Nitrogen fertilization form and energetic status as target points conditioning rice responsiveness to elevated [CO2]

The nitrogen (N) fertilization form and plant energy status are known to significantly influence plant responses to elevated atmospheric carbon dioxide (CO₂) concentrations. However, a close examination of the interplay between N sources under contrasting light intensity has been notably absent in the literature. In this study, we conducted a factorial experiment with rice plants involving two different light intensities (150 and 300 µmol m^-2 s^-1), inorganic N sources [nitrate (N-NO₃) or ammonium nitrate (N-NH₄NO₃)] at varying CO₂ levels (410 and 700 parts per million, ppm). The aim was to examine the individual and combined effects of these factors on the allocation of biomass in whole plants, as well as on leaf-level photosynthetic characteristics, chloroplast morphology and development, ATP content, ionomics, metabolomics, and hormone profiles. Our research hypothesis posits that mixed nutrition enhances plant responsiveness to elevated CO₂ (eCO₂) at both light levels compared to sole N-NO₃ nutrition, due to its diminished energy demands for plant assimilation. Our findings indicate that N-NO₃ nutrition does not promote the growth of rice, its photosynthetic capacity, or N content when exposed to ambient CO₂ (aCO₂), and is significantly reduced in low light (LL) conditions. Rice plants with N-NH₄NO₃ exhibited a higher carboxylation capacity, which resulted in larger biomass (total C, tiller number, and lower root-shoot ratio) supported by higher Calvin-cycle-related sugars. The lower leaf N content and overall amino acid levels at eCO₂, particularly pronounced in N-NO₃, combined with the lower ATP content (lowest at LL and N-NO₃), may reflect the higher energy costs of N assimilation at eCO₂. We also observed significant plasticity patterns in leaves under eCO₂. Our findings highlight the importance of a thorough physiological understanding to inform innovative management practices aimed at mitigating the negative effects of climate change on plant N use efficiency.

Introduction

Atmospheric carbon dioxide (CO₂) concentrations are rising, reaching unprecedented levels (IPCC, 2023). This increase is primarily attributed to human activities such as industrial processes, fossil fuel combustion, and energy production. The rise in atmospheric CO₂ concentrations exerts profound effects on plant development, resulting in alterations to growth dynamics, physiological processes, and broader ecosystem interactions (Aspray et al., 2023). Previous studies (Baslam et al., 2020; Ancín et al., 2024) have noted that elevated CO₂ (eCO₂) levels enhance photosynthetic efficiency, stimulate biomass accumulation, and affect nutrient distribution within plant tissues. As atmospheric CO₂ continues to increase, it becomes imperative to identify and understand the key factors influencing plant growth under these conditions. Critical determinants, such as specific nutrient availability, particularly nitrogen (N), play a pivotal role in modulating plant responses to eCO₂ and ultimately impact agricultural productivity (Porras et al., 2017). Additionally, persistent cloudy weather during crop growth stages—especially during the critical period—often leads to significant losses in grain quality and yield in rice due to low light conditions (Weng et al., 2017). As the Earth’s temperature continues to rise, altered weather patterns, such as increased cloud cover (Koshiro et al., 2022), will influence the amount of light available to plants (IPCC, 2023). Understanding how plant photosynthesis interacts with light patterns under a climate change scenario is essential to ensuring global food security (Murchie et al., 2009).

Nitrogen (N) remains a crucial nutrient for plants under both ambient and eCO₂ conditions. The combined presence of the two main primary inorganic N sources—ammonium (NH₄⁺) and nitrate (NO₃^-)—has a synergistic effect in promoting plant growth; however, the energy requirements for the assimilation of different N sources are markedly distinct (Bloom et al., 1992). Specifically, the photo-assimilation of NO₃^– to NH₄⁺ requires oxidation of NADPH or NADH and six reduced ferredoxins. Consequently, plants relying on NO₃^– as an N source must efficiently allocate reductant generated during the light reactions of photosynthesis to meet the additional energy demands of NO₃^– assimilation (Masclaux-Daubresse et al., 2010). Therefore, light intensity is likely to have a significant impact on plant preferences for N sources. Mitochondrial metabolism offers a promising avenue for enhancing N utilization efficiency (NUE) by modifying respiratory pathways, including the oxidative pentose phosphate pathway. These pathways can improve energy balance during N uptake (Foyer et al., 2011; Jauregui et al., 2017).

The influence of the N source extends to the plant’s carbon (C) balance. Plants can enhance their CO₂ uptake rate during the de novo assimilation of NO₃^- through photorespiration (Busch et al., 2018). This phenomenon is particularly critical for plants exposed to atmospheric eCO₂, as several authors have indicated that NO₃^- assimilation is significantly impaired under these conditions (Bloom et al., 2010, 2014; Jauregui et al., 2017). The assimilation of NO₃^- requires NADP which is generated by photosynthesis However, under eCO₂ conditions, C fixation is favored, and a greater proportion of reductant is utilized in this process. Additionally, as photorespiration decreases under eCO₂, less malate is exported from the chloroplast to the cytosol, disrupting the NADH/NAD ratio in the cytosol, which is critical for NO₃^- assimilation (Bloom et al., 2010). Other factors, such as reduced transpiration rates, further compound these metabolic imbalances (Jauregui et al., 2016). Remarkably, despite differences in N sources, C3 plants grown under higher than aCO₂ show an overall depletion in their ionome, both in plant tissues and grains (Loladze, 2014). Specifically, Tcherkez et al. (2020) demonstrated that eCO₂ reduces the concentrations of minerals in C3 plants (e.g., rice, wheat) by an average of 8%, while increasing the ratio of total non-structural carbohydrates to mineral content.

Rice, as a staple food for over half of the global population, plays a crucial role in food security, serving as a primary source of protein, carbohydrates, vitamins, and minerals. However, climate change poses significant challenges to rice production, with rising atmospheric CO₂ concentrations emerging as a key factor impacting both crop yield and grain quality (Tcherkez et al., 2020). Studies indicate that higher CO₂ levels increase carbohydrate content while reducing essential nutrients such as protein, zinc, and iron in rice grains, with profound implications for human health. Additionally, the reliance on chemical fertilizers in rice production exacerbates several environmental challenges. Excessive use of nitrogenous fertilizers can lead to nutrient imbalances, soil degradation, increased greenhouse gas emissions, and water contamination through runoff, resulting in eutrophication and harmful algal blooms. The interplay between climate change and rice cultivation techniques extends beyond the farm. A recent experiment (Xu et al., 2020) revealed that eCO₂ stimulates a unique cluster of anaerobic microorganisms from the Burkholderiales family, which play a pivotal role in the release of N gas through NH₄⁺ oxidation. This finding significantly enhances our understanding of the complex interactions between eCO₂ levels and the N cycle in rice systems.

Our study investigates the interplay among light intensity (LL: 150 µmol m⁻² s⁻¹; and CL: 300 µmol m⁻² s⁻¹), N sources (nitrate; N-NO₃ or ammonium nitrate; N-NH₄NO₃), and CO₂ concentrations (410 and 700 ppm) on growth, photosynthetic efficiency, and N utilization in rice plants. Our research hypothesis posits that mixed nutrition improves plant responsiveness to eCO₂ across varying light intensities compared to sole N-NO₃ nutrition, due to its lower energy demands for N assimilation. The findings indicate that N-NO₃ nutrition fails to enhance growth, photosynthetic efficiency, or N accumulation under aCO₂ conditions, particularly in low light (LL) environments. Conversely, the application of N-NH₄NO₃ increases biomass accumulation and carboxylation efficiency under eCO₂ conditions, supported by a corresponding increase in Calvin cycle-related sugar production. A decrease in leaf N and amino acid concentrations at eCO₂, particularly in plants supplied with N-NO₃, along with diminished ATP levels, suggests elevated energy demand for N assimilation. These findings highlight the critical role of N sources and light interactions in shaping plant adaptations to the challenges posed by climate change.

Materials and methods

Plant material and growth conditions

Seeds of Oryza sativa L. cv. Nipponbare were surface-sterilized with 2.5% (v/v) bleach and 0.02% (v/v) Triton X-100, then rinsed three times with sterile deionized water. Seeds were incubated at 30°C in the dark before transferring to 7.5-liter pots filled with nitrogen-free nursery culture soil (Kumiai Gousei Baido 4, JA, Tokyo, Japan). The plants were grown in a growth chamber (CFH-415; Tomy Seiko, Tokyo, Japan) under 12-hour light/dark cycles at 28/23°C, which contained a fluorescent lighting system as described by Inomata et al. (2018). Plants were watered twice a week.

The experimental design comprised four growth chambers with distinct environmental conditions to investigate plant responses to factorial conditions. A group of two chambers differed in light intensity (one at low light, LL, with 150 µmol m^-2 s^-1, and another at control light, CL, with 300 µmol m^-2 s^-1). Additionally, within each of the groups, one chamber functioned at atmospheric CO₂ concentrations (410 ppm CO₂; aCO₂) while the other functioned at elevated CO₂ concentrations (700 ppm CO2; eCO₂). In each chamber, eighteen pots were divided into two nitrogen treatment groups: nine pots received N as Ca(NO₃)₂4H₂O, while the other nine received NH₄NO₃ at 2.5 mM of N, with calcium being the element present at different concentrations between the nutrient solutions.

Growth measurements

At the end of the experiment (50 days after germination; DAG), fresh leaf material from the fully expanded leaf was collected and stored in liquid nitrogen; then, this material was lyophilized using a freeze-drier (Testlar LyoQuest, Spain) and used for the metabolite and mineral measurements. Additionally, plant height was measured from the base of the tillers to the tip of the tallest leaf using a ruler. Leaf area was determined by scanning leaves and analyzing images using ImageJ software. Then, fresh weight was taken and dried in an oven at 60°C for 48 hours to determine the dry mass of each plant. The tiller number was counted manually. Roots were carefully washed with H₂O to clean them. Fresh and dry weights were measured.

Photosynthetic measurements

Gas exchange parameters were evaluated on fully developed apical leaves using a LI-COR 6400XT portable photosynthesis system (LI-COR Biosciences, Lincoln, NE, USA). Measurements were performed between 09:00 to 11:00 AM to reduce diurnal fluctuation. The chamber was set up with a 2 cm² standard leaf chamber and a red/blue LED illumination source. Environmental conditions within the chamber were regulated as follows: CO₂ concentration at growth conditions (410 μmol mol−¹ or 700 μmol mol−¹) photosynthetic photon flux density (PPFD) at 1200 μmol m−² s−¹, and block temperature held at 25°C. Relative humidity was controlled to maintain leaf-to-air vapor pressure deficit between 1.0 and 1.5 kPa. Prior to measurements, the system was calibrated according to the manufacturer´s recommended protocols, including zeroing the CO₂ and H₂O analyzers and aligning the reference and sample gas analyzers. Leaves were acclimatized in the chamber for 5 minutes to stabilize gas exchange rates prior to data collection. Saturation CO₂ assimilation rate (Asat), and transpiration rate (T_r) were computed using the equations of von Caemmerer and Farquhar (1981). Afterwards, A/Cⁱ response curves were produced by varying the chamber CO₂ concentration from 50 to 1500 μmol mol−¹, with each step lasting 5 minutes to ensure equilibrium. Maximum carboxylation velocity (V_max) and maximum electron transport rate (J_max) were approximated by fitting the A/C_i data to the mechanistic model of C₃; photosynthesis as outlined by Sharkey et al. (2007). All measurements were replicated on five individual plants per treatment, and data were adjusted for potential leaks and standardized to standard temperature and pressure conditions.

ATP measurements

ATP quantification was conducted utilizing an ATP Determination Kit (A22066, Invitrogen) based on luciferase-based bioluminescence assay (Tatsumi et al., 1989). Approximately 50 mg of frozen rice leaf tissue was homogenized in 1 mL sonication solution (SONOP) buffer (0.372 g EDTA dissolved in 130 mL deionized distilled water H₂O, adjusted to pH 10.9 with NaOH, then mixed with 370 mL of 96% ethanol) using a gentleMACSTM Dissociator. The mixture was centrifuged at 13,000×g, and protein concentration in the supernatant was determined using the Pierce™ BCA™ Protein Assay kit (Thermo Fisher Scientific). The protein concentration was adjusted to 300 µg/mL with SONOP buffer. Samples were further diluted 10-fold in 100 µM phosphate buffer, and ATP concentrations were determined using a calibration curve corrected for protein content and expressed as nmol per gram of protein.

Mineral content

50 mg of lyophilized leaf sample was utilized, which is subjected to acid digestion utilizing a combination of concentrated HNO₃ and H₂O₂ (5:1 volume ratio) within a temperature-regulated digestion block maintained at 180°C for 4 hours; subsequently, it was diluted with ultrapure water to achieve a specified volume. The digested samples were assessed for mineral composition using inductively coupled plasma/optical emission spectrometry (ICP/OES) on an iCAP 6500 Duo apparatus (Thermo Fisher Scientific, Waltham, USA), where the plasma is sustained at approximately 7000-8000K and sample introduction is facilitated through a nebulizer apparatus. Regarding the determination of C and N concentration (%), a separate aliquot of the same lyophilized sample undergoes dynamic flash combustion at 1800°C in an elemental analyzer (FlashEA1112, ThermoFinnigan) outfitted with a MAS200R autosampler, wherein the sample is entirely oxidized into elemental gases (CO₂, H₂O, and N₂), which were subsequently separated via a chromatographic column and detected through thermal conductivity.

Hormone profile

About 40 mg of lyophilized leaf sample was mixed with 80% methanol-1% acetic acid and internal standards, shaken at 4°C for an hour, then stored at -20°C overnight, and centrifuged before drying the supernatant. The dried residue was dissolved in 1% acetic acid, passed through an Oasis HLB column; for abscisic acid (ABA) quantification, the eluate was processed with 5% acetonitrile-1% acetic acid and separated using utra-high-performance liquid chromatography (UHPLC). To analyze cytokinins an Oasis MCX column was utilized, and after elution with 60% methanol-5% NH₄OH, the basic fraction was dried, dissolved, and separated through UHPLC chromatography. Hormones were examined with a Q-Exactive mass spectrometer using targeted Selected Ion Monitoring, and their concentrations were determined using calibration curves generated with Xcalibur 4.0 and TraceFinder 4.1 SP1 software. Deuterium-labelled hormones were used as internal standards for quantification of each plant hormone.

Metabolome profile

For the rice leaf metabolome analyses, all steps were adapted from the original protocol described by Fiehn et al. (2006). The ground dried samples (15 mg DW) were resuspended in 1 mL of frozen (-20°C) Water: Acetonitrile : Isopropanol (2:3:3) containing Ribitol at 4 pg/mL and extracted for 10 min at 4°C with shaking at 1500 rpm in an Eppendorf Thermomixer. Insoluble material was removed by centrifugation at 13,500 RPM for 10 min. Then, 50 µL were collected and 10 µL of myristic acid d27 at 30 pg/mL was added as an internal standard for retention time locking. Extracts were dried for 4 hours at 35°C in a SpeedVac and stored at -80°C.

Samples were taken out of -80°C, warmed 15 min before opening and SpeedVac dried again for 1.5 h at 35°C before adding 10 µL of 20 mg/mL methoxyamine in pyridine to the samples and the reaction was performed for 90 min at 30°C under continuous shaking in an Eppendorf thermomixer. 90 µL of N-methyl-N-trimethylsilyl-trifluoroacetamide (MSTFA, Thermo Scientific SAS) were then added and the reaction continued for 30 min at 37°C. After cooling, 100 µL were transferred to an Agilent vial for injection.

4 hours after derivatization 1 µl of sample was injected in splitless mode on an Agilent 7890B gas chromatograph coupled to an Agilent 5977A mass spectrometer. The column was an Rxi-5SilMS from Restek. An injection in split mode with a ratio of 1:30 was systematically performed for saturated compounds quantification. Oven temperature ramp was 60°C for 1 min, then increased at 10°C min−¹ to 325°C, held for 10 min. Helium constant flow was 1.1 mL min^-1. Temperatures were the following: injector: 250°C, transfer line: 290°C, source: 230°C and quadrupole 150°C. The quadrupole mass spectrometer was switched on after a 5.90 min solvent delay time, scanning from 50-600 m/z. Absolute retention times were locked to the internal standard d27-myristic acid using the RTL system provided in Agilent’s MassHunter software. Retention time locking reduces run- to-run retention time variation. Samples were randomized. Fatty acid methyl esters mix (C8, C9, C10, C12, C14, C16, C18, C20, C22, C24, C26, C28, C30) was injected in the middle of the queue for external RI calibration.

Raw Agilent datafiles were analyzed with AMDIS http://chemdata.nist.gov/mass-spc/amdis/. The Agilent Fiehn GC/MS Metabolomics RTL Library (version June 2008) was employed for metabolite identification. Peak areas determined with the MassHunter Quantitative Analysis (Agilent) in splitless and split 30 modes. Resulting areas were compiled in one single Excel file for comparison. Peak areas were normalized to Ribitol and dry weight. Metabolite contents are expressed in arbitrary units (semi-quantitative determination). Additional information can be found in (Aranjuelo et al., 2015).

Statistical analysis

Data were analyzed using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test to determine significant differences between groups. All statistical analyses were performed using R software.

Data from metabolomics were analyzed using MetaboAnalyst 6.0 software (Pang et al., 2024) through multivariate (MVA) and univariate (UVA) analyses. The MVA involved normalization (by sum), logarithmic transformation (by log₁₀), and Pareto scaling, with principal component analysis (PCA) used for unsupervised modelling to ensure data quality and detect patterns. Discriminant models like PLS-DA and OPLS-DA were employed to identify group separations, while UVA utilized the t-test and Kruskal-Wallis test for group discrimination at a 95% confidence interval. The top 25 metabolites were identified using the Correlation and Partial Correlation Analysis based on Pearson’s R and considered significant if correlation coefficients were higher than ± 0.5. The fold change (FC) and percentage of variation (%) were calculated to determine differences between case and reference groups.

Results

Biomass accumulation

The results indicate that the light intensity has a significant effect on aboveground biomass accumulation (Figures 1A–C). As expected, biomass accumulation increased significantly under higher irradiance at 300 µmol m^-2 s^-1 (CL), with p < 0.002. Notably, a substantial accumulation of aboveground biomass, by an order of magnitude, was observed when plants were exposed to eCO₂ and low irradiance levels at 150 µmol m^-2 s^-1 (LL). At aCO₂, no significant differences in fresh weight (FW) were observed between the two N sources at the same light intensity. However, under eCO₂, FW showed a slight increase in plants supplemented with N-NH₄NO₃ compared to N-NO₃ at both light intensities. For dry weight (DW), while aboveground biomass was higher under eCO₂ than aCO₂, no statistically significant differences between N sources were detected at the same light intensity. Regarding root biomass accumulation (Figures 1A, D, E), significant increases in both FW and DW were observed in plants grown with N-NO₃ compared to N-NH₄NO₃ at both CO₂ levels (p-values < 0.0142), regardless of light intensity. Additionally, root biomass accumulation increased significantly under eCO₂ compared to aCO₂. For N-NH₄NO₃ under aCO₂, no differences were observed between light intensity treatments. However, higher root biomass was noted in LL × N-NO₃ and in all treatments under eCO₂ conditions.

Figure 1

Figure 1. Represents the combined effect of nitrogen (N) form, irradiance, and CO2 concentration on rice plant growth. (A) shows representative images of rice plants grown under different combinations of ninitrate (NO3) or ammonium nitrate (NH4NO3), at two light intensities (150 µmol m^-2 s^-1 , white bars; or 300 μmol m^-2 s^-1, grey bars) and under ambient (aCO2) or elevated (eCO2) CO2 concentrations. (B, C) quantify the fresh weight (FW) and dry matter (DM), respectively, of the above-ground tissues (shoots) of these plants; similarly, (D, E) present the FW and DM of the root tissues. Bar graphs within (B–E) display means ± SD derived from 9 replicates. The lowercase letters indicate statistically significant differences within the LL (Low Light) treatment, while the uppercase letters denote significant differences within the CL (control Light) treatment.

Plants showed increased height (Figures 2A) under LL x eCO₂ x N-NH₄NO₃ nutrition (p-value 0.0269). In contrast, no significant differences were observed between light treatments under aCO₂ or in plants grown with N-NO₃ under eCO₂ (p-values > 0.05). The shortest plant heights were consistently observed in plants grown with N-NO₃ and eCO₂, irrespective of light conditions. Root length followed a similar trend: plants grown with N-NH₄NO₃ under eCO₂ and CL displayed significantly greater root lengths (p-value 0.0269). Conversely, no significant differences in root length were observed between light treatments under aCO₂ or in eCO₂ × N-NO₃ treatments. Plants grown under eCO₂ × N-NH₄NO₃ exhibited shorter root systems at both light intensities (Figure 2B).

Figure 2

Figure 2. Represents the combined effect of N form, irradiance, and CO2 concentration on rice plant growth parameters. (A) A displays the plant height (cm) under various conditions, while (B) shows root length (cm). (C) represents the number of leaves per plant. In all panels, rice plants were grown with either nitrate (NO3) or ammonium nitrate (NH4NO3), at two light intensities (150 and 300 μmol m^-2 s^-1) represented by white and gray bars respectively, and under either ambient (aCO2) or elevated (eCO2) CO2 conditions. Bar graphs within (A-C) display means ± SD derived from 9 replicates. The lowercase letters indicate statistically significant differences within the LL (Low Light) treatment, while the uppercase letters denote significant differences within the CL (control Light) treatment.

The number of leaves per plant (Figure 2C) was significantly higher in plants grown under CL compared to LL (p-value <0.0004). These differences were more pronounced under eCO₂, but were also observed under aCO₂. Furthermore, plants grown under eCO₂ x N-NH₄NO₃ x LL had significantly higher leaf counts than those grown under LL x eCO₂ x N-NO₃, as indicated by the significance letters. The number of tillers per plant (Figure 2D) was significantly higher in plants grown under CL compared to LL (p-value < 0.0073). As with leaf count, these differences were more substantial under eCO₂ and less pronounced under aCO₂. Plants grown under eCO₂ × N-NH₄NO₃ exhibited the highest number of tillers per plant across all light conditions.

Plasticity of the leaves

Figure 3 illustrates the impact of the treatments on the width and length of the first, second, and third leaves of rice plants. The findings indicate that there was not a significant effect on the length of the flag leaf due to the treatments, in contrast to other growth parameters, as indicated by the p-values. Notably, the length of the flag leaf tended to increase under aCO₂ in comparison to eCO₂. Particularly, it was a notably higher flag leaf length at LL x aCO₂ x N-NO₃ compared to its counterpart under eCO₂. Conversely, the width was influenced by the N treatment: plants supplied with N-NO₃ exhibited a significantly narrower flag leaf width than those with N-NH₄NO₃ (p-value < 0.0024). Moreover, under conditions of LL x eCO₂, plants exhibited a notably wider width when subjected to a combination of N-NH₄NO₃ compared to solely N-NO₃. Similarly, the impact of the treatments on the 2^nd leaf length was found to be non-significant when comparing its counterpart at different LL, as denoted by the p-values. A larger 2^nd leaf was observed under HI x aCO₂ conditions when N-NH₄NO₃ was provided compared to N-NO₃ nutrition. Conversely, under LL x eCO₂, N-NH₄NO₃ nutrition led to an increase in 2^nd leaf length in comparison to N-NO₃. The extension patterns of 2^nd leaf length were comparable across different light intensities, showing no discernible differences. A notable increase in width was noted in plants receiving N-NH₄NO₃ as opposed to N-NO₃, specifically under eCO₂ and LL conditions. Conversely, the 3^rd leaf length and width exhibited significant variations due to the treatments. Plants grown under HI conditions displayed longer and wider leaves compared to those under LL conditions, particularly evident under eCO₂ with N-NO₃ at both light intensities (p-value < 0.044 for LL and < 0.0001 for HI). Furthermore, a reduction in the length and width of the upper third leaf was observed under eCO₂ and CL conditions when N-NH₄NO₃ was supplied.

Figure 3

Figure 3. Represents the combined effect of N form, irradiance, and CO² concentration on leaf length of rice plants. (A) shows representative images of the top, second, and third leaves of rice plants grown under different conditions. (B-D) present the length (cm) of the top, second, and third leaves respectively. In all panels, rice plants were grown with either nitrate (NO³) or ammonium nitrate (NH⁴ NO³), at two light intensities (150 and 300 μmol m^-2 s^-1) represented by white and gray bars respectively, and under either ambient (aCO²) or elevated (eCO²) CO² conditions. Bar graphs within (A-C) display means ± SD derived from 9 replicates. The lowercase letters indicate statistically significant differences within the LL (Low Light) treatment, while the uppercase letters denote significant differences within the CL (control Light) treatment.

Photosynthetic parameters and ATP

As anticipated, net photosynthesis at growing CO₂ conditions (410 ppm for aCO₂ and 700 ppm for eCO₂; Figures 4A, B) was markedly higher at HI. Notably, higher net photosynthetic rates at eCO₂ were observed only for N-NH₄NO₃, irrespective of light levels. Besides, the phenomenon of photosynthetic acclimation to eCO₂—reduction of the theoretically potential fixation capacity–was specifically evident in N-NO₃ plants. Hence, plants grown with eCO₂ x N-NH₄NO₃ exhibited the highest velocity of carboxylation (Vc_max; Figure 4C) at eCO₂ x N-NH₄NO₃ at both light levels. Interestingly, no significant differences were detected between light treatments, indicating potential constraints on carbon fixation capacity, such as the photosynthetic electron transport capacity (J_max; Figure 4D). It is noteworthy that plants supplied with N-NO₃ exhibited the lowest J_max under eCO₂ at both light levels. Furthermore, the J_max at CL showed near-significance for N-NO₃ x aCO₂ compared to plants at different light levels, and increased at CL for aCO₂ x N-NH₄NO₃, eCO₂ x N-NO₃, and eCO₂ x N-NH₄NO₃ (p values of 0.0119, 0.0198, and 0.0439, respectively). The transpiration rates (Supplementary Figure S1B) exhibited a comparable pattern to g_s, but the intriguing pattern of contrasting results in LL conditions was not statistically significant on this occasion. Finally, ratio of intercellular CO₂ to aCO₂ (the Ci/Ca ratio; Supplementary Figure S1C) increases in CL comparing to LL in aCO₂ x N-NO₃, aCO₂ x N-NH₄NO₃ and eCO₂ x N-NO₃, as denoted by the p-values. Moreover, the ratio of eCO₂ x N-NO₃ was found to be the lowest at LL, whereas aCO₂ x N-NO₃ exhibited the highest ratio at CL. The results indicate that the amount of light exposure profoundly impacts the ATP levels in leaves (Figure 4E). Interestingly, higher ATP levels were seen in plants with aCO₂ levels compared to eCO₂ levels in all experimental conditions. Additionally, under N-NH₄NO₃ x aCO₂, the ATP content is higher at CL than at LL (p-value 0.018); at eCO₂, ATP levels decreased at LL in N-NO₃ fed plants (p-value 0.0254).

Figure 4

Figure 4. Represents the combined effect of N form, irradiance, and CO₂ concentration on leaf length of rice plants. (A, B) shows assimilation rate (Asat) at growing CO₂. (C, D) the maximum carboxylation velocity (Vmax) and themaximum electron transport rate (Jmax) repectively. (E) represents adenosine triphosphate (ATP) levels. In all panels, rice plants were grown with either nitrate (NO₃) or ammonium nitrate (NH₄NO₃), at two light intensities (150 and 300 μmol m^-2 s^-1) represented by white and gray bars respectively, and under either ambient (aCO₂) or elevated (eCO₂) CO₂ conditions. Bar graphs within (A-D) display means ± SD derived from 5replicates while from ATP was derived from 3-4 replicates. The lowercase letters indicate statistically significant differences within the LL (Low Light) treatment, while the uppercase letters denote significant differences within the CL (control Light) treatment.

Ionomics

Figure 5 illustrates the impact of the treatments on selected leaf mineral content; the entire ionome profile is presented in Supplementary Figure S1. The findings indicated a significant rise in the total leaf carbon (C) content of plants grown under CL conditions at eCO₂ levels (Figure 5A). Additionally, N-NH₄NO₃ showed a trend towards enhancing the C content. The N content experienced a significant reduction in plants grown under N-NO₃ and eCO₂ levels regardless of light intensity. Additionally, a decrease in N content was noted in CL conditions when exposed to eCO₂ x N-NH₄NO₃ compared to its counterpart at LL. The potassium (K) content exhibited a remarkable decrease in plants cultivated under CL x eCO₂, in clear contrast to LL x eCO₂, at both N regimes. No discernible distinctions were observed for alternative combinations. An evident rise in calcium (Ca) concentrations was noted in plants grown under LL x eCO₂ x N-NO₃ compared to its counterpart at CL. Furthermore, a decline in Ca levels was identified in CL x aCO₂ x N-NH₄NO₃ compared to the same combination at LL. Elevated levels of magnesium (Mg) were observed in plants subjected to increased LL x eCO₂ x N-NH₄NO₃ compared to CL conditions. Moreover, sodium (Na) concentrations were notably higher in plants grown under LL conditions compared to CL in all cases. The phosphorus (P) content tended to increase with N-NH₄NO₃, but it remained comparable at CL x eCO₂. Additionally, at eCO₂ x N-NH₄NO₃, plants had higher P with LL. Finally, the sulfur (S) content decreased at CL compared to LL in aCO₂ for both N treatments.

Figure 5

Figure 5. Illustrates the impact of various N forms (NO₃ or NH₄NO₃) on selected leaf minerals (A–H) and phytohormones (I) contents of rice plants grown under contrasting irradiances (150 µmol m⁻² s⁻¹ or 300 mmol m⁻² s⁻¹) and cultivated in either ambient (aCO₂) or elevated (eCO₂) CO₂ conditions. The values presented are means ± SD derived from 3 replicates.

Hormone profile

A hormone profile was conducted in leaves in order to capture possible profile that explains the physiological responses recorded; the results are represented in a heatmap (Figure 5B). First, the result that stands out is the notable rise across multiple hormonal markers (excluding cis-Zeatin (cZ), isopentenyladenine (iP), isopentenyladenosine (iPR)) in the experimental conditions involving the LL x eCO₂ x N-NO₃. The only other treatment that demonstrated an overall rise in the hormone levels, albeit less pronounced— with increases in almost all hormones except for ABA— was CL x eCO₂ x N-NH₄NO₃. In relation to the other two treatments in eCO₂, there is a significant rise in salicylic acid (SA) observed for CL x eCO₂ x N-NO₃ and, a slight rise in Indole-3-acetic acid (IAA) and jasmonoyl-isoleucine (JAlle) for LL x eCO₂ x N-NH₄NO₃. In terms of the hormone levels in plants under aCO₂ conditions, what is notable is the overall decrease in hormones with the exception of certain specific groups: for CL x aCO₂ x N-NH₄NO₃ the zeatin cZ, the zeatin riboside (tZR) and cis-zeatin riboside (cZR) and cZR shows a significant increase, while a slight increase is observed for CL x aCO₂ x N-NO₃. iP has been largely accumulated in LL x aCO₂ x N-NH₄NO₃.

Metabolomics

Metabolites that were verified by the standard and tentatively identified through spectral similarity scores were the sole entities considered for inclusion in the statistical examination, which encompassed 108 metabolites.

Single factor analysis

When examining the metabolites influenced by the different treatments by analyzing the metabolites using 2-way ANOVA in Venn diagrams (Figure 6A), it was observed that only the CO₂ treatment led to significant differences, indicating the greatest impact of this treatment. Figure 6B summarizes how the top 25 metabolites respond to increased levels of CO₂, light, and different N sources; the entire heatmap with the entire metabolome can be seen in Supplementary Figure S2.

Figure 6

Figure 6. Illustrates the impact of various nitrogen forms (nitrate or ammonium nitrate) on selected leaf metabolites varying between treatments using (A) Venn diagram, (B) heatmap, and (C–E) the top 25 varying metabolites of rice plants grown under irradiances of 150 µmol m^-2 s^-1 (depicted by white bars) and 300 µmol m^-2 s^-1 (represented by gray bars), and cultivated in either ambient (aCO₂) or elevated (eCO₂) CO₂ conditions. The values presented are means ± SD derived from 3 replicates.

The foremost metabolites whose content was altered by the treatments CO₂, light, and N are depicted in Figures 6C–E; only metabolites that changed by a correlation coefficient greater than 0.5 were deemed significant. The compounds that escalated with the CO₂ treatment were: D-glucose 2, gluconic acid, ribonic acid-gamma, serotonin, gamma-aminobutyric, N-acetyl-D-glucose, beta-cyno–alanine, ribose, L-histidine, salicylic acid, putrescine, L-lysine, and fructose. Furthermore, the leading metabolites that were diminished with the CO₂ treatment were: phosphoric acid, dioctyl phthalate, D-glucose-6-phosphate, galactinol, linoleic acid, beta-sitosterol, myo-inositol, L-glutamic acid, stearic acid, glycine, palmitic acid, and glycerol 1-phosphate. Concerning illumination and nitrogen, only marginally significant findings were found: In the case of light, only xylose 2 correlated positively, and in the case of nitrogen, only fumaric acid correlated positively, while L-asparagine and allantoin correlated negatively.

Through the categorization of the metabolites using analyses of variance of 1-way and false discovery rate, 16 metabolites were identified as significant (Supplementary Figure S3). The pattern of accumulation of metabolites changes differentially across the treatments. D-glucose exhibits a substantial increase under eCO₂ levels in comparison to aCO₂. Additionally, under LL x eCO₂ x N-NO₃, the quantity of D-glucose is lower than in the remaining treatments. In line with D-glucose, gluconic acid lactone (aerobic oxidation of glucose) shows a notably higher content at eCO₂ relative to aCO₂. The concentration of ribonic acid-gamma-lactone increased at eCO₂ compared to aCO₂. Moreover, under LL x eCO₂ x N-NO₃ conditions, the concentration is lower compared to the other treatments at eCO₂. Serotonin displays a similar trend to ribonic acid-gamma-lactone; however, the variability under LL x eCO₂ x N-NO₃ hinders the distinction from aCO₂. Furthermore, at LL x aCO₂, the serotonin is accumulated at N-NH₄NO₃ nutrition compared to its counterpart. Both benzoic acid and salicylic acid tend to accumulate higher amounts at eCO₂ compared to aCO₂ counterpart. Additionally, under both CO₂ levels and CL, rice plants at N-NO₃ accumulated higher levels than at N-NH₄NO₃. In contrast, linoleic acid and phosphoric acid tend to gather lower quantities at eCO₂ compared to aCO₂. The level of the amino acid glycine rises under CL compared to LL at aCO₂; however, at eCO₂, while the glycine content is higher with N-NO₃ nutrition under LL, it is significantly reduced under CL. The metabolites beta-sitosterol, stigmasterol, stearic acid, and palmitic acid collectively display a similar trend: They show significantly higher contents at LL x aCO₂ compared to the other conditions. Slightly lower accumulation was found in CL x aCO₂ x N-NH₄NO₃ compared to its counterpart with N-NO₃ for beta-sitosterol. No other major distinctions were observed under eCO₂. In contrast, fumaric acid exhibits the lowest levels at LL x aCO₂, at both nitrogen regimes, while its level is stable across the other treatments. The levels of L-asparagine and maltose did not follow any of the patterns described above. Plants tend to accumulate higher amounts of L-asparagine at eCO₂ under low light, reaching the peak with N-NO₃. Additionally, at aCO₂ under LL x N-NH₄NO₃, the levels are higher than in the other aCO₂ treatments. The pattern of maltose is partially obscured by variability in two treatments, but, CL x aCO₂ x N-NH₄NO₃ tends to displays the lowest levels. The highest maltose levels are observed at eCO₂ under LL with N-NO₃.

Exploratory group analysis

A Partial Least Squares Discriminant Analysis (PLS-DA) is a supervised dimensionality reduction method that, unlike PCA, incorporates class labels in the analysis. First, the SPLS plot allows grouping the results in 2 dimensions, with component 1 explaining 16.1% of the variability and component 2 accounting for 9% (Supplementary Figure S4). Moreover, these clusters are distinctly separated from the eCO₂ treatments.

To understand the core metabolic changes that lead to the observed traits, it is necessary to study how different factors interact in detail. This is why the Simultaneous Component Analysis (ASCA) method, which merges analysis of variance (ANOVA) with Simultaneous Component Analysis (SCA), was chosen. By using this method, relationships between treatments can be explored with a significance level of p-value < 0.05. An ASC analysis was conducted to create leverage and squared prediction error (SPE) plots to pinpoint metabolites that conform to or deviate from ASCA patterns. Metabolites with a leverage threshold of 0.9 and an alpha threshold of 0.05 high leverage and low SPE values are considered well-modeled and are thus identified as influential compounds. Additionally, PLS aids in reducing dimensionality and can be tailored for feature selection and classification purposes. When contrasting the treatments CO₂ and light (Supplementary Figure S5A), the organic compounds N-acetyl-D-glucosamine, gluconic acid lactone, GABA and D-glucose were effectively simulated by factor CO₂; xylose, arabinose, beta-alanine and ethanolamine were effectively simulated by factor light; finally, L-asparagine and salicylic acid were effectively simulated by their interaction. By contrasting CO₂ and N (Supplementary Figure S5B), the substances N-acetyl-D-glucosamine, gluconic acid lactone, GABA and D-glucose were effectively simulated by the treatment CO₂; the substances fumaric acid, allantoin 1, gluconic acid 2, arachidic acid and allantoin 2 were effectively simulated with N; finally, the substances galacturonic acid, glycine and beta-sitosterol acid were effectively simulated with the interaction. By contrasting illumination and nitrogen using an ASC analysis, distinctions can be identified (Supplementary Figure S5C). The substances xylose 2, arabinose, beta-alanine 1 and ethanolamine were effectively simulated by the factor light; the substances fumaric acid, allatoin 1, gluconic acid 2, arachidic acid and allantoin 3 were effectively simulated by the factor nitrogen; the substances threonic acid 1,4-lactone, 2,3-butanediol, proline and 2,3- butanediol 2 were effectively simulated by the interaction.

Discussion

Despite its importance, limited information exists regarding how light intensity interacts with other factors influencing crop performance, such as fertilization and CO₂ levels (Aspray et al., 2023). This gap is significant because crop development is heavily impacted by reduced light intensity, which plays a crucial role in yield formation. In Southeast Asia, for instance, early and late monsoon periods are often characterized by prolonged overcast conditions. Building on the findings of Weng et al. (2017), and assuming that 47.5% of solar radiation falls within the PAR range, we simulated the lower irradiance conditions observed in this study at 8 MJ per day, corresponding to 150 µmol m^-2 s^-1 PAR. Notably, many days with solar irradiance below 8 MJ per day have been reported by Weng et al. (2017). According to Venkateswarlu et al. (1977), insufficient light negatively affects all stages of rice growth, reducing tillers and panicles during vegetative growth, while disrupting biochemical and physiological processes that lead to fewer spikelets, lower grain weight, and diminished grain quality during the reproductive stage. Our research builds on these findings, exploring not only the impact of low light but also the interplay between light intensity, CO₂ levels, and N supplementation. Specifically, our study was designed to investigate how different forms of N supplementation influence rice plant growth across varying CO₂ levels and light intensities, including LL conditions. The results revealed that N source significantly influences plant growth and physiological responses under changing climate conditions, particularly in LL environments. Notably, eCO₂ positively affected above-ground biomass under LL conditions, with the effect being more pronounced when N-NH₄NO₃ was used as the fertilizer. Additionally, both leaf count and tiller number increased under eCO₂ across light regimes, with greater enhancement observed in plants supplemented with N-NH₄NO₃. These findings underscore the importance of considering not only the direct effects of climate change but also the pivotal role of N fertilization in mediating plant responses to varying light intensities.

Previous studies have shown that while plants initially experience a boost in photosynthesis due to increased substrate availability under eCO₂, this enhancement tends to diminish over time (Ancín et al., 2024; Tcherkez et al., 2024). This phenomenon, known as photosynthetic acclimation, was first described by Webber et al. (1994). The accumulation of carbohydrates, particularly glucose, fructose, and starch, disrupts the balance of N compounds, including amino acids, and leads to a reduction in protein levels such as Rubisco. This reduction, in turn, lowers photosynthetic capacity in plants exposed to eCO₂ levels (Tcherkez et al., 2020). The current study demonstrated that crop responsiveness to N fertilization strategies is CO₂ dependent, consistent with previous findings in other crops (Porras et al., 2017; Domiciano et al., 2020; Collado-González et al., 2022). Under aCO₂, no significant variations were observed among the different N fertilization regimes, regardless of light intensity. However, under eCO₂, plants fertilized with N-NH₄NO₃ exhibited a significant enhancement in photosynthetic efficiency, irrespective of light intensity. In contrast, plants supplemented with N-NO₃ experienced a reduction in their carbon fixation ability due to adaptation to higher CO₂ levels, which led to decreased carboxylation velocity and, particularly, electron transport capacity. This was accompanied by a reduction in ATP levels under LL x eCO₂ x N-NO₃, indicating severe energy constraints in these plants. As noted by several researchers (Bloom, 2010; Jauregui et al., 2015, 2017; Rubio-Asensio and Bloom, 2017), the decrease in NO₃^- assimilation at higher CO₂ levels is attributed to decreased photorespiration, which disrupts the C/N balance and ultimately hampers carbon assimilation. However, Andrews et al. (2019) have argued that eCO₂ does not impede NO₃⁻assimilation in C₃ plants. It is worth noting that the study by Andrews et al. (2019) was conducted under high light conditions, exceeding ∼950 μmol photons m⁻² s⁻¹ of PAR, whereas Bloom´s and Jauregui’s research (Bloom, 2010; Jauregui et al., 2015, 2017; Rubio-Asensio and Bloom, 2017) was based on lower light levels of ∼350 µmol m^–2 s^–1 of PAR. Our findings suggest that low light levels, such as those used in this study, exacerbate energy constraints under eCO₂, particularly in plants supplied with N-NO₃ fertilization.

Various photosynthetic proteins, particularly those in the outer membrane of the chloroplast, are influenced by Ca²⁺ and play critical roles in responding to environmental cues and regulating photosynthesis. For instance, Hou et al. (2019) demonstrated that Ca²⁺ application is a target factor in photosynthetic responses to low light levels, highlighting its role in regulating gas exchange through fluid movement. In our experiment, an increase in Ca²⁺ levels was observed under LL conditions, but not in plants receiving N-NH₄NO₃. This is notable given the significant decrease in Ca²⁺ levels in leaves reported in a meta-analysis by Loladze (2014). The observed accumulation of Ca²⁺ under eCO₂ × N-NO₃ further supports the notion that reduced reductant power severely restricts photosynthesis and overall plant performance under these conditions. Recognizing the potential signaling role of minerals such as Ca²⁺, we expanded our study to analyze the phytohormone profile in leaves to explore the intricate interplay between mineral signaling and hormonal regulation in plant physiology. As anticipated, a pronounced increase in the phytohormone profile was observed under LL × eCO₂ × N-NO₃, indicating a stress adaptation response. This pattern was not evident under LL × aCO₂ × N-NO₃ or LL × aCO₂ × N-NH₄NO₃, further underscoring the unique interplay between light intensity, CO₂ levels, and N fertilization. Considering the significant changes in the hormone profile and the N limitations, it is not surprising that root biomass growth and plant height were greatest under eCO₂ x N-NO₃ at both light regimes. This finding suggests a strategic growth adjustment aimed at compensating for N deficiency in these plants. Our research underscores the complexity of plant adaptation mechanisms under future [CO₂] levels and highlights the necessity of considering multiple interacting variables when modeling plant responses to environmental stress.

The significant impact of CO₂ on the metabolome in the current study is evident from the enrichment analysis (Supplementary Figure 2), particularly in pathways such as glucose-fructose-mannose, arginine, and proline metabolism, which reflect a higher C status in rice plants. The metabolome exhibited minimal changes due to light treatments or N fertilization alone, indicating potential interactions among factors. To pinpoint the specific pathways affected by the combination of LL x N-NO₃, a one-factor ANOVA was performed. First, we found that although sugars such as glucose and glucono-delta-lactone increased under eCO₂ levels, their concentrations decreased at LL x N-NO₃. Serotonin, known to function as both a potent antioxidant and a signaling molecule regulating root system structure (Pelagio-Flores et al., 2011), was detected in high levels under LL x eCO₂ x N-NO₃. This corresponds to the expansion of the root system observed in our study and likely reflects either a stress-induced response or an adaptation mechanism in the plants. Additionally, benzoic acid and salicylic acid concentrations were highest under LL x eCO₂ x N-NO₃, aligning with the profound hormonal modifications reported under similar conditions by others (Li et al., 2022). The increase in glycine concentration under LL x eCO₂ x N-NO₃ likely reflects a decrease in photorespiration rates caused by eCO₂, compounded by limited reductant power available under LL conditions (Busch et al., 2018). Similarly, L-asparagine, which is indirectly related to photorespiration, followed a similar pattern to glycine under LL x eCO₂ x N-NO₃. These results mirror changes observed under aCO₂ at LL compared to aCO₂ at CL conditions (Rosa-Téllez et al., 2024). Overall, the physiological and hormonal changes recorded in our study illustrate the pronounced photosynthetic acclimation to eCO₂ observed in N-NO₃-fertilized plants.

Our findings emphasize the critical relationship between light intensity and various metabolic processes, particularly N metabolism, under eCO₂ conditions. When plants are supplied only with N-NO₃ as a nutrient, their ability to maintain optimal N levels becomes restricted, supporting the notion that hindered NO₃^- assimilation arises from a decrease in reductant power. In the restrictive LL treatment, plants underwent extensive hormonal signaling modifications to manage stress and enhance adaptability. These changes included alterations in tillering, leaf development, and shoot-to-root ratios, which collectively represent a sophisticated interplay among metabolic pathways aimed at overcoming these challenges.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Author contributions

IJ: Conceptualization, Data curation, Formal analysis, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. TM: Conceptualization, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing. BG: Investigation, Writing – original draft, Writing – review & editing. FG: Investigation, Writing – original draft, Writing – review & editing. IA: Conceptualization, Funding acquisition, Project administration, Writing – original draft, Writing – review & editing. MB: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing. CM: Data curation, Investigation, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The current study was carried out in the context of the CropYQualT-CEC funded by the Marie Curie RISE program (European Commission).

Acknowledgments

IJ thanks the Government of Navarra for his contract Atracción de Talento Senior Andia 2021. The authors sincerely thank the reviewers’ valuable feedback.

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.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1517360/full#supplementary-material

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