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Metabolic analysis of two contrasting wild barley genotypes grown hydroponically reveals adaptive strategies in response to low nitrogen stress
Accepted Manuscript Title: Metabolic analysis of two contrasting wild barley genotypes grown hydroponically reveals adaptive strategies in response to low nitrogen stress Author: Guoping Zhang Xiaoyan Quan Qiufeng Qian Zhilan Ye Jianbin Zeng Zhigang Han PII: DOI: Reference:
S0176-1617(16)30200-0 http://dx.doi.org/doi:10.1016/j.jplph.2016.07.020 JPLPH 52449
To appear in: Received date: Revised date: Accepted date:
25-4-2016 3-7-2016 28-7-2016
Please cite this article as: Zhang Guoping, Quan Xiaoyan, Qian Qiufeng, Ye Zhilan, Zeng Jianbin, Han Zhigang.Metabolic analysis of two contrasting wild barley genotypes grown hydroponically reveals adaptive strategies in response to low nitrogen stress.Journal of Plant Physiology http://dx.doi.org/10.1016/j.jplph.2016.07.020 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Metabolic analysis of two contrasting wild barley genotypes grown hydroponically reveals adaptive strategies in response to low nitrogen stress
Xiaoyan Quana, Qiufeng Qiana, Zhilan Yea, Jianbin Zenga, Zhigang Hana, Guoping Zhanga*
Agronomy Department, Institute of Crop Science, Zhejiang University, Hangzhou, 310058, China *Corresponding author.
E-mail address: G. Zhang, [email protected], Tel: +86
571 88982115
Abstract Nitrogen (N) is an essential macronutrient for plants. The increasingly severe environmental problems caused by N fertilizer application urge alleviation of N fertilizer dependence in crop production. In previous studies, we identified the Tibetan wild barley accessions with high tolerance to low nitrogen (LN). In this study, metabolic analysis was done on two wild genotypes (XZ149, tolerant and XZ56, sensitive) to understand the mechanism of LN tolerance, using a hydroponic experiment. Leaf and root samples were taken at seven time points within 18 d after LN treatment, respectively. XZ149 was much less affected by low N stress than XZ56 in plant biomass. A total of 51 differentially accumulated metabolites were identified between LN and normal N treated plants. LN stress induced tissue-specific changes in carbon and nitrogen partitioning, and XZ149 had a pattern of energy-saving amino acids accumulation and carbon distribution in favor of root growth that contribute to its higher LN tolerance. Moreover, XZ149 is highly capable of producing energy and maintaining the redox homeostasis under LN stress. The current results revealed the mechanisms underlying the wild barley in high LN tolerance and provided the valuable references
for developing barley cultivars with LN tolerance.
Key words: Tibetan wild barley, amino acid, carbon, low nitrogen tolerance
1. Introduction N is an essential mineral nutrient required for plant growth and development. N fertilization is a key factor affecting crop yield, with much increase achieved over the last half century because of extensive use of N fertilizer (Miller and Cramer, 2005). However, only less than half of the applied N is used by crops (Socolow, 1999), thus bringing the severe environmental problems while adding productive cost for farmers. Therefore, it is quite important to alleviate the dependence of crop production on N fertilizer (tolerance to LN stress) or to improve N use efficiency (NUE) of crops in order to ensure the agricultural sustainability. Development of the crop cultivars with high LN tolerance or NUE is a most fundamental and efficient approach for coping with low N availability in soils. N availability in soils varies drastically with space and time, and correspondingly plants have evolved versatile mechanisms/strategies to cope with N limitation. Moreover, it was found that such N limitation adaptability in crops is closely associated with their yield performance (McCullough et al., 1994; Ding, et al., 2005). Obviously genetic improvement of LN tolerance in crops is possible and practical. In addition, it has been well documented that NUE is a genetically controlled trait, differing dramatically among genotypes, in the crops including wheat, rice, maize and barley (Le et al., 2000; Anbessa et al., 2009; Namai et al., 2009; Presterl et al., 2003). However, genetic diversity in the cultivated barley becomes narrower, forming a bottleneck for genetic improvement (Ellis et al., 2000). On the other hand, the Tibetan annual wild barley, proved as one of the ancestors of the cultivated barley (Dai et al., 2012), is rich in genetic variation and shows much better adaption to poor soil fertility, such as K deficiency (Zeng et al., 2015) and N deficiency (Quan et al., 2016). In a previous study, we identified some wild barley genotypes with high LN tolerance (Yang et al., 2014), suggesting that the wild barley may provide the elite genetic materials or genes for
improving LN tolerance of barley as well as other cereal crops. It is therefore increasingly important to understand the mechanisms underlying the wild barley in high NUE or LN tolerance. The development of gas chromatography-mass spectrometry (GC-MS) technology has facilitated the comprehensive analysis of metabolite profile of a specific genotype or sample, allowing us to make insight into the multiple physiological processes in responses to various conditions. In recent years, metabolomics analysis has been widely used to investigate the tolerance of plants to various abiotic stresses, including temperature (Kaplan et al., 2004), drought (Guo et al., 2009), salt stress (Kim et al., 2007; Patterson et al., 2009), potassium nutrition (Armengaud et al., 2009) and phosphate deficiency (Huang et al., 2008). It has also been performed to dissect the metabolic changes of some crops under LN stress, such as tomato (UrbanczykWochniak et al., 2005), maize (Schlüter et al., 2012), cultivated barley (Comadira et al., 2015). However, no relevant study of metabolics has been done on the wild barley under LN stress, although it shows higher LN tolerance than the cultivated barley. In this study, a GC-MS-based method was used to investigate the impact of LN stress on metabolite profiles in different tissues of the two Tibetan wild barley accessions, so as to reveal the possible difference of metabolic profiles in response to LN stress between the two wild barleys differing in LN tolerance. 2. Materials and methods 2.1 Plant materials and treatments Healthy seeds of two Tibetan wild barley accessions, i.e. XZ149 (LN tolerant) and XZ56 (LN sensitive) were germinated in a plant growth chamber (22/18℃, day/night). Ten-day-old seedlings with uniform size were transplanted into black plastic containers (5 L) with aerated hydroponic solution in a greenhouse with natural light. The hydroponic solution was prepared according to Quan et al. (2016), and renewed every five days. Three-leaf-stage seedlings were exposed to 0.2 mM N (LN treatment) and 2 mM N (control). 2.2 Sampling and metabolite extraction The plant samples were taken at seven time points, namely 1, 3, 6, 9, 12, 15, and 18
d after LN treatment, respectively. Shoots and roots were separated, dried for constant weight at 80 °C, and then the plant biomass was recorded. For metabolite profiling analysis, the topmost fully expanded leaves in plants were used as leaf sample. All leaf and root samples with four biological replicates were frozen in liquid nitrogen. The metabolites were extracted according to Lisec et al. (2006) with small modification. The fresh samples were quickly milled in a mortar (pre-cooled) with liquid nitrogen. Then 100 mg accurately weighed fine powder was transferred to a 2ml, screw cap, round bottom tube, and extracted in 1400 μl of 100% methanol (precooled at -20 °C), and finally 60 μl of ribitol (0.2 mg/ml stock in dH2O) was subsequently added as internal quantitative standard. The mixture was shaken for 10 min at 70 °C in a thermo-mixer at 950 rpm, followed by centrifugation for 10 min at 11,000 g. In the obtained supernatant, 750 μl chloroform (-20 °C) and 1500 μl dH2O (4 °C) were added, then centrifuged for 15 min at 2, 200 g. Totally 150 μl supernatant was dried in a vacuum freeze dryer and the derivatization of the dried samples was initiated at 37 °C for 2 h by adding 40 μl of 20 mg/ml methoxyamine hydrochloride in pyridine (Sigma-Aldrich) and then treated with 70 μl MSTFA (Sigma-Aldrich) for 30 min. 2.3 GC–MS analysis Metabolites contents of the extracted samples were determined using 7890A/5975C GC-MS system (Agilent, USA). The prepared sample of 1 μl was injected into the HP5 capillary column and the injection temperature was set at 230 °C. The analysis was performed as following temperature-rising program: initial temperature of 80 °C for 2 min, then at 15 °C /min rate up to 300 °C, kept 300 °C for 10 min. Mass spectrometry was identified by fully-scanning method with range from 70 to 600 (m/z). The mass spectra data were analyzed for resolution of co-eluting peaks using AMDIS_32 software (Nashalian and Yaylayan, 2015). 2.4 Data analysis and statistics Significant difference of each metabolite between treatments was tested using a data processing system (DPS) software, and the difference at P < 0.05 and P < 0.01 was considered as significant and highly significant, respectively. Principle component and
heatmap analysis (PCA) of the identified metabolites was performed using Metaboanalyst 3.0 (Xia et al., 2015). 3. Results 3.1 The temporal changes of plant biomass in response to LN stress The two Tibetan wild barley genotypes, namely XZ149 and XZ56, were identified as LN tolerant and sensitive, respectively (Yang et al, 2014). In this study, LN stress reduced shoot dry weight of the two wild barley accessions, with the reduction being significant from 6 d after stress treatment (Fig. 1). The genotypic difference caused by LN stress become larger with the treated time, with no difference at 1 d and the reduction being 12.8% and 26.6% at 18 d for XZ149 and XZ56, respectively (Fig. 1). 3.2 The changes of metabolite profiles of the two genotypes in response to LN stress Totally 51 metabolites content changed significantly under LN stress relative to the control (C) in both leaves and roots of XZ149 and XZ56. Hierarchical clustering analysis clearly grouped 224 samples into two classes, namely leaves and roots (Fig. 2, Tables A1 and A2). Moreover, 112 samples were clearly divided into two groups, i.e. the samples taken at early stage (before 9 d after stress treatment, the left group) and those taken at late stage (12-18 d after treatment, the right group) (Figs. A1 and A2, Tables A1 and A2). Furthermore, four subclasses could be divided, mainly representing the control and LN stress in the two stages, as seen in Figs. A1 and A2. To identify the key factors affecting metabolome, principal component analysis (PCA) was conducted on the detected metabolites. The first PCA components could explain 38.7% and 40.6% of the variance in leaves and roots, respectively (Fig. 3, A and B), and predominantly reflects the difference of developmental stages, indicating that the large metabolic changes happened during plant growth, irrespectively of control and LN conditions. Clear separation between the control and LN stress (explaining 20.7% and 16% of the variance in leaves and roots, respectively) could be also detected in both genotypes from 3 d onwards after LN stress (Fig. 3, A and B), indicating that LN stress had substantial effect on metabolites. It was also noted that N-containing metabolites, such as amino acids (L-threonine (Thr), L-serine (Ser) and pyroglutamic acid) (Tables A3 and A4), contributed greatly to the separation of PC2-component in both plant
tissues. On the other hand, phosphoric acid, L-lysine (Lys) and glyceric acid-3phosphate in leaves (Table A3), and phosphoric acid, malic acid and fructose in roots (Table A4) were specifically loaded for the LN-related principal components. The similar effect on metabolite profiles could be found in XZ149 and XZ56 in the initiating time of LN treatment (1 d), and the difference between the two genotypes became larger over the time. Obviously, the response of metabolites to LN stress varied with plant tissue, growth stage, and genotype. 3.3 The response of amino acid level to LN stress High flexibility is one of major characteristics for amino acid pool in plants when exposed to the variable environments. A dramatic difference could be found in amino acids level between the two genotypes and plant tissues under LN stress. However, no consistent trend could be detected for most amino acids over the whole time-course of LN stress (Tables 1 and 2), indicating the complexity of amino acid change in response to LN stress. In leaves, levels of all major amino acids were decreased at the late stage, but increased frequently at the early stage under LN stress (Table 1). It is interestingly found that relative content (calculated by the formula: log2 (LN stress /control)) of glutamic acid (Glu) and aspartic acid (Asp) was consistently lower in XZ149 than in XZ56 (Table 1). On the other hand, levels of some minor acids increased consistently during the whole LN stress treatment (Table 1). The contents of leucine (Leu) and lysine (Lys) were generally increased in XZ56, while changed irregularly in XZ149 during the treatment (Table 1 and Fig. 4). The contents of phenylalanine (Phe) and tyrosine (Tyr) decreased or remained without change in XZ149, while increasing in XZ56 at the early stage (Table 1). In roots, both minor and major amino acids increased at some time points of the late stage. Relative content of Asp was always higher in XZ149 than in XZ56 (Table 2). A strong increase was detected for Lys and Leu contents during 1 d to 18 d after treatment, showing the opposite trend as pyroglutamic acid did (Table 2). 3.4 The response of organic acid levels to LN stress On the whole, LN stress caused a dramatic increase for the most organic acids involved in tricarboxylic acid cycle (TCA) at the early stage, but at the later stage, a
distinct reduction was found (Figs. 4 and 5, Tables A5 and A6). Moreover, these metabolites showed the different responses to LN stress between leaves and roots. In leaves, citric acid content increased consistently during the whole treatment. Succinic acid content decreased in XZ149, but remained little changed in XZ56 at 1 d after treatment, and then increased at other earlier time points, and again decreased in the two genotypes (Table A5). Fumaric acid and malic acid contents kept increasing until 12 and 15 d after treatment (Table A5). Surprisingly, 2-ketoglutaric acid (2-KG) showed the dramatic decrease in the two genotypes, with XZ149 being larger than XZ56 (Table A5). In roots, the TCA cycle was enhanced in the two genotypes under LN stress, as shown by higher citric acid level compared to the control at early stage (Fig. 5 and Table A6). However, citric acid level kept high in XZ149, while decreased or remained little change in XZ56 under LN stress at late stage (Fig. 5 and Table A6). Other TCA intermediates, including 2-KG, succinic acid and fumaric acid, showed downaccumulation at 1 d after LN stress, and then up-accumulation, and then again downaccumulation in XZ149. The two genotypes showed the similar trend, with XZ56 showing larger decline at late stage (Fig. 5 and Table A6). It should be noted that the content of malic acid, as a down-stream metabolite of TCA cycle, was consistently higher in LN stress than in control for the two genotypes (Fig. 5 and Table A6). In this study, increased shikimic acid content enhanced shikimate pathway in roots and leaves, and the similar increase was observed for the related secondary metabolites, such as quinic acid (Figs. 4 and 5). 3.5 The response of sugar and its related metabolites to LN stress Under LN stress, accumulation of glucose-6-P (G-6-P) involved in glycolysis and pentose phosphate (PPP) pathway was greatly enhanced at early stage for both genotypes, then reduced in XZ56 and remained little change in XZ149 from 12 d onwards in roots (Fig. 5 and Table A6). On the other hand, accumulation of G-6-P coupled with the glyceric acid-3-P was consistently enhanced in the leaves of both XZ149 and XZ56 over the whole treatment (Fig. 4 and Table A5). In both leaves and roots, the content of soluble sugar, except sucrose, increased
dramatically in XZ149 under LN stress, but decreased obviously at many time points in XZ56 (Figs. 4 and 5, Tables A5 and A6). The contents of myo-inositol, galactonic acid, 2-keto-L-gluconic acid, and threonic acid in leaves showed a significant increase in both genotypes (Fig. 4 and Table A5), while contents of 2-keto-L-gluconic acid, glyceric acid, and threonic acid in roots increased in XZ149 and decreased in XZ56 at some time points (Fig. 5 and Table A6). 4. Discussion It is well documented that there is a large difference among genotypes within a plant species in LN tolerance (Le et al., 2000; Anbessa et al., 2009; Namai et al., 2009; Presterl et al., 2003). In the present study, the difference in LN response between the two wild barley genotypes was again demonstrated, as reflected by plant biomass, with XZ149 being more tolerant to LN stress than XZ56. Metabolites profiles between control and LN stress could not be separated until the 3rd day after stress treatment according to hierarchical clustering analysis (Fig. 3). We analyzed the root and leaf responses separately to identify the difference in metabolic response to LN stress between the two genotypes. Amino acids level in leaves showed highly individual response at early stage of treatment, but mainly exhibited a significant decrease at late stages under LN stress (Tables 1 and 2). It is commonly expected that amino acids are less accumulated after long LN stress, and our results are in agreement with previous reports (Amiour et al., 2012; Schlüter et al., 2012; Krapp et al., 2011; Howarth et al., 2008 and Fritz et al., 2006a). At early stage, inhibition of plant growth may allow the accumulation of some amino acids (Tschoep et al., 2009). However, it is quite perplexing that all major amino acids were down-accumulated in leaves while some of them were up-accumulated in roots at later stages. Amino acid synthesis happened mainly in leaves and was energy consuming. Therefore, relatively less accumulation of amino acids in leaves may reduce energy cost. In addition, some amino acids have the function of alleviating abiotic stress (Sharma et al., 2006). It may be assumed that amino acids accumulation in roots might be beneficial for developing a defense against LN stress in the way we still do not know up to date. Currently, XZ149 had lower and higher relative contents of some major
amino acids in leaves and roots than XZ56, respectively, fitting to the difference in LN tolerance between the two genotypes. N assimilation in plants is mediated by the GS/GOGAT cycle and depends on the availability of energy, reducing power and C skeletons. Glu is a primary amino acid produced by the cycle, acting as an immediate donor of amino group for the synthesis of most other amino acids. It is reported that Glu shows quite stable in changing amino acid profiles, suggesting that its homeostasis in plants is regulated by many mechanisms. It was reported that the plant PII-like protein serves as the part of a complex signal transduction network perceiving availability of carbon and organic nitrogen (Hsieh et al., 1998; Fritz et al., 2006a; Krapp et al., 2011). However, in the present study, Glu level was markedly affected by LN stress (Table 1). Meanwhile, 2-KG is an immediate acceptor for ammonium in the GS/GOGAT pathway, and its importance as a key regulator of carbon and nitrogen interactions has been noted in many studies (Stitt and Fernie, 2003). In this study, the changing trend of 2-KG content was not mirrored by Glu content in leaves, which did not agree with the previous study that Glu and2-KG often changed in parallel (Mueller et al., 2001; Novitskaya et al., 2002; Fritz et al., 2006b). In this study, more Glu synthesis in leaves could be indicated by the consistent increase of citric acid, an up-stream metabolite in the TCA cycle, and consistent decrease of 2-KG, a Glu precursor under LN stress (Table A5). In view of the fact that Glu provides amino group for the synthesis of most other amino acids, the detected Glu content was not high. Moreover, XZ149 had the lower relative content of Glu under LN in comparation with XZ56, indicating its quick use in amino acid synthesis. Glycolysis, TCA cycle, and PPP pathway in leaves shares the same metabolic trend in the response to LN stress (Figs. 4 and 5). Carbon precursors for amino acid biosynthesis are the organic acids involved in these pathways (Figs. 4 and 5), and the influence of LN on the organic acid metabolism seems to be a central point in the coordination of C and N metabolisms (Hodges, 2002). In the non-photosynthetic tissue, these pathways supply energy and/or reduce the power for N assimilation. In our previous study, we found that a gene encoding 6-phosphogluconate dehydrogenase, the key enzyme in PPP pathway, was up-regulated only in the roots of XZ149 (Quan et al.,
2016). Here, the roots of XZ149 showed consistently higher relative contents of both glucose-6-P and citric acid than XZ56 during the whole stress treatment, except at 1th day (Table A6), suggesting its higher capacity of producing energy and maintenance of cell redox homeostasis, thereby beneficial for its LN tolerance. Carbohydrates are synthesized in plant shoots and more sucrose will be translocated to the roots when plants suffer from N deficiency, as the roots become the main growing sink under LN stress (Krapp et al., 2011). In this study, sucrose was up-accumulated in roots consistently during LN stress, and its relative content in XZ149 was much higher than that in XZ56 (Fig. 5), whereas in leaves, sucrose was down-accumulated in XZ149 until 15 d after LN treatment (Fig. 4), suggesting that the allocation proportion of sucrose was larger in the roots of XZ149 compared with that in XZ56 at the earlier stage of LN stress. Sucrose could provide carbon source and energy for roots, playing an important role in plant growth and development. While invertase could irreversibly catalyze the hydrolysis of sucrose, thus affecting the use and transportation of sucrose (Koch, 2004). It was reported that invertase activities increased when plants were exposed to abiotic stress conditions (Yamada et al., 2010). Our previous study showed that the gene encoding invertase was up-regulated only in roots of XZ149 (Quan et al, 2016). Hence it can be assumed that the use of sucrose in the roots of XZ149 was much greater compared to that in X56 under LN stress. Moreover, the sucrose translocated to the roots also acts as a signal molecule for plant growth and development (Chiou and Bush, 1998) and is involved in root growth under nutrition deficiency (Jain et al, 2007; Hammond and White, 2008; Wang et al, 2015). Thus, the current results suggest that greater accumulation and use of sucrose in XZ149 are attributed to better root growth under LN stress. In addition, the content of soluble sugars, such as glucose, fructose, and raffinose increased consistently during the whole LN treatment in XZ149, but decreased frequently in XZ56 (Tables A5 and A6). Raffinose serves not only as a carbon store, but also has a protective function (Hannah et al., 2006; Krapp et al., 2011; Schlüter et al., 2012). Hence relatively higher raffinose content in the roots and leaves of XZ149 may be attributed to its greater LN stress tolerance. Malic acid, an intermediate in the TCA cycle, has been thought to play important
roles in plant metabolism, acting as a reducing equivalent, osmoticum, and pH regulator (Fernie and Martinoia, 2009). Schlüter et al (2012) reported that malic acid was continuously reduced in maize leaves under N deficiency.
The demand for malate in
the C4-specific shuttle, acting as a counterion of balancing hydroxyl ions produced through nitrate reduction declined under LN conditions. Although hydroxyl ions produced through nitrate reduction declined, reactive oxygen species (ROS) was accumulated under LN conditions (Shin et al., 2005), due to the reduction in the frequency of electron carriers of electron transport systems (Grossman and Takahashi, 2001). Dehydrogenation of malic acid was accompanied by the generation of NADH in plants, and the latter is crucial to maintain the cellular redox state and/or antioxidative capacity (Kirsch and De Groot, 2001; Petrat et al., 2003). In the present study, malic acid content in roots was continuously increased under LN stress (Table A6). Moreover, XZ149 maintained higher relative content of malic acid in comparison with XZ56. Therefore, it may be suggested that XZ149 have the stronger ability of maintaining redox homeostasis in the roots relative to XZ56. The genes encoding the enzymes of the shikimate pathway are induced in parallel to the genes encoding phenylpropanoid synthesis under low N conditions (Scheible et al., 2004; Amiour et al., 2012). In this study, the precursor of shikimate, shikimic acid, was increased in roots and leaves of the both genotypes under LN stress, and the similar increase was observed for the related secondary metabolites, such as quinic acid (Figs. 4 and 5). Relative content of Phe was consistently lower in XZ149 than in XZ56 (Table 2). Our previous study showed that the synthesis pathways of both phenylpropanoid and flavonoid mediated by PAL were much more active in the LN tolerant genotype than in sensitive one (Quan et al., 2016). Thus, it may be concluded that the lower relative content of Phe in XZ149 should be associated with its more utilization. 5. Conclusion Based on the metabolic analysis, the possible mechanisms of low N tolerance in XZ149 could be assumed as follows: (1) an energy saving pattern of amino acids accumulation; (2) a pattern of carbon allocation in favor of plant growth; (3) a high capacity of producing energy; and (4) a higher ability to maintain redox homeostasis.
All of these in turn contributed to a higher biomass of XZ149 under LN stress in comparison with XZ56.
Abbreviations N, nitrogen; LN, low nitrogen; G-6-P, glucose-6-phosphate; 2-KG, 2-ketoglutaric acid; PPP, pentose phosphate; Glu, glutamic acid; Gln, glutamine; Asp, aspartic acid; Asn, asparagine; Ser, serine; Gly, glycine; Ala, alanine; Leu, leucine; Thr, threonine; Lys, lysine; Val, valine; Phe, phenylalanine; Tyr, tyrosine; GABA, 4-aminobutyric acid.
Acknowledgments We thank Prof. Dongfa Sun (Huazhong Agricultural University, China) for providing Tibetan wild barley accessions and Hangzhou PTM Biolabs Inc. for excellent technical assistance. This work was supported by the by Natural Science Foundation of China (31330055), China Agriculture Research System (CARS-05) and Jiangsu Collaborative Innovation Center for Modern Crop Production (JCIC-MCP).
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Figure legends: Fig. 1 The temporal changes of plant biomass in the two barley genotypes under normal N (C) and low N (LN) conditions. The lowcase letters represent significant difference (P, 0.05) among treatments and genotypes at each time point (n=4, bars show the SD). Fig. 2 Heatmap and hierarchical cluster analysis for detected metabolites of leaves and roots in the two wild barley genotypes. The samples from left to right: 1-9 d in roots (G, H, AE, O, W, AC, E, U, F, AD, AF, X, V, M, P, N); 12-18 d in roots (AV, AT, BA, AS, AL, AN, BD, BB, BC, AK, AU, AM); 1-9 d in leaves (D, C, B, A, Y, AA, Q, S, I, J, K, L, T, R, AB, Z); 12-18 d in leaves (AG, AW, AO, AI, AQ, AY, AZ, AH, AG, AX, AR, AU). The samples corresponding letter number and the metabolite names from 1 to 51 were listed in Tables A1 and A2. Fig. 3 Principal component analysis (PCA) of metabolic profiles in roots and leaves of XZ149 and XZ56 under control and low N conditions (four biological replicates). (A) PCA in leaves; (B) PCA in roots. PC1, the first principal component; PC2, the second principal component. Fig. 4 Changes in metabolites mapped to the metabolic pathways in leaves of XZ149 and XZ56 under LN stress. Relative contents of metabolites areshown by a color gradient from low (green) to high (red) as presented in the color scale. Relativecontent of each metabolite was calculated by log2 (LN/C) in two genotypes at each time point. Grey color means corresponding metabolite not detected. For each heatmap from left to right: 1 d (first column), 3 d (second column), 6 d (third column), 9 d (fourth column), 12 d (fifth column), 15 d (sixth column), 18 d (seventh column); and from top to bottom: (top column), XZ56 (bottom column). Fig. 5 Changes in metabolites mapped to the metabolic pathways in roots of XZ149 and XZ56 under LN stress. Relative contents of metabolites are shown by a color gradient from low (green) to high (red) as presented in the color scale. Relative content of each metabolite was calculated by log2 (LN/C) in two genotypes at each time point. Grey color means corresponding metabolite not detected. For each heatmap from left to right: 1 d (first column), 3 d (second column), 6 d (third column), 9 d (fourth column),
12 d (fifth column), 15 d (sixth column), 18 d (seventh column) ; and from top to bottom: (top column), XZ56 (bottom column).
Fig. 1
Fig. 2
Fig. 3 A
B
Fig. 4 2-Keto-gluconic acid
Ascorbic acid
Glycerol
Threonic acid
Galactose Glyceric acid
Glucose
Threonic acid-1,4-lactones
Glucose-1-P Hexadecanoic acid
Sucrose
Ribose
Xylitol
Glucose-6-P
Ribitol
Raffinose Fructose
Inositol
Fructose-6-P
Xylose L-Phenylalanine
Glycine
Glyceric acid-3-P
L-Serine
Quinic acid
PEP L-Leucine
Pyruvate
L-Lysine
Shikimic acid
L-Alanine Tyrosine
Acetyl-CoA Valine
Oxosuccinic acid L-Aspartic acid Glyoxylic acid
Citric acid Pyroglutamic acid
Malic acid
L-Threonine Glycolic acid
Isocitric acid
L-Proline
0
3
Ketoglutaric acid Ornithine
Succinic acid
-3
Relative content
Fumaric acid
GABA
L-Glutamic acid
Glutamine
Fig. 5 2-Keto-gluconic acid
Ascorbic acid Galactose
Glycerol Glyceric acid
Glucose
Sucrose
Hexadecanoic acid
Threonic acid
Glucose-1-P
Ribose
Ribitol
Xylitol
Glucose-6-P Raffinose
Fructose
Inositol
Xylose
Fructose-6-P
L-Serine
Glycine
Glyceric acid-3-P Tyrosine
L-Lysine
L-Aspartic acid
Quinic acid
PEP
L-Leucine
Pyruvate
Shikimic acid
L-Alanine L-Phenylalanine
L-Threonine
Acetyl-CoA L-Asparagine
Valine
Oxosuccinic acid
Glyoxylic acid Malic acid
Citric acid Isocitric acid
3
Fumaric acid
L-Proline Ketoglutaric acid Putrescine
Ornithine 0
Succinic acid Maleic acid
Arginine
-3
Relative content
Glycolic acid
Pyroglutamic acid
GABA
L-Glutamic acid
Glutamine
Table 1. Effects of LN stress on amino acid content of barley leaves Amino acidsa
Log2 (fold change)b
Genotypes 1d
Glu
Gln
Asp
Gly
Ala
Ser
Leu
Lys
Thr
Val
Phe
Tyr
GABA
Pyroglutamic acid
3d
6d
9d
12d
15d
18d
X149
0.09
-0.35**c
0.14**
-0.03
-0.66**
-1.36**
-0.93**
X56
-0.08
0.09
0.68**
0.51*
-0.21**
-0.78**
-0.27*
X149
-0.49**
-0.78**
-1.62**
-1.93**
-1.12**
-0.8**
-4.05**
X56
0.35**
-0.89**
-1.09**
-3.41**
-1.62**
-1.13**
0.94**
X149
1.31**
-0.18*
0.69**
0.04
-1.06**
-2.72**
-1.44**
X56
0.4*
0.09
3.89**
0.39**
-0.78**
-2.69**
-0.89**
X149
-0.82**
-1.88**
1.27**
-1.63**
-0.19**
-1.51**
-1.14**
X56
0.25*
-0.9**
0.55**
-0.61*
-0.21*
-1.9**
-0.54**
X149
0.4**
-0.93**
-0.27**
-1.02**
-1.91**
-3.04**
-2.61**
X56
0.11*
-0.63**
-0.51*
-1.56**
-1.61**
-2.52**
-0.5**
X149
-0.39**
-0.96**
-0.72**
-1.1**
-0.91**
-3.79**
-2.13**
X56
0.1
-1.13**
-0.48**
-1.77**
-1.44**
-3.37**
-1.23**
X149
-0.19
-0.19*
-0.73**
1.18**
-0.57**
1.47**
-0.71
X56
0.26*
0.8**
-0.34*
1.25**
0.13
1.88**
1.29**
X149
-0.19
-0.35*
-1.69**
0.11*
-0.46**
1.74**
-0.32
X56
0.17
1.62**
-1.33**
1.63**
1.24**
3.3**
0
X149
-0.19**
-0.78**
-0.31**
-0.8**
-1.76**
-3.47**
-1.62**
X56
-0.01
-0.87**
-0.62**
-1.01**
-1.39**
-2.97**
-0.61**
X149
0.05
-0.48**
-0.41*
0.16*
-0.01
0.24*
-1.08**
X56
0.06
0.3**
-0.65**
0.48**
-0.82**
0.54**
0.53**
X149
0.13
0.05
-0.39**
-0.66**
-0.09
-1**
-0.42**
X56
0.43*
0.53**
0.89**
0.32**
-0.06
-0.94**
0.37*
X149
0.03
-0.47**
-0.13
0.54*
-0.11**
0.64**
-0.78*
X56
-0.03
0.31*
0.25*
0.98**
-0.1
-0.03
0.46*
X149
-
-
-
-
-0.5*
-1.48**
-0.19*
X56
-
-
-
-
-0.52**
-1.22**
-0.1
X149
-0.5**
-0.74**
-0.37*
-0.89**
-1.15**
-2.82**
-1.92**
X56
0.21
-1.25**
-1.22**
-1.61**
-1.01**
-2.71**
-0.52*
a Amino acids are indicated using the standard three letter code. GABA, 4-aminobutyric acid. b Fold change=LN/C. c * and ** indicate significant (P<0.05) and highly significant (P<0.01) differences between treatments, respectively (n = 4).
Table 2. Effects of LN stress on amino acid content of barley roots Amino acidsa
Glu
Gln
Asp
Asn
Gly
Ala
Ser
Leu
Lys
Thr
Val
Phe
Tyr
GABA
Pyroglutaminc acid
Log2 (fold change)b
Genotypes 1d
3d
6d
X149
-0.02
0.15*c
0.02
X56
-0.02
0.28**
X149
-0.7**
X56
9d
12d
15d
18d
-0.03
-0.48**
-0.8**
0.06
0.4**
-0.56**
-0.43**
-1.25**
0.3*
-0.19*
-1.5**
-2.33**
-5.16**
-4.39**
-2.05**
1.33**
0.68**
0.7*
-2.79**
-4.25**
-4.68**
-0.35
X149
0.8
2.71**
3.05**
3.01**
-0.38**
0.1
2.02**
X56
0.16
1.3**
3.83**
0.93**
-1.03**
-0.67**
1.89**
X149
1.38*
1.46**
0.13
1.29**
-0.85**
0
0
X56
1.12**
0.52*
1.41**
-0.45*
0.36*
0.36**
0
X149
0.26*
0.56*
0.85**
0.71*
-0.41**
0.32*
-0.01
X56
0.39**
0.76**
1.27*
0.02
-0.04
-0.16
-0.68**
X149
-0.45**
-0.36**
-0.04
-0.19
0
-0.45**
0.37**
X56
-0.35*
1.06**
0.53**
-0.22
-0.13
-0.65**
-1.13**
X149
-0.53**
0.13*
-1.06**
-0.96**
-1.88**
-1.82**
-2.39**
X56
0
0.08
-0.81**
-2.26**
-2.13**
-1.97**
-2.47**
X149
0.04
0.67**
-0.25
0.02
-0.03
1.98**
1.1**
X56
0
1.12**
1.02**
1.3**
0.28
1.49**
1.21**
X149
0.52**
0.46**
0.83**
0.51*
0.16
2.16**
0.71*
X56
0.65**
1.47**
1.91**
0.74**
-0.3**
1.01**
0.07
X149
-0.27**
0.36**
-0.6**
-0.53*
-1.6**
-0.59**
-2.11**
X56
0.04
0.55**
-0.41**
-1.45**
-1.73**
-1.24**
-1.72**
X149
-0.55**
0.11
-0.33**
-0.45**
-0.19**
0.6**
0.91**
X56
-0.06
0.38*
0.32**
0.7**
-0.08
0.98**
0.44**
X149
0.05
0.81**
0.23
0.1
-0.73**
0.19*
-0.26**
X56
0.02
0.22**
1.22**
0.27*
-0.7**
0.23**
-0.01
X149
-0.12
0.24*
-0.21**
0.63**
-0.52**
0.66
-0.23*
X56
0.31*
0.33*
0.17
0.32*
-0.24*
0.24
-0.04
X149
-0.58**
-0.19**
0.33**
0.52**
-0.28**
-0.24**
0.56**
X56
-0.22**
0.7**
0.09
0.23*
-0.24**
-1.28**
-0.76**
X149
-0.3*
-0.54**
-0.67**
-1.52**
-1.65**
-3.31**
-1.79**
X56
0.33
-0.32**
-0.81**
-1.66**
-1.57**
-3.08**
-1.49**
a Amino acids are indicated using the standard three letter code. GABA, 4-aminobutyric acid. b Fold change=LN/C. c * and ** indicate significant (P<0.05) and highly significant (P<0.01) differences between treatments, respectively (n = 4).
S0176-1617(16)30200-0 http://dx.doi.org/doi:10.1016/j.jplph.2016.07.020 JPLPH 52449
To appear in: Received date: Revised date: Accepted date:
25-4-2016 3-7-2016 28-7-2016
Please cite this article as: Zhang Guoping, Quan Xiaoyan, Qian Qiufeng, Ye Zhilan, Zeng Jianbin, Han Zhigang.Metabolic analysis of two contrasting wild barley genotypes grown hydroponically reveals adaptive strategies in response to low nitrogen stress.Journal of Plant Physiology http://dx.doi.org/10.1016/j.jplph.2016.07.020 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Metabolic analysis of two contrasting wild barley genotypes grown hydroponically reveals adaptive strategies in response to low nitrogen stress
Xiaoyan Quana, Qiufeng Qiana, Zhilan Yea, Jianbin Zenga, Zhigang Hana, Guoping Zhanga*
Agronomy Department, Institute of Crop Science, Zhejiang University, Hangzhou, 310058, China *Corresponding author.
E-mail address: G. Zhang, [email protected], Tel: +86
571 88982115
Abstract Nitrogen (N) is an essential macronutrient for plants. The increasingly severe environmental problems caused by N fertilizer application urge alleviation of N fertilizer dependence in crop production. In previous studies, we identified the Tibetan wild barley accessions with high tolerance to low nitrogen (LN). In this study, metabolic analysis was done on two wild genotypes (XZ149, tolerant and XZ56, sensitive) to understand the mechanism of LN tolerance, using a hydroponic experiment. Leaf and root samples were taken at seven time points within 18 d after LN treatment, respectively. XZ149 was much less affected by low N stress than XZ56 in plant biomass. A total of 51 differentially accumulated metabolites were identified between LN and normal N treated plants. LN stress induced tissue-specific changes in carbon and nitrogen partitioning, and XZ149 had a pattern of energy-saving amino acids accumulation and carbon distribution in favor of root growth that contribute to its higher LN tolerance. Moreover, XZ149 is highly capable of producing energy and maintaining the redox homeostasis under LN stress. The current results revealed the mechanisms underlying the wild barley in high LN tolerance and provided the valuable references
for developing barley cultivars with LN tolerance.
Key words: Tibetan wild barley, amino acid, carbon, low nitrogen tolerance
1. Introduction N is an essential mineral nutrient required for plant growth and development. N fertilization is a key factor affecting crop yield, with much increase achieved over the last half century because of extensive use of N fertilizer (Miller and Cramer, 2005). However, only less than half of the applied N is used by crops (Socolow, 1999), thus bringing the severe environmental problems while adding productive cost for farmers. Therefore, it is quite important to alleviate the dependence of crop production on N fertilizer (tolerance to LN stress) or to improve N use efficiency (NUE) of crops in order to ensure the agricultural sustainability. Development of the crop cultivars with high LN tolerance or NUE is a most fundamental and efficient approach for coping with low N availability in soils. N availability in soils varies drastically with space and time, and correspondingly plants have evolved versatile mechanisms/strategies to cope with N limitation. Moreover, it was found that such N limitation adaptability in crops is closely associated with their yield performance (McCullough et al., 1994; Ding, et al., 2005). Obviously genetic improvement of LN tolerance in crops is possible and practical. In addition, it has been well documented that NUE is a genetically controlled trait, differing dramatically among genotypes, in the crops including wheat, rice, maize and barley (Le et al., 2000; Anbessa et al., 2009; Namai et al., 2009; Presterl et al., 2003). However, genetic diversity in the cultivated barley becomes narrower, forming a bottleneck for genetic improvement (Ellis et al., 2000). On the other hand, the Tibetan annual wild barley, proved as one of the ancestors of the cultivated barley (Dai et al., 2012), is rich in genetic variation and shows much better adaption to poor soil fertility, such as K deficiency (Zeng et al., 2015) and N deficiency (Quan et al., 2016). In a previous study, we identified some wild barley genotypes with high LN tolerance (Yang et al., 2014), suggesting that the wild barley may provide the elite genetic materials or genes for
improving LN tolerance of barley as well as other cereal crops. It is therefore increasingly important to understand the mechanisms underlying the wild barley in high NUE or LN tolerance. The development of gas chromatography-mass spectrometry (GC-MS) technology has facilitated the comprehensive analysis of metabolite profile of a specific genotype or sample, allowing us to make insight into the multiple physiological processes in responses to various conditions. In recent years, metabolomics analysis has been widely used to investigate the tolerance of plants to various abiotic stresses, including temperature (Kaplan et al., 2004), drought (Guo et al., 2009), salt stress (Kim et al., 2007; Patterson et al., 2009), potassium nutrition (Armengaud et al., 2009) and phosphate deficiency (Huang et al., 2008). It has also been performed to dissect the metabolic changes of some crops under LN stress, such as tomato (UrbanczykWochniak et al., 2005), maize (Schlüter et al., 2012), cultivated barley (Comadira et al., 2015). However, no relevant study of metabolics has been done on the wild barley under LN stress, although it shows higher LN tolerance than the cultivated barley. In this study, a GC-MS-based method was used to investigate the impact of LN stress on metabolite profiles in different tissues of the two Tibetan wild barley accessions, so as to reveal the possible difference of metabolic profiles in response to LN stress between the two wild barleys differing in LN tolerance. 2. Materials and methods 2.1 Plant materials and treatments Healthy seeds of two Tibetan wild barley accessions, i.e. XZ149 (LN tolerant) and XZ56 (LN sensitive) were germinated in a plant growth chamber (22/18℃, day/night). Ten-day-old seedlings with uniform size were transplanted into black plastic containers (5 L) with aerated hydroponic solution in a greenhouse with natural light. The hydroponic solution was prepared according to Quan et al. (2016), and renewed every five days. Three-leaf-stage seedlings were exposed to 0.2 mM N (LN treatment) and 2 mM N (control). 2.2 Sampling and metabolite extraction The plant samples were taken at seven time points, namely 1, 3, 6, 9, 12, 15, and 18
d after LN treatment, respectively. Shoots and roots were separated, dried for constant weight at 80 °C, and then the plant biomass was recorded. For metabolite profiling analysis, the topmost fully expanded leaves in plants were used as leaf sample. All leaf and root samples with four biological replicates were frozen in liquid nitrogen. The metabolites were extracted according to Lisec et al. (2006) with small modification. The fresh samples were quickly milled in a mortar (pre-cooled) with liquid nitrogen. Then 100 mg accurately weighed fine powder was transferred to a 2ml, screw cap, round bottom tube, and extracted in 1400 μl of 100% methanol (precooled at -20 °C), and finally 60 μl of ribitol (0.2 mg/ml stock in dH2O) was subsequently added as internal quantitative standard. The mixture was shaken for 10 min at 70 °C in a thermo-mixer at 950 rpm, followed by centrifugation for 10 min at 11,000 g. In the obtained supernatant, 750 μl chloroform (-20 °C) and 1500 μl dH2O (4 °C) were added, then centrifuged for 15 min at 2, 200 g. Totally 150 μl supernatant was dried in a vacuum freeze dryer and the derivatization of the dried samples was initiated at 37 °C for 2 h by adding 40 μl of 20 mg/ml methoxyamine hydrochloride in pyridine (Sigma-Aldrich) and then treated with 70 μl MSTFA (Sigma-Aldrich) for 30 min. 2.3 GC–MS analysis Metabolites contents of the extracted samples were determined using 7890A/5975C GC-MS system (Agilent, USA). The prepared sample of 1 μl was injected into the HP5 capillary column and the injection temperature was set at 230 °C. The analysis was performed as following temperature-rising program: initial temperature of 80 °C for 2 min, then at 15 °C /min rate up to 300 °C, kept 300 °C for 10 min. Mass spectrometry was identified by fully-scanning method with range from 70 to 600 (m/z). The mass spectra data were analyzed for resolution of co-eluting peaks using AMDIS_32 software (Nashalian and Yaylayan, 2015). 2.4 Data analysis and statistics Significant difference of each metabolite between treatments was tested using a data processing system (DPS) software, and the difference at P < 0.05 and P < 0.01 was considered as significant and highly significant, respectively. Principle component and
heatmap analysis (PCA) of the identified metabolites was performed using Metaboanalyst 3.0 (Xia et al., 2015). 3. Results 3.1 The temporal changes of plant biomass in response to LN stress The two Tibetan wild barley genotypes, namely XZ149 and XZ56, were identified as LN tolerant and sensitive, respectively (Yang et al, 2014). In this study, LN stress reduced shoot dry weight of the two wild barley accessions, with the reduction being significant from 6 d after stress treatment (Fig. 1). The genotypic difference caused by LN stress become larger with the treated time, with no difference at 1 d and the reduction being 12.8% and 26.6% at 18 d for XZ149 and XZ56, respectively (Fig. 1). 3.2 The changes of metabolite profiles of the two genotypes in response to LN stress Totally 51 metabolites content changed significantly under LN stress relative to the control (C) in both leaves and roots of XZ149 and XZ56. Hierarchical clustering analysis clearly grouped 224 samples into two classes, namely leaves and roots (Fig. 2, Tables A1 and A2). Moreover, 112 samples were clearly divided into two groups, i.e. the samples taken at early stage (before 9 d after stress treatment, the left group) and those taken at late stage (12-18 d after treatment, the right group) (Figs. A1 and A2, Tables A1 and A2). Furthermore, four subclasses could be divided, mainly representing the control and LN stress in the two stages, as seen in Figs. A1 and A2. To identify the key factors affecting metabolome, principal component analysis (PCA) was conducted on the detected metabolites. The first PCA components could explain 38.7% and 40.6% of the variance in leaves and roots, respectively (Fig. 3, A and B), and predominantly reflects the difference of developmental stages, indicating that the large metabolic changes happened during plant growth, irrespectively of control and LN conditions. Clear separation between the control and LN stress (explaining 20.7% and 16% of the variance in leaves and roots, respectively) could be also detected in both genotypes from 3 d onwards after LN stress (Fig. 3, A and B), indicating that LN stress had substantial effect on metabolites. It was also noted that N-containing metabolites, such as amino acids (L-threonine (Thr), L-serine (Ser) and pyroglutamic acid) (Tables A3 and A4), contributed greatly to the separation of PC2-component in both plant
tissues. On the other hand, phosphoric acid, L-lysine (Lys) and glyceric acid-3phosphate in leaves (Table A3), and phosphoric acid, malic acid and fructose in roots (Table A4) were specifically loaded for the LN-related principal components. The similar effect on metabolite profiles could be found in XZ149 and XZ56 in the initiating time of LN treatment (1 d), and the difference between the two genotypes became larger over the time. Obviously, the response of metabolites to LN stress varied with plant tissue, growth stage, and genotype. 3.3 The response of amino acid level to LN stress High flexibility is one of major characteristics for amino acid pool in plants when exposed to the variable environments. A dramatic difference could be found in amino acids level between the two genotypes and plant tissues under LN stress. However, no consistent trend could be detected for most amino acids over the whole time-course of LN stress (Tables 1 and 2), indicating the complexity of amino acid change in response to LN stress. In leaves, levels of all major amino acids were decreased at the late stage, but increased frequently at the early stage under LN stress (Table 1). It is interestingly found that relative content (calculated by the formula: log2 (LN stress /control)) of glutamic acid (Glu) and aspartic acid (Asp) was consistently lower in XZ149 than in XZ56 (Table 1). On the other hand, levels of some minor acids increased consistently during the whole LN stress treatment (Table 1). The contents of leucine (Leu) and lysine (Lys) were generally increased in XZ56, while changed irregularly in XZ149 during the treatment (Table 1 and Fig. 4). The contents of phenylalanine (Phe) and tyrosine (Tyr) decreased or remained without change in XZ149, while increasing in XZ56 at the early stage (Table 1). In roots, both minor and major amino acids increased at some time points of the late stage. Relative content of Asp was always higher in XZ149 than in XZ56 (Table 2). A strong increase was detected for Lys and Leu contents during 1 d to 18 d after treatment, showing the opposite trend as pyroglutamic acid did (Table 2). 3.4 The response of organic acid levels to LN stress On the whole, LN stress caused a dramatic increase for the most organic acids involved in tricarboxylic acid cycle (TCA) at the early stage, but at the later stage, a
distinct reduction was found (Figs. 4 and 5, Tables A5 and A6). Moreover, these metabolites showed the different responses to LN stress between leaves and roots. In leaves, citric acid content increased consistently during the whole treatment. Succinic acid content decreased in XZ149, but remained little changed in XZ56 at 1 d after treatment, and then increased at other earlier time points, and again decreased in the two genotypes (Table A5). Fumaric acid and malic acid contents kept increasing until 12 and 15 d after treatment (Table A5). Surprisingly, 2-ketoglutaric acid (2-KG) showed the dramatic decrease in the two genotypes, with XZ149 being larger than XZ56 (Table A5). In roots, the TCA cycle was enhanced in the two genotypes under LN stress, as shown by higher citric acid level compared to the control at early stage (Fig. 5 and Table A6). However, citric acid level kept high in XZ149, while decreased or remained little change in XZ56 under LN stress at late stage (Fig. 5 and Table A6). Other TCA intermediates, including 2-KG, succinic acid and fumaric acid, showed downaccumulation at 1 d after LN stress, and then up-accumulation, and then again downaccumulation in XZ149. The two genotypes showed the similar trend, with XZ56 showing larger decline at late stage (Fig. 5 and Table A6). It should be noted that the content of malic acid, as a down-stream metabolite of TCA cycle, was consistently higher in LN stress than in control for the two genotypes (Fig. 5 and Table A6). In this study, increased shikimic acid content enhanced shikimate pathway in roots and leaves, and the similar increase was observed for the related secondary metabolites, such as quinic acid (Figs. 4 and 5). 3.5 The response of sugar and its related metabolites to LN stress Under LN stress, accumulation of glucose-6-P (G-6-P) involved in glycolysis and pentose phosphate (PPP) pathway was greatly enhanced at early stage for both genotypes, then reduced in XZ56 and remained little change in XZ149 from 12 d onwards in roots (Fig. 5 and Table A6). On the other hand, accumulation of G-6-P coupled with the glyceric acid-3-P was consistently enhanced in the leaves of both XZ149 and XZ56 over the whole treatment (Fig. 4 and Table A5). In both leaves and roots, the content of soluble sugar, except sucrose, increased
dramatically in XZ149 under LN stress, but decreased obviously at many time points in XZ56 (Figs. 4 and 5, Tables A5 and A6). The contents of myo-inositol, galactonic acid, 2-keto-L-gluconic acid, and threonic acid in leaves showed a significant increase in both genotypes (Fig. 4 and Table A5), while contents of 2-keto-L-gluconic acid, glyceric acid, and threonic acid in roots increased in XZ149 and decreased in XZ56 at some time points (Fig. 5 and Table A6). 4. Discussion It is well documented that there is a large difference among genotypes within a plant species in LN tolerance (Le et al., 2000; Anbessa et al., 2009; Namai et al., 2009; Presterl et al., 2003). In the present study, the difference in LN response between the two wild barley genotypes was again demonstrated, as reflected by plant biomass, with XZ149 being more tolerant to LN stress than XZ56. Metabolites profiles between control and LN stress could not be separated until the 3rd day after stress treatment according to hierarchical clustering analysis (Fig. 3). We analyzed the root and leaf responses separately to identify the difference in metabolic response to LN stress between the two genotypes. Amino acids level in leaves showed highly individual response at early stage of treatment, but mainly exhibited a significant decrease at late stages under LN stress (Tables 1 and 2). It is commonly expected that amino acids are less accumulated after long LN stress, and our results are in agreement with previous reports (Amiour et al., 2012; Schlüter et al., 2012; Krapp et al., 2011; Howarth et al., 2008 and Fritz et al., 2006a). At early stage, inhibition of plant growth may allow the accumulation of some amino acids (Tschoep et al., 2009). However, it is quite perplexing that all major amino acids were down-accumulated in leaves while some of them were up-accumulated in roots at later stages. Amino acid synthesis happened mainly in leaves and was energy consuming. Therefore, relatively less accumulation of amino acids in leaves may reduce energy cost. In addition, some amino acids have the function of alleviating abiotic stress (Sharma et al., 2006). It may be assumed that amino acids accumulation in roots might be beneficial for developing a defense against LN stress in the way we still do not know up to date. Currently, XZ149 had lower and higher relative contents of some major
amino acids in leaves and roots than XZ56, respectively, fitting to the difference in LN tolerance between the two genotypes. N assimilation in plants is mediated by the GS/GOGAT cycle and depends on the availability of energy, reducing power and C skeletons. Glu is a primary amino acid produced by the cycle, acting as an immediate donor of amino group for the synthesis of most other amino acids. It is reported that Glu shows quite stable in changing amino acid profiles, suggesting that its homeostasis in plants is regulated by many mechanisms. It was reported that the plant PII-like protein serves as the part of a complex signal transduction network perceiving availability of carbon and organic nitrogen (Hsieh et al., 1998; Fritz et al., 2006a; Krapp et al., 2011). However, in the present study, Glu level was markedly affected by LN stress (Table 1). Meanwhile, 2-KG is an immediate acceptor for ammonium in the GS/GOGAT pathway, and its importance as a key regulator of carbon and nitrogen interactions has been noted in many studies (Stitt and Fernie, 2003). In this study, the changing trend of 2-KG content was not mirrored by Glu content in leaves, which did not agree with the previous study that Glu and2-KG often changed in parallel (Mueller et al., 2001; Novitskaya et al., 2002; Fritz et al., 2006b). In this study, more Glu synthesis in leaves could be indicated by the consistent increase of citric acid, an up-stream metabolite in the TCA cycle, and consistent decrease of 2-KG, a Glu precursor under LN stress (Table A5). In view of the fact that Glu provides amino group for the synthesis of most other amino acids, the detected Glu content was not high. Moreover, XZ149 had the lower relative content of Glu under LN in comparation with XZ56, indicating its quick use in amino acid synthesis. Glycolysis, TCA cycle, and PPP pathway in leaves shares the same metabolic trend in the response to LN stress (Figs. 4 and 5). Carbon precursors for amino acid biosynthesis are the organic acids involved in these pathways (Figs. 4 and 5), and the influence of LN on the organic acid metabolism seems to be a central point in the coordination of C and N metabolisms (Hodges, 2002). In the non-photosynthetic tissue, these pathways supply energy and/or reduce the power for N assimilation. In our previous study, we found that a gene encoding 6-phosphogluconate dehydrogenase, the key enzyme in PPP pathway, was up-regulated only in the roots of XZ149 (Quan et al.,
2016). Here, the roots of XZ149 showed consistently higher relative contents of both glucose-6-P and citric acid than XZ56 during the whole stress treatment, except at 1th day (Table A6), suggesting its higher capacity of producing energy and maintenance of cell redox homeostasis, thereby beneficial for its LN tolerance. Carbohydrates are synthesized in plant shoots and more sucrose will be translocated to the roots when plants suffer from N deficiency, as the roots become the main growing sink under LN stress (Krapp et al., 2011). In this study, sucrose was up-accumulated in roots consistently during LN stress, and its relative content in XZ149 was much higher than that in XZ56 (Fig. 5), whereas in leaves, sucrose was down-accumulated in XZ149 until 15 d after LN treatment (Fig. 4), suggesting that the allocation proportion of sucrose was larger in the roots of XZ149 compared with that in XZ56 at the earlier stage of LN stress. Sucrose could provide carbon source and energy for roots, playing an important role in plant growth and development. While invertase could irreversibly catalyze the hydrolysis of sucrose, thus affecting the use and transportation of sucrose (Koch, 2004). It was reported that invertase activities increased when plants were exposed to abiotic stress conditions (Yamada et al., 2010). Our previous study showed that the gene encoding invertase was up-regulated only in roots of XZ149 (Quan et al, 2016). Hence it can be assumed that the use of sucrose in the roots of XZ149 was much greater compared to that in X56 under LN stress. Moreover, the sucrose translocated to the roots also acts as a signal molecule for plant growth and development (Chiou and Bush, 1998) and is involved in root growth under nutrition deficiency (Jain et al, 2007; Hammond and White, 2008; Wang et al, 2015). Thus, the current results suggest that greater accumulation and use of sucrose in XZ149 are attributed to better root growth under LN stress. In addition, the content of soluble sugars, such as glucose, fructose, and raffinose increased consistently during the whole LN treatment in XZ149, but decreased frequently in XZ56 (Tables A5 and A6). Raffinose serves not only as a carbon store, but also has a protective function (Hannah et al., 2006; Krapp et al., 2011; Schlüter et al., 2012). Hence relatively higher raffinose content in the roots and leaves of XZ149 may be attributed to its greater LN stress tolerance. Malic acid, an intermediate in the TCA cycle, has been thought to play important
roles in plant metabolism, acting as a reducing equivalent, osmoticum, and pH regulator (Fernie and Martinoia, 2009). Schlüter et al (2012) reported that malic acid was continuously reduced in maize leaves under N deficiency.
The demand for malate in
the C4-specific shuttle, acting as a counterion of balancing hydroxyl ions produced through nitrate reduction declined under LN conditions. Although hydroxyl ions produced through nitrate reduction declined, reactive oxygen species (ROS) was accumulated under LN conditions (Shin et al., 2005), due to the reduction in the frequency of electron carriers of electron transport systems (Grossman and Takahashi, 2001). Dehydrogenation of malic acid was accompanied by the generation of NADH in plants, and the latter is crucial to maintain the cellular redox state and/or antioxidative capacity (Kirsch and De Groot, 2001; Petrat et al., 2003). In the present study, malic acid content in roots was continuously increased under LN stress (Table A6). Moreover, XZ149 maintained higher relative content of malic acid in comparison with XZ56. Therefore, it may be suggested that XZ149 have the stronger ability of maintaining redox homeostasis in the roots relative to XZ56. The genes encoding the enzymes of the shikimate pathway are induced in parallel to the genes encoding phenylpropanoid synthesis under low N conditions (Scheible et al., 2004; Amiour et al., 2012). In this study, the precursor of shikimate, shikimic acid, was increased in roots and leaves of the both genotypes under LN stress, and the similar increase was observed for the related secondary metabolites, such as quinic acid (Figs. 4 and 5). Relative content of Phe was consistently lower in XZ149 than in XZ56 (Table 2). Our previous study showed that the synthesis pathways of both phenylpropanoid and flavonoid mediated by PAL were much more active in the LN tolerant genotype than in sensitive one (Quan et al., 2016). Thus, it may be concluded that the lower relative content of Phe in XZ149 should be associated with its more utilization. 5. Conclusion Based on the metabolic analysis, the possible mechanisms of low N tolerance in XZ149 could be assumed as follows: (1) an energy saving pattern of amino acids accumulation; (2) a pattern of carbon allocation in favor of plant growth; (3) a high capacity of producing energy; and (4) a higher ability to maintain redox homeostasis.
All of these in turn contributed to a higher biomass of XZ149 under LN stress in comparison with XZ56.
Abbreviations N, nitrogen; LN, low nitrogen; G-6-P, glucose-6-phosphate; 2-KG, 2-ketoglutaric acid; PPP, pentose phosphate; Glu, glutamic acid; Gln, glutamine; Asp, aspartic acid; Asn, asparagine; Ser, serine; Gly, glycine; Ala, alanine; Leu, leucine; Thr, threonine; Lys, lysine; Val, valine; Phe, phenylalanine; Tyr, tyrosine; GABA, 4-aminobutyric acid.
Acknowledgments We thank Prof. Dongfa Sun (Huazhong Agricultural University, China) for providing Tibetan wild barley accessions and Hangzhou PTM Biolabs Inc. for excellent technical assistance. This work was supported by the by Natural Science Foundation of China (31330055), China Agriculture Research System (CARS-05) and Jiangsu Collaborative Innovation Center for Modern Crop Production (JCIC-MCP).
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Figure legends: Fig. 1 The temporal changes of plant biomass in the two barley genotypes under normal N (C) and low N (LN) conditions. The lowcase letters represent significant difference (P, 0.05) among treatments and genotypes at each time point (n=4, bars show the SD). Fig. 2 Heatmap and hierarchical cluster analysis for detected metabolites of leaves and roots in the two wild barley genotypes. The samples from left to right: 1-9 d in roots (G, H, AE, O, W, AC, E, U, F, AD, AF, X, V, M, P, N); 12-18 d in roots (AV, AT, BA, AS, AL, AN, BD, BB, BC, AK, AU, AM); 1-9 d in leaves (D, C, B, A, Y, AA, Q, S, I, J, K, L, T, R, AB, Z); 12-18 d in leaves (AG, AW, AO, AI, AQ, AY, AZ, AH, AG, AX, AR, AU). The samples corresponding letter number and the metabolite names from 1 to 51 were listed in Tables A1 and A2. Fig. 3 Principal component analysis (PCA) of metabolic profiles in roots and leaves of XZ149 and XZ56 under control and low N conditions (four biological replicates). (A) PCA in leaves; (B) PCA in roots. PC1, the first principal component; PC2, the second principal component. Fig. 4 Changes in metabolites mapped to the metabolic pathways in leaves of XZ149 and XZ56 under LN stress. Relative contents of metabolites areshown by a color gradient from low (green) to high (red) as presented in the color scale. Relativecontent of each metabolite was calculated by log2 (LN/C) in two genotypes at each time point. Grey color means corresponding metabolite not detected. For each heatmap from left to right: 1 d (first column), 3 d (second column), 6 d (third column), 9 d (fourth column), 12 d (fifth column), 15 d (sixth column), 18 d (seventh column); and from top to bottom: (top column), XZ56 (bottom column). Fig. 5 Changes in metabolites mapped to the metabolic pathways in roots of XZ149 and XZ56 under LN stress. Relative contents of metabolites are shown by a color gradient from low (green) to high (red) as presented in the color scale. Relative content of each metabolite was calculated by log2 (LN/C) in two genotypes at each time point. Grey color means corresponding metabolite not detected. For each heatmap from left to right: 1 d (first column), 3 d (second column), 6 d (third column), 9 d (fourth column),
12 d (fifth column), 15 d (sixth column), 18 d (seventh column) ; and from top to bottom: (top column), XZ56 (bottom column).
Fig. 1
Fig. 2
Fig. 3 A
B
Fig. 4 2-Keto-gluconic acid
Ascorbic acid
Glycerol
Threonic acid
Galactose Glyceric acid
Glucose
Threonic acid-1,4-lactones
Glucose-1-P Hexadecanoic acid
Sucrose
Ribose
Xylitol
Glucose-6-P
Ribitol
Raffinose Fructose
Inositol
Fructose-6-P
Xylose L-Phenylalanine
Glycine
Glyceric acid-3-P
L-Serine
Quinic acid
PEP L-Leucine
Pyruvate
L-Lysine
Shikimic acid
L-Alanine Tyrosine
Acetyl-CoA Valine
Oxosuccinic acid L-Aspartic acid Glyoxylic acid
Citric acid Pyroglutamic acid
Malic acid
L-Threonine Glycolic acid
Isocitric acid
L-Proline
0
3
Ketoglutaric acid Ornithine
Succinic acid
-3
Relative content
Fumaric acid
GABA
L-Glutamic acid
Glutamine
Fig. 5 2-Keto-gluconic acid
Ascorbic acid Galactose
Glycerol Glyceric acid
Glucose
Sucrose
Hexadecanoic acid
Threonic acid
Glucose-1-P
Ribose
Ribitol
Xylitol
Glucose-6-P Raffinose
Fructose
Inositol
Xylose
Fructose-6-P
L-Serine
Glycine
Glyceric acid-3-P Tyrosine
L-Lysine
L-Aspartic acid
Quinic acid
PEP
L-Leucine
Pyruvate
Shikimic acid
L-Alanine L-Phenylalanine
L-Threonine
Acetyl-CoA L-Asparagine
Valine
Oxosuccinic acid
Glyoxylic acid Malic acid
Citric acid Isocitric acid
3
Fumaric acid
L-Proline Ketoglutaric acid Putrescine
Ornithine 0
Succinic acid Maleic acid
Arginine
-3
Relative content
Glycolic acid
Pyroglutamic acid
GABA
L-Glutamic acid
Glutamine
Table 1. Effects of LN stress on amino acid content of barley leaves Amino acidsa
Log2 (fold change)b
Genotypes 1d
Glu
Gln
Asp
Gly
Ala
Ser
Leu
Lys
Thr
Val
Phe
Tyr
GABA
Pyroglutamic acid
3d
6d
9d
12d
15d
18d
X149
0.09
-0.35**c
0.14**
-0.03
-0.66**
-1.36**
-0.93**
X56
-0.08
0.09
0.68**
0.51*
-0.21**
-0.78**
-0.27*
X149
-0.49**
-0.78**
-1.62**
-1.93**
-1.12**
-0.8**
-4.05**
X56
0.35**
-0.89**
-1.09**
-3.41**
-1.62**
-1.13**
0.94**
X149
1.31**
-0.18*
0.69**
0.04
-1.06**
-2.72**
-1.44**
X56
0.4*
0.09
3.89**
0.39**
-0.78**
-2.69**
-0.89**
X149
-0.82**
-1.88**
1.27**
-1.63**
-0.19**
-1.51**
-1.14**
X56
0.25*
-0.9**
0.55**
-0.61*
-0.21*
-1.9**
-0.54**
X149
0.4**
-0.93**
-0.27**
-1.02**
-1.91**
-3.04**
-2.61**
X56
0.11*
-0.63**
-0.51*
-1.56**
-1.61**
-2.52**
-0.5**
X149
-0.39**
-0.96**
-0.72**
-1.1**
-0.91**
-3.79**
-2.13**
X56
0.1
-1.13**
-0.48**
-1.77**
-1.44**
-3.37**
-1.23**
X149
-0.19
-0.19*
-0.73**
1.18**
-0.57**
1.47**
-0.71
X56
0.26*
0.8**
-0.34*
1.25**
0.13
1.88**
1.29**
X149
-0.19
-0.35*
-1.69**
0.11*
-0.46**
1.74**
-0.32
X56
0.17
1.62**
-1.33**
1.63**
1.24**
3.3**
0
X149
-0.19**
-0.78**
-0.31**
-0.8**
-1.76**
-3.47**
-1.62**
X56
-0.01
-0.87**
-0.62**
-1.01**
-1.39**
-2.97**
-0.61**
X149
0.05
-0.48**
-0.41*
0.16*
-0.01
0.24*
-1.08**
X56
0.06
0.3**
-0.65**
0.48**
-0.82**
0.54**
0.53**
X149
0.13
0.05
-0.39**
-0.66**
-0.09
-1**
-0.42**
X56
0.43*
0.53**
0.89**
0.32**
-0.06
-0.94**
0.37*
X149
0.03
-0.47**
-0.13
0.54*
-0.11**
0.64**
-0.78*
X56
-0.03
0.31*
0.25*
0.98**
-0.1
-0.03
0.46*
X149
-
-
-
-
-0.5*
-1.48**
-0.19*
X56
-
-
-
-
-0.52**
-1.22**
-0.1
X149
-0.5**
-0.74**
-0.37*
-0.89**
-1.15**
-2.82**
-1.92**
X56
0.21
-1.25**
-1.22**
-1.61**
-1.01**
-2.71**
-0.52*
a Amino acids are indicated using the standard three letter code. GABA, 4-aminobutyric acid. b Fold change=LN/C. c * and ** indicate significant (P<0.05) and highly significant (P<0.01) differences between treatments, respectively (n = 4).
Table 2. Effects of LN stress on amino acid content of barley roots Amino acidsa
Glu
Gln
Asp
Asn
Gly
Ala
Ser
Leu
Lys
Thr
Val
Phe
Tyr
GABA
Pyroglutaminc acid
Log2 (fold change)b
Genotypes 1d
3d
6d
X149
-0.02
0.15*c
0.02
X56
-0.02
0.28**
X149
-0.7**
X56
9d
12d
15d
18d
-0.03
-0.48**
-0.8**
0.06
0.4**
-0.56**
-0.43**
-1.25**
0.3*
-0.19*
-1.5**
-2.33**
-5.16**
-4.39**
-2.05**
1.33**
0.68**
0.7*
-2.79**
-4.25**
-4.68**
-0.35
X149
0.8
2.71**
3.05**
3.01**
-0.38**
0.1
2.02**
X56
0.16
1.3**
3.83**
0.93**
-1.03**
-0.67**
1.89**
X149
1.38*
1.46**
0.13
1.29**
-0.85**
0
0
X56
1.12**
0.52*
1.41**
-0.45*
0.36*
0.36**
0
X149
0.26*
0.56*
0.85**
0.71*
-0.41**
0.32*
-0.01
X56
0.39**
0.76**
1.27*
0.02
-0.04
-0.16
-0.68**
X149
-0.45**
-0.36**
-0.04
-0.19
0
-0.45**
0.37**
X56
-0.35*
1.06**
0.53**
-0.22
-0.13
-0.65**
-1.13**
X149
-0.53**
0.13*
-1.06**
-0.96**
-1.88**
-1.82**
-2.39**
X56
0
0.08
-0.81**
-2.26**
-2.13**
-1.97**
-2.47**
X149
0.04
0.67**
-0.25
0.02
-0.03
1.98**
1.1**
X56
0
1.12**
1.02**
1.3**
0.28
1.49**
1.21**
X149
0.52**
0.46**
0.83**
0.51*
0.16
2.16**
0.71*
X56
0.65**
1.47**
1.91**
0.74**
-0.3**
1.01**
0.07
X149
-0.27**
0.36**
-0.6**
-0.53*
-1.6**
-0.59**
-2.11**
X56
0.04
0.55**
-0.41**
-1.45**
-1.73**
-1.24**
-1.72**
X149
-0.55**
0.11
-0.33**
-0.45**
-0.19**
0.6**
0.91**
X56
-0.06
0.38*
0.32**
0.7**
-0.08
0.98**
0.44**
X149
0.05
0.81**
0.23
0.1
-0.73**
0.19*
-0.26**
X56
0.02
0.22**
1.22**
0.27*
-0.7**
0.23**
-0.01
X149
-0.12
0.24*
-0.21**
0.63**
-0.52**
0.66
-0.23*
X56
0.31*
0.33*
0.17
0.32*
-0.24*
0.24
-0.04
X149
-0.58**
-0.19**
0.33**
0.52**
-0.28**
-0.24**
0.56**
X56
-0.22**
0.7**
0.09
0.23*
-0.24**
-1.28**
-0.76**
X149
-0.3*
-0.54**
-0.67**
-1.52**
-1.65**
-3.31**
-1.79**
X56
0.33
-0.32**
-0.81**
-1.66**
-1.57**
-3.08**
-1.49**
a Amino acids are indicated using the standard three letter code. GABA, 4-aminobutyric acid. b Fold change=LN/C. c * and ** indicate significant (P<0.05) and highly significant (P<0.01) differences between treatments, respectively (n = 4).