Higher Atmospheric CO2 Levels Favor C3 Plants Over C4 Plants in Utilizing Ammonium as a Nitrogen Source

Photosynthesis of wheat and maize declined when grown with NH₄⁺ as a nitrogen (N) source at ambient CO₂ concentration compared to those grown with a mixture of NO₃^– and NH₄⁺, or NO₃^– as the sole N source. Interestingly, these N nutritional physiological responses changed when the atmospheric CO₂ concentration increases. We studied the photosynthetic responses of wheat and maize growing with various N forms at three levels of growth CO₂ levels. Hydroponic experiments were carried out using a C₃ plant (wheat, Triticum aestivum L. cv. Chuanmai 58) and a C₄ plant (maize, Zea mays L. cv. Zhongdan 808) given three types of N nutrition: sole NO₃^– (NN), sole NH₄⁺ (AN) and a mixture of both NO₃^– and NH₄⁺ (Mix-N). The test plants were grown using custom-built chambers where a continuous and desired atmospheric CO₂ (C_a) concentration could be maintained: 280 μmol mol^–1 (representing the pre-Industrial Revolution CO₂ concentration of the 18th century), 400 μmol mol^–1 (present level) and 550 μmol mol^–1 (representing the anticipated futuristic concentration in 2050). Under AN, the decrease in net photosynthetic rate (P_n) was attributed to a reduction in the maximum RuBP-regeneration rate, which then caused reductions in the maximum Rubisco-carboxylation rates for both species. Decreases in electron transport rate, reduction of electron flux to the photosynthetic carbon [Je(PCR)] and electron flux for photorespiratory carbon oxidation [Je(PCO)] were also observed under AN for both species. However, the intercellular (C_i) and chloroplast (C_c) CO₂ concentration increased with increasing atmospheric CO₂ in C₃ wheat but not in C₄ maize, leading to a higher Je(PCR)/ Je(PCO) ratio. Interestingly, the reduction of P_n under AN was relieved in wheat through higher CO₂ levels, but that was not the case in maize. In conclusion, elevating atmospheric CO₂ concentration increased C_i and C_c in wheat, but not in maize, with enhanced electron fluxes towards photosynthesis, rather than photorespiration, thereby relieving the inhibition of photosynthesis under AN. Our results contributed to a better understanding of NH₄⁺ involvement in N nutrition of crops growing under different levels of CO₂.

Introduction

The application of chemical nitrogen (N) fertilizers has greatly increased global crop yields and decreased world hunger over the past five decades (Gong et al., 2011). However, only 30–40 % of applied N is utilized by crops; most is lost in numerous ways, including run-off, leaching, denitrification and volatilization, which together lead to a range of environmental problems (Richter and Roelcke, 2000; Xing and Zhu, 2000). Thus, increasing plant nitrogen use efficiency (NUE) is crucial for the development of sustainable agriculture. Unlike nitrate (NO₃^–), ammonium (NH₄⁺) can be assimilated by plants without further chemical reduction (Mehrer and Mohr, 1989; Onoda et al., 2004). NH₄⁺ can be provided by both manure and urea fertilizers (Xu et al., 2012; Coskun et al., 2017). A promising future strategy for improving agronomic NUE is the application of stabilized-NH₄⁺-based fertilizers together with other active compounds, such as nitrification inhibitors, which can inhibit the nitrification of NH₄⁺, thereby maintaining a high soil N content in the form of NH₄⁺ over extended periods (IPCC, 2007; Ariz et al., 2011). Some crops, including wheat and maize, are able to grow well when provided with a mixture of NO₃^– and NH₄⁺ (Mix-N), or NO₃^– as the sole N source (NN) (Miller and Cramer, 2005). Under certain environmental conditions, NH₄⁺ may reduce growth by decreasing photosynthesis, thereby lowering crop productivity (Britto and Kronzucker, 2002). Since urea and NH₄⁺-based N fertilizers are used commonly to support the growth of cereals, vegetables and fruits, a better understanding of the toxic effects of NH₄⁺ in plant nutrition should facilitate better crop productivity (Miller and Cramer, 2005; Fernández-Crespo et al., 2012).

Photosynthesis is a synergistic process that involves electron harvesting, transport, and utilization (Kühlbrandt et al., 1994). Photosynthetic electron transport typically involves two reaction centers: photosystems I and II (PSI and PSII, respectively). The D1 protein of PSII is sensitive to NH₄⁺, and a loss of PSII function occurs when NH₄⁺-based fertilizers are applied in excessive quantities (Drath et al., 2008). When this occurs, impairment of the photosynthetic electron transport chain will lead to a decrease in photochemical efficiency (Φ_PSII) and the electron transport rate (J_t), in turn leading to a deficiency in NADPH and ATP for CO₂ assimilation (Wang et al., 2019). The atmospheric CO₂ concentration (C_a) has increased from 280 μmol mol^–1 in pre-industrial times to 400 μmol mol^–1 at present, and is expected to reach 550 μmol mol^–1 by the 2050 s (IPCC, 2013). The elevation of CO₂ concentration at the sites of Rubisco carboxylation alters plant photosynthetic sensitivity, potentially modulating sensitivity to a diversity of N sources. Due to the importance of food production security and crops in the global carbon cycle, an improved understanding of C_a changes on the N nutrition of C₃ and C₄ plants will become more and more crucial (Lloyd and Farquhar, 1996; Ghannoum et al., 2000). In general, plants with the C₄ photosynthetic pathway have anatomical and biochemical traits that increase CO₂ levels around the carboxylating Rubisco enzyme (Hatch and Slack, 1966). Bloom et al. (2014) and Dier et al. (2018) showed that NO₃^– assimilation is inhibited by elevated CO₂ concentrations in field-grown C₃ wheat plants. We postulated that an elevated C_a increases photosynthesis to produce more carbon skeletons, which in turn would increase NH₄⁺ assimilation, thereby ameliorating the possible physiological stress of having excess free NH₄⁺ in C₃ plants. The detailed comparative physiological responses of C₃ and C₄ plants to NH₄⁺ fertilization under elevated C_a are still not fully understood (Bloom et al., 2002, 2010; Cousins and Bloom, 2003; Bloom, 2015).

A wide variety of equipment, including open-top chambers (OTC), controlled-environment (CE) systems, and free-air CO₂ enrichment (FACE) systems, has been used to study the effects of elevated C_a. In the OTC system, plants are held in a chamber with an open top that facilitates gas exchange with the atmosphere (Owensby et al., 1999). However, the temperature is generally higher inside the chamber than outside, inevitably increasing plant transpiration, which influences plant growth rates. The FACE system minimally perturbs the plant growth environment and is suitable for long-term experiments under elevated C_a. However, the FACE system could not be used to study the effects of sub-ambient CO₂ concentrations. Controlled environment experiments can also be performed in greenhouses and artificial growth chambers (Yong et al., 2000: Aranjuelo et al., 2009; Kanemoto et al., 2009; Robredo et al., 2011). To design a system having minimal impact on natural temperature, light and seasonality, while also having the capacity to provide either sub-ambient or elevated levels of CO₂, we custom-fabricated experimental chambers in which the inside temperature and humidity were kept similar to outside open field conditions while regulating the C_a.

Our primary objective was to improve understanding of (i) the physiological mechanisms underlying the photosynthetic inhibition caused by NH₄⁺ nutrition and (ii) the mechanism by which C_a affects the NH₄⁺ tolerance of C₃ and C₄ plants. We therefore studied the photosynthetic responses of a C₃ plant (wheat, Triticum aestivum L.) and a C₄ plant (maize, Zea mays L.). Both these species prefer NO₃^– as the N nutrient source and we grew them under three C_a concentrations (280, 400, or 550 μmol mol^–1) combined with three forms of N nutrition: Mix-N, NN and sole NH₄⁺ nitrogen (AN). Our other objective was to provide new knowledge facilitating (i) directed breeding programs aiming to produce N-efficient cultivars, and (ii) the development of sustainable crop N management strategy to adapt to a future with elevated C_a, as predicted by current models of global climate change.

Materials and Methods

Plant Materials and Experimental Design

Wheat (T. aestivum cv. Chuanmai 58) and maize (Z. mays cv. Zhongdan 808), two common crop species in Chongqing, China, were grown under hydroponic experimental conditions. Seeds of both species, of uniform size, were sterilized in 20% (v/v) H₂O₂ for 10 min, rinsed with distilled water, and germinated in darkness in culture dishes covered with wet sterile gauze. When the cotyledons were 1.0 cm long, the seedlings were transferred to silica sand (previously soaked in 1% HCl for 2 days, followed by flushing with copious amounts of water to remove all traces of HCl) and watered twice daily with distilled water. Uniform 14-day-old (two-leaf stage) seedlings were transplanted into opaque plastic growth containers containing a modified Hoagland’s solution (Wang et al., 2016a; Gao et al., 2018), with three N sources: Mix-N, NN or AN. The composition of the Mix-N solution was as follows: macronutrients were provided as 5.0 mM N in the form of Ca(NO₃)₂, KNO₃ and (NH₄)₂SO₄ (the ratio of NO₃^– to NH₄⁺ in the Mix-N was 3 : 2), 3.0 mM K in the form of KH₂PO₄ and KNO₃, 1.5 mM Ca as Ca(NO₃)₂ and CaCl₂, 1.0 mM Mg as MgSO₄, 1.0 mM P as KH₂PO₄, and 0.6 mM Na as NaCl. Micronutrients were provided as 0.1 mM Fe as Fe-EDTA, 455 × 10^–3 mM Mn as MnSO₄, 38.1 × 10^–6 mM Zn as ZnSO₄, 15.6 × 10^–6 mM Cu as CuSO₄, 2.31 × 10^–3 mM B as H₃BO₃, and 6.2 × 10^–6 mM Mo as MoO₃. Macronutrients were provided in the NO₃^–-source solution as 5.0 mM N in the form of Ca(NO₃)₂ and KNO₃, 3.0 mM K in the form of KH₂PO₄ and KNO₃, 1.5 mM Ca as Ca(NO₃)₂, 1.0 mM Mg as MgSO₄, 1.0 mM P as KH₂PO₄, and 0.5 mM Na as NaCl. Macronutrients were provided in the NH₄⁺-source solution as 5.0 mM N in the form of (NH₄)₂SO₄, 3.0 mM K as KH₂PO₄ and K₂SO₄, 1.5 mM Ca as CaCl₂ and CaSO₄, 1.0 mM Mg as MgSO₄, 1.0 mM P as KH₂PO₄, and 0.5 mM Na as NaCl. Micronutrients in NN or AN source solution were identical to those in the Mix-N source solution; and then kept in chambers (Figure 1A) with the following CO₂ levels: 280 μmol mol^–1 [pre-industrial revolution (i.e., 1840) concentration], 400 μmol mol^–1 (current level), and 550 μmol mol^–1 (projected concentration by the 2050 s) (IPCC, 2013). The temperature and humidity inside the chambers were automatically maintained to match those of the atmosphere outside (Figure 1B). During the night, the CO₂ gradients were held at concentrations 150 ± 1 μmol mol^–1 above daytime levels (Anderson et al., 2001).

FIGURE 1

Figure 1. Custom-built chambers (A) that automatically maintained desired CO₂ levels, while temperature and humidity of the chambers were similar to the outside atmospheric environment and (B) mean CO₂ concentrations maintained inside the CO₂ chamber; intensity of photon flux intensity (PPFD); humidity and temperature inside and outside the chambers over a period of 21 days of growth.

A nitrification inhibitor (dicyandiamide, 1 mM) was added to the nutrition solutions to prevent microbial oxidation of NH₄⁺. The pH of the Hoagland’s solution was adjusted daily to 5.5 using 0.1 mM NaOH for the plants treated with NH₄⁺, or with 0.1 mM H₂SO₄ for those treated with NO₃^–. The N concentrations were kept constant by replacing the media at 3-day intervals; aeration was provided continuously. We used three CO₂ treatments, with three replicates each in separate chambers (9 chambers in total), and deployed three N treatments for each species in each chamber. Ideally, each experiment should be repeated at least once in every chamber, for each of the planned treatments, to eliminate any intrinsic technical effect of each chamber on plant growth; this represented a potential small shortcoming of the current study in our facility.

Experimental Field Chambers

The automatically CE facility consisted of a CO₂ control system (2543CN; Shengsen Corp., Qingdao, China; Supplementary Figures S1-1) and a CO₂ generator (12864; Shengsen Corp.; Supplementary Figure S1-2). The CO₂ generator comprised of several components, i.e., an electric connection point pressure meter (STC90C516RD; Shengsen Corp.), pressure sensors (13864; Shengsen Corp.), and Na₂CO₃ and H₂SO₄ feeding inlets (Supplementary Figures S1-5, 6), where the CO₂ was generated according to the following equation: Na₂CO₃ + H₂SO₄ = Na₂SO₄ + H₂O + CO₂ The CO₂ generator was connected to the CO₂ control system to maintain the CO₂ concentration in the chambers within a desired set range; CO₂ was delivered through pipes into each chamber.

The chambers were built using toughened glass (10 mm thick, 99% transmittance) walls and roofs (Supplementary Figure S1-13). The floors were covered using a polyvinyl chloride (PVC) material (Supplementary Figure S1-7). Each chamber measured 1,500 × 1,000 × 2,000 mm (length × width × height). CO₂, temperature and humidity sensors were mounted on both the outer and inner surfaces of the walls (Supplementary Figures S1-8–10). The environmental control mechanism in this system operated automatically and regulated the internal CO₂ concentration (±2.5 μmol mol^–1), air temperature (±0.5°C) and humidity (±5%) (Figure 1B). The CO₂ control system sensed and assessed the chamber environmental data; these data were later used to regulate the CO₂ injection process and attaining the desired chamber CO₂ levels. When the CO₂ concentration in a chamber exceeded the set concentration, the air was filtered through 1.0 mol mol^–1 NaOH solution using a pump controlled by a mini-computer. When the humidity of a chamber exceeded the set concentration, the air was filtered through dry calcium carbonate using a separate pump controlled by the mini-computer.

Plant Sampling

The seedlings were sampled between 10:00 and 11:00 at the 21st day of the experiment. Plant organs were separated into two portions: the first was immersed in liquid-N and then stored at –80°C for later chemical analyses, and the second portion was oven-dried at 105°C for 20 min, and then at 75°C for at least 48 hours. The dried material was used later for different chemical analyses. Fresh leaf area was measured using a leaf area scanning device (Li-3000; Li-Cor Inc., Lincoln, NE, United States).

Gas-exchange Measurements

After 21 days of growth under different N source and CO₂ level conditions, we measured gas exchange and chlorophyll fluorescence simultaneously on the first fully developed leaves during the morning (09:00–11:00) using a Li-Cor 6400 infrared gas analyzer (Li-Cor 6400; Li-Cor Inc.). The leaf temperature during measurements was maintained at 25.0 ± 0.5^oC. Leaves were illuminated with a steady red and blue light source at a photosynthetic photon flux density (PPFD) of 1,500 μmol m^–2 s^–1 (Yong et al., 2000, 2010). The reference CO₂ concentrations in the cuvettes matched the treatment CO₂ concentrations to which samples had been previously subjected (C_treatment), i.e., 280 ± 2.5, 400 ± 2.5 or 550 ± 2.5 μmol mol^–1. The vapor pressure deficit (Vpdl) was 1.1 ± 0.05 kPa, and the relative humidity was in the range 55–65%. The gas exchange instrument was calibrated each day before the measurements and matched at least twice a day (between the curves). Data were recorded after sample acclimation in the cuvette for at least 15 min.

Two types of curves were plotted: net photosynthesis (A_n) vs. intercellular CO₂ concentrations (C_i, Supplementary Figure S2), and A_n vs. PPFD. Simultaneous measurements of chlorophyll fluorescence and parameters for plotting the A/C_i curves were made on the same leaf using the Li-Cor 6400 infrared gas analyzer. Leaf temperature, PPFD, Vpdl and relative humidity were maintained as indicated above. Prior to measurement, leaves were held in the cuvette at a reference CO₂ concentration of C_treatment for at least 10 min. The reference CO₂ concentration was controlled across a series of C_treatment values: 200, 150, 100, 50, 400, 600, 800, 1,000, 1,200, and 1,500 μmol mol^–1. Data were collected after the prevailing CO₂ had reached a steady state (2–3 min).

Method for Determining Photosynthetic Parameters of C₃ and C₄ Plants

The parameters for the C₃ wheat plant photosynthesis model were calculated using the equations of Farquhar et al. (1980); Long and Bernacchi (2003), Gao et al. (2018), and Wang et al. (2019).

According to the photosynthesis model that we used for C₄ maize plants (von Caemmerer and Farquhar, 1999), the rates of phosphoenolpyruvate (PEP) and Rubisco carboxylation (V_p and V_c, respectively) are the major determinants of the net CO₂ assimilation rate. The Rubisco carboxylation rate (V_cmax), the maximal rate of PEP carboxylation (V_pmax), maximum RuBP-regeneration rate (J_max) and CO₂ concentration in the bundle sheath (C_s) for maize were calculated using the following equations. The photosynthetic rate was expressed mathematically as:

$A=V_p-R_m-L(1)$

and

$A=V_c-0.5V_0-R_d(2)$

where R_m is the mitochondrial respiration of the mesophyll cells, L is the rate of CO₂ leakage from the bundle sheath into the mesophyll, and R_d is the mitochondrial respiration rate in the light.

$L=g_{bs}\times \left(C_s-C_m\right)(3)$

$C_m=C_i-\frac{A}{g_i}(4)$

where g_bs is the bundle sheath conductance for CO₂, g_i is the mesophyll conductance for CO₂, C_s is the CO₂ concentration in the bundle sheath, and C_m is the CO₂ concentration in the mesophyll cells. V_o is the rate of Rubisco oxygenation:

$V_0=\frac{2\mathrm{\gamma }*O}{C_s}\times V_c(5)$

where γ^∗ is one half of the reciprocal of Rubisco specificity (S_c/o), and O is the oxygen concentration in the bundle sheath cells, which matches the oxygen concentration in the mesophyll cells. By fitting equation (3) to equation (1), and equation (5) to equation (2), we obtained the following expressions:

$A=V_p-R_m-g_{bs}\times \left(C_s-C_m\right)(6)$

and

$A=V_c\times \left(1-\frac{\mathrm{\gamma }*O}{C_s}\right)-R_d(7)$

V_p and V_c depend on V_cmax, V_pmax, J_max, the Michaelis constants for O₂ and CO₂ (K_o, K_c and K_p), and the relative specificity of Rubisco (S_c/o).

To calculate V_cmax and V_pmax, we used the enzyme-limited expressions of V_p and V_c:

$V_p=\frac{C_m{V}_{p_{\max }}}{C_m+K_p}(8)$

$V_c=\frac{C_s{V}_{c\max }}{C_s+K_p\left(1+\frac{0}{K_0}\right)}(9)$

By fitting equation (8) to equation (6), and equation (9) to equation (7), we obtained the following expressions:

$A=\frac{C_m{V}_{p\max }}{C_m+K_p}-R_m-g_{bs}\times \left(C_s-C_m\right)(10)$

and

$A=\frac{C_s{V}_{c\max }}{C_s+K_c\left(1+\frac{0}{K_0}\right)}\times \left(1-\frac{\mathrm{\gamma }*O}{C_s}\right)-R_d(11)$

In equations (4), (10) and (11), g_i, g_bs, R_m, K_p, K_c, K_o, O, R_d and γ^∗ were constant parameters at a given temperature [as described by von Caemmerer and Farquhar (1999)], A and C_i were measured values, and C_m, C_s, V_cmax and V_pmax were unknowns. Two pairs of (A, C_i) (with C_i limited to 40–80 μmol mol^–1) were then inserted into two sets of equations (5), (8) and (9), following which we obtained six equations and six unknowns (V_cmax, V_pmax, C_s1, C_s2, C_m1 and C_m2) using the Matlab software (MathWorks, Natick, MA, United States).

To calculate J_max, we used the electron transport limited expressions of V_p and V_c:

$V_p=\frac{x{J}_t}{2}(12)$

$V_c=\frac{\left(1-x\right)J_t}{3\left(1+\frac{7\mathrm{\gamma }*O}{3C_s}\right)}(13)$

where x is a partitioning factor of electron transport, and J_t is the electron transport rate, given by:

$J_t=\frac{I_2+J_{\max }-\sqrt{{\left(I_2+J_{\max }\right)}^2-4\mathrm{\theta }{I}_2{J}_{\max }}}{2\mathrm{\theta }}(14)$

where I₂ is the total absorbed irradiance, which is a function of the incident irradiance I, and θ is an empirical curvature factor.

By fitting equation (11) to equation (6), and equation (12) to equation (7), we obtained:

$A=\frac{x{J}_t}{2}-R_m-g_{bs}\times \left(C_s-C_m\right)(15)$

and

$A=\frac{\left(1-x\right)J_t}{3\left(1+\frac{7{\mathrm{\gamma }}^{*}O}{3C_s}\right)}-R_d(16)$

C_m was obtained from equation (4). In equations (15) and (16), x and γ^∗ are constants at a given temperature, A, C_m and I (PPFD) are known values, and J_max and C_s are unknown entities. With two equations and two unknowns, and incorporating the single values of A and I (PPFD), we obtained the values for J_max and C_s.

Chlorophyll Fluorescence Measurements

Light adapted chlorophyll fluorescence was measured with a Li-Cor 6400 infrared gas analyzer while simultaneously measuring gas exchange, as described above. Steady-state fluorescence (F_s) was measured under actinic light. A saturating light pulse (∼8,000 μmol photons m^–2 s^–1) was applied for 0.7 s to obtain the maximum fluorescence (F_m’). After removing the actinic light and applying 3 s of far-red light, the minimal fluorescence of the light-adapted state (F_o’) was obtained. The quantum efficiency of PSII (Φ_PSII) and J_t were calculated using equations (17) and (18), respectively, following Genty et al. (1989) and Li et al. (2009):

${\mathrm{\varphi }}_{PSII}=\frac{F_m^{\prime }-F_s}{F_m^{\prime }}(17)$

$J_t=\frac{F_m^{\prime }-F_s}{F_m^{\prime }}\times PPFD\times 0.85\times 0.5(18)$

The central portion of the same leaf (∼70% leaf area) was chosen for measurement of dark-adapted and light-adapted chlorophyll fluorescence parameters using a Fluor imager (CF Imager; Technologia Ltd., Colchester, United Kingdom). The minimum and maximum chlorophyll fluorescence (F_o and F_m, respectively) values were determined after full dark adaptation for at least 30 min. F_s, F_m’, and F_o’ were obtained as described above. The maximum quantum efficiency of PSII (F_v/F_m) was calculated using equation (32) of Genty et al. (1989):

$F_v/F_m=\frac{F_m-F_o}{F_m}(19)$

Photochemical quenching (qL) was calculated using equation (20) and non-photochemical quenching (NPQ) was calculated using equation (21), following Kramer et al. (2004).

$qL=\frac{F_o^{\prime }}{F_s}\times \frac{F_m^{\prime }-F_s}{F_m^{\prime }-F_o^{\prime }}(20)$

$NPQ=\frac{F_m-F_m^{\prime }}{F_m^{\prime }}(21)$

Calculating Electron Flux to the Photosynthetic Carbon Reduction Cycle [Je(PCR)], and Electron Flux to the Photorespiratory Carbon Oxidation Cycle [Je(PCO)]

The J_t in the photosynthetic carbon reduction and photorespiratory carbon oxidation cycles were expressed as follows (Zhou et al., 2004):

$Je\left(PCR\right)=4\times {\mathrm{\nu }}_c=4\times \frac{A+R_d}{1-\frac{\mathrm{\Gamma }*}{C_i}}(22)$

$Je\left(PCO\right)=4\times {\mathrm{\nu }}_o(23)$

Determination of Free NH₄⁺ and Soluble Sugar Concentrations

The free NH₄⁺ in plant tissues was determined according to Balkos et al. (2010) with some modifications. Briefly, plant tissues were desorbed in 10 mM CaSO₄ for 5 min, and then rinsed with deionized water to remove any extracellular NH₄⁺. Approximately 0.5 g of fresh material was homogenized with liquid nitrogen; NH₄⁺ was then extracted in 5 ml of 10 mM formic acid. Supernatants were collected after centrifugation at 10,000 g (4°C) for 15 min, transferred to 5-ml polypropylene tubes after filtration through 0.45-μm organic ultra-filtration membranes, and re-centrifuged at 50,000 g (4°C) for 10 min. An O-phthalaldehyde (OPA) reagent was prepared by combining 200 mM potassium phosphate buffer (equimolar amounts of potassium dihydrogen phosphate and potassium monohydrogen phosphate), 3.75 mM OPA, and 2 mM 2-mercaptoethanol (v/v/v = 1:1:1). Prior to adding 2-mercaptoethanol, the pH was adjusted to 7.0 using 1 M NaOH, and the solution was then filtered through two layers of filter paper. A 10-μl aliquot of tissue extract was mixed with 3 ml of OPA reagent. The color was developed in darkness at 25°C for 30 min before carrying out absorbance measurements at 410 nm using a spectrophotometer (model UV-2401, Shimadzu Corp., Kyoto, Japan).

Soluble sugar concentrations were measured following the method of Wang et al. (2016a). Dry powdered shoot and root samples (0.5 g) were extracted in 80% (v/v) ethanol at 80°C for 30 min. The extracts were later centrifuged at 3000 g for 10 min and the supernatants were collected. This extraction procedure was repeated three times to ensure all soluble sugars were extracted. The supernatants were evaporated on china dishes in a hot water bath. Residues were then re-dissolved in 1-3 ml of distilled water and filtered through 0.4-μm film to assay soluble sugars. Concentrations of soluble sugar were measured using the anthrone method. Anthrone sulfuric acid (5 ml) solution (75% v/v) was added to 0.1 ml of supernatant and heated to 90°C for 15 min. Absorbance at 620 nm was measured using a spectrophotometer (model UV-2401, Shimadzu Corp., Kyoto, Japan).

Statistical Analysis

We found significant effects of N forms (Mix-N, NN, AN) and CO₂ levels on the measured parameters in wheat and maize using the two-way ANOVA (P < 0.05, n = 3). Significant pairwise differences between means were identified with Dunnett’s multiple comparisons test (P < 0.05). The proportion of variation (%) explainable by each factor was estimated as the total sums of squares. Calculations were performed with SPSS software (SPSS, Inc., Chicago, United States). Graphs were plotted using SigmaPlot 10.0 software (Systat Software, Inc., Chicago, IL, United States).

Results

Dry Biomass, Leaf Area and Free NH₄⁺

Compared with the Mix-N treatment, AN significantly reduced the shoot and root biomass of both wheat and maize plants (Figure 2). However, with increasing CO₂ concentration, wheat shoot and root biomass increased significantly, although these biomass parameters did not differ significantly according to CO₂ levels in maize. Shoot biomass in wheat under AN was reduced by 38%, 27%, and 14% at CO₂ concentrations of 280, 400 and 550 μmol mol^–1, respectively (in comparison with the Mix-N treatment). The decreases were larger in maize (46%, 44% and 44% at CO₂ concentrations of 280, 400, or 550 μmol mol^–1, respectively) (Figure 2). The AN treatment reduced the total leaf area, where the reduction was again greater in maize than in wheat. With increasing CO₂ concentrations, the total foliage area of wheat increased significantly, but this was not the case for maize. Free NH₄⁺ concentrations did not differ significantly in either species between the Mix-N and NN treatments with increasing CO₂ concentration (Figure 2) whereas, in comparison with the other two treatments, the AN increased free NH₄⁺ in shoots and roots. The concentration of free NH₄⁺ decreased significantly with increasing CO₂ concentration in wheat, but not in maize.

FIGURE 2

Figure 2. Effects of CO₂ levels on shoot and root biomass (A,B, mg DW plant^–1), total foliage area (C, cm² plant^–1) and free NH₄⁺ (D, μg g^–1 FW) of C₃ wheat and C₄ maize seedlings after 21 days of treatment with different N-sources. Data are means ± SE (n = 3); Lower-case letters indicate a significant difference at p < 0.05.

Only the N form had significant effects on maize shoot dry biomass; in wheat, the N form, CO₂ level, and their interaction significantly affected shoot biomass (Table 1). Changes in CO₂ levels caused a higher proportion of the variance in wheat shoot dry biomass than did changes in N form. Total leaf area in the two species was significantly affected by both N form and CO₂ level. N form explained a larger proportion of the variance in total leaf area in both species. Alterations in CO₂ levels explained a much greater proportion of the variance in total leaf area variation in wheat (38%) than in maize (0.5%). In both species, the quantity of free NH₄⁺ in shoots and roots differed significantly according to the form of N supplied. Interestingly, the CO₂ level had significant effects on free NH₄⁺ in tissues of wheat, but not in maize.

TABLE 1

Table 1. F-values in two-way ANOVA analysis of biomass, total foliage area and free NH₄⁺ in newly expanded leaves of C₃ wheat and C₄ maize seedlings after 21 days of treatment with different N-sources.

Photosynthesis and Its Related Parameters

Compared with Mix-N and NN, AN treatment reduced the net photosynthetic rate (P_n) of both maize and wheat, although the effect was greater in the former species (Figure 3). The P_n of wheat plants growing under 550 μmol CO₂ mol^–1 was significantly higher than that of plants grown under 280 and 400 μmol CO₂ mol^–1. Conversely, the P_n of maize plants under AN did not differ significantly according to the CO₂ level. On day 21 of the experiment, the P_n of maize under AN was reduced in comparison with those under the Mix-N treatment, by 34%, 32% and 32% at CO₂ concentrations of 280, 400, and 550 μmol mol^–1, respectively. The respective reductions in wheat were 30%, 27 % and 19%.

FIGURE 3

Figure 3. Effects of CO₂ levels on P_n (A, μmol CO₂ m^–2 s^–1), g_s (B, mol H₂O m^–2 s^–1), C_i (C, μmol mol^–1), C_s (D, μmol mol^–1), V_cmax (E, μmol CO₂ m^–2 s^–1), V_pmax (F, μmol CO₂ m^–2 s^–1), and J_max (G, μmol protons m^–2 s^–1) in newly expanded leaves of C₃ wheat and C₄ maize seedlings after 21 days of treatment with different N-sources. Data are means ± SE (n = 3); Lower-case letters indicate a significant difference at p < 0.05.

The g_s values did not differ significantly by CO₂ concentration, but were significantly reduced by AN treatment compared with the other two treatments, in both species (Figure 3). C_i increased significantly with increasing CO₂ concentration in wheat, but not in maize. The AN treatment significantly increased C_i in both species in comparison with the other two treatments. On day 21 of the experiment, we found no significant differences in C_s according to either the treatment or CO₂ concentration. V_pmax did not vary significantly by CO₂ concentration, but was significantly reduced under AN in comparison with the other two treatments.

The V_cmax and J_max of wheat increased significantly with increasing CO₂ concentration within the same level of the N form factor, but this was not the case for maize (Figure 3). In comparison with the other two treatments, AN reduced V_cmax and J_max in both species. The reduction in V_cmax associated with AN was larger in maize than in wheat, while the reduction in J_max was smaller in maize than in wheat. On day 21 of the experiment, AN reduced V_cmax in maize in comparison with Mix-N, by 32%, 33% and 32% at CO₂ concentrations of 280, 400, and 550 μmol mol^–1, respectively. The respective reductions in wheat were 19%, 17%, and 18%. Moreover, AN reduced the J_max of maize in comparison with Mix-N, by 25%, 25%, and 24% at CO₂ concentrations of 280, 400, and 550 μmol mol^–1, respectively. The respective reductions in wheat were 36%, 36%, and 32%.

The P_n, g_s, C_i, V_cmax and J_max varied significantly according to both the N treatment type and the CO₂ level. The N form accounted for a larger proportion of the variance in these parameters in both species (Table 2). The effect of CO₂ level was much greater in wheat than in maize. The V_pmax of maize was significantly affected only by the N form. The C_s in maize was not affected by the N form, CO₂ level, or their interaction, nor by the interaction between pH and the N form.

TABLE 2

Table 2. F-values in two-way ANOVA analysis of P_n, g_s, C_i, C_s, V_cmax, V_pmax, and J_max in newly expanded leaves of C₃ wheat and C₄ maize seedlings after 21 days of treatment with different N-sources.

Electron Transport Parameters

Under Mix-N and NN, the F_v/F_m, Φ_PSII and qL values of the two species did not vary significantly across different CO₂ concentration (Figures 4A–C). The values of these parameters decreased with increasing CO₂ concentration in maize plants under AN, but increased in wheat plants as CO₂ concentrations rose. NPQ increased significantly under AN in comparison with the other treatments, but did not differ significantly by CO₂ level (Figure 4D). The F_v/F_m, Φ_PSII and qL values of wheat varied significantly by both N form and CO₂ concentration, as observed for Φ_PSII and qL in maize (but not for Fv/Fm). N form explained a larger proportion of the variance in these parameters in both species than CO₂ concentration (Table 3). CO₂ level accounted for a larger proportion of the variance in electron transport in wheat (8.9% for F_v/F_m, 5.1% for Φ_PSII and 5.5% for qL) than in maize (0.0% for F_v/F_m, 0.5% for Φ_PSII and 0.4% for qL).

TABLE 3

Table 3. F-values in two-way ANOVA analysis of F_v/F_m, Φ_PSII, NPQ and qL in newly expanded leaves of C₃ wheat and C₄ maize seedlings after 21 days of treatment with different N-sources.

FIGURE 4

Figure 4. Effects of different CO₂ levels on F_v/F_m (A), Φ_PSII (B), NPQ (C), and qL (D) in newly expanded leaves of C₃ wheat and C₄ maize seedlings after 21 days of treatment with different N-sources. Data are means ± SE (n = 3); Lower-case letters indicate a significant difference at p < 0.05.

Under Mix-N and NN, the values of J_t for both species did not differ significantly across different CO₂ concentration (Figure 5A). However, while the values of J_t for wheat plants under AN increased significantly with increasing CO₂ concentration, this was not the case for maize. On day 21 of the experiment, AN reduced the J_t values to below those of plants under Mix-N, by 31%, 32% and 32% in maize at CO₂ concentrations of 280, 400, or 550 μmol mol^–1, respectively. The respective reductions in wheat were 38%, 30%, and 21%. The Je(PCR) values of wheat were higher under CO₂ concentrations of 400 and 550 μmol mol^–1 than under a concentration of 280 μmol mol^–1. CO₂ level had no significant effect on the Je(PCR) values of maize (Figure 5B). In comparison with the other treatments, AN significantly reduced Je(PCR) in both species. Under AN, the Je(PCR) values of wheat increased significantly with increasing CO₂ concentration, but this was not the case for maize. When compared to other N form treatments, AN significantly reduced Je(PCO) in both species (Figure 5C) on day 21 of the experiment. The Je(PCR)/Je(PCO) ratio increased significantly with increasing CO₂ concentration in wheat. The ratio in maize was unaffected by either CO₂ level or N form (Figure 5D).

FIGURE 5

Figure 5. Effects of different CO₂ levels on J_t (A), Je(PCR) (B), Je(PCO) (C), and ratio of Je(PCR)/ Je(PCO) (D) in newly expanded leaves of C₃ wheat and C₄ maize seedlings after 21 days of treatment with different N-sources. Data are means ± SE (n = 3); Lower-case letters indicate a significant difference at p < 0.05.

J_t, Je(PCR) Je(PCO) and the Je(PCR)/Je(PCO) ratio were significantly affected by N form and CO₂ level in both species. N form explained a larger proportion of the variance in these parameters in both species (Table 4). In wheat, 40% of the variance in the Je(PCR)/Je(PCO) ratio could be attributed to CO₂ level, but this factor accounted for only 2.9% of the variance in maize (Table 4).

TABLE 4

Table 4. F-values in two-way ANOVA analysis of J_t, Je(PCR), Je(PCO), and ratio of Je(PCR)/ Je(PCO) in newly expanded leaves of C₃ wheat and C₄ maize seedlings after 21 days of treatment with different N-sources.

Soluble Sugars

In comparison with the other N form treatments, AN markedly reduced the soluble sugar concentration in both species. Soluble sugar levels in the shoots and roots of wheat increased with increasing CO₂ concentration, but this was not the case in maize (Figure 6).

FIGURE 6

Figure 6. Effects of different CO₂ levels on soluble sugar concentrations (mg g^–1 DW) in shoots (A,B) and roots (C,D) of C₃ wheat and C₄ maize seedlings after 21 days of treatment with different N-sources. Data are means ± SE (n = 3); Lower-case letters indicate a significant difference at p < 0.05.

Discussion

Increased Atmospheric CO₂ Concentrations Offset NH₄⁺-Linked Stress in C₃ Wheat but Not in C₄ Maize

Unlike field or pot experiments, hydroponic experiments remove any potential complex interaction between ions and soil particles that might affect nutrient availability, and thus plant growth and development (Conn et al., 2013; Nguyen et al., 2016). We harnessed the hydroponic approach to study the responses of C₃ wheat and C₄ maize to different N forms and three levels of CO₂ concentrations. In previous studies, the most obvious effect of AN was reduced biomass production (Britto and Kronzucker, 2002; Li et al., 2011; Wang et al., 2016b, 2019). In the present study, the Mix-N and NO₃^–-fed C₃ wheat plants produced more dry biomass when the corresponding C_a concentration was increased, but this was not the case for C₄ maize, corroborating the findings of Leakey et al. (2006). When C_a was low, the leaf-free NH₄⁺ content was highest in C₄ maize with NH₄⁺ as the sole N source (Figure 2); concomitantly under these conditions, we recorded the lowest P_n values for maize (Figure 3).

In C₃ wheat, AN-induced photosynthetic inhibition was ameliorated by increasing the C_a concentration, but this effect was insignificant in C₄ maize (Figure 3). Franks et al. (2013) found that change in C_a concentration changes the rates of carboxylation by Rubisco (in C₃ plants) and PEP carboxylase (in C₄ plants); each of these enzymes has a crucial limiting step in the photosynthetic pathway. An initial increase in P_n (less pronounced in C₄ plants) at or above the ambient C_a concentration occurs because of the unique CO₂-concentrating mechanism associated with C₄ photosynthesis (Ghannoum et al., 1997; von Caemmerer and Farquhar, 1999). In our study, we further explored (i) the potential limiting factors of P_n when C₃ and C₄ plants were grown under condition of NH₄⁺-N nutrition, and (ii) the way in which changes in C_a concentration affected the potential NH₄⁺ tolerance of C₃ and C₄ plants.

Impaired Electron Transfer Associated With NH₄⁺ Inhibited Photosynthesis

Under atmospheric CO₂ conditions, the carboxylation ability of Rubisco is the key factor limiting C₃ photosynthesis (Li et al., 2009; Carmo-Silva et al., 2015). V_cmax, which represents the apparent Rubisco activity in vivo (Long and Bernacchi, 2003), increases with increasing CO₂ concentration (Jordan and Ogren, 1984). We found that both wheat and maize plants had lower g_s and V_cmax values under AN than under the other two N treatments, but C_i values were elevated under AN (Figure 3). Early CO₂ enrichment experiments using crops and tree saplings demonstrated that g_s was generally reduced by elevated CO₂ concentrations; we noted a similar phenomenon in wheat (Figure 3) (Yong et al., 1997; Medlyn et al., 2001). In contrast, the C_i and C_s of maize changed less with increases in C_a, regardless of the type of N nutrition. Li et al. (2015) suggested that the increases in C_i may result from decreases in the rate of the photosynthetic dark CO₂ reduction when g_s is reduced in C₃ plants.

Under ambient conditions, 44% of the absorbed light at peak PPFD was used for photosynthetic electron transport (25% for CO₂ fixation, 19% for photorespiration), and the remaining 56% was dissipated by chlorophyll fluorescence and thermal energy generation (Demmig-Adams and Adams, 1992). The balance between photosynthetic electron harvesting and transport within the chloroplasts is important for CO₂ assimilation based on the Calvin cycle (Demmig-Adams et al., 1989; Fryer et al., 1998; Shikanai, 2011). We found that Fv/Fm, Φ_PSII, qL and J_t were reduced under conditions of NH₄⁺ nutrition (Figures 4A–C, 5A), indicating that the energy available for CO₂ assimilation was limited. Similar responses under conditions of NH₄⁺ nutrition were reported by Johnson et al. (2011), where the photosynthetic electron transport chain was interrupted on the PSII side. The oxygen-evolving complex of PSII may be a direct target of NH₄⁺, causing a marked decline in photosynthesis (Drath et al., 2008). We found significant reductions in J_max, Φ_PSII and J_t for both wheat and maize (Figures 3, 4B, 5A); the reductions for maize were especially marked, and led to deficiencies in NADPH and ATP availability for CO₂ assimilation (Gao et al., 2018). Cousins and Bloom (2003) suggested that NO₃^– assimilation increases linear electron transfer and alleviates the photosynthetic ATP limitation in maize. In NH₄⁺-fed plants, the inadequate energy supply for CO₂ carboxylation may be a result of interruptions in the electron transport chain (Wang et al., 2019). With an impaired PSII, plants have a reduced capacity to dissipate excitation energy through qL, resulting in a surplus of light energy (Kim and Apel, 2013). We found that NPQ increased in both species (Figure 4D) via a process involving the scavenging of excess light energy through heat dissipation under conditions of NH₄⁺ nutrition. This finding was consistent with a previous report showing that plants can dissipate excess excitation energy in the form of heat through NPQ when they encountered abiotic stresses (Demmig-Adams and Adams, 1992).

Higher C_a Enhanced CO₂ Assimilation, Which Provided Additional C Skeletons for NH₄⁺ Assimilation in C₃ Plants

Under AN, the J_max, Φ_PSII and J_t values of wheat increased with increasing CO₂ concentration (Figures 3, 4B, 5A), indicating that the interruption in electron transport can be offset by higher CO₂ concentration; these parameters did not differ significantly for maize grown under different CO₂ levels. At low atmospheric CO₂ levels, Rubisco utilizes both CO₂ and O₂ (Edwards et al., 2010). The process of uptaking O₂ leads to photorespiration, resulting in net losses of ≤40% of photosynthetic carbon under present day CO₂ levels of 400 μmol mol^–1 (Andrews and Lorimer, 1978; Sage, 2004; Bloom, 2015). C₄ photosynthesis suppresses photorespiration by concentrating CO₂ internally (Andrews and Lorimer, 1978; Ehleringer et al., 1991). Conversely, higher C_a increases the CO₂ assimilation of C₃ plants and thereby inhibiting photorespiration; C₄ plants do not respond in this way (Andrews and Lorimer, 1978). We found that the C_i and C_c values of wheat under AN increased significantly with increased C_a, leading to increases in Je(PCR), whereas Je(PCO) did not change significantly (Figure 5D). As a result, the Je(PCR)/Je(PCO) ratio increased with increasing CO₂ concentration under AN (Figure 5C). These findings indicated that the electron flux to CO₂ assimilation was increased at higher CO₂ concentrations, which may compensate for the decrease in electron transport ability seen under AN, thereby sustaining carbon assimilation. In maize, there were no significant differences in C_i, C_s or Je(PCR)/Je(PCO) by CO₂ level on day 21 of the experiment (Figures 2, 5D).

Bloom et al. (2010) and Bloom (2015) found that (i) elevated CO₂ inhibits nitrite (NO₂^–) transport into chloroplasts, (ii) the chloroplast stroma compete for reduced ferredoxin (Fdr), and (iii) elevated CO₂ levels decrease photorespiration, thereby inhibiting shoot NO₃^– assimilation in C₃ plants under elevated CO₂ concentrations. In contrast, the first carboxylation reaction in the C₄ carbon fixation pathway generates ample quantities of malate and NADH in the cytoplasm of mesophyll cells. This explains adequately the CO₂-independent shoot NO₃^– assimilation in C₄ plants (Bloom et al., 2010). However, since N assimilation occurs rapidly when NH₄⁺ is the sole source of N nutrition, an adequate C skeleton supply for NH₄⁺ assimilation is required to facilitate general physiological homoeostasis under elevated NH₄⁺ concentrations (Ariz et al., 2013). Therefore, the carbohydrate status of plant tissues has an important role in the transition and adaptation to AN nutrition. A shortage of carbon assimilation for NH₄⁺-form has been associated with a reduced level of soluble sugars in NH₄⁺-grown plants (Setien et al., 2013). We found a significant decrease in the soluble sugar concentration in both species, especially in roots, under AN and ambient CO₂ conditions (Figure 6). With increasing C_a, an increase in soluble sugar concentration and a decrease in free NH₄⁺ concentration occurred in wheat, possibly because of an increase in CO₂ photosynthetic capacity (Ariz et al., 2010, 2013). Therefore, in wheat, an increased P_n under AN, which was driven by elevated C_a levels, increased the supply of carbon skeleton for NH₄⁺ assimilation, which in turn reduced the NH₄⁺ concentrations and thereby ameliorating the NH₄⁺ stress.

Conclusion

In conclusion, under ambient CO₂ conditions and AN nutrition, electron transport was reduced in both the C₃ wheat and C₄ maize plants, leading to a suppression of photosynthetic carbon assimilation. In wheat growing under elevated atmospheric CO₂ concentrations (C_a), increased C_i and C_c values improved electron flux to CO₂ assimilation rather than to photorespiration, thus sustaining photosynthesis and alleviating NH₄⁺-induced stress. In contrast, elevated C_a had a negligible effect on C_i and C_s in maize and, consequently, minor effects on photosynthesis. Therefore, future increases in atmospheric C_a should provide C₃ plants with more opportunities to use NH₄⁺ rather than relying on NO₃^– as a source of N fertilizers for crop production. Analyses using molecular biology and mutants to explain the possible physiological mechanisms in NH₄⁺ tolerance of crop cultivars.

Data Availability Statement

All datasets generated for this study are included in the manuscript/Supplementary Material.

Author Contributions

FW and XH conceived the original screening and research plans. FW, JG, and XH supervised the experiments. FW performed most of the experiments, conceived the project, and wrote the article with salient contributions from all the authors in specific areas. FW, JG, JY, QW, JM, and XH supervised and completed the writing. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the 100 Talents Program of Chongqing (20710940) and the World-Class Biological Science Discipline Development Program at the Southwest University, China (100030/2120054019), the Chinese Postdoctoral Science Foundation (2017M622948) and the Chongqing Postdoctoral Science Foundation (Xm2017147), and Key R & D project of Zhejiang Province “R & D and application of green, safe and efficient fertilizers” (2020R20A50B01).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank Professor Hans Lambers (School of Biological Sciences, University of Western Australia) for guidance in writing the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.537443/full#supplementary-material

Abbreviations

Γ^∗, the CO2 compensation point related to Ci; φ, apparent quantum yield; Φ PSII, quantum efficiency of PSII; Ca, atmospheric CO2 concentration; Cc, chloroplastic CO2 concentration; CE, carboxylation efficiency; Ci, intercellular CO2 concentration; Cm, CO2 concentrations in the mesophyll cells; Cs, CO2 concentrations in the bundle sheath; C treatment, the CO2 concentration treatment of each sample; FACE, free-air CO2 enrichment; Fm, the maximum chlorophyll fluorescence with dark-adaptation; Fm’, the maximum fluorescence with light-adaptation; Fo, the minimum chlorophyll fluorescence with dark-adaptation; Fo’, the minimum chlorophyll fluorescence with light-adaptation; Fs, steady state fluorescence with light-adaptation; Fv/Fm, maximum quantum efficiency of PSII; gm, mesophyll conductance; gs, stomatal conductance; Ja, alternative electron flux; Jcmax, maximum carboxylation rates limited by RuBP regeneration; Je(PCO), electron fluxes to photosynthetic carbon oxidation; Je(PCR), electron fluxes to photorespiratory carbon reduction; Jmax, maximum electron transport rates; LHC, light-harvesting complex; Mix-N, a mixture of both NO3– and NH4+ source; NPQ, non-photochemical quenching; NUE, nitrogen use efficiency; OTC, open-top chambers; Pn, net photosynthetic rate; PPFD, photon flux intensity; PSII, photosystem II; PVC, polyvinyl chloride; qL, photochemical quenching; Rd, mitochondrial respiration rate in the light; ROS, reactive oxygen species; Rubisco, ribulose-1,5-bisphosphate carboxylase/oxygenase; vc, the carboxylation rate; Vcmax, maximum carboxylation rate limited by Rubisco; vo, the oxygenation rate; Vpdl, the vapor pressure deficit; Vpmax, the maximal rate of PEP carboxylation; wc, the potential Rubisco carboxylation rate; wj, the potential RuBP regeneration rate; wp, the potential triose-phosphate utilization rate.

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