Download Nitrogen-use efficiency in maize (Zea mays L

Journal of Experimental Botany, Vol. 65, No. 19, pp. 5657–5671, 2014
doi:10.1093/jxb/eru227 Advance Access publication 26 May, 2014
v
Review Paper
Nitrogen-use efficiency in maize (Zea mays L.): from ‘omics’
studies to metabolic modelling
Margaret Simons1, Rajib Saha1, Lenaïg Guillard2, Gilles Clément3, Patrick Armengaud2, Rafael Cañas2,
Costas D. Maranas1, Peter J. Lea4 and Bertrand Hirel2,*
1 Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Institut Jean-Pierre Bourgin,
Institut National de la Recherche Agronomique (INRA), Centre de Versailles-Grignon, RD 10, 78026 Versailles cedex, France
3 Plateau Technique Spécifique de Chimie du Végétal, Institut National de la Recherche Agronomique (INRA), Centre de VersaillesGrignon, Unité Mixte de Recherche 1318, INRA-Agro-ParisTech, Route de St Cyr, F-78026 Versailles Cedex, France
4 Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
2 * To whom correspondence should be addressed. E-mail: [email protected]
Received 13 January 2014; Revised 1 April 2014; Accepted 28 April 2014
Abstract
In this review, we will present the latest developments in systems biology with particular emphasis on improving
nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The
review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome,
and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. ‘Omics’ data
are hard to interpret in the absence of metabolic flux information within genome-scale models. These models, when
integrated with ‘omics’ data, can serve as a basis for generating predictions that focus and guide further experimental studies. By simulating different nitrogen (N) conditions at a pseudo-steady state, the reactions affecting NUE and
additional gene regulations can be determined. Such models thus provide a framework for improving our understanding of the metabolic processes underlying the more efficient use of N-based fertilizers.
Key words: Maize, metabolome, metabolic modelling, proteome, systems biology, transcriptome.
Introduction
Over the last decade, it has become possible to construct
complete plant genome annotations for a wide variety of
model and crop species (Jackson et al., 2011), and to use new
high-throughput tools such as the transcriptomics, proteomics, and metabolomics, to unravel the processes controlling
plant productivity (Fukushima et al., 2009; Kusano and
Fukushima, 2013). These processes have been shown to be a
multitude of complex networks and interdependent pathways
involving many genes, proteins, enzymes, and metabolites,
rather than distinct linear pathways (Fernie and Stitt, 2012).
As in a large number of cases, this non-linear complexity has
hindered plant metabolic manipulation experiments focused
on the original agronomic target of improving water-use
efficiency (Ashraf, 2010) or nitrogen-use efficiency (NUE)
(Pathak et al., 2011; McAllister et al., 2012). Hence, the
need to associate a biological function to a gene, the corresponding translation product, and finally the synthesis of a
desired metabolite has led to the development of various systems biology approaches based on integrated ‘omics’ studies.
These studies have taken advantage of an increasing number
of gene expression and metabolic databases (http://www.
hsls.pitt.edu/obrc/index.php?page=metabolic_pathway). In
Abbreviations: C, carbon; Dof1, DNA-binding with one finger; GIMME, gene inactivity moderated by metabolism and expression; GS, glutamine synthetase; GS2,
plastidic glutamine synthetase; iMAT, integrative metabolic analysis tool; MADE, metabolic adjustment by differential expression; miRNA, microRNA; N, nitrogen;
NMR, nuclear magnetic resonance; NUE, nitrogen-use efficiency; PEPC, phosphoenolpyruvate carboxylase; PROM, probabilistic regulation of metabolism.
© The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved.
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5658 | Simons et al.
particular, a comprehensive data collection called OPTIMAS
Data Warehouse (OPTIMAS-DW) that includes transcriptomes, metabolomes, ionomes, proteomes, and phenomes has
recently been released to support systems biology research
in maize (Colmsee et al., 2012). The resource is available at
http://www.optimas-bioenergy.org/optimas_dw.
One of these systems biology approaches consists of
linking genes and metabolic functions to physiological or
agronomic traits through the construction of genome-scale
metabolic models (Ruppin et al., 2010). Such metabolic models at the interfaces between computation, biology, genetics,
and agronomy are currently being developed to advance our
ability to maximize phenotypic and agronomic traits in a
rational manner. Construction of such metabolic models is
generally conducted using as a working basis the most prominent models built from unicellular organisms such as bacteria and yeast and expanded in a stepwise manner to make
them applicable to model plants such as Arabidopsis thaliana
and then to crops (Seaver et al., 2012). The ultimate goal of
developing such models is to provide a new tool for predicting
crop yields that will allow the selection of crops adapted to
lower inputs and to particular environmental conditions. The
knowledge gained from such modelling approaches could
ultimately allow for the identification of key developmental
and metabolic components involved in the elaboration of
complex agronomic traits such as NUE and water-use efficiency (Baldazzi et al., 2012; Shachar-Hill, 2013). The identification of such components and a better understanding of
their regulations should provide additional tools for developing marker-assisted selection strategies for breeders, and for
exploiting the possibilities offered by genetics, including natural variability, mutagenesis, and genetic manipulation (Hirel
et al., 2007).
Why improve NUE in a crop such as maize?
Both from an agronomic and economic point of view, the
main driver for crop improvement over the last century has
been yield (Conant et al., 2013). During this period, the rate of
yield improvement has accelerated due primarily to the introduction of an increasingly scientific approach to plant breeding but also through the extensive use of fertilizers (Tilman
et al., 2011; Andrews and Lea, 2013). Among these fertilizers, nitrogen (N) is a major factor in agricultural production,
where it can be supplied through chemical synthesis (Andrews
et al., 2013), organic rotation (Tuomisto et al., 2012), or biological N fixation (Vitousek et al., 2013). However, this extensive use of N fertilizers has caused major detrimental impacts
on the diversity and functioning of non-agricultural bacterial,
animal, and plant ecosystems (Erisman et al., 2013; Galloway
et al., 2013). In addition, fertilizer-derived nitrous oxide emissions into the atmosphere contribute to the depletion of the
ozone layer, while volatilized ammonia is returned as wet or
dry deposition, which can cause acidification and eutrophication (Cameron et al., 2013; Fowler et al., 2013). An excellent
overview of the different possible strategies to optimize the
use of N fertilizers worldwide for both economic and environmental benefits has recently been published by Good and
Beatty (2011). This review emphasizes that implementing
the best N management practices together with crop genetic
improvements adapted for each country can substantially
reduce excess N fertilizer applications without compromising
crop yields.
At present, a mixture of converging global factors is putting unprecedented pressure on agricultural productivity.
These factors include increasing demand for human food and
animal feed in developing nations with large populations,
diminishing supplies, and the rising cost of fossil fuel energy
that is required for fertilizer production. Since cereals such
as maize, wheat, and rice are the basis of most human food
in the world, improving their NUE is a major challenge for a
sustainable agriculture (Hirel et al., 2007; Kant et al., 2011;
McAllister et al., 2012). It is therefore necessary to select and
release new varieties requiring less N-based fertilizer, while
maintaining high yields and grain quality (protein content, in
particular). For this reason, several public institutions and all
major seed breeding companies are investing in crop genome
research, and applying molecular marker and transgenic
techniques to identify genes that can be used to improve NUE
further (Edgerton, 2009; Xu et al., 2012; Fischer et al., 2013).
Improving NUE is particularly relevant for maize, as large
amounts of N fertilizer are required to obtain the maximum
yield and for which global NUE, as with other crops, has been
estimated on average to be less than 50% (Raun and Johnson,
1999). Recent studies have demonstrated that there are large
differences in maize lines and hybrids in their ability to grow
and yield well on soils with low mineral nutrient availability,
which depends on both N-uptake efficiency and N-utilization
efficiency (Hirel and Gallais, 2011). Maize is recognized not
only as a major crop but also as a model species that is well
adapted for fundamental research, especially for understanding the genetic basis of yield performance. Many tools are
available in maize such as mutant collections, a wide genetic
diversity, recombinant inbred lines, straightforward transformation protocols, and physiological, biochemical, and
‘omics’ data, as well as its genome sequence (Hirel and Lea,
2011) and more recently genome-scale metabolic models
(Saha et al., 2011).
NUE: from ‘omics’ studies to systems
biology approaches
Due to the complexity of the biological systems involved in
the control of NUE at the cellular, organ, and whole-plant levels, the emerging research field of systems biology was developed for both model and crop species. This has allowed the
researcher to focus on a holistic understanding of N-regulatory
networks from the genomic to agronomic traits such as biomass production or yield. Such an approach consists of taking
advantage of the various transcriptome, proteome, metabolome, and fluxome datasets that can be further analysed in an
integrated manner through the utilization of various mathematical, bioinformatics, and computational tools (Gutiérrez,
2012). Ultimately such integrated analyses, possibly combined
with whole-plant physiology and quantitative genetic studies,
Maize | 5659
may allow the identification of key individual or common regulatory elements that are involved in the control of complex
biological processes (Saito and Matsuda, 2010).
Transcriptome studies
To identify some of the regulatory and structural elements
representing the physiological changes associated with NUE,
several studies have been carried out to evaluate modifications in gene expression under low- and high-N conditions.
In an increasing number of model and crop species, transcriptome studies have highlighted the complexity of the
regulatory mechanisms involved in the control of leaf or root
gene expression under N-limiting and non-limiting conditions (Wang et al., 2003; Krapp et al., 2011; Amiour et al.,
2012; Wei et al., 2013). In mutants, transcriptome studies
have also revealed deficiencies in key reactions or key regulatory proteins involved in primary N metabolism (Beatty
et al., 2009; Castaings et al., 2009; Wang et al., 2009; Kissen
et al., 2010). Several classes of N-responsive genes have been
identified, including those involved in a variety of metabolic
and regulatory pathways. It is hoped that such a transcriptome approach could ultimately help to identify the genes
and proteins required for a N-use-efficient phenotype under
different environmental soil conditions (Ruzicka et al., 2010)
up to the agronomic level (Tenea et al., 2012). For example,
protein kinases such as AtCIPK8 (Hu et al., 2009) or transcription factors such as NLP7 (Marchive et al., 2013), identified following whole-genome/transcriptome approaches,
were shown to be key players in nitrate sensing and signalling in Arabidopsis. The target of rapamycin (TOR) signalling pathway seems also to be involved in the regulation of N
assimilation in proliferating and conductive tissues, thus playing an important role in controlling long-distance and shortdistance nutrient exchanges (Robaglia et al., 2012). Improved
NUE was obtained when OsENOD93-1, a gene encoding
another N-responsive transcription factor, was overexpressed
in rice (Bi et al., 2009), strengthening the finding that regulatory proteins are as important as enzymes in the control of N
metabolism. In line with this finding, the maize DNA-binding
with one finger gene (Dof1) was expressed in Arabidopsis
under the control of a maize pyruvate phosphate dikinase
promoter. The transformed plants exhibited a considerable
elevation in the concentration of soluble amino acids, especially glutamine, and increased growth under low-N conditions (Yanagisawa et al., 2004). When the same maize ZmDof1
gene was expressed in rice under the control of the ubiquitin
promoter, increases in photosynthesis, N assimilation, and
growth were detected under low-N conditions (Kurai et al.,
2011). The latest advances in our understanding of N signalling in plants have been reviewed by Castaings et al. (2011),
highlighting the roles of transcription factors, nitrate transporters, and kinases in their interactions with hormones or
N-containing molecules in the regulation of N assimilation.
Plants are able to use both nitrate and ammonium ions
as N sources, as well as various organic sources (Andrews
et al., 2013). Distinct signalling pathways and transcriptome
response signatures for ammonium- and nitrate-supplied
Arabidopsis have been identified. The data indicated that
there is an ammonium- and a nitrate-specific pattern of gene
expression, as well as a general inorganic N gene response
(Patterson et al., 2010). Such observations suggest that the
regulation of gene expression under agronomic conditions,
when the two sources of inorganic N will be present in variable proportions, depending on the type of fertilizer used, is
probably more complex than that occurring in plants grown
under controlled conditions on a single N source.
Considering the agronomic importance and economic
value of maize worldwide, an increasing number of wholegenome transcriptome approaches have also been developed
to identify genome-wide transcriptional circuits in various
organs and tissues during maize development (Sekhon et al.,
2011; Downs et al., 2013) and particularly those related to
N-responsive genes. Depending both on the duration and
intensity of the N-limiting stress applied, most of the studies
in maize have ended up with a portfolio of genes involved in a
variety of developmental, metabolic, and regulatory functions
(Amiour et al., 2012; Humbert et al., 2013). In some cases, a
number of these N-responsive genes were also found in dicot
species, but their level of response to the N feeding conditions
appeared to be largely dependent on both the genotype and
the experimental conditions (Table 1). Nevertheless among
these genes, those encoding carbonic anhydrase, which plays
an important role in the delivery of CO2 for carbon assimilation (Moroney et al., 2001), and plastidic glutamine synthetase (GS2), which assimilates or reassimilates ammonium
(Hirel and Lea, 2001), were found to be upregulated under
non-limiting N conditions. Those encoding germin-like proteins, which are involved in various developmental and stress
responses (Bernier and Berna, 2001; Wang et al., 2013), and
those encoding peroxiredoxins, which are known to play a
major role in controlling organelle redox metabolism (König
et al., 2012), were also found to be N responsive. The developmental stage of the plant appears to be very important in
some circumstances, since genes that responded to N-limiting
stress at the vegetative stage of maize were different from
those that were responsive to N at the late grain-filling stage
(Amiour et al., 2012). Interestingly, a number of these genes
were found to respond similarly to varying N nutrition conditions in different genotypes and under both controlled and
field-growth conditions. This finding led Yang et al. (2011)
to propose that a small set of N-responsive genes could be
used as biomarkers to monitor the in planta status of maize
N. A number of these genes were also found in the study of
Amiour et al. (2012), thus strengthening the idea that they
could be used as agronomic tools both for breeding purposes
and for optimizing fertilizer usage.
More recently, evidence showing the importance of microRNAs (miRNAs) in the regulation of several abiotic stresses
has rekindled the interest of research groups in the epigenetic
regulation of NUE and its potential use for NUE improvement (Fischer et al., 2013). As revealed in studies performed
on maize, the occurrence of miRNA-mediated control of
gene expression could represent an important biological
component of NUE that has hitherto been overlooked using
standard transcriptome approaches (Trevisan et al., 2011;
5660 | Simons et al.
Table 1. Transcripts exhibiting significant increase following transfer from limiting to non-limiting N feeding conditions in different studies
and across different species
The numbers on the right side of the panel correspond to: 1, Wang et al. (2003) in Arabidopsis; 2, Scheible et al. (2004) in Arabidopsis; 3, Bi
et al. (2007) in Arabidopsis; 4, Peng et al. (2007) in Arabidopsis; 5, Krapp et al. (2011) in Arabidopsis; 6, Cai et al. (2012) in rice; 7, Amiour
et al. (2012) in maize. ID, gene identification number. A cross (×) indicates that the gene was identified in one of the seven studies. A number of
transcripts for ribosomal proteins were also found in the different studies but did not correspond exactly to the gene annotation in Arabidopsis
and in maize.
Arabidopsis ID
Maize ID
Gene annotation
1
2
3
4
5
At3g01500
At5g20630
At5g35630
At1g03600
At4g09650
At4g28660
At3g11630
At5g15350
At5g62720
TC259341
TC259932
TC271006
TC216153
TC249593
BM381938
TC262220
BG319827
TC265222
CA1 (CARBONIC ANHYDRASE 1); carbonate dehydratase/zinc ion binding
GLP3 (GERMIN-LIKE PROTEIN 3); manganese ion binding/nutrient reservoir
GS2 (GLUTAMINE SYNTHETASE 2); glutamate-ammonia ligase
Photosystem II family protein
ATP synthase delta chain, chloroplast, putative / H(+)-transporting two-sector ATPase
PSB28, photosystem II reaction centre W (PsbW) family protein
2-Cys peroxiredoxin, chloroplast (BAS1)
Plastocyanin-like domain-containing protein
Integral membrane HPP (Human Proteome Project) family protein
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
Zhao et al., 2012). Such a putative regulatory function mediated by the action of miRNAs was highlighted by the finding that significant differences in their accumulation were
observed according to the level of N nutrition, as well as their
spatiotemporal expression pattern in root tissues (Trevisan
et al., 2012; Zhao et al., 2012). Although the genes targeted
by miRNAs had various and ubiquitous functions, encompassing a variety of developmental and metabolic processes
that were not necessarily directly linked to NUE (Xu et al.,
2011), the genetic manipulation of the expression of miRNAs could be an alternative method of improving NUE in
crops (Fischer et al., 2013).
In order to shed light on the dynamics of transcription
in response to various environmental stimuli or stresses
such as N limitation, tools such as MapMan were originally developed to visualize large gene-expression datasets
in Arabidopsis in order to search for similar global responses
across large numbers of microarrays (Usadel et al., 2005).
Such a tool, which has now been adapted for maize (Usadel
et al., 2009) and solanaceous species (Urbanczyk-Wochniak
et al., 2006; Ling et al., 2013), will provide information
about the response of the expression of the whole genome
to N nutrition in relation to other cellular and metabolic
processes. Several software and visualization tools have
been developed to interpret more easily large ‘omics’ datasets and also to identify genes, gene networks, and regulatory hubs that control plant growth and development. For
example, VirtualPlant (Katari et al., 2010), Geneinvestigator
(Hruz et al., 2008), and Cytoscape (Killcoyne et al., 2009)
have been used in an increasing number of studies aimed at
identifying gene regulatory networks involved in N metabolism in both model and crop species. These visualization
tools have been used extensively to decipher the relationship
between N-responsive gene networks and other biological
processes linked to carbon (C) availability (Krouk et al.,
2010; McIntyre et al., 2011), external signals such as light
(Krouk et al., 2009), and internal signals such as hormones
(Nero et al., 2009; Krouk et al., 2010). They have also proved
×
×
×
×
×
×
6
×
×
7
×
×
×
×
×
×
×
×
×
particularly useful for investigating the natural variation of
the response of Arabidopsis to N availability (Ikram et al.,
2012). Two excellent reviews presenting current knowledge
on the regulatory components controlling the response of
Arabidopsis to N, together with the response networks corresponding to metabolic, physiological, growth, and developmental pathways, has recently been published (Gutiérrez,
2012; Canales et al., 2014). This review article highlights the
function of receptors, transcription factors, and other putative signalling components of N signalling pathways, deciphered by means of the integrated systems biology approach
described above.
Although much more informative than conventional transcriptome studies, such whole-genome expression approaches
have remained confined to deciphering regulatory circuits at
the transcriptional level, since only the steady state of transcripts has been considered. Such an approach, originally
developed for Arabidopsis by virtue of the wealth of information available, when transferred to crops may help in
identifying key master genes involved in the control of NUE
(Gutiérrez, 2012). Nevertheless, transcriptome, metabolome,
and even fluxome co-expression network analyses will certainly be necessary in order to enhance our knowledge of the
genes and metabolic pathways linked to NUE in crops such
as maize (Saito and Matsuda, 2010).
Proteome studies
Although a number of proteome databases are now available
on the world wide web (Jorrín-Novo et al., 2009), in comparison with the numerous transcriptome studies, there is
much less information available on the proteome concerning NUE in both model and crop species, as time-consuming
and difficult techniques are required. Moreover, at best, fewer
than a thousand proteins can usually be separated by twodimensional gel electrophoresis and identified using either the
available databases or mass spectrometry techniques (Jorrín
et al., 2007). However, with second-generation quantitative
Maize | 5661
proteome techniques, the coverage of the plant cell proteome
has increased considerably (Jorrín-Novo et al., 2009). Under
abiotic stress conditions, proteome studies are able to provide
additional information on the quantity of expressed proteins and post-translational modifications such as phosphorylation and glycosylation that cannot be identified by only
determining mRNA transcription. Analysis of the proteome
has identified protein response pathways shared by different plant species, as well as pathways that are unique to a
given stress (Kosová et al., 2011). With the improvements
in mass spectrometry-based proteome and phosphoproteome analyses, it is now possible to explore various areas
of maize biology including the impact of N-deficiency stress
on the plant proteome (Facette et al., 2013; Pechanova et al.,
2013). The first proteome studies performed on wheat grown
under N-deficiency stress conditions showed that the concentrations of enzymes and proteins involved in C metabolism were the most strongly reduced (Bahrman et al., 2004).
Later, changes in the protein profile were also examined in the
roots and shoots of maize (Prinsi et al., 2009; Amiour et al.,
2012), rice (Kim et al., 2009), barley (Møller et al., 2011), and
Arabidopsis (Wang et al., 2012), when the plants were grown
under a low- or a high-N supply. Results from these studies
showed that the amounts of enzyme proteins that have a pivotal role in N assimilation such as glutamine synthetase (GS)
and in C metabolism such as phosphoenolpyruvate carboxylase (PEPC) were higher when plants were fed with nitrate, in
agreement with a previous study (Sugiharto and Sugiyama,
1992). Many other proteins involved in a number of photosynthetic reactions, in maintaining the energy and redox status of the cell, and in signal transduction were also shown
to be N responsive. Such data confirm the tight relationship
that exists between N and other metabolism found at the
transcriptional level (Gutiérrez, 2012). In the vast majority
of the proteome investigations into the response of a plant to
N limitation, there was no direct relationship with transcriptome or metabolome studies. It was therefore difficult to tell
if the regulation of protein synthesis occurred at the translational or post-translational level, or if the amount of protein
was correlated with the amount of corresponding mRNA. In
the study of Amiour et al. (2012) on maize, no simple and
direct relationship among transcript, protein and metabolite
accumulation was found. In a similar manner to wheat, this
finding suggests that post-transcriptional modifications may
be positively or negatively regulated by the N metabolite concentration in the plant (Bahrman et al., 2005). In addition,
complex and still uncharacterized network interactions are
probably occurring between gene transcription and protein
and metabolite accumulation (Fernie and Stitt, 2012). It is
likely that advanced proteome tools will be used more widely
in the analysis of signalling and developmental processes
in plants, as they are in medical research (Choudhary and
Mann, 2010). When integrated with other ‘omics’ data and
with information from quantitative genetics, proteomes will
be able to contribute to our understanding of complex regulatory networks underlying important phenotypic traits such
as yield and nutrient perception and utilization (Kaufmann
et al., 2011; Verma et al., 2013).
Metabolome studies
Over the last 5 years, an increasing number of metabolome
studies have been carried out for both model and crop plants,
with the aim of identifying changes in metabolite concentrations under various biotic (Balmer et al., 2013) and abiotic
stresses including N deficiency (Kusano et al., 2011; Obata
and Fernie, 2012). These have also been valuable in improving our understanding of the interactions between C and N
metabolism (Fait et al., 2011). Such approaches have allowed
the identification of new compounds that accumulate in
response to a given stress, as well as those sharing a common
pattern of accumulation across various stress conditions. In
addition, a number of plant metabolic databases are now
available that will facilitate the development of plant systems
biology approaches (Fukushima and Kusano, 2013).
Up until now, the vast majority of metabolome studies have
been carried out using Arabidopsis, but more recently these
have been extended to a wider range of plants including cereals
such as rice and maize (Kusano et al., 2011; Lisec et al., 2011;
Amiour et al., 2012; Riedelsheimer et al., 2012a). Exhaustive
metabolic profiling using separation techniques based on gas
chromatography coupled to mass spectrometry has provided
information for plant phenotyping and the exploitation of
genetic variability (Saito and Matsuda, 2010). Liquid chromatography coupled to mass spectrometry has also been used
frequently for metabolome analysis (Rohrmann et al., 2011;
Tohge et al., 2011). In parallel, 1H-nuclear magnetic resonance (NMR) spectroscopy approaches have been developed,
which are less sensitive but non-invasive, compared with those
requiring the extraction of plant material (Kim et al., 2010).
1
H-NMR metabolomics appears to be an attractive technique for the development of mapping approaches (Graham
et al., 2009) and could support breeding for improved NUE
through the establishment of metabolite databases. 1H-NMR
has also been used successfully to improve the characterization of GS-deficient mutants of maize, indicating that, in
addition to the glutamine-derived amino acid biosynthetic
pathways, lignin biosynthesis was also altered (Broyard et al.,
2009). Such techniques have also been used to demonstrate
that, in tobacco, the enzyme glutamate dehydrogenase does
not assimilate ammonia, even when it is overexpressed several
fold (Labboun et al., 2009).
The effect of N starvation on the plant metabolic profile
has been examined in a few studies using Arabidopsis as a
model species (Krapp et al., 2011) or maize as a crop (Amiour
et al., 2012; Schlüter et al., 2012, 2013). In all these studies, it
was observed that, in leaves, N deprivation caused a general
decrease in most of the metabolites involved in both C and
N primary assimilation, thus impacting on either biomass or
grain production. An accumulation of starch and of a variety of stress-related carbohydrates was also a characteristic
metabolic symptom induced by N deficiency. Interestingly, in
the three studies performed on maize, it was observed that
the accumulation of secondary metabolites, particularly
those used as precursors for cell-wall synthesis, was strongly
reduced in line with the work on the maize GS-deficient
mutants (Broyard et al., 2009). This observation partly
5662 | Simons et al.
explains why N deficiency, or a perturbation of primary N
assimilation, has a strong impact on maize growth and development through an altered synthesis of metabolites used as
the precursors required for lignin and cellulose production.
Moreover, Amiour et al. (2012) showed that the response of
the leaf metabolite content of maize to N deficiency varied
according to the plant developmental stage. Thus, it is essential that changes in the metabolome must be followed over
a long developmental period when studying environmental
effects in field trials with genotypes, or transgenic plants with
varied NUE (Asiago et al., 2012).
Integrating ‘omics’ data
Although the main metabolic functions that were altered
as a result of N deficiency were conserved across different
‘omics’, there was very little correlation among mRNA transcript, protein and metabolite content. This would suggest
that other regulatory elements such as uncharacterized genes
or metabolites may have important functions within the biological networks involved (Urano et al., 2010). Moreover, it is
generally admitted that ‘omics’ studies only provide a narrow
and static picture of the physiological status of a given organ
at a particular stage of plant development (Fernie and Stitt,
2012). Thus, additional fluxomics studies based on the use of
15
N- and 13C-labelled compounds may represent an interesting
complementary approach to metabolomics, since these techniques can provide additional information on the metabolic
fluxes occurring in mutants, genetically modified crops, or
genotypes exhibiting contrasting NUE (Kruger and Ratcliffe,
2012; Masakapalli et al., 2013). Moreover, such fluxomics
techniques are potentially able to provide additional information on the turnover and remobilization of metabolites in a
given cellular compartment during the day/night cycle and at
critical periods of plant development when N is required for
optimal plant growth and development (Gauthier et al., 2010;
O’Grady et al., 2012).
In addition to examining the impact of N deficiency,
metabolome studies are becoming more and more extensively used for the high-throughput phenotyping necessary
for large-scale molecular and quantitative genetic studies
aimed at identifying candidate genes involved in the control of plant productivity (Kliebenstein, 2009), even when
these studies are not necessarily focused on NUE (Meyer
et al., 2007; Lisec et al., 2008). Figure 1 illustrates the genetic
variability of leaf metabolite content, enzyme activities, and
biomass components in 19 selected maize lines, which are
representative of American and European plant diversity
and used as a core collection for association genetic studies
(Camus-Kulandaivelu et al., 2006). Such a large genetic variability could be used to obtain a better understanding of the
control of NUE, since we observed that in this core collection there was almost no difference in PEPC activity, whereas
the asparagine content varied by up to 200-fold. In agreement with this observation, it is well known that the asparagine content can vary considerably from one plant species to
another and that its concentration can change dramatically
depending on the physiological condition of the plant (Lea
et al., 2007). Interestingly, we also observed that, of the different classes of metabolites analysed, the amounts of polyamines, secondary metabolites, and unknown metabolites were
the most variable (Fig. 1). Such findings strengthen the idea
that further work is required to identify their role in plant
productivity in order to provide the necessary information
required for a full characterization of the metabolic and regulatory networks involved. Moreover, the range of variation
observed for both biochemical and biomass-related traits
was different, depending on the developmental stage of the
plant. These data confirm that ‘omics’-based studies should
be performed over a sufficiently long developmental period
to ensure that the critical physiological stages during which
there is a progressive switch between N assimilation and N
remobilization are included (Hirel et al., 2007). In addition,
environment-dependent changes of the underlying metabolic networks need to be taken into account when investigating the relationship between plant metabolism and plant
biomass production (Sulpice et al., 2013). Leaf metabolite
profiling techniques have recently been used successfully to
dissect complex traits in maize through the use of genomewide association mapping in both maize lines (Riedelsheimer
et al., 2012a) and hybrids (Riedelsheimer et al., 2012b). Thus,
metabolome-assisted breeding techniques, in addition to
genome-assisted selection of superior hybrids, are promising for narrowing the genotype/phenotype gap of complex
traits such as NUE (DellaPenna and Last, 2008; Fernie and
Schauer, 2008; Lisec et al., 2011).
Metabolic modelling as a tool to unravel
the limiting steps in NUE
Due to the ever-accelerating pace of genome sequencing and
annotation in the past few years, researchers have made significant advances in mapping plant genes to metabolic functions. Nevertheless, efforts to engineer plant metabolism,
in general and N metabolism in particular, have on most
occasions been met with limited success (Good et al., 2004;
Hirel et al., 2007). Due to the built-in metabolic redundancy,
genetic interventions often do not bring about the desired
effect in plant metabolism (Sweetlove et al., 2003; Gutiérrez
et al., 2005). Therefore, by taking into account the complete
inventory of metabolic transformations of a given plant species, a genome-scale metabolic reconstruction has the potential to make valuable advances, such as improvement of yield,
NUE, and nutritional quality in crops.
Unlike gene and protein regulatory networks that denote
putative interactions (Cho et al., 2007), metabolic network
models capture the interconversion of metabolites through
chemical transformations catalysed by enzymes. Therefore,
their topology is generally better characterized than the one
of regulatory networks. A genome-scale metabolic model
is constructed by encompassing the widest possible list of
biotransformations present in the organism as supported
by annotation and homology evidence. Thus, these models
attempt to map the entire chemical repertoire of a specific
organism (see Fig. 3).
Maize | 5663
Fig. 1. Changes in metabolite content, enzyme activities, and biomass-related components in leaves of 19 selected maize lines covering their genetic
diversity (Camus-Kulandaivelu et al., 2006) at two key stages of plant development. The top of the figure shows the variation coefficient (expressed
as %) of the biomass-related components in red (C, total carbon; N, total nitrogen; WC, water content) including yield. Enzyme activities are in blue
italics (PEPC, phophoenolpyruvate carboxylase; GS, glutamine synthetase; PPDK, pyruvate phosphate dikinase; MDH, NADP-dependent malate
dehydrogenase; GDH, glutamate dehydrogenase, GOGAT, ferredoxin-dependent glutamate synthase; AspAT, aspartate aminotransferase; NR, nitrate
reductase). Metabolites and classes of metabolites are in black (2-OG, 2-oxoglutarate; Cit, citrate; Suc, sucrose; Pyr, pyruvate; Glu, glutamate; Gln,
glutamine; OA, total organic acids; AA, total amino acids; Fruc, fructose; Gluc, glucose; unknown, unidentified metabolites). At the bottom of the figure
is shown an overview representation of the average of variation coefficients (from 0 to 80%) for the main classes of metabolites, enzyme activities, and
biomass components.
Large amounts of data relating to metabolites, reactions,
and their associated enzymes/genes are currently available
through generic databases such as the Kyoto Encyclopedia of
Genes and Genomes (KEGG; Kanehisa et al., 2012), SEED
(Devoid et al., 2013), Metacyc (Karp and Caspi, 2011),
Brenda (Schomburg et al., 2013), Universal Protein Resource
(Uniprot; Consortium, 2012), PubChem (Wang et al., 2014),
ChemSpider (Pence and Williams, 2010), and plant-specific
databases such as MaizeCyc (Monaco et al., 2013), MetaCrop
(Schreiber et al., 2012), Corncyc (for maize; Dreher, 2014), and
the Plant Metabolic Network (PMN; Dreher, 2014), which is
a combination of 18 plant databases. However, incompatibilities of representation (e.g. metabolites with multiple names/
chemical formulae across databases), stoichiometric errors
(i.e. elemental or charge imbalances) and generic metabolite
descriptions (e.g. absence of stereospecificity or use of generic
side chains) are key bottlenecks for the rapid reconstruction
of new high-quality metabolic models. Of late, a database
called MetRxn was developed to address these issues by integrating information (of metabolites and reactions) from eight
such databases and 44 published metabolic models (Kumar
et al., 2012). In addition to gene/protein/reaction information, knowledge of the subcellular localization of enzymes
is critical for the development of plant metabolic models.
Towards this end, there exists protein localization databases
such as the Plant Proteome DataBase (PPDB; Hieno et al.,
2014) and SUBcellular localization database for Arabidopsis
proteins (SUBA; Tanz et al., 2013) for the two plant species
Arabidopsis and maize. As displayed in Fig. 2, the combination of biological databases, localization databases, and literature evidence comprise the initial information required to
create a genome-scale model.
5664 | Simons et al.
Encouragingly, an increasing number of genome-scale metabolic models of micro-organisms and multicellular organisms
are emerging, and the applications of these models are expanding (de Oliveira Dal’Molin and Nielsen, 2013; McCloskey
et al., 2013). These models are being developed in an iterative
manner using literature evidence in tandem with genome, transcriptome, proteome, and metabolome data. By using flux balance analysis (FBA), combined with the pseudo-steady-state
assumption (Orth et al., 2010), metabolic fluxes (or feasible
ranges thereof) can be calculated using a fitness optimization
proxy, such as biomass yield maximization. Under the pseudosteady-state assumption, FBA assumes that each metabolite
that is produced must be consumed at an identical rate. To
investigate the growth phenotype of the cell via FBA, a reaction that contains all required precursors for cell growth in
experimentally measured proportions is generated and added
to the model. This reaction is known as the ‘biomass’ reaction
and its flux is an abstraction of the cell biomass composition.
Constraints in FBA are represented: (i) as equations balancing
metabolite production to consumption via all possible reactions
in the model, and (ii) as inequalities imposing bounds (i.e. the
maximum or minimum allowable fluxes) in the system, such as
uptake or secretion of specific metabolites, or upper and lower
bounds of reaction fluxes (based on reaction thermodynamics).
All these balances and bounds determine the feasible solution
space, i.e. the allowable flux distributions of the model, and an
optimal solution is found under the specific objective (e.g. biomass yield maximization), as displayed in Fig. 3.
Genome-scale models can be tested by comparing the in
vivo growth of knockout strains with in silico growth under
corresponding conditions, or by comparing fluxome data
with simulated fluxes (McCloskey et al., 2013). Saha et al.
(2011) compared experimental results with in silico model
predictions in 17 of 21 cases by comparing the directional
change of the maximum theoretical yield of lignins, sugars,
and crude protein between the wild type and two mutant
strains. The C4GEM model quantitatively showed a strong
correlation between the differential expression of proteins
or protein complexes and predicted flux differences between
the bundle sheath and mesophyll cell types in 50 of 66 cases
(de Oliveira Dal’Molin et al., 2010b). These models can then
guide physiological characterization, metabolic engineering, and discovery (Oberhardt et al., 2009). Mintz-Oron
et al. (2012) demonstrated the use of genome-scale models
by suggesting the knockout of 71 target enzymes to increase
vitamin E accumulation in Arabidopsis. Furthermore,
Fig. 2. Iterative process of genome-scale model building. A combination of biological and localization databases with published literature provides
information for the initial genome-scale model. This gene, protein, and reaction information is combined with a biomass equation, which contains userspecified stoichiometries, to give the stoichiometric matrix. Using flux balance analysis with an objective function, typically maximizing biomass, a set of
simulated reaction fluxes is determined. Transcriptome, proteome, and metabolome data constrain the model either by turning on or off reactions in a
‘switch’ approach or by modifying the allowable flux through a reaction in a ‘valve’ approach. The model is validated using knockout data or fluxomics
data and, depending on these results, iterations continue until a high-quality genome-scale model is developed.
Maize | 5665
Fig. 3. FBA using a metabolic model. A simplified metabolic model is displayed encompassing multiple tissue types, cell types, and compartments,
as well as the transporters between them. FBA assumes that each metabolite is produced and consumed at equal rates, as required by the pseudosteady-state assumption. This constraint is imposed for every metabolite and, along with the flux bounds of each reaction determined by reaction
thermodynamics, creates the feasible solution space. By optimizing an objective function, typically biomass production, a flux value is predicted for each
reaction within the model.
tissue-specific-type (Jerby et al., 2010) and multitissue-type
(Jerby et al., 2010; Thiele et al., 2013) models have also been
developed for Homo sapiens and employed for studying metabolic interactions and therapeutic applications.
Metabolic modelling of plants is a rapidly developing field. Models of Arabidopsis (Poolman et al., 2009;
de Oliveira Dal’Molin et al., 2010a; Radrich et al., 2010;
Mintz-Oron et al., 2012), rice (Poolman et al., 2013), barley (Grafahrend-Belau et al., 2009, 2013), rapeseed (Pilalis
et al., 2011), sorghum (de Oliveira Dal’Molin et al., 2010b),
sugarcane (de Oliveira Dal’Molin et al., 2010b), and maize
(de Oliveira Dal’Molin et al., 2010b; Saha et al., 2011)
have already been developed. Although the majority of
these available models focus on a single tissue, whole-plant
models are beginning to emerge. For instance, GrafahrendBelau et al. (2013) developed a whole-plant model of barley,
including specific models for the leaf, stem, and seed tissues,
to analyse seed development, crop improvement, and yield
stability. To this end, a whole-plant metabolic model of
maize would be useful not only to characterize metabolism
but also to highlight the limiting steps in NUE. Integration
of high-throughput ‘omic’ information with metabolic models contributes towards improving the genotype–phenotype
relationship and prediction accuracy. Plant genome-scale
metabolic model development is currently exploring ways
of incorporating such experimental data by adopting
approaches developed for microbial organisms (Töpfer
et al., 2013).
Incorporating transcriptome, proteome,
and metabolome data into models
Incorporating transcriptome, proteome and metabolome
data can increase the predictive accuracy of genome-scale
models. Utilizing such high-throughput ‘omics’ information
not only provides regulation (for tissue-specific models) but
also ensures that the correct reactions and metabolites are
represented within specific tissue types (for multiple tissue/
whole-plant models). To this end, Jerby et al. (2010) developed the model-building algorithm (MBA) to reconstruct
a tissue-specific model from a generic model by combining
‘omics’ (i.e., transcriptome, proteome, and metabolome)
information with the published literature. Furthermore,
many other approaches have been developed for capturing
‘omics’ data in the form of regulation on the model (Blazier
5666 | Simons et al.
and Papin, 2012; Hyduke et al., 2013). Two main modelling philosophies that abstract regulation as either an on/off
‘switch’ or a continuous flow ‘valve’ have been put forth, as
shown in Fig. 2. The gene inactivity moderated by metabolism and expression (GIMME; Becker and Palsson, 2008),
integrative metabolic analysis tool (iMAT; Shlomi et al.,
2008; Zur et al., 2010), and metabolic adjustment by differential expression (MADE; Jensen and Papin, 2011) algorithms
use a ‘switch’ approach to turn reactions on or off, based on
differential expression changes. The GIMME approach simply turns off reactions based on a user-specified threshold for
expression data. The iMAT approach discretizes the expression data into low-level, moderately, and highly expressed
genes, and then utilizes an algorithm to turn on the smallest number of low-level expressed genes required to achieve
a specified metabolic function (i.e. a user-specified biomass
objection function). The MADE algorithm employs multiple datasets from two or more related conditions to activate
or repress appropriate reactions for simulating the progression of experimental conditions. Only statistically significant
changes in expression levels will convert an activated reaction to a repressed state, or vice versa, when comparing one
experimental condition with another. All ‘switch’ approaches
require essential reactions to remain active in the model,
regardless of their expression level, to ensure that biomass
is produced. Contrary to these ‘switch’ approaches, E-FLUX
(a combination of flux and expression data; Colijn et al.,
2009) and probabilistic regulation of metabolism (PROM;
Chandrasekaran and Price, 2010) algorithms adopt a ‘valve’
approach, by modifying the allowable range of the flux of
any reaction (i.e. the upper and lower flux bounds of the reaction) based on gene/protein expression data. The E-FLUX
algorithm incorporates a single dataset (i.e. one experimental
condition) and requires a user-specified function to convert
expression levels to flux constraints. The PROM algorithm
incorporates multiple datasets to set maximum reaction
flux levels based on the probability that the gene is active
among all experimental datasets. Recently, Lee et al. (2012)
developed another ‘valve’ approach by using absolute geneexpression levels to regulate reaction fluxes. The integration
of metabolome data with transcriptome or proteome data
can further increase the accuracy of the genome-scale metabolic models. The integrative omics-metabolome analysis
(IOMA) algorithm uses a Michaelis–Menten-type rate equation to calculate an empirical reaction flux using metabolome
and proteome data. Each empirical reaction flux includes an
error correction that is used to account for missing experimental metabolite concentrations and errors in experimental
measurements. An algorithm is then used to minimize the
error in reaction flux predictions with an additional biomass
constraint (Yizhak et al., 2010). Overall, these advancements
to integrate ‘omics’ data into microbial models have resulted
in a collection of data integration techniques that have set
the stage for similar implementations in plant models.
While many of these algorithms have not yet been
applied to plants, Töpfer et al. (2013) applied the E-FLUX
algorithm to the Arabidopsis model developed by MintzOron et al. (2012) to predict the maximum flux through
metabolic pathways altered under eight varying light and
temperature conditions. Of the 167 metabolic functions or
pathways studied, 37 functions resulted in a differential
capacity in at least one of the eight conditions modelled,
meaning that the flux through the pathway changed more
than the expected flux variations from random chance. With
the successful incorporation of E-FLUX in Arabidopsis,
more plant models employing transcriptome, proteome,
and metabolome data are expected to emerge. For a more
in-depth review of integrating ‘omics’ data with genomescale models of model organism, see the recent review by
Saha et al. (2014).
Concluding remarks
Understanding the complexity of the control of NUE of
model crop species such as maize requires a holistic understanding of N flow and associated regulation at the cellular,
organ, and whole-plant levels. In this review, we have highlighted the current status of plant ‘omics’ and critically analysed the importance of metabolic modelling in the study of
NUE and other agronomic traits such as biomass and grain
yield. While the integration of several biological databases,
model-building strategies, and high-throughput ‘omics’ procedures are already available, there is still no whole-plant model
that has been developed for maize. Therefore, by applying a
combinatorial semi-automated (Suthers et al., 2009; Jerby
et al., 2010) model-building workflow, a high-quality wholeplant model could be developed for maize. Then, the ‘omics’
data obtained by growing plants under varying N conditions
and by analysing genetically modified plants and mutants
altered in the expression of structural or regulatory genes for
N uptake, assimilation, and remobilization can be incorporated in the model. Such data could provide a more accurate
simulation of the effect of N on the metabolic interactions
and flow throughout the plant and subsequently could identify the key reactions (i.e. genes) controlling NUE. Ultimately,
an integrated model combined with quantitative genetic studies may identify possible genetic interventions to improve
NUE. Exploiting natural and created genetic variability could
then be tested experimentally either to verify the model or to
provide new information to resolve discrepancies of model
predictions, thereby increasing the model fidelity in the future.
In addition, genome-wide association studies combined
with metabolic and gene-expression analyses are becoming
more commonly implemented for screening large collections
of genotypes and hybrids for their potential productivity
(Riedelsheimer et al., 2012b; Wen et al., 2014). Such studies
also focus on the effect of the environment on plant phenotypic plasticity under various N regimes and environmental conditions (Brunetti et al., 2013; Gifford et al., 2013).
In a similar manner to gene-expression studies, it is also
now possible to study the relationship between measured
metabolite contents in order to interpret complex datasets
and identify key network components for further practical
metabolic engineering (Toubiana et al., 2013; YonekuraSakakibara et al., 2013).
Maize | 5667
Thus, the next major challenge for plant biologists and
breeders will consist of integrating full ‘omics’ datasets into
the modelling, population structure, and selection strategies
(Langridge and Fleury, 2011).
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