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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. 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