Growth and nitrogen metabolism are associated with nitrogen-use efficiency in cotton genotypes

Plant Physiology and Biochemistry 149 (2020) 61–74

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Research article

Growth and nitrogen metabolism are associated with nitrogen-use efficiency in cotton genotypes

T

Asif Iqbal (PhD)1, Dong Qiang1, Wang Zhun, Wang Xiangru (PhD), Gui Huiping, Zhang Hengheng, Pang Nianchang, Zhang Xiling (PhD)∗∗, Song Meizhen∗ State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, PR China

A R T I C LE I N FO

A B S T R A C T

Keywords: Cotton genotypes Nitrate Root architecture Nitrogen metabolism Nitrogen-use efficiency

Crops, including cotton, are sensitive to nitrogen (N) and excessive use can lead to an increase in production costs and environmental problems. We hypothesized that the use of cotton genotypes with substantial root systems and high genetic potentials for nitrogen-use efficiency (NUE) would best address these problems. Therefore, the interspecific variations and traits contributing to NUE in six cotton genotypes having contrasting NUEs were studied in response to various nitrate concentrations. Large genotypic variations were observed in morphophysiological and biochemical traits, especially shoot dry weight, root traits, and N-assimilating enzyme levels. The roots of all the cotton genotypes were more sensitive to low-than high-nitrate concentrations, and the genotype CCRI-69 had the largest root system irrespective of the nitrate concentration. The root morphological traits were positively correlated with N-utilization efficiency and were more affected by genotype than nitrate concentration. Conversely, growth and N-assimilating enzyme levels were more affected by nitrate concentration and were positively correlated with N-uptake efficiency. Based on shoot dry weight, CCRI-69 and XLZ-30 were identified as N-efficient and N-inefficient genotypes, respectively, and these results were confirmed by their contrasting root systems, N metabolism, and NUEs. In the future, multi-omics techniques will be performed to identify key genes/pathways involved in N metabolism, which may have the potential to improve root architecture and increase NUE.

1. Introduction Nitrogen (N) is an important and limiting factor of plant growth and productivity (Iqbal et al., 2019a). It is a key input in agricultural production and is the main component of several macro-molecules, metabolites and signaling compounds that are necessary for plant growth and productivity (Hawkesford et al., 2012). N fertilization has significantly increased crop production and thus reduced the pressure of global population growth (Gojon, 2017). However, suboptimal N availability is a major restriction in crop production and can lead to yield decreases of almost 50% (Jones et al., 2013; Iqbal et al., 2015). Thus, large amounts of nitrogenous fertilizers are applied to improve growth and productivity (Glass, 2003; Sarasketa et al., 2014), and their use is expected to increase by threefold in the future (Good et al., 2004). However, the excessive use of chemical N fertilizers reduces the N-use efficiency (NUE) (Miao et al., 2011) and causes serious environmental pollution, such as groundwater contamination and soil acidification

(Qiao et al., 2012). In addition, the intensive use of N fertilizers significantly increases production costs (Hou et al., 2007). Thus, to reduce this costly component of crop production, there is an immediate need to identify and develop crop genotypes with higher NUEs (Edgerton, 2009; Den Herder et al., 2010; Kant et al., 2010). NUE is a complex trait with two main components N-uptake efficiency (NUpE) and N-utilization efficiency (NUtE) and is influenced by biochemistry, phenology, architecture, and responses to the environment (Hawkesford, 2017; Hawkesford and Griffiths, 2019). The roles of NUpE and NUtE in determining the overall NUE are influenced by the N supply (Garnett et al., 2015). Generally, plants have evolved versatile mechanisms to increase N use (Bascuñán-Godoy et al., 2018). Efficient genotypes have specific physiological mechanisms enabling them to access sufficient N quantities (uptake efficiency) and/or to more effectively use their N uptake (utilization efficiency) (Sattelmacher et al., 1994). NUpE is associated with root growth and architecture (Xu et al., 2012). The root is the main organ of N uptake and, therefore, its

∗

Corresponding author. Corresponding author. E-mail addresses: [email protected] (A. Iqbal), [email protected] (Z. Xiling), [email protected] (S. Meizhen). 1 These authors equally contributed to this work. ∗∗

https://doi.org/10.1016/j.plaphy.2020.02.002 Received 27 November 2019; Received in revised form 1 February 2020; Accepted 2 February 2020 Available online 04 February 2020 0981-9428/ © 2020 Elsevier Masson SAS. All rights reserved.

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traits associated with NUpE and NUtE among cotton genotypes; and 3) to identify the most distinct N-efficient and N-inefficient genotypes for further molecular investigation of key genes and pathways responsible for NUE.

morphological characteristics, such as root length and area, determine a plant's ability to acquire N (Jiang et al., 2017). The greater allocation of biomass to root for the development of a branched and dense root system results in an increased NUpE and limits the impact of N fertilization on the environment (Robinson, 2004). N uptake is also dependent on the root absorption rate, which is affected by energy supply and feedback from root N assimilation (Jiang et al., 2017). The induction of root extension by low nitrate levels has been reported. Luo et al. (2015) found that root lengths and surface area increased 54% and 49% under low-N conditions, while Bahrman et al. (2005) demonstrated a decreased root length, but increased root/shoot ratio, under the same conditions. Similarly, Lynch (2013) showed that the frequency and lengths of roots are important for N capture. Therefore, clarifying the changes in cotton root system architecture under various nitrate concentrations will provide information on root N-uptake ability. However, improving NUtE is a much more complex task (He et al., 2017). Gene engineering has been used to improve the plant processes of carbon/N storage as well as the signaling and regulation of N metabolism, translocation, remobilization, and senescence, which are related to NUtE (Chardon et al., 2012; McAllister et al., 2012). Detailed investigations to identify the underlying traits of NUtE under various nitrate concentrations are required to pinpoint the critical factors of N utilization in cotton. A study was conducted previously on total plant dry matter, N absorption and other morphological traits contributing to NUE (Lea and Azevedo, 2006). However, little is known about plant N metabolism (Forde and Lea, 2007), specifically, the key enzymes related to the primary N assimilation, such as nitrate reductase (NR), nitrite reductase, glutamine synthetase (GS), glutamate synthase (GOGAT) and glutamate dehydrogenase (GDH), which play central roles in the primary assimilation of N (Krapp et al., 2011; Gu et al., 2013). During the primary assimilation of N, ammonium is converted into an amino acid or other organic N molecules. These molecules are then translocated from sources to sinks (Gupta et al., 2012), in which they act as precursors for the biochemical synthesis of nitrogenous compounds and become part of the internal N pool (Luo et al., 2015). This internal N pool regulates N uptake, assimilation, and metabolism in the plants based on external N availability. In addition, these N-containing compounds (amino acids, proteins, etc.) are also used as indicators to detect genotypic responses to the N supply (Quan et al., 2017). Therefore, different steps involved in the uptake and assimilation of N may help to identify the most important features contributing to NUE (Xu et al., 2012). Thus investigating the levels of these indicators in cotton with contrasting N-efficient genotypes will increase our understanding of N metabolism and may be useful in identifying and improving NUE in response to various nitrate concentrations. Cotton (Gossypium L.) has great economic and social importance worldwide because it is among the ten largest sources of wealth in the agricultural sector (de Oliveira Araújo et al., 2013). However, the high cost of production, especially of N fertilization, is the main problem for cotton growers (Zhang et al., 2018). To reduce this costly component of crop production, there is an immediate need to identify and develop cotton genotypes having high NUEs. Studies on N-efficient genotypes have been performed in many crops, including rice (Cheng et al., 2011), maize (Gallais and Hirel, 2004), rapeseed (Bouchet et al., 2016), tomato (Abenavoli et al., 2016), barley (Anbessa et al., 2010; Quan et al., 2017), wheat (Gaju et al., 2011; Hitz et al., 2017) and Arabidopsis (Chardon et al., 2010). However, N-efficient cotton cultivars have not yet been identified and developed because of our limited knowledge regarding the mechanisms and traits contributing to NUE (Zhang et al., 2018). Therefore, we hypothesized that the use of genotypes having profound root systems and high genetic efficiencies for N metabolism, NUpE and NUtE would best address this problem. Consequently, the objectives of our study were: 1) to identify the genotypic variations in cotton affecting root morphology, N metabolism, NUpE and NUtE in response to various nitrate concentrations; 2) to find the most essential

2. Materials and methods 2.1. Plant materials The experiment was set up in the growth chamber at the Cotton Research Institute of the Chinese Academy of Agricultural Sciences, Anyang, China. Six cotton genotypes comprising of three N-efficient (CCRI-69, Z-1017 & ZMS-49) and three N-inefficient (LU-21, GD-89 & XLZ-30) were selected from the previously screened genotypes based on biomass and NUE in the pot experiment (Zhang et al., 2018). Healthy seeds of each genotype were placed in a mixture of sand and vermiculate for one week in a germinator. After the full opening of two cotyledons, seedlings with uniform height were selected and transplanted into 7 L containers in the growth chamber (16/8 h light/dark cycle, 28 °C temperature, 60% relative humidity). At the first week after transplanting, seedlings were supplied with 1/2-strength, followed by a full strength-Hoagland solution (Iqbal et al., 2019b). At two true leaves stage, seedlings were exposed to various nitrate concentrations 0, 0.5, 1, 2, 5, and 10 mM as Ca(NO3)2. Moreover, in low N treatment, a total concentration of 0–4 mM L−1 CaCl2 was added to equalize calcium concentration between the treatments (Jiang et al., 2017). The solutions were replaced on a weekly basis and aerated with an electric pump. The position of each plant was randomly changed every week to eliminate the effect of position. After four weeks, plants displayed obvious symptoms of N treatment and eight plants from each group were harvested for further analysis. 2.2. Plant morphological characteristics After the plants were harvested, shoot length was measured through a ruler by randomly taking four plants in each replication as described by Shao et al. (2016). Similarly, the lengths and widths of each leaf were measured and the mean single leaf area was obtained from the product of length, width and correction factor (0.75) as described by Iqbal et al. (2019b). The harvested plants were then put in the oven at 105 °C for 1 h followed by 80 °C for 48 h. The root, shoot and total plant dry weight of each plant was measured with the help of an electronic balance. At the end of the experiment, the root morphological parameters were measured by using a special image analysis software program WinRHIZO (Win/Mac RHIZO Pro V. 2002c Regent Instruments Inc., Québec, QC G1V 1V4, Canada) in combination with Epson Expression 11000XL scanner. The samples were each (one after the other) placed in the scanner's tray. Water was added and with the aid of a plastic forceps, the roots were homogeneously spread across the tray; and the scanning and analysis have done from the WinRhizo system's interface on a computer connected to the scanner, and the root characteristics were calculated according to the established method (Ulas et al., 2019). 2.3. Measurement of gas exchange parameters Before harvest, six plants from each treatment were used for gas exchange analysis. For each plant, the top three mature leaves that had formed after N treatment were used to measure photosynthesis-related parameters. Net photosynthetic rate, stomatal conductance, transpiration rate, and intercellular CO2 concentration were analyzed with the help of a portable photosynthesis system (Li-Cor-6800; Li-Cor, Inc., Lincoln, NE, USA) with an attached red-blue light leaf chamber from 9:00 to 11:00 a.m. in the growth chamber, according to a previously reported method (He et al., 2011). The CO2 concentration inside the chamber was controlled at 400 ± 1 μmol CO2 (mol air)−1. The blue 62

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(v:v) 2-mercaptoethanol, was used to determine the activity of the GDH and GOGAT enzymes. In addition, the reaction solution of GOGAT was 2.5 mM α-ketoglutarate, 100 μM NADH, 10.0 mM L-glutamine, and 1.0 mM aminooxyacetate, and that of GDH was 2.5 mM α-ketoglutarate, 100.0 μM NADH, and 100.0 mM (NH4+)2SO4.

light was employed to ensure a maximal stomatal opening. The light intensity was set as 1000 μmol photon m−2 s−1, as recommended by Cao et al. (2012). 2.4. Measurement of N concentration and NUE traits N concentrations of plant tissues were measured through the Kjeldahl method. The dried samples of shoot and root were grounded into a fine powder and around 0.2 g of each sample powder was weighed, digested with H2SO4–H2O2 and were then analyzed for N concentration (Li et al., 2006) using the Bran + Luebbe ContinuousFlow AutoAnalyzer III (AA3). Shoot dry weight (SDW) data of the cotton genotypes were plotted as a function of various nitrate concentrations. The curves were defined by non-linear regression analysis, where α shows the maximum shoot dry weight and β represents the nitrate concentrations at half maximum shoot dry weight or the rate of nitrate concentration at which α was reached (Fig. 5A). The performance of genotypes was analyzed by directly comparing the values of α and β in a way that is similar to the well-known Michaelis-Menten enzyme kinetics Vmax and Km (Gourley et al., 1994). The total N accumulation was obtained as the product of N concentrations and plant total dry weight (Lawlor, 2002). NUtE was measured as total plant dry weight divided by N concentrations (Siddiqi and Glass, 1981) and NUpE was determined as total N accumulation divided by root dry weight (Elliot and Laüchli, 1985).

2.6. Measurement of total soluble protein, free amino acids and total soluble sugars Total soluble protein was measured according to the supposed method by Bradford (1976) using Coomassie Brilliant Blue (G-250) as a dye and albumin as a standard (Theymoli and Sadasivam, 1987). Root and shoot samples (0.5 g) were grounded in liquid nitrogen and homogenized in phosphate buffer (5 mL). The samples were then placed in a water bath 100 °C (10 min) and then centrifuged at 3000 g for 5 min at 22–25 °C. Reaction mixture composed of 2 mL d H2O, enzyme extract (20 μL) and Bradford reagent (0.5 mL). Finally, the values were recorded at 595 nm wavelength using distilled water as a blank control and bovine albumin (BSA) as a standard with the help of a spectrophotometer (UV-2600). For the determination of total free amino acids, the ninhydrin method was used as described previously (Yokoyama and Hiramatsu, 2003) with some modifications (Sun et al., 2006). Extraction buffer composed of Acetic acid/sodium acetate (pH 5.4) and the final values of free amino acids were detected at 580 nm with the help of a spectrophotometer (UV-2600). Total soluble sugar was determined according to the method suggested by Shields and Burnett (1960) followed with slight modifications. The shoots and roots samples (0.5 g) were grounded into fine powder in liquid nitrogen using mortar and pestle. The samples were homogenized in 90% ethanol (3 ml) and then incubated for 1 h at 60–70 °C. After incubation, 90% ethanol was again added to the extract in a volumetric flask (final vol. 25 ml). Each sample (1 ml) was then mixed anthrone solution (5 ml) and sulphuric acid (5 ml). The final value was then observed at 485 nm using glucose as standard.

2.5. Measurement of N assimilating enzymatic activities Nitrate reductase (NR: EC 1.7.1.3) activity was measured according to Silveira et al. (2001) and expressed as μg nitrogen dioxide (NO2−) h−1 g−1 fresh weight (FW). Fresh tissue sample (0.2 g) added with 2.0 mL extraction (25.0 mM phosphate buffer (pH 7.5), 5.0 mM cysteine, and EDTA-Na2). It was ground in an ice bath and centrifuged at 8000 rpm for 10 min at 4 °C. Then, 0.4 mL of the supernatant was mixed with 1.6 mL of a mixture (1.2 mL of 0.1 M KNO3 phosphate buffer and 0.4 mL of 2.0 mg mL−1 NADH solution). The control received 0.4 mL of 0.1 mM phosphate buffer without the NADH solution. Both treatment and control were kept in a 30 °C bath for 30 min. Then, 1.0 mL of 1% p-aminobenzene sulfonic acid and 0.2% α-naphthylamine were added to the supernatant, color developed for 20 min, and centrifuged for 5 min at 4000 rpm. The absorbance was determined by calorimetry at a wavelength of 540 nm. The determination method for GS (EC 6.3.1.2) enzyme activity was as described by Wang et al. (2014). One unit of glutamine synthetase (GS) activity (U) was defined as a 0.01 change in A540 per minute per mL reaction system. Fresh samples (0.2 g) were mixed with 2.0 mL of an extract (0.05 M Tris–HCl buffer, pH 8.0, 2.0 mM MgSO4, 2.0 mM DTT, and 0.4 M sucrose), minced in an ice-cold mortar, and centrifuged at 15,000 rpm for 20 min at 4 °C. Then, 0.7 mL of the supernatant was mixed with 1.6 mL of 0.1 M Tris–HCL buffer (pH 7.4, 80.0 mM MgSO4, 20.0 mM sodium glutamate, 20.0 mM cysteine, 2.0 mM EDTA, containing 80.0 mM HONH3Cl) and 0.7 mL of 40.0 mM ATP solution. The mixture was placed in water bath for 30 min at 25 °C, to which 1.0 mL of a chromogenic reagent (0.2 M trichloroacetic acid, 0.37 M FeCl3, and 0.6 M HCl) was added, incubated for 15 min, and centrifuged at 5000 rpm for 10 min at 25 °C, then the supernatant was collected and the absorbance was measured at a wavelength of 540 nm. The reaction mixture of 1.6 mL of 0.1 M Tris–HCl solution (pH 7.4, not containing 80.0 mM HONH3Cl) was added as control. GOGAT (EC 1.4.7.1) and GDH (EC 1.4.1.2) activities were determined by spectrophotometry according to the absorbance of NADH at a wavelength of 340 nm (Groat and Vance, 1981). One unit of GOGAT and one unit of GDH were calculated the oxidation of 1.0 nmol of NADH per min. Sample weighing, adding extract and centrifugation methods for determining GOGAT and GDH activities were the same as those of GS. Then, 100 mM K+-phosphate (pH 7.6), containing 0.1%

2.7. Statistical analysis A two-way ANOVA with a split-plot arrangement was performed to analyze the impacts of nitrate concentrations on cotton genotypes using Statistix 10 software. Where nitrate concentrations were used as the main plot, while cotton genotypes as subplot factor during analysis. Means were separated using a least significant test (LSD) at a 5% level of significance. Principal component analysis (PCA) was calculated in OriginPro (2015) (b9.2.214, OriginLab Corporation, Northampton, MA, USA). Morphophysiological and biochemical traits were used to calculate the correlation relationships in R with the GeneNT package (version 1.4.1) (Zhu et al., 2005), and the results were visualized in Cytoscape (version 3.5.0) according to the method of Shannon et al. (2003). The figures were drawn with GraphPad Prism 7. All the data results are expressed as mean ± standard error (SE) of three technical and biological replications. 3. Results 3.1. Genotypic variations in plant morphology and physiology At the end of the experiment, the N-deficiency symptoms were clearer in N-inefficient genotypes compared with N-efficient genotypes. The plants grown in low-nitrate concentration (0.5 mM) were short in stature with large root systems compared with those grown in moderate- (1–2 mM) and high-nitrate concentrations (5–10 mM) (Fig. 1). To assess these changes, various morphological traits were analyzed. Under the no (0 mM) and low-nitrate (0.5 mM) concentrations, shoot length (cm), total dry matter and single leaf area were markedly decreased; however, the reduction was more prominent in N-inefficient 63

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percent increase in each root trait was greater in genotype CCRI-69 compared with other genotypes, especially XLZ-30 (Fig. 2). Thus, the larger root system of CCRI-69 may aid in efficient N uptake and utilization, followed by growth and NUE. The leaf gaseous exchange traits, like photosynthetic rate, stomatal conductance, transpiration rate, and intercellular CO2 concentration of cotton genotypes were differentially influenced by various nitrate concentrations (Fig. 3). In particular, CCRI-69 had the highest photosynthetic rate, stomatal conductance, and transpiration rate, whereas XLZ-30 had the lowest values for these three traits at each nitrate concentration. However, no and low-nitrate concentrations greatly inhibited the photosynthetic rate, stomatal conductance, and transpiration rate compared with moderate- and high-nitrate concentrations (Fig. 3A and B,D). The high intercellular CO2 concentration level under no and low-nitrate concentrations, especially in N-inefficient genotypes, indicated their lower efficiencies in carboxylating the available carbon dioxide (Fig. 3C). 3.2. Genotypic variations in NUE traits Different nitrate concentrations significantly affected N concentrations and accumulations in the plant tissues. Generally, under no and low-nitrate concentrations, significant reductions in N concentrations and accumulations were observed (Fig. 4). However, the extents of the declines varied among the genotypes and plant tissues (root and shoot) (Fig. 4). Among the genotypes, the variation in shoot N concentration was less as compared with that in the root (Fig. 4 A, B). An N-efficient genotype, CCRI-69, showed significantly higher N concentrations and accumulations in both plant tissues compared with all the other genotypes, especially XLZ-30 (Fig. 4). Shoot dry weight was considered a good trait for identifying the Nefficient genotypes, and it was plotted as α in a nonlinear regression curve in response to various nitrate concentrations, indicated by β (Table 2 and Fig. 5A). The maximum shoot dry weight ranged from 1.41 to 2.88 g for XLZ-30 and CCRI-69, which had the lowest and highest values among all the genotypes, respectively. The nitrate concentration values mostly ranged from 0.392 to 0.545 (LU-21, GD-89, Z1017 and CCRI-69), with XLZ-30 having a greater value of 0.885 mM. Thus, there were significant differences among the genotypes. N-inefficient genotypes, particularly XLZ-30, needed significantly more nitrate to reach their half -maximum dry weights than the N-efficient genotype CCRI-69 (Table 2 and Fig. 5A). Thus, the genotypes CCRI-69 and XLZ-30 exhibited contrasting responses (α and β) and were considered the most distinct N-efficient and N-inefficient genotypes, respectively (Fig. 5A). Root, shoot and mean NUtE values of cotton genotypes were significantly affected by nitrate concentrations (Fig. 5B and Fig. S1). Genotype CCRI-69 had the highest root, shoot and mean NUtE values of all the genotypes, while XLZ-30 had the lowest values for the same traits (Fig. 5B and Fig. S1). Additionally, the increases in nitrate concentrations significantly reduced tissue NUtE irrespective of the genotype (Fig. S1). In contrast, NUpE increased as the nitrate concentrations increased. However, the increase was more obvious for CCRI-69 compared with other genotypes, especially GD-89 and XLZ-30 (Fig. 5C).

Fig. 1. Representative root phenotype of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5, and 10 mM). Table 1 Shoot length (cm), total plant dry matter (g plant−1), and single leaf area (cm2) of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5, and 10 mM). Cotton genotypes (G)

Shoot length

ZMS-49 16.1 ± 0.5c CCRI-69 20.4 ± 1.2a Z-1017 18.0 ± 1.0b LU-21 18.0 ± 1.3b GD-89 15.7 ± 0.8cd XLZ-30 14.7 ± 1.2d LSD 1.1 Nitrate concentrations (N, mM) 0 5.0 ± 0.5d 0.5 13.9 ± 0.8c 1 16.8 ± 1.0b 2 21.8 ± 1.2a 5 22.8 ± 1.2a 10 22.6 ± 1.3a LSD 1.12 NxG ** CV-I (%) 8.9 CV-II (%) 9.6

Total dry weight

Single leaf area

1.64 2.38 2.13 2.14 1.62 1.36 0.17

± ± ± ± ± ±

0.03 0.05 0.03 0.05 0.03 0.05

c a b b c d

49.3 80.0 57.6 67.7 60.1 51.9 4.8

± ± ± ± ± ±

2.9 3.9 4.1 4.3 3.5 4.0

d a c b c d

0.89 1.67 1.88 2.28 2.19 2.36 0.07 ** 2.9 13.2

± ± ± ± ± ±

0.03 0.03 0.03 0.05 0.05 0.05

f e d c b a

23.3 43.7 51.4 76.2 79.4 92.6 9.7 *** 20.4 11.8

± ± ± ± ± ±

2.9 3.5 4.1 3.9 4.0 4.5

d c c b b a

Note: Means followed by the same letters within the same category in the same columns are not different statistically. ** Significant at P ≤ 0.01. *** Significant at P ≤ 0.001. ns = non-significant at P ≥ 0.05. Data indicate means ± SD (n = 9).

genotypes compared with N-efficient genotypes (Table 1). The shortest shoot length, least total dry matter, and the smallest single leaf area were from genotype XLZ-30, while the greatest values were from genotype CCRI-69 (Table 1). Root morphology under various nitrate concentrations was compared among contrasting N-efficient cotton genotypes grown in hydroponic cultures (Figs. 1 and 2). Increasing nitrate concentrations in the medium greatly inhibited root growth as indicated by shorter root lengths, smaller surface areas, and root volumes compared with those of plants grown in moderate- and low-nitrate concentrations (Table S1). However, the root diameter significantly increased as nitrate concentrations increased (Table S1). At each nitrate concentration, the

3.3. Genotypic variation in N-assimilating enzymes and products After uptake, N is assimilated through various enzymes. Therefore, to predict a genotype N-assimilation potential, enzymes related to N assimilation, like NR, GS, GOGAT, and GDH, were determined. Shoot and root NR and GS activities were higher in CCRI-69 than in the other genotypes at each nitrate concentration (Fig. 6A and B). Interestingly, both NR and GS activities were lower under no and low-nitrate concentrations; however, as nitrate concentrations increased the activities of both enzymes significantly increased (Fig. 6A and B). The activities of GOGAT and GDH in both shoots and roots were highest in CCRI-69 64

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Fig. 2. Percent variation in the roots of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5, and 10 mM). Root length (RL, cm); root surface area (RSA, cm2); root diameter (RD, mm); root volume (RV cm3).

Fig. 3. (A) Photosynthetic rate (μmol m−2 s−1), (B) stomatal conductance (mmol H2O m⁻2 s⁻1), (C) intercellular CO2 (μmol CO2 mol⁻1 air) and (D) transpiration rate (mmol m⁻2 s⁻1) of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5 or 10 mM). The values are presented as mean ± SE (n = 9).

65

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Fig. 4. (A) Shoot N concentration (%), (B) root N concentration (%), (C) root N accumulation (mg N) and (D) shoot N accumulation (mg N) of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5 or 10 mM). The values are presented as mean ± SE (n = 9).

NUtE. However, most of the root traits (root dry weight, root length, root volume, and root surface area) accounted for over 30% of the total variance. In short, root morphological traits showed more genotypic effects than shoot traits. Interaction effects also had large roles in the total variation and the maximum interaction effect was noted for NUtE, which explained 21% of the total variance (Fig. 10). To reveal the correlations between the various morphophysiological and biochemical traits and both NUpE and NUtE, correlation networks were constructed (Fig. 11). Two correlation networks with 528 correlations (edges) among 31 traits (nodes) were formed (Fig. 11). Out of the total direct correlations, NUpE had the strongest positive correlations (r = 0.52–0.94) with 24 traits, medium-strong correlations (r = 0.37–0.40) with two traits, low correlations (r = 0.07–0.28) with four traits and a strong negative correlation (r = −0.80) with only one trait (Fig. 11A; Table S2). In addition, root total soluble sugar, shoot total soluble sugar and shoot free amino acid had negative correlations with more than five traits in the network (Fig. 11A). The N-assimilating enzymes were strongly associated with NUpE and maybe target traits for increasing NUE (Fig. 11A). For NUtE, strong positive correlations (r = 0.50–0.79) were found with 12 traits, medium-strong correlations (r = 0.30–0.49) with 14 traits, low correlations (r = 0.21–0.26) with 4 traits and a medium negative correlation (r = −0.31) with only one trait (Fig. 11B and Table S3). In addition, root total soluble sugar, shoot total soluble sugar and shoot free amino acid had negative correlations with more than five traits in the network (Fig. 11B). Notably, the root morphological traits (root dry weight, root surface area, root length, and root volume) were strongly correlated with NUtE, and they could be used as target traits for increasing NUE (Fig. 11B).

and lowest in XLZ-30 (Fig. 7). The activities of both enzymes increased as the nitrate concentrations in the medium increased (Fig. 7). N-assimilation compounds, like total soluble proteins and total free amino acids, in the shoot as well as in the root were significantly affected by nitrate concentrations (Fig. 8). A significant increase in total soluble protein and free amino acids was found in CCRI-69 compared with other genotypes, especially XLZ-30 (Fig. 8). In general, as nitrate concentrations increased, total free amino acid and shoot soluble protein levels significantly increased. However, the root total soluble protein was significantly reduced as the nitrate concentration increased (Fig. 8B). Significant differences among genotypes were also noted for total soluble sugar levels (Fig. 9). In both plant tissues, CCRI-69 and XLZ-30 had the highest and lowest total soluble sugar values, respectively (Fig. 9). The increase in the nitrate concentration had a negative effect on total soluble sugar, with both plant tissues showing reductions as the nitrate concentration increased, irrespective of the genotype (Fig. 9).

3.4. Multivariate analysis of trait mining for NUE The various studied traits showed significant nitrate concentration, genotype, and interaction (nitrate concentration × genotype) effects. Nitrate concentrations significantly affected all the detected traits and accounted for 13%–99% of the total variation (Fig. 10). For most of the traits, the effects of nitrate concentrations accounted for more than 50% of the total variance (Fig. 10). The genotypic effects were also significant for all the studied traits and described 3%–45% of the total variance (Fig. 10). The maximum genotypic difference was noted for 66

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Fig. 5. (A) Relationship between shoot dry weight and nitrate concentrations for cotton genotypes used to calculate the kinetic parameters α and β shown in Table 1. R2 or coefficient of determination was p < 0.05 for all genotypes, (B) N utilization efficiency (g2 TDW mg−1) and (C) N uptake efficiency (mg N g−1 RDW) of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5 or 10 mM). The values are presented as mean ± SE (n = 9).

especially N-efficient genotypes to low-rather than high-nitrate concentrations (Fig. 12) (see Fig. 13).

Table 2 Kinetic parameters for contrasting N-efficient cotton genotypes of the shoot dry weight (g plant−1) under various nitrate concentrations (R2 or the coefficient of determination was P < 0.05 for all genotypes). Genotypes

α

ZMS-49 CCRI-69 Z-1017 LU-21 GD-89 XLZ-30

1.85 2.88 2.34 2.27 1.88 1.41

β ± ± ± ± ± ±

0.30bc 0.45a 0.23b 0.07b 0.23bc 0.15c

0.545 0.449 0.369 0.392 0.416 0.885

4. Discussion

R2 ± ± ± ± ± ±

0.262b 0.083b 0.030b 0.023b 0.054b 0.079a

4.1. Variations in morphophysiological capacities among cotton genotypes

0.79 0.91 0.83 0.88 0.66 0.63

Most plants show some behavioral changes in response to various levels of supplied N (Sakakibara et al., 2006). Roots play a crucial role in adapting to changes in N availabilities and can signal the plant to alter the root system (Gruber et al., 2013; Rellán-Álvarez et al., 2016). A remarkable level of variation in root morphology exists among Arabidopsis accessions in response to various levels of N availability (De Pessemier et al., 2013; Kellermeier et al., 2013). Similarly, considerable variations in root morphological traits were observed in cotton genotypes in response to various nitrate concentrations. Among these genotypes, CCRI-69 showed the greatest induction of root morphological traits at low-nitrate concentration (Fig. 1 and Table S1), indicating that the roots of CCRI-69 were more sensitive to low-nitrate concentration than other genotypes, which was consistent with our previous results (Iqbal et al., 2019b). Additionally, the highest root dry weight and root morphological traits were detected in CCRI-69, irrespective of the nitrate concentration, indicating that this genotype had the largest root system. Moreover, other morphological traits, like shoot length, single leaf area, and root, shoot and total plant dry weights, increased as the nitrate concentration increased, which was consistent with previous findings (Iqbal et al., 2019b; Luo et al., 2015). In addition, an ANOVA revealed that genotype effects accounted for 7%–33% of the total

Note: Means followed by the same letters within the same columns are not different statistically at P ≥ 0.05. Data indicate means ± SD (n = 9).

Based on the correlation analysis, a principal component analysis (PCA) was performed to investigate the response patterns of cotton genotypes to various nitrate concentrations and the most important traits contributing to NUE. The loading plots of PCs 1 and 2 of the PCA analysis included 33 selected traits obtained from the average values of the six genotypes under various nitrate concentrations (Fig. 12). Nitrate concentrations were associated with PC1 and explained 66.64% of the variation. Cotton genotypes were associated with PC2 and contributed 17.85% of the total variation. N-assimilating enzymes and NUpE were the key contributors to PC1, while the root morphological traits and NUtE were the key contributors to PC2 (Fig. 12). The larger distance between no/low (0–0.5 mM) and moderate (1–2 mM) nitrate-treated plants than between moderate (1–2 mM) and high (5–10 mM) nitratetreated plants indicated that cotton genotypes were highly responsive, 67

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Fig. 6. (A) Shoot NR activity (μg g−1 FW h−1), (B) root NR activity (μg g−1 FW h−1) (C) shoot GS activity (μmol g−1 FW h−1) and (D) root GS activity (μmol g−1 FW h−1) of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5 or 10 mM). The values are presented as mean ± SE (n = 9).

efficiency as the intercellular CO2 concentration increases, as reported in rice and sunflower (Huang et al., 2004). Thus, N is important for photosynthesis, which is the basis for increasing crop growth, productivity, and NUE.

variations in these morphological traits, indicating the variations were mainly determined by the genotype. In the PCA analysis, PC2 clearly determined the genotypic effect, and root morphological traits were considered essential factors contributing to the genotypic variation. Thus, cotton genotypes showed considerable variation in root morphology in response to various nitrate concentrations, and among the studied traits, root morphology contributes more to the genotypic variation. Similarly, photosynthesis is very sensitive to changes in N availability (Qin et al., 2018; Xu et al., 2015), because 57% of the N in the leaves is located in the chloroplasts and is used for the synthesis of photosynthetic components and related enzymes (Ziadi et al., 2008; Xu et al., 2012). The photosynthetic capacity is positively correlated with leaf N content (Ghannoum et al., 2005; Egli and Schmid, 1999), and consistently, a positive correlation was noted between N concentration and photosynthetic activity. Increasing trends were observed for the photosynthetic activity of CCRI-69 and nitrate concentrations, which is consistent with our previous results (Iqbal et al., 2019b) as well as results in poplar species (Li et al., 2012; Luo et al., 2015). In contrast, the photosynthetic activity of XLZ-30 decreased, which may be associated with an inhibited photosystem, because many genes involved in photosystem were downregulated under high-N conditions (Luo et al., 2015). As in cotton, the photosynthetic activities were significantly reduced in Arabidopsis, rice, maize, wheat, and other nitrogen-deficient plants (Beatty and Good, 2018; Makino, 2011; Vidal et al., 2015; Markelz et al., 2011). The reduction in the overall photosynthetic efficiency levels of cotton genotypes under no and low-nitrate concentrations may also result from the decrease in carboxylation

4.2. Variations in N metabolism are tightly associated with NUE Increasing the NUE is important to maintain a high productivity level with a comparatively low N supply (Xu et al., 2012). Consequently, identifying the genotypic responses to various N conditions is the best way to understand the mechanisms and traits contributing to NUE (Hajari et al., 2015). Many methods have been used to identify genotypes having good NUE values, such as shoot dry weight, which was previously described for tomato cultivars (Gourley et al., 1994; Abenavoli et al., 2016). Similarly, here, a large genotypic difference was found in shoot dry weights and the amount of nitrate used to achieve the maximum shoot dry weight among cotton genotypes (Table 2). On the basis of this method, CCRI-69 and XLZ-30 showed contrasting phenotypes, irrespective of the nitrate concentration. The contrasting behavior of these genotypes was confirmed from their N uptake, utilization and metabolism levels. The lower potentials of Ninefficient genotypes, especially XLZ-30, might result from poor root architecture for N uptake and low photosynthetic activity for efficient N utilization (Uribelarrea et al., 2009; Mu et al., 2016). Thus, it was confirmed that CCRI-69 and XLZ-30 are the most distinct N-efficient and N-inefficient genotypes, respectively. In addition, large genotype-dependent differences were noted in the key enzymes regulating N metabolism. After uptake, N is reduced in the 68

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Fig. 7. (A) Shoot GOGAT activity (U mg−1 protein), (B) root GOGAT activity (U mg−1 protein), (C) shoot GDH activity (U mg−1 protein) and (D) root GDH activity (U mg−1 protein) of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5 or 10 mM). The values are presented as mean ± SE (n = 9).

The final products of N metabolism are amino acids and proteins (Miller and Cramer, 2005; Cañas et al., 2009). Significant reductions in the free amino acids and total soluble proteins in the shoot occurred under low-nitrate concentration, whereas in the roots, a dramatic increase occurred in CCRI-69, which was consistent with the results in maize ears (Yu et al., 2016). This dramatic increase in soluble protein may result from higher accumulations of carbohydrates in the roots. As a result, root NUtE and total soluble sugars increased under low-nitrate concentration (Fig. S1A and Fig. 9). Moreover, the high protein levels in the roots act as N sources for photosynthetic processes. Thus, CCRI-69 transferred more N to shoots and, in return, more carbon to roots, resulting in a more efficient root system (Fig. 1 and Table S1). In addition, two other scenarios may occur. The high nitrate concentrations may lead to increases in transcripts encoding various proteins (Stitt, 1999) or the higher protease activity in low nitrate-treated plants may lead to a greater level of protein degradation (Galangau et al., 1988). Thus, clear genotypic differences in cotton were noted for N metabolism and its importance in increasing NUE.

roots or translocated to the shoots for assimilation (Xu et al., 2012), where it is converted into glutamine and glutamate, amino acids and other N compounds are synthesized (Pratelli and Pilot, 2014). This process requires many important enzymes, including NR, GS, GOGAT and GDH (Hakeem et al., 2011; Funayama et al., 2013) (Fig. 14). Large genotypic variations were observed among the key enzymes regulating N metabolism. Interestingly, enzymatic activities in CCRI-69 were the highest among the studied genotypes, indicating its greater potential for N metabolism (Figs. 6 and 7). As in our results, high N-based enzymatic activities were found in Arabidopsis (Lemaître et al., 2008) and N-efficient Brassica napus genotypes (Ye et al., 2010). The variations in Nbased enzymatic activities among the genotypes may be associated with differences in regulation of N transporters or N fluxes in the roots (Britto and Kronzucker, 2002). This assumption is based on the Km data of rice and Arabidopsis nitrate transporters as described in a previous study (Gupta et al., 2012). Similar to the Km value, the α value for CCRI-69 was the highest among the genotypes; therefore, the activities of cotton transporters may be similar to those of rice transporters. Thus, in CCRI-69, the low-affinity transporters may be more active in the roots and shoots, which might lead to the high N uptake and assimilation (Gupta et al., 2012; Remans et al., 2006). In addition, the high-N metabolism in CCRI-69 might result from the constant conversion of nitrate to nitrite, and then to ammonia and amino acids, even at lownitrate concentrations (Ali et al., 2007; Vijayalakshmi et al., 2015). Additionally, the high-N efficiency of CCRI-69 may result from a wellcoordinated system of N uptake and assimilation, resulting in low ammonia levels in the tissues (Yun et al., 2008), which was explained by the high enzymatic activities (Figs. 6 and 7).

4.3. Multivariate analysis identified key traits associated with NUE The PCA showed that most of the morphophysiological and biochemical traits were strongly positively correlated with nitrate concentrations and contributed to the total variations explain by PC1. Most of the root-related traits and NUtE contributed to PC2, which was mainly associated with cotton genotypes (Table S4). NUtE and root morphological traits were negatively correlated with increasing nitrate concentrations. This negative correlation between NUtE and the N 69

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Fig. 8. (A) Shoot total soluble protein (mg g−1), (B) root total soluble protein (mg g−1), (C) shoot free amino acid (mg g−1) and (D) root free amino acid (mg g−1) of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5 or 10 mM). The values are presented as mean ± SE (n = 9).

NUpE and NUtE, correlation networks were constructed (Fig. 11). The N-assimilating enzymes were strongly associated with NUpE and shared a large N effect, which indicated that N uptake and metabolism were more affected by nitrate concentration than genotype. Thus, N-assimilating enzymes may be targets for increasing NUE (Fig. 11A). Moreover, root morphological traits and NUtE shared a large genotypic effect, suggesting that cotton genotypes were mainly different in root morphology followed by NUtE. Consistent with previous studies, the

supply has been extensively studied in many plant species (Li et al., 2012; Fu et al., 2017; Jankowski et al., 2018). The reduction in NUtE as the nitrate concentrations increased may result from the excessive storage of external N beyond plant requirements, and thus, the external N cannot stimulate further growth (Luo et al., 2019). As a result, lower growth, with a high tissue N concentration, may reduce NUtE under high-nitrate concentrations. Moreover, to determine the correlations between traits and both

Fig. 9. (A) Shoot total soluble sugar (mg g−1) and (B) root total soluble sugar (mg g−1) of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5 or 10 mM). The values are presented as mean ± SE (n = 9). 70

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Fig. 10. Global ANOVA of morphological, physiological and biochemical traits of contrasting N-efficient cotton genotypes in response to various nitrate concentrations (0, 0.5, 1, 2, 5 and 10 mM). The main effects (nitrate concentrations (N), genotypic effects (G), interaction (N × G), and residual (R) represent as a percentage of type III sums of squares. P-values of the F-test are indicated. *: P < 0.05; **: P < 0.01; ***: P < 0.001; ns: not significant. RGS, root GS activity; SGS, shoot GS activity; RGDH, root GDH activity; SGDH, shoot GDH activity; RGOGAT, root GOGAT activity; SGOGAT, shoot GOGAT activity; RNR, root NR activity; SNR, shoot NR activity; RTSS, root total soluble sugar; STSS, shoot total soluble sugar; SAA, shoot free amino acid; RAA, root free amino acid; RTSP, root total soluble protein; STSP, shoot total soluble protein; NUtE, N utilization efficiency; NUpE, N uptake efficiency; SNA, shoot N accumulation; RNA, root N accumulation; E, transpiration rate; Ci, intercellular CO2 concentration; gs, stomatal conductance; Pn, net photosynthesis; RN, root N concentration; SN, shoot N concentration; RV, root volume, RD, root diameter; RSA, root surface area; RL, root length; SLA, single leaf area, TDM, total plant dry matter; RDW, root dry weight; SDW, shoot dry weight; SL, shoot length.

low-availability conditions (De Pessemier et al., 2013). Recently, natural variations in Arabidopsis accessions were explored to define ideotypes for increasing yield (Chardon et al., 2012). In a similar manner, we can learn from the genetic and environmental regulation of root growth and development in cotton genotypes. CCRI-69, which produced a highly branched and long root system at low- and moderatenitrate concentrations, can form an interesting ideotype. Previous studies have also linked NUE with the physiological and biochemical processes related to carbon and N metabolism (Li et al., 2012). Thus, increases in the activities of enzymes related to N metabolism, total soluble protein, and sugar, may be an adaptive strategy that helps the N-efficient cotton genotypes promote more efficient root

variation in NUtE was high under the low-N supply compared with the high-N supply (Li et al., 2012; Gan et al., 2015). The increases in NUtE and root morphology at low-nitrate concentrations may also be associated with a high protease activity that can lead to high protein degradation (Galangau et al., 1988). Notably, the root morphological traits may be used as target traits for increasing NUE (Fig. 11B). The root is considered the key to a new revolution in agriculture in which crops are developed that produce high yields with low fertilizer inputs or NUEs (Den Herder et al., 2010; Gewin, 2010). The influence of N on root development is still not clearly understood. Modifying the root architecture is a good strategy for developing crops that can efficiently uptake nutrients even under

Fig. 11. Correlation network represents the relationships among morphophysiological and biochemical traits with NUpE (A) and NUtE (B). Nodes or detail of the traits are shown in Tables S2 and S3. 71

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were positively correlated with NUtE, whereas N-assimilating enzymes and shoot-related traits were positively correlated with NUpE. Interestingly, roots had more differences in physiological and biochemical traits than shoots, suggesting the importance of root over shoot in NUE, especially at low-nitrate concentration. Based on shoot dry weight, CCRI-69 and XLZ-30 were identified as the most distinct Nefficient and N-inefficient genotypes, respectively, and these results were confirmed by their contrasting root systems, N metabolism, and NUEs. In the future, after identifying cotton genotypes with contrasting phenotypes, we will analyze quantitative traits associated with root morphology. The identification of key developmental genes and pathways involved in N metabolism may have great potential for improving root characteristics and increasing NUE.

CRediT authorship contribution statement Asif Iqbal: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review & editing. Dong Qiang: Conceptualization, Methodology, Project administration, Resources, Software, Supervision, Writing - original draft, Writing - review & editing. Wang Zhun: Data curation, Formal analysis. Wang Xiangru: Data curation, Resources, Software, Visualization. Gui Huiping: Formal analysis, Methodology. Zhang Hengheng: Data curation, Formal analysis. Pang Nianchang: Supervision. Zhang Xiling: Funding acquisition, Project administration, Resources, Software, Supervision, Writing - review & editing. Song Meizhen: Conceptualization, Funding acquisition, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing.

Fig. 12. Principal component analysis (PCA) of morphophysiological and biochemical traits of contrasting N-efficient cotton genotypes under various nitrate concentrations (0, 0.5, 1, 2, 5 and 10 mM). The PCA shows the biplot of the first two principal components. The eigenvectors are shown in Table S1.

systems for enhancing N uptake, assimilation and finally NUE (CastroRodríguez et al., 2017). These results suggest that the NUpEs of cotton genotypes are likely determined by N-assimilating enzymes and the NUtEs by root morphological traits. Therefore, increasing N metabolism and altering root morphology through genetic approaches might increase NUE.

5. Conclusions

Acknowledgments

Contrasting N-efficient cotton genotypes displayed considerable variations in shoot dry weight, root morphological traits, and enzymatic activities regulating N metabolism in response to various nitrate concentrations. Root-related traits were the most affected by genotype and

This work was supported by the National Key Research and Development Program of China (Grant No. 2017YFD0101600) and the State Key Laboratory of Cotton Biology, Institute of Cotton Research, Anyang, China (Grant No. CB2019C17) for their financial support.

Fig. 13. The summery of the physiological mechanism of NUE in cotton. Taken together, an N-efficient cotton genotype has the characteristics of (1) a large root system for efficient N uptake/accumulation; (2) efficient N utilization through N assimilating enzymes and (3) high photosynthetic efficiency for biomass production. 72

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Fig. 14. The central role of N assimilating enzymes in the complex matrix of plant N metabolism. A diagrammatic representation of the relationship of tissue N concentrations with N metabolism. Nitrate is taken up by the roots and transported to the shoots. Nitrate reductase (NR) reduces nitrate to nitrite. Nitrite is then reduced to ammonium by nitrite reductase (NiR). Ammonium is then incorporated into glutamate by the glutamine synthetase (GS)/glutamate synthase (GOGAT) action. Glutamate dehydrogenase (GDH) plays a role to provide 2-OG to the TCA cycle during carbon starvation. Amino acids are either supplied to sink as N source or utilized to synthesized protein in the source. Values in the box are determination coefficients of regression showing the direct linear relationship of all N assimilating enzymatic activities with the shoot (green) and root (red) N concentrations. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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