Download 21. Toshihiko Okamotofi`1 Yo Isogai,$2 and T6111 K0izumi*1 :

Plant Mol Biol (2016) 92:293–312
DOI 10.1007/s11103-016-0512-5
Nitrogen assimilation system in maize is regulated
by developmental and tissue-specific mechanisms
Darren Plett1,2 · Luke Holtham1,2 · Ute Baumann1,2 · Elena Kalashyan1,2 ·
Karen Francis2 · Akiko Enju1,2 · John Toubia1,2,3,4 · Ute Roessner5,6 · Antony Bacic6,7 ·
Antoni Rafalski8 · Kanwarpal S. Dhugga9,10 · Mark Tester11 · Trevor Garnett1,2,12 ·
Brent N. Kaiser2,13
Received: 23 December 2015 / Accepted: 10 July 2016 / Published online: 10 August 2016
© Springer Science+Business Media Dordrecht 2016
Abstract
Key message We found metabolites, enzyme activities
and enzyme transcript abundances vary significantly
across the maize lifecycle, but weak correlation exists
between the three groups. We identified putative genes
regulating nitrate assimilation.
Abstract Progress in improving nitrogen (N) use efficiency (NUE) of crop plants has been hampered by the complexity of the N uptake and utilisation systems. To understand this complexity we measured the activities of seven
enzymes and ten metabolites related to N metabolism in the
leaf and root tissues of Gaspe Flint maize plants grown in
0.5 or 2.5 mM NO3− throughout the lifecycle. The amino
Electronic supplementary material The online version of this
article (doi:10.1007/s11103-016-0512-5) contains supplementary
material, which is available to authorized users.
Trevor Garnett
[email protected]
1
Australian Centre for Plant Functional Genomics, Waite
Research Institute, University of Adelaide, Adelaide,
SA 5064, Australia
2
School of Agriculture, Food and Wine, Waite Research
Institute, University of Adelaide, Adelaide,
SA 5064, Australia
3
ACRF South Australian Cancer Genomics Facility, Centre
for Cancer Biology, SA Pathology, Adelaide,
SA 5000, Australia
4
Present address: School of Molecular and Biomedical
Science, The University of Adelaide, Adelaide,
SA 5000, Australia
5
Australian Centre for Plant Functional Genomics, School of
BioSciences, The University of Melbourne, Parkville,
VIC 3010, Australia
acids had remarkably similar profiles across the lifecycle
except for transient responses, which only appeared in the
leaves for aspartate or in the roots for asparagine, serine
and glycine. The activities of the enzymes for N assimilation were also coordinated to a certain degree, most noticeably with a peak in root activity late in the lifecycle, but
with wide variation in the activity levels over the course of
development. We analysed the transcriptional data for gene
sets encoding the measured enzymes and found that, unlike
the enzyme activities, transcript levels of the corresponding genes did not exhibit the same coordination across the
lifecycle and were only weakly correlated with the levels
of various amino acids or individual enzyme activities.
We identified gene sets which were correlated with the
enzyme activity profiles, including seven genes located
6
Metabolomics Australia, School of BioSciences, The
University of Melbourne, Parkville, VIC 3010, Australia
7
ARC Centre of Excellence in Plant Cell Walls, School of
BioSciences, The University of Melbourne, Parkville,
VIC 3010, Australia
8
DuPont Pioneer, Wilmington, DE 19803, USA
9
DuPont Pioneer, Johnston, IA 50131, USA
10
Present address: International Maize and Wheat
Improvement Center (CIMMYT), Carretera México
Veracruz, Km. 45, El Batán, Texcoco,
Estado De México 56237, USA
11
Center for Desert Agriculture, King Abdullah University of
Science and Technology, Thuwal 23955-6900, Saudi Arabia
12
Present address: The Plant Accelerator, Australian Plant
Phenomics Facility, The University of Adelaide, PMB 1,
Glen Osmond 5064, Australia
13
Present address: Centre For Carbon Water and Food, The
Faculty of Agriculture and Environment, The University of
Sydney, Camden, NSW 2570, Australia
13
294
within previously known quantitative trait loci for enzyme
activities and hypothesise that these genes are important
for the regulation of enzyme activities. This work provides
insights into the complexity of the N assimilation system
throughout development and identifies candidate regulatory genes, which warrant further investigation in efforts to
improve NUE in crop plants.
Keywords Nitrogen use efficiency · NUE ·
Nitrogen metabolism · Amino acids · Enzyme activity ·
Transcript abundance
Introduction
Nitrogen uptake, utilisation and remobilisation are the fundamental mechanisms which determine the NUE of a crop
plant (Garnett et al. 2009; Good et al. 2004; Hawkesford
2011). I mproving NUE of crop plants is one of the most
important goals in agriculture, along with water use efficiency and its interactions with NUE (Dhugga and Waines
1989; Ober and Parry 2011). Since crop plants are inefficient users of N fertiliser (Sylvester-Bradley and Kindred
2009), significant amounts of N are lost from the root zone
and can pollute waterways or be lost as gaseous N instead of
improving grain yield (Sebilo et al. 2013). Improved NUE
will reduce the direct cost, and minimise the sheer scale, of
N fertilisers applied to crops in order to improve yield (FAO
2013).
Nitrate (NO3−) is the predominant form of N taken up
by plants in most agricultural cropping systems (Miller et
al. 2007; Wolt 1994). Much of this NO3− is transported by
the NRT1/PTR (now NPF (Léran et al. 2014)) and NRT2
families of NO3− transporters (Krouk et al. 2010a). In Arabidopsis, uptake of NO3− from the soil occurs via the low
affinity NO3− transporters NRT1.1 (NPF6.3) and NRT1.2
(NPF4.6) and the high affinity NRT2.1 and NRT2.2 transporters (Wang et al. 2012). Uptake of ammonium (NH4+) is
important in certain agricultural settings such as the paddy
rice system where NH4+ uptake occurs predominantly
through AMT transporters (Ranathunge et al. 2014; von
Wiren et al. 2000). Regulation of N transporters is complex
and involves multiple interactions with other environmental
factors (Gutiérrez 2012; Wang et al. 2012; Xu et al. 2012).
The complexity and lack of understanding of the system(s)
controlling plant N uptake is an important reason behind
the delay in delivering new crop cultivars with improved
N uptake efficiency (McAllister et al. 2012). Furthermore,
the fact that no more than two-thirds of the maize leaf N is
remobilized for grain development under limiting or normal soil N further strengthens the case to enhance nitrate
uptake and assimilation for grain yield stability (DeBruin
et al. 2013).
13
Plant Mol Biol (2016) 92:293–312
Inorganic N (NO3− and NH4+) taken up by the plant must
first be assimilated into amino acids before it can be utilised
by the plant for synthesising proteins for growth. A number of comprehensive reviews discussing the N assimilation
system have been published (Lam et al. 1996; McAllister et
al. 2012; Xu et al. 2012). Briefly, NO3− is reduced to NO2−
by nitrate reductase (NR) in the cytoplasm (Lea et al. 2006)
and further reduced to NH4+ by nitrite reductase (NiR) in
the plastid/chloroplasts (Takahashi et al. 2001). The NH4+
is assimilated into amino acids by the glutamine synthetase
(GS)/glutamate synthase (GOGAT) system in the plastids/
chloroplasts into glutamine (Gln) and glutamate (Glu) (Bernard et al. 2008; Martin et al. 2006; Swarbreck et al. 2011;
Yamaya and Kusano 2014). Aspartate (Asp) and asparagine
(Asn) can be produced from these amino acids by asparagine synthetase (AS) (Gaufichon et al. 2013) and aspartate
aminotransferase (AspAT) (de la Torre et al. 2014a, b).
Together these are the four primary amino acids for transport between, and storage within, plant organs. Alanine
aminotransferase (AlaAT) catalyses the reversible reaction
between glutamate and pyruvate on the one hand, and alanine (Ala) and alpha-ketoglutarate on the other (Beatty et al.
2009; McAllister et al. 2012; Shrawat et al. 2008).
An understanding of the complexity of the N assimilation
regulatory system is beginning to emerge but, despite important advances in understanding the control points of this system (Liseron-Monfils et al. 2013; Schlüter et al. 2012; Valadier
et al. 2008; Zanin et al. 2015), there has been little progress in
improving N utilisation efficiency in crops (McAllister et al.
2012). I n particular, matching amino acid levels with either
related enzyme activities or the transcript abundance of genes
encoding the relevant proteins has been difficult (Fernie and
Stitt 2012; Stitt 2013). As an example, a forward genetics
approach to discover regulatory elements involved the measurement of activities of several of the central N assimilation
enzymes in the leaves of the IBM B73 x Mo17 maize mapping
population (Zhang et al. 2010). The study identified 81 quantitative trait loci (QTL) important for such regulation; however
only three of these were cis-QTL, meaning the gene encoding the relevant structural enzyme was located within the QTL
interval. This suggests that there are other proteins involved in
regulating the actual enzyme activity.
Our recent work described the physiological and molecular responses of Gaspe Flint (Gaspe) maize plants to N supply and demand across the lifecycle (Garnett et al. 2013).
We demonstrated that plants grown in low N were able to
meet demand and maintain growth and grain yield of those
grown in higher N by increasing their NO3− uptake capacity. These changes in uptake capacity were correlated with
the transcript abundance of genes encoding the high-affinity
nitrate transporters (NRT2). We also undertook an analysis
of the transcriptional landscape of the plant’s response to N
supply and demand and discovered an extremely dynamic
Plant Mol Biol (2016) 92:293–312
transcriptional response across the lifecycle (Plett et al.
2016). Here we extend these studies by characterising the
N assimilation machinery of maize to determine whether
this response to low N also required an adaptation of these
processes. We measured the tissue levels of amino acids and
quantified the activity of the central N assimilation enzymes
across the entire lifecycle of Gaspe maize. Further, we analysed the microarray data for transcript levels of the genes
encoding these N assimilation enzymes to determine if
the transcriptional response was related to the response of
maize to low N. Finally, we analysed the transcriptional data
for genes which have transcript abundance profiles similar
to those of the enzyme activities, foreseeing a putative role
in regulating the activity of the enzyme. We present a short
list of such genes from our work, which can now be considered as candidate genes underlying enzyme activity QTL
described in previous studies of maize N assimilation.
295
Results
Fig. 1 Simplified schematic representation of the major biochemical
pathways of N assimilation in plants. Additional roles of enzymes and
biochemical intermediates specific to C4 plants (e.g. photosynthesis)
have not been included (Pick et al. 2011; Wang et al. 2014). Included
in colour are the amino acids (blue) and enzyme activities (red), which
were measured in this study
We measured the amino acid quantities and enzyme activities in the youngest fully emerged leaf blade (YEB) and
roots of Gaspe maize plants grown hydroponically in either
low (0.5 mM) or adequate (2.5 mM) NO3− across the lifecycle (Garnett et al. 2013). The choice of concentrations was
based on preliminary experiments, which suggested that the
threshold NO3− concentration eliciting a major N response
was 0.5 mM. The tissue levels of amino acids were quantified and the activities of the core group of enzymes involved
in N assimilation were measured including NR, NiR, GS,
GOGAT, AS, AspAT and AlaAT (Fig. 1).
The amino acids with the highest tissue concentrations
were Ala, Asn, Asp, Serine (Ser), Glu, Glycine (Gly), Threonine (Thr), Gln, Valine (Val) and Tyrosine (Tyr) (Fig. 2).
The level of most amino acids in the YEB was higher in the
plants grown in 2.5 than 0.5 mM at several points across the
lifecycle, however there was a similar trend between treatments for most AAs with a steady increase in level until the
final time point when YEB levels were much higher than in
the root. The amino acid pool was greater in the YEB than in
the roots, consisting of approximately 25 % Ala. Asp deviated from the total amino acids profile with stable to slightly
decreasing levels in the YEB over time. The level of most
amino acids in the roots decreased after day seven for both
treatments and remained relatively stable for the rest of the
lifecycle except for a small peak in the level of most AAs
in the 0.5 mM plants at day 20 and in the level of Asn, Ser
and Gly in 2.5 mM at day 36. The roots had a higher level
of Gln until later in the lifecycle where it showed a transient
peak in 0.5 mM at day 22. The peak in total AA in the root
at day 36 in the 2.5 mM plants mainly consisted of Asn, Ser
and Gly.
The activity of most of the core group of N assimilation
enzymes was higher in the roots than in the leaves with the
exception of NR (Fig. 3a). There were very few differences
in activity between the treatments in either tissue. I n the
YEB, NR activity was highest in the early time points and
decreased steadily from day 15. As well, the 2.5 mM plants
had higher YEB NR activity at day 11 than the 0.5 mM
plants. NR activity was higher in the roots of plants grown
in 2.5 mM than 0.5 mM at days 15, 18, 20 and 25, with activity in both treatments gradually declining over the lifecycle.
In order to analyze the gene expression differences from
our recently produced microarray dataset, we first identified
the genes encoding the enzymes that we assayed. I mportantly, the data presented in this study are derived from the
same tissue samples used in our previous physiological and
transcriptomic characterization of Gaspe maize grown at
two steady-state levels of NO3− across the lifecycle (Garnett
et al. 2013; Plett et al. 2016). We also established the microarray data was robust by confirming abundances of a set
of transcripts from the same samples via quantitative-PCR
(Plett et al. 2016). We used all known Arabidopsis genes
encoding the proteins for these enzymes and employed a
reciprocal best-hit approach (Plett et al. 2010) to identify
the orthologous genes from the maize genome. A total of 30
maize genes were identified, ranging from two to six encoding the individual enzyme types, with predicted subcellular
localizations in chloroplast, mitochondria and cytoplasm
(Supplementary Table 1). Of these, 23 were represented on
the microarray we used for the transcriptional analysis previously (Plett et al. 2016).
Three of the four NR genes are present on the microarray
(Supplementary Fig. 1). ZmNR1 had much higher transcript
13
296
Fig. 2 Tissue levels of the top ten amino acids across the lifecycle.
a Ala, b Asn, c Asp, d Ser, e Glu, f Gly, g Thr, h Gln, i Val and j Tyr
were quantified in the youngest fully emerged leaf blade (YEB) (green
lines) and roots (o range lines) of plants grown in either 0.5 (open
13
Plant Mol Biol (2016) 92:293–312
symbols) or 2.5 mM (filled symbols) NO3− across the lifecycle. Values
are the mean ± SEM (n = 4) with stars indicating a significant difference (see “Materials and methods” for details) between treatments in
YEB (green) or roots (orange)
Plant Mol Biol (2016) 92:293–312
297
Fig. 3 Nitrate reductase (NR) activity and transcript abundance
profiles of genes encoding NR proteins across the lifecycle. a NR
enzyme activity was quantified in the youngest fully emerged leaf
blade (YEB) (green lines) and roots (o range lines) of plants grown
in either 0.5 (open symbols) or 2.5 mM (filled symbols) NO3− for the
entire lifecycle. b–d Transcript abundance data (log2) was mined from
microarray analysis described previously (Plett et al. 2016). Values are
the mean ± SEM (n = 4) with stars indicating a significant difference
(see “Materials and methods” for details) between treatments in YEB
(green) or roots (orange)
abundance in both YEB and root than ZmNR3, while ZmNR3
had higher root transcript abundance than YEB transcript
abundance (Fig. 3b–d). ZmNR1 YEB transcript abundance
profile matched YEB NR activity, but none of the NR genes
matched root activity. All three genes had similar root transcript abundance profiles with significantly higher transcript
levels in the 2.5 mM treatment at day 29. Transcript abundance of ZmNR2 was similar between tissues, however,
transcript abundance in the YEB leaves from 0.5 mM plants
peaked at day 18 and in the roots from 2.5 mM plants at day
29. A decrease in expression was observed in roots from
plants grown in 0.5 mM at day 29.
The activity of NiR was higher in the roots than in the
YEB across the lifecycle (Fig. 4a). There was no difference in activity between treatments and activity showed
one gradual peak in the YEB days 11–29, while there was
a distinct peak in root activity days 29–43. One of two NiR
genes is represented on the microarray (Supplementary
Fig. 2). Transcript abundance of ZmNiR1 was generally
stable except for a decrease in transcript abundance in the
roots at day 29 in the 0.5 mM treatment (Fig. 4b). Neither root nor shoot transcript abundance matched enzyme
activity.
The GS activity was similar to several of the other
enzymes measured in that there was very little difference
between the treatments for both the YEB and roots and the
distinct peak in activity occurred in the roots at days 29–43
(Fig. 5a). All six maize GS genes are represented on the
microarray (Supplementary Fig. 3). ZmGS1-3, ZmGS1-4 and
ZmGS1-5 had similar YEB transcript abundance profiles,
especially in 2.5 mM grown plants (Fig. 5b–g). ZmGS1-3
and ZmGS1-4 had similar root transcript abundance profiles at both concentrations. ZmGS1-2 and ZmGS2 had
similar root transcript abundance profiles with a decrease in
0.5 mM root transcript abundance at day 29. Four genes had
higher transcript abundance in roots, however ZmGS1-2 and
ZmGS2 had higher transcript abundance in the YEB. None
of the transcript abundance patterns were similar to enzyme
activity patterns either in YEB or root.
There was little difference in ferrodoxin-dependent
GOGAT enzyme activity between the YEB and roots
except days 29–43 where the root activity peaked while the
activity in the YEB did not (Fig. 6a). Three of four maize
GOGAT genes are represented on the microarray (Supplementary Fig. 4). ZmGOGAT1 (encodes ferrodoxin dependent GOGAT) had higher transcript abundance in the YEB
13
298
Fig. 4 Nitrite reductase (NiR) activity measurement and transcript
abundance profiles of genes encoding NiR proteins across the lifecycle.
a NiR enzyme activity was quantified in the youngest fully emerged
leaf blade (YEB) (green lines) and roots (orange lines) of plants grown
in either 0.5 (open symbols) or 2.5 mM (filled symbols) NO3− for the
entire lifecycle. b Transcript abundance data (log2) was mined from
microarray analysis described previously (Plett et al. 2016). Values are
the mean ± SEM (n = 4) with stars indicating a significant difference
(see “Materials and methods” for details) between treatments in YEB
(green) or roots (orange)
compared to root, while ZmGOGAT2 and ZmGOGAT3
(both encode NADH dependent GOGAT) had higher transcript abundances in root (Fig. 6b–d). ZmGOGAT2 and
ZmGOGAT3 had nearly identical YEB and root profiles
and all three genes show a decrease in transcript abundance
level at day 29 in the 0.5 mM roots. None of the transcript
abundance profiles matched the GOGAT activity profile.
The AS activity measurements showed little difference
between treatments in the YEB or root and shared the peak
in root activity at days 29–43 (Fig. 7a). Three of four AS
genes are represented on the microarray (Supplementary
Fig. 5). Similar YEB transcript abundance profiles exist
between ZmAS2 and ZmAS3 with a slow increase over time
(Fig. 7b–c). Root activity did not match any of the transcript
abundance profiles. There was a large increase in YEB transcript abundance of ZmAS3 (log2 7–13) in 0.5 mM plants at
day 32. Transcript abundance levels of all three genes were
higher in the root than in the YEB.
13
Plant Mol Biol (2016) 92:293–312
No treatment differences were detected in AspAT activity in the YEB or roots (Fig. 8a). A peak observed in root
activity appeared narrower than those of the other aforementioned enzymes. All five AspAT genes are represented
on the microarray (Supplementary Fig. 6). The transcript
abundances of ZmAspAT1.1 and ZmAspAT2.1 were higher
in the root, while transcript abundance of ZmAspAT1.2 was
higher in the YEB (Fig. 8b–f). Transcript abundances of the
genes ZmAspAT1.1, ZmAspAT1.3, ZmAspAT2.1 and ZmAspAT2.2 decreased in the YEB at day 18. Activity profiles did
not correlate with the transcript abundances for any of the
genes encoding AspAT.
AlaAT activity was similar between the treatments or
tissues until the end of the lifecycle when the root activity
increased (Fig. 9a). Two of the five AlaAT genes are represented on the microarray (Supplementary Fig. 7). A general
decrease in ZmAlaAT4 transcript abundance occurred over
time in the YEB (Fig. 9b–c). A peak in transcript abundance
of ZmAlaAT5 in the YEB was observed at day 18 for the
0.5 mM grown plants. The AlaAT activity profile did not
correlate with transcript abundances for either gene.
I n order to determine whether the N assimilation system was operating in a coordinated manner we looked
for correlations amongst all the data from the amino acid
level, enzyme activity and transcript abundance analyses.
Data sets from the YEB and root were analyzed independently of each other to determine whether there were
tissue-specific responses involved in N assimilation. In
the YEB, the NR enzyme activity clustered with ZmNR1
(GRMZM2G589636), a gene encoding NR. For all other
enzyme activities, no genes encoding the respective enzyme
proteins clustered together in either the YEB or root (Supplementary Fig. 8). Several enzyme activity profiles clustered together in the root, and the amino acids clustered
together in both the YEB and roots. Additionally, clusters
of genes encoding enzyme proteins were observed, which
shared similar transcript abundance profiles. However, little
clustering was noticed among amino acids, enzyme activities and transcript abundances of the respective genes.
We next determined whether genes not directly related
to the studied enzymes shared transcript abundance profiles
with the enzyme activity profiles. The underlying hypothesis was that enzyme activities might be affected by transacting factors, which were under the control of signaling
pathways regulated by classic signaling molecules, for
example, kinases and phosphatases. Using a program developed “in-house” (NUEcorr—see “Materials and methods”
for details) we identified all genes with either significant
positive (r > 0.95) or negative (r < −0.95) correlations to
the enzyme activities in all tissues and treatments (Table 1).
Visual inspection of transcript profiles correlated with
enzyme activities at a range of correlation coefficients indicated the chosen values were appropriate to limit inclusion
Plant Mol Biol (2016) 92:293–312
299
Fig. 5 Glutamine synthetase (GS) activity measurement and transcript
abundance profiles of genes encoding GS proteins across the lifecycle.
a GS enzyme activity was quantified in the youngest fully emerged
leaf blade (YEB) (green lines) and roots (orange lines) of plants grown
in either 0.5 (open symbols) or 2.5 mM (filled symbols) NO3− for the
entire lifecycle. b–g Transcript abundance data (log2) was mined from
microarray analysis described previously (Plett et al. 2016). Values are
the mean ± SEM (n = 4) with stars indicating a significant difference
(see “Materials and methods” for details) between treatments in YEB
(green) or roots (orange)
of ‘false positives’. Figure 10 is an example of this search
and identifies the transcript abundance profiles, which are
positively or negatively correlated with NR activity in the
roots from plants grown in either 0.5 or 2.5 mM NO3−.
Numbers of transcripts with correlated abundance profiles
varied greatly among enzymes, tissues and treatments. For
example, two correlated with the NR activity profile in the
YEB in 0.5 mM treated plants and 665 co-expressed with
13
300
Plant Mol Biol (2016) 92:293–312
Fig. 6 Glutamate synthase (GOGAT) activity measurement and transcript abundance profiles of genes encoding GOGAT proteins across
the lifecycle. a GOGAT enzyme activity was quantified in the youngest fully emerged leaf blade (YEB) (green lines) and roots (orange
lines) of plants grown in either 0.5 (open symbols) or 2.5 mM (filled
symbols) NO3− for the entire lifecycle. b–d Transcript abundance data
(log2) was mined from microarray analysis described previously (Plett
et al. 2016). Values are the mean ± SEM (n = 4) with stars indicating a significant difference (see “Materials and methods” for details)
between treatments in YEB (green) or roots (orange)
the NiR activity profile in the roots of 0.5 mM treated plants.
Even for individual enzymes the variation between tissues
and treatments could be large, with NiR varying widely
in number of correlated genes from very low (32) in the
YEB in 0.5 mM treated plants to very high (665) for roots
in 0.5 mM treated plants. The individual lists of genes summarized in Table 1 are presented in Supplementary Table 2
along with an image of each of the profiles. To examine the
putative function of the genes in each list we completed a
Gene Ontology (GO) enrichment analysis. We found 16 of
the 64 lists were enriched for GO terms, for example, the
list of genes negatively correlated with NiR activity in the
roots from plants grown under low N (NiR_R_0.5neg) was
enriched for amino acid biosynthetic genes (Supplementary
Table 2). We determined the level of similarity between the
lists by collating all 64 lists (5984 total genes) and found
864 present twice, 180 present three times, 53 present four
times, eight present five times and two were on six of the
lists, meaning 76 % of the genes were on only one list.
In a previous study, several N assimilation enzyme activities were measured in an IBM population derived from B73
and Mo17 (Zhang et al. 2010). Activities were measured in
the youngest expanded leaf from the seedlings grown at one
N level and QTL were identified for the individual activities
(Zhang et al. 2010). In this study, we used our lists of genes
with transcript abundance patterns positively and negatively
correlated to individual enzyme activities (YEB only) and
determined the chromosomal location for each gene. We
then determined which of the genes on our lists were located
within the QTL intervals described previously (Zhang et
al. 2010). We found seven such genes (one for GS; two
for AspAT, the transcript abundance of which were negatively correlated with AspAT activity; and four for AlaAT)
(Table 2). Four of these had some functional assignment (no
exine formation [NEF1] ortholog [GRMZM2G056103],
serine/threonine-protein phosphatase [GRMZM2G080083],
a proline-rich family protein [GRMZM2G150217] and a
plasma membrane ATPase [GRMZM2G455557]). Of note,
three of the four AlaAT-related genes were found to be in
close proximity on chromosome five (within 25 genes),
while two of these three genes are next to each other on the
chromosome.
13
Plant Mol Biol (2016) 92:293–312
301
Fig. 7 Asparagine sythetase (AS) activity measurement and transcript
abundance profiles of genes encoding AS proteins across the lifecycle.
a AS enzyme activity was quantified in the youngest fully emerged
leaf blade (YEB) (green lines) and roots (orange lines) of plants grown
in either 0.5 (open symbols) or 2.5 mM (filled symbols) NO3− for the
entire lifecycle. b–d Transcript abundance data (log2) was mined from
microarray analysis described previously (Plett et al. 2016). Values are
the mean ± SEM (n = 4) with stars indicating a significant difference
(see “Materials and methods” for details) between treatments in YEB
(green) or roots (orange)
Discussion
changes were observed through development in root
activity than in the YEB, with NR being the exception. The most noticeable trend was the large increase
in root enzyme activity for six of the seven N assimilatory enzymes measured after day 30. This was not completely unexpected given the development profile of the
plants, which were well into their reproductive phase at
the completion of the experiment. However, the response
could be interpreted in two ways. Firstly, the increased
enzymatic activity does closely follow an increase in both
NO3− uptake capacity and NRT2 transcript abundance discovered previously by Garnett et al. (2013). In young vegetative YEB and root tissues (<20 days), NR activity is
enhanced and would appear to be responsive to measured
patterns of NO3− uptake shown across representative time
points by Garnett et al. (2013) and similar to the increase
in the N assimilation system following NO3− resupply
in ‘primary nitrate response’ studies (Li and Oaks 1993;
Wang et al. 2004). However, NR activity in the root did
not increase after day 30, which suggests NO3− influx just
prior to anthesis may result from N demand in aerial parts
of the plant rather than in the roots. This hypothesis is
AAs and enzyme activities vary widely across the
lifecycle, but not in response to NO3− treatment
We measured AAs, enzyme activities and transcript abundance of genes encoding enzymes involved in N assimilation in the youngest emerged, fully expanded leaf blade
(YEB), and whole roots across the Gaspe maize lifecycle in
response to N supply and demand. Amino acid levels varied widely in YEB and root across the lifecycle. The levels
generally increased towards maturity in YEB but stayed
predominantly stable in the root. This suggests that the
root amino acid levels were maintained at an optimal level
across the lifecycle, while excess amino acids were being
exported to young developing tissues (YEB). We observed
some reductions in amino acid (Ala, Asp, Ser and Gly) levels in YEB tissues at day 20, but the general trend was for
an increase as the plants matured.
A similar degree of variation existed for the enzyme
activity profiles across the lifecycle; however, the trends
were opposite to those for the amino acids. Much larger
13
302
Plant Mol Biol (2016) 92:293–312
Fig. 8 Aspartate aminotransferase (AspAT) activity measurement and
transcript abundance profiles of genes encoding AspAT proteins across
the lifecycle. a AspAT enzyme activity was quantified in the youngest fully emerged leaf blade (YEB) (green lines) and roots (orange
lines) of plants grown in either 0.5 (open symbols) or 2.5 mM (filled
symbols) NO3− for the entire lifecycle. b–f Transcript abundance data
(log2) was mined from microarray analysis described previously (Plett
et al. 2016). Values are the mean ± SEM (n = 4) with stars indicating a significant difference (see “Materials and methods” for details)
between treatments in YEB (green) or roots (orange)
supported by an increase in NR activity in YEB tissues
during this period.
Alternatively, enhanced root enzymatic activity (postanthesis) may be related to the degradation of root proteins and the associated enzymatic processes linked with
N-remobilisation as has been described previously in leaves
(Avila-Ospina et al. 2014; Gaufichon et al. 2010; MasclauxDaubresse et al. 2014). The strong response in both AS and
GS activity in roots would support this suggestion. This
hypothesis is also supported by the general increase in YEB
amino acids at this time, putatively resulting from transport
of newly remobilised root amino acids.
An interesting observation was the relationship between NR
and NiR activity in YEB and root tissues. Traditionally, both
NR and NiR activities respond to NO3− supply and have been
shown to be tightly correlated so that NO2− produced by NR
activity is reduced to avoid accumulation to toxic levels in the
tissue (Beevers and Hageman 1980; Foyer et al. 1994; Solomonson and Barber 1990; Ward et al. 1995). Surprisingly we
found both YEB and root tissues displayed contrasting NR and
NiR activities. In roots, NR activity remained stable across the
lifecycle while NiR steadily increased (day 20–30) followed by
a rapid decline through to harvest (day 40). In contrast, YEB
NR activity fluctuated and was strikingly similar to the root
13
Plant Mol Biol (2016) 92:293–312
303
Fig. 9 Alanine aminotransferase (AlaAT) activity measurement and
transcript abundance profiles of genes encoding AlaAT proteins across
the lifecycle. a AlaAT enzyme activity was quantified in the youngest fully emerged leaf blade (YEB) (green lines) and roots (orange
lines) of plants grown in either 0.5 (open symbols) or 2.5 mM (filled
symbols) NO3− for the entire lifecycle. b–c Transcript abundance data
(log2) was mined from microarray analysis described previously (Plett
et al. 2016). Values are the mean ± SEM (n = 4) with stars indicating a significant difference (see “Materials and methods” for details)
between treatments in YEB (green) or roots (orange)
NO3− uptake profiles as observed previously for Gaspe maize
plants (Garnett et al. 2013). In YEB tissues, NiR activity was
relatively unchanged across the lifecycle. These observations
suggest NR activity in YEBs is closely linked to root NO3−
uptake while root NR activity is not. The increase in root NiR
activity may be in response to a liberation of stored vacuolar
root NO3− and its subsequent reduction to NO2− by constitutive root NR activity. Similar activation profiles of root GS,
GOGAT, AS, AspAT and AlaAT would suggest roots postanthesis are undergoing a spike in reduced N flux that mostlikely is supporting reproductive N demands in the shoots.
The similarity in Fd-GOGAT activity levels between
YEB and root was unexpected given that previous studies
report greater activity levels in leaves than roots (Matoh and
Takahashi 1982; Suzuki and Rothstein 1997). The assays
were conducted in vitro under identical conditions, but it
is possible, that in the intact plant, ferrodoxin-dependent
GOGAT activity in the YEB would be significantly higher
than in the root, as expected.
We observed the transcript profiles of genes encoding N
assimilation enzymes across the lifecycle. I n most cases,
when correlation was observed between the transcript
abundances of genes, it was due to flat unresponsive profiles that gave an appearance of correlation. However, in the
root the expression levels of all the genes encoding NR were
correlated (Supplementary Fig. 8), implying the importance
of a unified and co-ordinated response between NR genes
expressed in roots throughout the lifecycle.
Transcript levels for genes encoding N assimilation
enzymes were generally more N-responsive than the associated enzyme activities, suggesting that transcript abundance is not the most influential factor regulating enzyme
activity. I t should be noted that amino acids, enzyme
activities and transcript abundance did differ between
treatments on individual sampling days, however, these
differences were inconsequential in the context of the
entire lifecycle. The fact that the leaf we sampled was
always the YEB may bias against differences associated
with older tissues (those undergoing senescence across
the lifecycle). A common mechanism for plants to cope
with low N provision is to degrade protein to remobilise amino acids from older leaves to support the growth
of younger leaves (Andrews et al. 2004). Perhaps the
data for the older leaves from the plants grown in low
13
304
Plant Mol Biol (2016) 92:293–312
Table 1 The number of genes with transcript abundance profiles correlated positively (r > 0.95) and negatively (r < −0.95) with individual enzyme
activity profiles measured in the youngest fully emerged leaf blade (YEB) and root from plants grown in 0.5 or 2.5 mM NO3−
Enzyme
Tissue/treatment
Postively correlated transcripts
(r > 0.95)
Negatively correlated transcripts (r < −0.95)
Total correlated
transcripts
NR
YEB 0.5
YEB 2.5
Root 0.5
Root 2.5
YEB 0.5
YEB 2.5
Root 0.5
Root 2.5
YEB 0.5
YEB 2.5
Root 0.5
Root 2.5
YEB 0.5
YEB 2.5
Root 0.5
Root 2.5
YEB 0.5
YEB 2.5
0
45
2
111
9
38
420
329
121
36
102
153
77
12
168
141
49
83
2
23
5
114
23
27
245
222
162
26
43
51
51
6
74
76
65
94
2
68
7
225
32
65
665
551
283
62
145
204
128
18
242
217
114
177
Root 0.5
Root 2.5
YEB 0.5
YEB 2.5
Root 0.5
Root 2.5
YEB 0.5
YEB 2.5
Root 0.5
Root 2.5
302
106
42
5
32
279
297
111
85
454
141
28
70
16
87
368
120
45
93
97
443
134
112
21
119
647
417
156
178
551
NiR
GS
GOGAT
AS
AspAT
AlaAT
NO3− would have shown higher enzyme activities, lower
protein and lower amino acid content than those grown
in higher NO3−. However, we believe YEB still provided
a useful tissue to look at N assimilatory pathways as it
acts as both a source and a sink tissue, where root and
shoot activities across the lifecycle support its development and it, in turn, supports the development of leaves
younger than itself. Also, we did not detect any growth or
yield differences and found only small differences in tissue N content between treatments for the plants harvested
in this study (Garnett et al. 2013). The uptake capacity of
the low NO3− grown plants was significantly greater than
those grown in higher NO3−. This suggests that the plants
grown in low NO3− relied on increased NO3− uptake and
did not need to alter N assimilation and remobilisation to
maintain growth and yield at a level similar to the plants
grown in high NO3− which may explain the lack of treatment differences in this study.
13
While correlations exist within AAs, enzyme activities
and enzyme gene transcript abundance profiles, the
three groups show little correlation among them
Many studies suggest that transcript levels and their relevant enzyme activities are not tightly correlated, but
rather have individual regulation profiles and amplitude of transcript changes often greater relative to those
of enzyme activity (Gibon et al. 2004). By examining a
plant’s lifecycle, we attempted to look for correlation
between transcript abundance and enzyme activity, but
without success (Supplementary Fig. 8). This needs to be
put into context, however, as enzyme activity measured
was a contribution from all isoforms active in the tissue. We did not have the tools to separate these isoforms
although we can infer from the complete lack of some
transcripts in the gene expression profiles as to which
of the isoforms might have contributed to activity. This
Plant Mol Biol (2016) 92:293–312
305
Fig. 10 Representative results of analysis to identify transcript abundance profiles correlated with enzyme activity across the Gaspe maize
plant lifecycle. Positively (r > 0.95) and negatively (r < −0.95) correlated transcripts are presented for NR activity in the roots of plants
grown in either 0.5 or 2.5 mM NO3−. Activity measurements are presented as log2 values for the 7 days with available transcript abundance
data (Plett et al. 2016). A complete set of figures for all enzyme activities are provided in Supplementary Table 2
suggests that, even at the resolution of this experiment,
transcriptional responses show limited correlation with
enzymatic activity. This difficulty in connecting metabolic
and transcription information has been described recently
in Arabidopsis and maize (Amiour et al. 2012; Krapp et
al. 2011). Further, other studies have found very little correlation between enzyme activities and metabolites (Sulpice et al. 2010). Similarly, a recent review describes the
discordance between metabolomic, proteomic and transcriptomic data and the difficulty in correlating data across
platforms to generate meaningful models of metabolism
(Fernie and Stitt 2012). This problem plagues most similar
studies of metabolism on different levels and is related to
lack of: knowledge of even well-characterised metabolic
pathways (especially the matter of metabolic flux within
these pathways), comparable instrument capabilities, tissue specificity in measurements, and capacity in model
development. Given the amount of variability in AAs and
enzyme activities in plants, across a developmental profile,
we can also add that insufficient time scale consideration
is hampering the efforts to develop meaningful models of
N assimilation. The capacity for this type of integration is
increasing, however, and systems approaches are making
inroads (Fukushima and Kusano 2014; Simons et al. 2014)
with the goal of generating models with complexity and
completeness, of which a number are emerging in bacteria
(Cho et al. 2012).
Putative candidate genes regulating enzyme activities
In an effort to discover the regulatory genes behind enzyme
activities we surmised that post-translational control is
important for enzyme activity (Chubukov et al. 2013; Fernie and Stitt 2012; Oliveira et al. 2012). We reasoned that
the machinery driving post-translational regulation may be
transcriptionally regulated in a similar manner to the actual
enzyme activity. Thus, we identified all the genes from our
previous transcriptional analyses of the expression profiles
of the maize plants (Plett et al. 2016) that were highly correlated to the enzyme activity profiles we measured. We
considered the genes which were positively or negatively
correlated with respective enzyme activities to capture both
putative positive and negative regulators of enzyme function. Interestingly, we found significant variation in the number of genes correlated with each individual enzyme activity
(Table 1). The number varied greatly among enzymes, but
even for individual enzymes the number of genes varied
between tissues, NO3− treatments and between positive and
13
13
22
14,210,373
13,331,063
0.5
GRMZM2G455557
AlaAT
9
13,769,806
13,773,642
111,334,026
106,931,390
GRMZM2G150217
AspAT (neg)
8
109,897,754
109,898,919
0.5
120
13,111.m03165|protein NEF1, putative, expressed; 0
13,101.m06208|protein expressed protein; 9E-91
13,102.m01045|protein expressed protein; 2E-67
13,102.m01051|protein ribosome, putative, expressed; 1E-27
13,102.m01084|protein serine/threonine-protein phosphatase
2 A regulatory subunit B subunitbeta, putative, expressed; 0
13,105.m01565|protein proline-rich family protein, putative,
expressed; 4E-61
13,106.m00868|protein plasma membrane ATPase, putative,
expressed; 0
241
26
358
358
358
229,414,867
188,830,505
136,661,511
136,661,511
136,661,511
222,216,549
188,168,358
92,564,391
92,564,391
92,564,391
0.5
0.5
0.5
0.5
0.5
GRMZM2G056103
GRMZM2G002165
GRMZM2G377553
GRMZM2G076263
GRMZM2G080083
GS
AspAT (neg)
AlaAT
AlaAT
AlaAT
2
3
5
5
5
223,398,926
188,589,432
99,491,397
99,500,710
101,636,835
223,400,560
188,595,258
99,494,857
99,502,928
101,643,469
QTL end
QTL start
Gene
Enzyme
Chrom
Start
End
Treatment
mM
Genes
under
QTL
BLASTx against rice genes
Plant Mol Biol (2016) 92:293–312
Table 2 List of genes which have transcript abundance profiles positively (r > 0.95) or negatively (r < −0.95) correlated with enzyme activity profiles and which are located in the appropriate
enzyme activity QTL interval described in (Zhang et al. 2010)
306
negatively correlated genes for the same tissue and treatment combinations. We found it surprising that 76 % of the
genes across all the lists of were unique to a single list. We
expected a greater degree of overlap between lists considering the enzymes function co-ordinately to assimilate N and
the individual lists come from the same tissue in two different treatments. This highlights the complexity of the N
assimilation regulatory system.
These gene lists may contain candidate regulatory networks for enzyme activity and the numbers of genes in each
category may indicate the complexity of each regulatory network, however this would require significant further work
to confirm. For example, the NR activity in the roots from
plants grown in 0.5 mM NO3− only had, respectively, two and
five genes positively and negatively correlated with transcript
abundance profiles, whereas the roots from plants grown in
2.5 mM NO3− had 111 and 114 genes in respective categories. If the number of correlated genes are indeed involved in
regulating a given enzyme activity, this suggests the NR regulatory network is much more complex for plants grown with
adequate compared to limiting NO3−. These lists of genes
could be used to explore the regulatory network further using
a systems biology approach by selecting a putative regulatory gene from the list, altering its transcript abundance level
and measuring the effect on the entire set of regulatory genes
shown in the list (Alvarez et al. 2014; Krouk et al. 2010b).
A small number of genes co-regulated with shoot
enzyme activity are in close proximity to activity QTL,
thus are possible candidates for transgenic analysis
A recent study measured the activity of several N assimilation enzymes in the shoots of maize plants in the vegetative
growth stage from the I BM population between B73 and
Mo17 (Zhang et al. 2010). They found that of the 73 QTL
detected in the population for the various enzyme activities only three were cis-QTL and the other 70 were transQTL, meaning very few of the QTL were related to the gene
encoding the actual enzyme protein. We found the genomic
locations of all the genes from our study which had transcript abundance profiles correlated with the enzyme activity profiles and searched for genes which were located in the
relevant QTL intervals (Zhang et al. 2010). We found seven
genes from our analysis, five of which were positively correlated with enzyme activity (one for GS and four for AlaAT)
and two that were negatively correlated (both for AspAT).
Interestingly, three of the four genes related to AlaAT activity were clustered on chromosome five, with two occurring
in tandem. This type of metabolic gene cluster has been
described previously for several other plant metabolite biosynthetic pathways (Boycheva et al. 2014; Chiasson et al.
2014; Nützmann and Osbourn 2015) and suggests that a
similar operon-like cluster may exist for AlaAT in maize.
Plant Mol Biol (2016) 92:293–312
None of the genes from our study have been functionally
characterised, but their current functional assignments
suggest one is involved in signalling (serine/threonine
phosphatase), another may be involved in regulating primary NO3− uptake or pH regulation of NO3− assimilation
(plasma membrane ATPase). Future functional analysis will
be required to determine whether these genes are important
for regulation of the associated enzyme activities.
Conclusion
We found much greater variation in the levels of amino acids,
enzyme activities and transcript abundance of the genes
encoding the enzymes between YEB (leaves) and roots and
across the lifecycle of maize than between N treatments. This
was surprising considering the large treatment differences we
measured in the NO3− uptake system previously, and suggests
the N uptake and assimilation systems are regulated by distinct
mechanisms. This finding also suggests that future studies in
N assimilation in plants should use caution in drawing conclusions based on one or few sampling time points and or tissue
types. It is clear from our work that the N assimilation system
is highly dynamic; meaning that at least several developmental
stages must be considered to avoid skewing interpretations of
correlations among metabolites, enzymes and genes. Another
important implication of this variation over the lifecycle for
amino acids, enzyme activities and genes encoding these
enzymes is that it is critical to sample at precisely the same
developmental time point to avoid interpreting differences
between treatments as relevant in the context of the entire lifecycle. This may be one of the causes behind the paucity of new
plant varieties with improved N assimilation efficiency. Our
finding of large numbers of genes correlated with the enzyme
activities suggests there might be complex regulatory systems
controlling the enzyme activities. A systems-based approach
to understanding these regulatory systems further, may be useful to identify the key regulatory elements behind an enzyme
activity, since it appears the actual transcription of the gene
encoding the enzyme is not a crucial step in the activity of N
assimilation enzymes. Our list of candidate genes provides a
starting point for deconstructing the regulatory systems for the
enzymes of N assimilation, which may lead to improved N
assimilation efficiency in plants.
Materials and methods
Plant growth
Dwarf maize (Zea mays L. var Gaspe Flint) were grown in
hydroponic systems as described previously (Garnett et al.
2013). Plants were sampled between 5 and 7 h after the start
307
of the light period (06:00). The whole root and the (YEB)
were excised, snap frozen in liquid N and stored at −80 °C.
Amino acid analysis
Tissue amino acid levels were determined using high pressure liquid chromatography electrospray ionization-mass
spectrometry as described by (Boughton et al. 2011) once
the samples had been derivatised following the method of
(Cohen and Michaud 1993).
Enzyme activity assays
For the NR activity assay, cryo-frozen and ground tissue
(30–35 mg) was measured into 1.1 ml tubes in a 96-well format and 140 µl protein extraction buffer was added to each
tube. Each 10 ml of protein extraction buffer was comprised
of 250 µl 1 M Tris–HCl (pH 8.5), 50 µl EDTA (200 mM),
2 µl FAD (100 mM), 0.1 g BSA, 10 µl DTT (100 mM),
1 ml l-Cysteine (100 mM) and 10 µl leupeptin (10 mM) for
YEB tissue or 10 µl chymostatin (10 mM) for root tissue
extractions. A small ball bearing was added to each tube and
samples were shaken vigorously for 15 min at 4 °C. Samples
were centrifuged at 4000×g for 15 min at 4 °C, supernatant
was transferred to fresh 1.1 ml tubes and centrifuged again
at 4000×g for 15 min at 4 °C to ensure supernatant was completely clarified. Supernatant was diluted to 1/3 concentration with the extraction buffer and protease inhibitors above
and transferred to 96-well PCR plates and stored at −80 °C.
The NR assay was undertaken in deep well optical plates.
Both phosphorylated and non-phosphorylated NR activities
were measured since Mg2+ was not added to the assay (Lea
et al. 2006). Protein extract (10 µl) and 70 µl assay buffer
was added to each well of an optical plate. Each 17.5 ml of
assay buffer was comprised of 2.5 ml 0.65 mM Hepes buffer (pH 7.0), 2.5 ml 0.1 M KNO3, 1.25 ml 200 mM NADH
(in 0.04 M KPO4 pH 7.0) and 1.25 ml 200 mM NADPH (in
0.04 M KPO4 pH 7.0). Plates were sealed and incubated in a
water bath (28 °C) in the dark for 20 min. The reaction was
stopped by adding 5 µl alcohol dehydrogenase (in 0.1 M
KPO4 pH 7.0) and 5 µl 2 % (v/v) acetylaldehyde to each well.
Following this 100 µl of a 50:50 mixture N-ethylamedine
(0.01 % (w/v)) and sulphanilamide in HCl (1 % (w/v)) was
added to each well and the plates remained at room temperature for 30 min. Each of the standards (70 µl added to each
well) was comprised of 0–500 µl 1 M KNO2 (0–100 µM)
625 µl 0.65 M Hepes buffer (pH 7.0), 625 µl of the extraction buffer above, 625 µl 0.1 M KNO3, and was made up
to 4.375 ml with water. At the start of the assay 10 µl of a
50:50 mix of 200 mM NADH and 200 mM NADPH was
added to each well of the standards. Blank wells contained
10 µl of extraction buffer (and protease inhibitor) and 70 µl
of the assay buffer. Plates were centrifuged at 1000×g for
13
308
20 s to remove bubbles and absorbance was measured at
540 nm on a Polarstar optical plate reader.
For the remaining enzyme activity assays, cryo-frozen
and ground tissue (100 mg) was measured into 1.1 ml tubes
in a 96-well format. Protein extraction buffer (600 µl) was
added to each tube and the rack of tubes was shaken vigorously for 15 min at 4 °C. Extraction buffer was comprised of
50 mM HEPES (pH 7.5), 20 % (v/v) glycerol, 1 mM EDTA,
1 mM EGTA, 0.1 % (v/v) Triton X-100, 1 mM benzamidine,
1 mM 6-aminohexanoic acid and 5 µl/ml protease inhibitor cocktail (Sigma–Aldrich). Racks were centrifuged at
4000×g for 30 min and supernatant was transferred to fresh
tubes. Racks were centrifuged at 4000×g for an additional
30 min. Supernatant was transferred to 96-well PCR plates,
snap frozen in liquid nitrogen and stored at −80 °C.
NiR activity was assayed in a solution containing 50 mM
Pi buffer (pH 7.1), 0.2 mM KNO2 and 2 mM methyl viologen. Each 100 ml of Pi buffer was comprised of 13.1 ml 1 M
K2HPO4, 6.9 ml KH2PO4 and 80 ml of H2O to achieve a pH
of 7.1. Protein extract (10 µl) was added to 90 µl of the assay
solution. 10 µl of 100 mM sodium dithionite in 50 mM Pi
buffer (pH 7.1) was added to start the reaction. The optical
plate was covered with plastic film and placed into a 25 °C
water bath for 20 min. The plates were then immediately
shaken to stop the reaction and were left at room temperature for 30 min. Following this 100 µl of a 50:50 mixture
N-ethylamedine (0.15 % (w/v)) and sulphanilamide (7.5 %
(w/v)) was added to each well and absorbance was measured
at 540 nm on a Polarstar optical plate reader. Activities were
determined based on the difference in amount of nitrite left
in solution subtracted from the initial nitrate added to the
assay solution. KNO2 standards in extraction buffer were
added to each plate in the range 0–350 µM. Activities were
expressed as nmol nitrite reduced per g of fresh tissue.
AlaAT activity was assayed in conditions adapted from
(Gibon et al. 2004). The assay solution contained 50 mM
Tris–Cl (pH 7.8), 10 mM l-alanine, 2.5 mM α-ketoglutarate
and 2 mM EDTA. Protein extract (15 µl + 10 µl dH2O) was
added to 25 µl of the assay solution and PCR plates were
incubated at room temperature for 30 min. The reaction was
stopped by adding 20 µl of 0.2 M N-ethylmaleimide and
plates were sealed with aluminium seals. Plates were heated
to 95 °C in a PCR machine for 10 min, then cooled and centrifuged at 4000×g for 2 min. Determination assays were
performed by transferring the entire assay solution to optical
plates. The determination solution was comprised of 100 mM
Tricine (pH 8.5), 0.6 mM MTTox, 1.8 mM NAD, 0.3 %
(v/v) Triton X-100 and 0.15 U diaphorase. The determination solution (100 µl) was added to each well and absorbance
was measured at 570 nm. After 5–10 min 10 µl of a solution
comprised of 1U/10 µl glutamate dehydrogenase in 50 mM
Tricine (pH 8.5) was added to each well and plates were incubated at room temperature in darkness for 1 h. Absorbance
13
Plant Mol Biol (2016) 92:293–312
was measured at 570 nm and the first absorbance measurement was subtracted from the second to derive a value for
activity calculations. Glutamate standards (0–9 mM in extraction buffer) were run in parallel. Activities were expressed as
nmol glutamate produced per g of fresh tissue.
AspAT activity was assayed in conditions adapted from
(Gibon et al. 2004). The assay solution contained 100 mM
Tricine (pH 8.0), 2.5 mM aspartic acid, 0.1 mM NADH and
2 mM α-ketoglutarate. Contaminating NAD+ was removed
from the NADH stock solution (in NaOH) prior to the assay by
heating at 95 °C for 10 min (Gibon et al. 2004). Protein extract
(10 µl + 10 µl dH2O) was added to 20 µl of the assay solution
in PCR plates and the α-ketogluarate was added to start the
reaction. Plates were incubated at room temperature for 30 min
and the reaction was stopped by adding 10 µl of 0.2 M N-ethylmaleimide and 10 µl of 1 M HCl. Plates were sealed with
aluminium foil, heated in a PCR machine to 95 °C for 10 min,
cooled and centrifuged at 4000×g for 2 min. Determination
assays and activity calculations were the same as for AlaAT.
AS activity was assayed in conditions adapted from (Joy
and I reland 1990) The assay solution contained 50 mM
Tris–Cl (pH 7.8), 5 mM glutamine, 5 mM ATP, 10 mM Asp,
10 mM MgSO4 and 2 mM DTT. Protein extract (15 µl + 10 µl
dH2O) was added to 25 µl of assay solution in PCR plates.
Plates were incubated at room temperature for 30 min and
the reaction was stopped by adding 10 µl of 0.2 M N-ethylmaleimide to each well. Plates were sealed with aluminium
foil, heated in a PCR machine to 95 °C for 10 min, cooled
and centrifuged at 4000×g for 2 min. Determination assays
and activity calculations were the same as for AlaAT.
GS activity was assayed in a solution containing 100 mM
MOPS (pH 7), 30 mM l-glutamine, 1 mM MnCl2, 0.4 mM
ADP, 10 mM sodium arsenate and 80 mM hydroxylamine.
Protein extract (20 µl of extract diluted 10× in extraction
buffer) was added to 80 µl of the assay solution in PCR
plates. Plates were incubated at room temperature for
20 min, then 100 µl of a solution containing 0.37 M FeCl3,
0.67 M HCl and 0.2 M trichloroacetic acid was added to
each well. Plates were mixed and centrifuged at 4000×g for
5 min. Supernatant (150 µl) was transferred to optical plates
and absorbance was measured at 540 nm on a Polarstar
optical plate reader. Activities were determined based on
the amount of γ-glutamylhydroxamate (γ-GHA) produced
in the assay and samples were compared against γ-GHA
standards (0–10 mM) in extraction buffer. Activities were
expressed as nmol γ-GHA produced per g of fresh tissue.
Ferrodoxin dependent GOGAT activity was assayed in
conditions adapted from (Gibon et al. 2004). The assay
solution contained 50 mM HEPES (pH 7.5), 10 mM l-glutamine, 5 mM methyl viologen, 3 mM α-ketoglutarate and
1 mM amino-oxyacetate. Two assays were run for each
sample, one assay contained l-glutamine and the other did
not. Protein extract (15 µl) was added to 25 µl of the assay
Plant Mol Biol (2016) 92:293–312
309
solution in PCR plates and 10 µl of a solution containing
125 mM sodium dithionite and 250 mM sodium bicarbonate was added to start the reaction. Plates were incubated at
room temperature for 30 min and the reaction was stopped
by adding 20 µl of 0.2 M N-ethylmaleimide to each well.
Plates were sealed with aluminium foil, heated in a PCR
machine for 10 min at 95 °C, cooled and centrifuged at
4000×g for 2 min. Determination assays and activity calculations were the same as for AlaAT with the addition that
activities were determined by subtracting activity in the
assay without added l-glutamine from the assay with added
l-glutamine.
All chemicals were obtained from Sigma-Aldrich unless
otherwise noted.
Identification of homologues
Arabidopsis genes encoding NR, NiR, GS, GOGAT, AS,
AspAT and AlaAT enzymes were used in a modified reciprocal best-hit approach to query the maize genome (B73
AGPv3 assembly) (http://www.phytozome.jgi.doe.gov)
and all sequence curation and alignment and tree building
were as described previously (Plett et al. 2010). Trees were
built using AlignX (Invitrogen) and visualised and modified
using FigTree (http://tree.bio.ed.ac.uk/software/figtree/).
Protein subcellular localization predictions were made
using TargetP 1.1 (Emanuelsson et al. 2000).
Correlation analysis of genes, enzyme activities and
amino acids
Transcript abundance values were obtained from previously
published data (Plett et al. 2016). Heatmaps and hierarchical clustering analysis on mean-centred probe set data were
undertaken using Genesis (Sturn et al. 2002).
I n order to investigate whether the correlation exists
between expression profiles of NUE microarray experiment (set1, 7 time points) and activity profiles of enzymes
involved in nitrogen metabolism (set2, 20 time points) we
decided to employ a commonly used and effective technique
based on Pearson product-moment correlation coefficient r
that measures the similarity in shape between two profiles.
The formula for a sample Pearson correlation coefficient as
the mean of the products of the standard scores is:
n
r=
1
∑ xi − X
n − 1 i =1  S x
  yi − Y
 
 Sy

,


where X = { x1 , … xn } and Y = { y1 , … yn } , two datasets
containing n values; X = 1 / n∑
n
x,
(i =1) i
the sample mean of
1
n
2
X values; S x =
∑ i =1 ( xi − x ) , the sample standard
n
−
1
deviation.
As evident from the formula, r corrects for differences
in variance and thus different datasets, even measured in
different units, do not need to be ‘normalised’. However,
considering that r is not robust to outliers, but taking into
account relatively small size of set2, we adopted manual
pattern curation instead of relying on Bootstrap, Jackknife,
Spearman rank or other methodology to reject false positives. A simple calculator, NUEcorr, was developed. Given
the target activity profile from set2 as input, it computes
Pearson correlation coefficient, based on the common seven
time points of both sets, to all expression profiles in set1.
NUEcorr then selects profiles from set1 that are highly correlated (either positive or negative) with the target profile
from set2 up to a given r-value cut-off. Auto-generated chart
of selected profiles, each ‘centred’ on zero mean, allows for
visual/manual assessment and adjustment.
Gene Ontology enrichment analysis was completed
using AgriGO (Du et al. 2010). A singular enrichment
analysis (complete GO) was undertaken on the query lists
using the ‘Z. mays ssp V5a’ species and the ‘Maize genome
v5a transcript ID’ reference background. The Fisher statistical method was chosen with the Hochberg (FDR) multitest
adjustment at a significance of 0.05. Chromosomal positions of all genes with transcript abundance profiles correlated (correlation coefficient greater than 0.95 or smaller
than −0.95) with enzyme activity profiles in the YEB were
obtained from Gramene (B73 AGPv3 assembly) (http://
ensembl.gramene.org/Zea_mays/). Chromosomal positions of these genes were compared against the appropriate QTL intervals described in (Zhang et al. 2010) for NR,
GS, AspAT and AlaAT and genes with transcript abundance
profiles significantly correlated with the appropriate enzyme
activity profile and located within the appropriate QTL
interval were collated.
Statistical analyses
Statistical analyses of amino acid levels and enzyme activities were completed using multiple t tests (P < 0.05) to identify treatment differences in each tissue on each sampling
day. Differently expressed genes were identified by the
moderated t-statistic implemented in the LIMMA package
(Smyth 2005). P values were adjusted to control the false
discovery rate (Benjamini and Hochberg 1995). Genes were
considered differentially expressed between the two conditions when their adjusted P values were less or equal to 0.01.
Acknowledgments The project was funded by the Australian Centre
for Plant Functional Genomics, DuPont Pioneer, Australian Council
Linkage Grant (LP0776635) to BNK, MT (University of Adelaide)
13
310
and AR, KSD (DuPont Pioneer). The authors gratefully acknowledge
the assistance of Lynne Fallis, Hari Kishan Rao Abbaraju, Vanessa
Conn, Stephanie Feakin, Jaskaranbir Kaur, Simon Conn, Mary Beatty,
and Kevin Hays. The authors also thank Ms Priyanka Reddy and Ms
Chia Ng, Metabolomics Australia, School of BioSciences, The University of Melbourne, for sample preparation and amino acid analysis. UR
and AB are also grateful to Victorian Node of Metabolomics Australia,
which is funded through Bioplatforms Australia Pty Ltd, a National
Collaborative Research I nfrastructure Strategy (NCRI S), 5.1 biomolecular platforms and informatics investment, and co-investment from
the Victorian State government and The University of Melbourne.
References
Alvarez JM et al (2014) Systems approach identifies TGA1 and TGA4
transcription factors as important regulatory components of the
nitrate response of Arabidopsis thaliana roots. Plant J 80:1–13.
doi:10.1111/tpj.12618
Amiour N et al (2012) The use of metabolomics integrated with
transcriptomic and proteomic studies for identifying key steps
involved in the control of nitrogen metabolism in crops such as
maize. J Exp Bot 63:5017–5033. doi:10.1093/jxb/ers186
Andrews M, Lea PJ, Raven JA, Lindsey K (2004) Can genetic manipulation of plant nitrogen assimilation enzymes result in increased
crop yield and greater N-use efficiency? An assess Ann Appl Biol
145:25–40
Avila-Ospina L, Moison M, Yoshimoto K, Masclaux-Daubresse C
(2014) Autophagy, plant senescence, and nutrient recycling. J
Exp Bot 65:3799–3811. doi:10.1093/jxb/eru039
Beatty PH, Shrawat AK, Carroll RT, Zhu T, Good AG (2009) Transcriptome analysis of nitrogen-efficient rice over-expressing
alanine aminotransferase. Plant Biotechnol J 7:562–576.
doi:10.1111/j.1467-7652.2009.00424.x
Beevers L, Hageman RH (1980) Nitrate and Nitrite Reduction. I n:
Miflin BJ (ed) The Biochemistry of Plants, vol 3. Academic
Press, New York, pp 115–168
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate:
a practical and powerful approach to multiple testing J R Stat Soc
Ser B (Methodol) 57:289–300 doi:10.2307/2346101
Bernard S et al (2008) Gene expression, cellular localisation and function of glutamine synthetase isozymes in wheat (Triticum aestivum
L.) Plant Mol Biol 67:89–105. doi:10.1007/s11103-008-9303-y
Boughton BA et al (2011) Comprehensive profiling and quantitation of
amine group containing metabolites. Anal Chem 83:7523–7530.
doi:10.1021/ac201610x
Boycheva S, Daviet L, Wolfender J-L, Fitzpatrick TB (2014) The rise
of operon-like gene clusters in plants. Trends Plant Sci 19:447–
459. doi:10.1016/j.tplants.2014.01.013
Chiasson DM et al (2014) Soybean SAT1 (Symbiotic Ammonium
Transporter 1) encodes a bHLH transcription factor involved
in nodule growth and NH4 + transport. Proc Natl Acad Sci
111:4814–4819. doi:10.1073/pnas.1312801111
Cho B-K, Federowicz S, Park Y-S, Zengler K, Palsson BØ
(2012) Deciphering the transcriptional regulatory logic of
amino acid metabolism Nat Chem Biol 8:65–71 http://www.
nature.com/nchembio/journal/v8/n1/abs/nchembio.710.
html#supplementary-information
Chubukov V et al (2013) Transcriptional regulation is insufficient to
explain substrate-induced flux changes in Bacillus subtilis. Mol
Syst Biol 9:709–709. doi:10.1038/msb.2013.66
Cohen SA, Michaud DP (1993) Synthesis of a fluorescent derivatizing reagent, 6-aminoquinolyl-n-hydroxysuccinimidyl carbamate,
and its application for the analysis of hydrolysate amino acids
13
Plant Mol Biol (2016) 92:293–312
via high-performance liquid chromatography. Anal Biochem
211:279–287. doi:10.1006/abio.1993.1270
de la Torre F, Cañas RA, Pascual MB, Avila C, Cánovas FM (2014a)
Plastidic aspartate aminotransferases and the biosynthesis
of essential amino acids in plants. J Exp Bot 65:5527–5534.
doi:10.1093/jxb/eru240
de la Torre F, El-Azaz J, Ávila C, Cánovas FM (2014b) Deciphering the role of aspartate and prephenate aminotransferase activities in plastid nitrogen metabolism. Plant Physiol 164:92–104.
doi:10.1104/pp.113.232462
DeBruin J et al (2013) N distribution in maize plant as a marker for
grain yield and limits on its remobilization after flowering. Plant
Breed 132:500–505. doi:10.1111/pbr.12051
Dhugga KS, Waines JG (1989) Analysis of nitrogen accumulation
and use in bread and durum wheat. Crop Sci 29:1232–1239.
doi:10.2135/cropsci1989.0011183X002900050029x
Du Z, Zhou X, Ling Y, Zhang Z, Su Z (2010) agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res
38:W64–W70. doi:10.1093/nar/gkq310
Emanuelsson O, Nielsen H, Brunak S, von Heijne G (2000) Predicting subcellular localization of proteins based on their N-terminal
amino acid sequence. J Mol Biol 300:1005–1016. doi:10.1006/
jmbi.2000.3903
FAO (2013) FAO statistical yearbook 2013: world food and agriculture. Food and Agriculture Organization of the United Nations,
Rome, Italy
Fernie AR, Stitt M (2012) On the discordance of metabolomics with
proteomics and transcriptomics: coping with increasing complexity in logic, chemistry, and network interactions. Plant Physiol
158:1139–1145. doi:10.1104/pp.112.193235
Foyer CH, Noctor G, Lelandais M, Lescure JC, Valadier MH, Boutin JP, Horton P (1994) Short-term effects of nitrate, nitrite and
ammonium assimilation on photosynthesis, carbon partitioning
and protein phosphorylation in maize. Planta 192:211–220
Fukushima A, Kusano M (2014) A network perspective on nitrogen
metabolism from model to crop plants using integrated ‘omics’
approaches. J Exp Bot 65:5619–5630. doi:10.1093/jxb/eru322
Garnett T, Conn V, Kaiser BN (2009) Root based approaches to
improving nitrogen use efficiency in plants. Plant Cell Environ
32:1272–1283
Garnett T et al (2013) The response of the maize nitrate transport system to nitrogen demand and supply across the lifecycle. New
Phytol 198:82–94. doi:10.1111/nph.12166
Gaufichon L, Reisdorf-Cren M, Rothstein SJ, Chardon F, Suzuki A
(2010) Biological functions of asparagine synthetase in plants.
Plant Sci 179:141–153. doi:10.1016/j.plantsci.2010.04.010
Gaufichon L et al (2013) Arabidopsis thaliana ASN2 encoding asparagine synthetase is involved in the control of nitrogen assimilation
and export during vegetative growth. Plant Cell Environ 36:328–
342. doi:10.1111/j.1365-3040.2012.02576.x
Gibon Y et al (2004) A Robot-based platform to measure multiple
enzyme activities in arabidopsis using a set of cycling assays:
comparison of changes of enzyme activities and transcript levels during diurnal cycles and in prolonged darkness. Plant Cell
16:3304–3325. doi:10.1105/tpc.104.025973
Good AG, Shrawat AK, Muench DG (2004) Can less yield more? Is
reducing nutrient input into the environment compatible with
maintaining crop production? Trends Plant Sci 9:597–605.
doi:10.1016/j.tplants.2004.10.008
Gutiérrez RA (2012) Systems biology for enhanced plant nitrogen
nutrition. Science 336:1673–1675. doi:10.1126/science.1217620
Hawkesford MJ (2011) An overview of nutrient use efficiency and
strategies for crop improvement. I n: The Molecular and Physiological Basis of Nutrient Use Efficiency in Crops. Wiley-Blackwell, pp 3–19. doi:10.1002/9780470960707.ch1
Plant Mol Biol (2016) 92:293–312
Joy KW, I reland RJ (1990) 17-Enzymes of Asparagine Metabolism.
In: P. J LEA (ed) Methods in Plant Biochemistry, vol 3. Academic
Press, pp 287–296. doi:10.1016/B978-0-12-461013-2.50024-1
Krapp A et al (2011) arabidopsis roots and shoots show distinct temporal adaptation patterns toward nitrogen starvation. Plant Physiol
157:1255–1282. doi:10.1104/pp.111.179838
Krouk G, Crawford NM, Coruzzi GM, Tsay Y-F (2010a) Nitrate signaling: adaptation to fluctuating environments. Curr Opin Plant
Biol 13:265–272
Krouk G, Mirowski P, LeCun Y, Shasha D, Coruzzi G (2010b) Predictive network modeling of the high-resolution dynamic plant
transcriptome in response to nitrate. Genome Biol 11:R123
Lam H-M, Coschigano KT, Oliveira I C, Melo-Oliveira R, Coruzzi
GM (1996) The molecular-genetics of nitrogen assimilation into
amino acids in higher plants. Annu Rev Plant Physiol Plant Mol
Biol 47:569–593. doi:10.1146/annurev.arplant.47.1.569
Lea US, Leydecker M-T, Quillere I , Meyer C, Lillo C (2006) Posttranslational regulation of nitrate reductase strongly affects the
levels of free amino acids and nitrate, whereas transcriptional regulation has only minor influence. Plant Physiol 140:1085–1094.
doi:10.1104/pp.105.074633
Léran S et al (2014) A unified nomenclature of NITRATE TRANSPORTER 1/PEPTI DE TRANSPORTER family members in
plants. Trends Plant Sci 19:5–9. doi:10.1016/j.tplants.2013.08.008
Li XZ, Oaks A (1993) Induction and turnover of nitrate reductase in
zea mays (influence of NO3-). Plant Physiol 102:1251–1257.
doi:10.1104/pp.102.4.1251
Liseron-Monfils C et al (2013) Nitrogen transporter and assimilation
genes exhibit developmental stage-selective expression in maize
(Zea mays L.) associated with distinct cis-acting promoter motifs.
Plant Signal Behav 8:e26056
Martin A et al (2006) Two cytosolic glutamine synthetase isoforms of
maize are specifically involved in the control of grain production.
Plant Cell Online 18:3252–3274 doi:10.1105/tpc.106.042689
Masclaux-Daubresse C et al (2014) Stitching together the multiple
dimensions of autophagy using metabolomics and transcriptomics reveals impacts on metabolism, development, and plant
responses to the environment in arabidopsis. Plant Cell Online
26:1857–1877 doi:10.1105/tpc.114.124677
Matoh T, Takahashi E (1982) Changes in the activities of ferredoxinand NADH-glutamate synthase during seedling development of
peas. Planta 154:289–294. doi:10.1007/bf00393905
McAllister CH, Beatty PH, Good AG (2012) Engineering nitrogen
use efficient crop plants: the current status. Plant Biotechnol J
10:1011–1025. doi:10.1111/j.1467-7652.2012.00700.x
Miller AJ, Fan X, Orsel M, Smith SJ, Wells DM (2007) Nitrate transport and signalling. J Exp Bot 58:2297–2306
Nützmann H-W, Osbourn A (2015) Regulation of metabolic gene
clusters in Arabido psis thaliana. New Phytol 205:503–510.
doi:10.1111/nph.13189
Ober E, Parry MAJ (2011) Drought and implications for nutrition. I n: The Molecular and Physiological Basis of Nutrient Use Efficiency in Crops. Wiley-Blackwell, pp 429–441.
doi:10.1002/9780470960707.ch18
Oliveira AP, Ludwig C, Picotti P, Kogadeeva M, Aebersold R, Sauer U
(2012) Regulation of yeast central metabolism by enzyme phosphorylation. Mol Syst Biol 8:623–623. doi:10.1038/msb.2012.55
Pick TR et al (2011) systems analysis of a maize leaf developmental
gradient redefines the current c4 model and provides candidates for
regulation Plant Cell 23:4208–4220. doi:10.1105/tpc.111.090324
Plett D, Toubia J, Garnett T, Tester M, Kaiser BN, Baumann U (2010)
Dichotomy in the NRT gene families of dicots and grass species.
PLoS One 5:e15289
Plett D et al (2016) Maize maintains growth in response to decreased
nitrate supply through a highly dynamic and developmental
311
stage-specific transcriptional response. Plant Biotechnol J
14:342–353. doi:10.1111/pbi.12388
Ranathunge K, El-kereamy A, Gidda S, Bi Y-M, Rothstein SJ (2014)
AMT1;1 transgenic rice plants with enhanced NH4 + permeability show superior growth and higher yield under optimal and suboptimal NH4 + conditions. J Exp Bot 65:965–979. doi:10.1093/
jxb/ert458
Schlüter U, Mascher M, Colmsee C, Scholz U, Bräutigam A, Fahnenstich H, Sonnewald U (2012) Maize source leaf adaptation to
nitrogen deficiency affects not only nitrogen and carbon metabolism but also control of phosphate homeostasis. Plant Physiol
160:1384–1406. doi:10.1104/pp.112.204420
Sebilo M, Mayer B, Nicolardot B, Pinay G, Mariotti A (2013) Longterm fate of nitrate fertilizer in agricultural soils. Proc Natl Acad
Sci 110:18185–18189. doi:10.1073/pnas.1305372110
Shrawat AK, Carroll RT, DePauw M, Taylor GJ, Good AG (2008)
Genetic engineering of improved nitrogen use efficiency in rice
by the tissue-specific expression of alanine amino transferase.
Plant Biotechnol J 6:722–732
Simons M et al (2014) Nitrogen-use efficiency in maize (Zea mays L.):
from ‘omics’ studies to metabolic modelling. J Exp Bot 65:5657–
5671. doi:10.1093/jxb/eru227
Smyth GK (2005) limma: linear models for microarray data. I n:
Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S (eds) Bioinformatics and computational biology solutions using r and bioconductor statistics for biology and health. Springer New York,
pp 397–420. doi:10.1007/0-387-29362-0/23
Solomonson LP, Barber MJ (1990) Assimilatory nitrate reductase:
functional properties and regulation. Annu Rev Plant Physiol
Plant Mol Biol 41:225–253
Stitt M (2013) Progress in understanding and engineering primary plant
metabolism. Curr Opin Biotechnol 24:229–238. doi:10.1016/j.
copbio.2012.11.002
Sturn A, Quackenbush J, Trajanoski Z (2002) Genesis: cluster analysis of microarray data. Bioinformatics 18:207–208. doi:10.1093/
bioinformatics/18.1.207
Sulpice R et al (2010) Network analysis of enzyme activities and
metabolite levels and their relationship to biomass in a large
panel of Arabidopsis accessions. Plant Cell Online 22:2872–2893
doi:10.1105/tpc.110.076653
Suzuki A, Rothstein S (1997) Structure and regulation of ferredoxindependent glutamase synthase from Arabido psis Thaliana. Eur
J Biochem 243:708–718. doi:10.1111/j.1432-1033.1997.00708.x
Swarbreck SM, Defoin-Platel M, Hindle M, Saqi M, Habash DZ
(2011) New perspectives on glutamine synthetase in grasses. J
Exp Bot 62:1511–1522. doi:10.1093/jxb/erq356
Sylvester-Bradley R, Kindred DR (2009) Analysing nitrogen responses
of cereals to prioritize routes to the improvement of nitrogen use
efficiency. J Exp Bot 60:1939–1951. doi:10.1093/jxb/erp116
Takahashi M, Sasaki Y, Ida S, Morikawa H (2001) Nitrite reductase
gene enrichment improves assimilation of NO2 in Arabidopsis.
Plant Physiol 126:731–741
Valadier M-H et al (2008) Implication of the glutamine synthetase/glutamate synthase pathway in conditioning the amino acid metabolism in bundle sheath and mesophyll cells of maize leaves. FEBS
J 275:3193–3206. doi:10.1111/j.1742-4658.2008.06472.x
von Wiren N, Gazzarrini S, Gojon A, Frommer WB (2000) The molecular physiology of ammonium uptake and retrieval. Curr Opin
Plant Biol 3:254–261
Wang R et al (2004) Genomic analysis of the nitrate response using
a nitrate reductase-null mutant of Arabidopsis. Plant Physiol
136:2512–2522. doi:10.1104/pp.104.044610
Wang Y-Y, Hsu P-K, Tsay Y-F (2012) Uptake, allocation and signaling of nitrate. Trends Plant Sci 17:458–467. doi:10.1016/j.
tplants.2012.04.006
13
312
Wang Y, Bräutigam A, Weber APM, Zhu X-G (2014) Three distinct
biochemical subtypes of C4 photosynthesis? A modelling analysis. J Exp Bot 65:3567–3578. doi:10.1093/jxb/eru058
Ward MP, Abberton MT, Forde BG, Sherman A, Thomas WTB, Wray
JL (1995) The Nir1 locus in barley is tightly linked to the nitrite
reductase apoprotein gene Nii. Mol Gen Genetics 247:579–582
Wolt J (1994) Soil solution chemistry:applications to environmental
science and agriculture. Wiley, New York
Xu G, Fan X, Miller AJ (2012) Plant nitrogen assimilation and use
efficiency. Annu Rev Plant Biol 63:153–182. doi:10.1146/
annurev-arplant-042811-105532
Yamaya T, Kusano M (2014) Evidence supporting distinct functions of
three cytosolic glutamine synthetases and two NADH-glutamate
13
Plant Mol Biol (2016) 92:293–312
synthases in rice. J Exp Bot 65:5519–5525. doi:10.1093/jxb/
eru103
Zanin L, Zamboni A, Monte R, Tomasi N, Varanini Z, Cesco S, Pinton
R (2015) Transcriptomic analysis highlights reciprocal interactions of urea and nitrate for nitrogen acquisition by maize roots.
Plant Cell Physiol 56:532–548. doi:10.1093/pcp/pcu202
Zhang N et al (2010) Fine quantitative trait loci mapping of carbon
and nitrogen metabolism enzyme activities and seedling biomass
in the maize ibm mapping population. Plant Physiol 154:1753–
1765. doi:10.1104/pp.110.165787