower temporal
variation with respect to all other metabolites (Töpfer
et al., 2013). These findings are thus in concordance
with theories of network rigidity and pathway robustness (Stephanopoulos and Vallino, 1991; Rontein et al.,
2002; Williams et al., 2008). Furthermore, considerable
evidence suggests that the levels of specific metabolites,
such as Ala, pyruvate, 2-oxoglutarate, Gln, and spermidine, are exceptionally stable across a massive range
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Integrative Studies of Metabolism
of cellular circumstances (Geigenberger, 2003; Stitt
and Fernie, 2003). They also are in keeping with
observations that the levels of metabolites such as Ser
coordinately control the levels of expression of genes
encoding multiple steps of the pathways to which they,
themselves, belong (Timm et al., 2013). The high stability of these metabolites is in keeping with their requirement across a range of different stresses. It also
highlights the fact that the robust metabolites may well
be the most biologically relevant for metabolic regulation; this is an important point, since it is at odds with
the manner in which the majority of the metabolomics
community assesses their data. This observation additionally highlights the potential difficulties and challenges in interpreting data from a single level of the
cellular hierarchy and thus provides further grounds
for integrated models.
CURRENT AND FUTURE CHALLENGES IN
DATA INTEGRATION
The above sections document that integrative approaches to further our understanding of metabolism
have proven very successful over the last decade or so,
particularly when linked with genetic and/or environmental experiments. To date, the approaches taken
have been relatively straightforward and have generally not been performed at a high level of spatial resolution. Several methods currently exist to obtain data
from all of the methods described here at the tissue,
cellular, and even subcellular levels (Aharoni and
Brandizzi, 2012); while still technically challenging, it
seems conceivable that such methods could provide
data required to better understand the cell specialization of metabolism. In addition, methods to gain accurate metabolic flux estimates following 13CO2 labeling
have recently been established (Young et al., 2008;
Szecowka et al., 2013; Ma et al., 2014) but are not yet
fully integrated with protein or transcript data. However, it is important to note that such experiments, albeit using [13C]Glc as a precursor, have already been
carried out in in vitro-cultivated Brassica napus embryos, providing considerable insight into the systemslevel regulation of this organ (Schwender et al., 2015). It
additionally seems highly likely that future research
will draw more heavily on archived genomics data than
it has to date; thus, the continued availability and
quality-control curation of such data sets are imperative
if we are going to fully exploit their value.
Received July 7, 2015; accepted September 10, 2015; published September 14,
2015.
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