The effects of photosynthetic rate on respiration in light, starch/sucrose partitioning, and other metabolic fluxes within photosynthesis

The effects of photosynthetic rate on respiration in light, starch/sucrose partitioning, and other metabolic fluxes within photosynthesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The effects of photosynthetic rate on respiration in light, starch/sucrose partitioning, and other metabolic fluxes within photosynthesis Yuan Xu, Joshua Kaste, Sean Weise, Yair Shachar-Hill, Thomas Sharkey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3995199/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In the future, plants may encounter increased light and elevated CO 2 levels. How consequent alterations in photosynthetic rates will impact fluxes in photosynthetic carbon metabolism remains uncertain. Respiration in light ( R L ) is pivotal in plant carbon balance and a key parameter in photosynthesis models. Understanding the dynamics of photosynthetic metabolism and R L under varying environmental conditions is essential for optimizing plant growth and agricultural productivity. However, measuring R L under high light and high CO 2 (HLHC) conditions poses challenges using traditional gas exchange methods. In this study, we employed isotopically nonstationary metabolic flux analysis (INST-MFA) to estimate R L and investigate photosynthetic carbon flux, unveiling nuanced adjustments in Camelina sativa under HLHC. Despite numerous flux alterations in HLHC, R L remained stable. HLHC affects several factors influencing R L , such as starch and sucrose partitioning, v o / v c ratio, triose phosphate partitioning, and hexose kinase activity. Analysis of A/C i curve operational points reveals that HLHC's major changes primarily stem from CO 2 suppressing photorespiration. Integration of these fluxes into a simplified model predicts changes in CBC labeling under HLHC. This study extends our prior discovery that incomplete CBC labeling is due to unlabeled carbon reimported during R L , offering insights into manipulating labeling through adjustments in photosynthetic rates. Biological sciences/Biochemistry Biological sciences/Plant sciences elevated CO2 high light metabolic flux analysis photosynthesis plant central metabolism starch/sucrose partitioning Figures Figure 1 Figure 2 Figure 3 Introduction The atmospheric concentration of CO 2 is rapidly increasing due to human activities, such as fossil fuel combustion and deforestation 1 , driving global climate change and impacting various ecosystems 2,3 . CO 2 levels have exceeded 420 parts per million (mole fraction ppm) as of 2023, a sharp rise from pre-industrial levels of around 280 ppm 4 . Projections suggest CO 2 concentrations could reach 670 ppm (under RCP 6.0) or even 936 ppm (under RCP 8.5) by the end of the century 5 . Additionally, solar radiation reaching Earth's surface is expected to increase, a phenomenon known as global brightening, following a period of global dimming from the 1950s to the 1980s. This trend, driven by reduced air pollution and changes in cloud cover, has been observed in regions like Europe 6 . Climate models project significant increases in solar radiation in parts of Europe, North America, and Southeast China 7 . As a result, future conditions are likely to involve high light and high CO 2 levels (HLHC), impacting plant metabolism. Although increased light and CO 2 can enhance photosynthesis 8 , their effects on specific metabolic pathways associated with photosynthesis remain unclear. It is expected that HLHC will stimulate photosynthesis but inhibit photorespiration. Less clear is how HLHC will affect respiration in the light ( R L ). This CO 2 release during photosynthesis represents a small yet critical CO 2 flux from the leaf 9 and is pivotal in photosynthesis biochemical models 10,11 . Understanding R L is essential for accurate carbon balance estimates 12,13 but remains challenging because of the simultaneous CO 2 uptake by photosynthesis and CO 2 release by photorespiration 9,14 . Traditionally, R L is measured through gas exchange methods such as Laisk, Kok, and Yin methods. The Laisk method measures net CO 2 assimilation ( A ) in response to changes in intercellular CO 2 concentrations ( A / C i curves) at low CO 2 to estimate R L and Γ * 15–17 . Kok and Yin methods estimate R L from the response of A to low levels of irradiance, with Yin accounting for declining photosystem II electron transport efficiency ( Φ 2 ) with light 18–20 . Recent advancements like the dynamic assimilation technique (DAT) offer faster and more efficient methods for R L measurements based on nonsteady-state assumptions and CO 2 mass balance principles 21–23 . However, these methods all measure at very low photosynthetic rates near or below the compensation point. Accurately measuring R L under conditions of HLHC has been a challenge. To estimate R L in HLHC we have reported using isotopically nonstationary metabolic flux analysis (INST-MFA). Our results indicate that R L results in large measure from the oxidative pentose phosphate pathway (OPPP) in the cytosol forming a shunt from glucose 6-phosphate (G6P) to ribulose 5-phosphate (Ru5P) bypassing the plastidial non-oxidative pentose phosphate pathway reactions of the Calvin-Benson Cycle 24,25 . To address this, our study used gas exchange techniques, 13 C and 14 C isotope labeling, and INST-MFA to precisely measure metabolic fluxes within photosynthesis, revealing intricate adjustments in plant metabolism under HLHC conditions. Gas exchange indicated that the high CO 2 component of HLHC was nearly entirely responsible for the increased photosynthetic rate observed. The effect of HLHC on starch/sucrose ratio was determined by 14 C feeding. The rate of R L was unchanged by HLHC but the source of the glucose 6-phosphate to begin the OPPP shunt changed in HLHC. Results Metabolic fluxes of central metabolism Metabolic fluxes of central metabolism were globally assessed for differences between control and HLHC conditions through a comprehensive, model-based analysis of isotope labeling dynamics provided by INST-MFA (Fig. 1). INST-MFA was performed using a previously developed reaction network model integrating the Calvin-Benson Cycle (CBC), photorespiration, the tricarboxylic acid (TCA) cycle, the G6P/OPPP shunt, cytosolic and vacuolar sugar pools, and biosynthesis pathways for starch, sucrose, amino acids, and intracellular metabolic transport. Constraints were applied based on gas exchange measurements, with the net photosynthetic rate recorded as 15.0 ± 0.8 µmol m -2 s -1 for control and 22.0 ± 2.2 µmol m -2 s -1 for HLHC (Table 1 ). The oxygenation to carboxylation rate ratio by rubisco ( v o / v c ) was constrained to 0.30 ± 0.03 for control and 0.20 ± 0.01 for HLHC based on gas exchange measurement (Table 1 ). Table 1 Photosynthesis and A / C i curve parameters for plants assayed in control (n = 11, ± SD) and high light high CO 2 (n = 8, ± SD) conditions. Significant differences between control and high light high CO 2 conditions are marked with asterisks (Student’s two tailed t-test, P ≤ 0.05). Parameters Unit Control High light high CO 2 A µmol m − 2 s − 1 15.0 ± 0.8 22.0 ± 2.2 * g mol m − 2 s − 1 0.26 ± 0.05 0.19 ± 0.02 * Ci Pa 27.8 ± 1.9 39.2 ± 1.8 * v o / v c 0.30 ± 0.03 0.20 ± 0.01 * A 13 CO 2 labeling experiment collected data at 10 time points, analyzing 43 fragment ions using LC-MS/MS and GC-MS. While various intermediates within CBC, photorespiration, starch, and sucrose biosynthesis pathways exhibited significant 13 C labeling, the TCA cycle intermediates analyzed, along with most amino acids derived from them, displayed minimal labeling within the 30-min time frame (Fig. 2 a). A gradual transition of mass isotopologue distributions (MIDs) towards heavier isotopologues was noted over time. The fitted labeling kinetics for most metabolites were found to be in good agreement with the measured MIDs (Supplementary Figs. S1, S2, and S3). The degree of this fit was quantitatively determined by the sum-of-squared residuals (SSR), which assesses the total variance between the measured and simulated kinetics, with an emphasis on minimizing this discrepancy to refine the flux solution 44 . The χ2 goodness-of-fit test accepted SSR values of 825 and 566 for the control and HLHC models, respectively. HLHC influenced most CBC intermediates (3-phosphoglycerate, PGA; the sum of glyceraldehyde 3-phosphate and dihydroxyacetone phosphate, GAP/DHAP; the sum of G6P and fructose 6-phosphate, F6P; the sum of ribose 5-phosphate and ribulose 5-phosphate R5P/Ru5P), exhibiting faster labeling initially but similar levels to the control after 30 min (Fig. 2 b1). In photorespiratory intermediates, HLHC led to varied effects: serine labeling increased, glycine decreased, and 2-phosphoglycolate remained unchanged compared to the control (Fig. 2 a and Fig. 2 b1). Regarding sugars, HLHC showed inconsistent trends: sucrose glucosyl and fructosyl moieties increased (Fig. 2 b2), while glucose and fructose decreased in 13 C label incorporation (Fig. 2 b3). The labeling kinetics of TCA cycle intermediates and amino acids were significantly altered in HLHC (Fig. 2 ). Citrate and malate exhibited higher labeling in HLHC (2% each at 30 min) compared to the control (0% and 1%). Threonine, glutamine, and glutamate from the TCA cycle intermediates displayed slower labeling, with 3%, 4%, and 6% enrichment in HLHC, respectively, versus 0%, 0%, and 1% in the control at 30 min. Alanine achieved 34% enrichment in HLHC compared to 22% in the control (Fig. 2 a and Fig. 2 b3), while aspartate reached 53% compared to 57% at 30 min (Fig. 2 a and Fig. 2 b2). Characterization of photosynthesis and A/Ci curve fitting Under HLHC, we observed an increase in A , a higher intercellular CO 2 ( c i ) concentration, lower stomatal conductance ( g ), and a reduced v o / v c ratio. At normal light and CO 2 , photosynthesis operated (solid arrow in Fig. 3 ) very near the point where RuBP regeneration and rubisco activity co-limit the photosynthetic rate. This confluence of limitations occurred at 84% of the potential rate of triose phosphate use (TPU) capacity and so TPU was unlikely to have any control over the rate at normal light and CO 2 . When both light and CO 2 were increased, photosynthesis increased as expected. Photosynthesis increased along the RuBP-regeneration limited line (blue), indicating that the effect was mostly, if not entirely, explained by the increase in CO 2 ; the increased light was only of secondary importance, if at all. The ratio of v O to v C decreased with increasing CO 2 from 0.30 to 0.20 (Table 1 ). Photosynthesis approached the TPU limitation at high light and CO 2 as shown by the dashed black arrow indicating the operating point. The operating point was very close to the confluence of the RuBP regeneration line and TPU limitation line. Respiration in the light Quantitative analysis of metabolic fluxes was conducted to determine the rates of non-photorespiratory CO 2 release from reactions contributing to R L (Fig. 1 and Supplementary Table S1 ). The reaction network model considered three main processes of R L , including decarboxylation reactions in the TCA cycle 26–28 , pyruvate decarboxylation for fatty acid synthesis 29 , and the OPPP 24,30–32 . R L was calculated as the aggregate of estimated non-photorespiratory fluxes producing CO 2 , amounting to 8.1 umol CO 2 g − 1 FW h − 1 in the control condition and 9.0 µmol CO 2 g − 1 FW h − 1 in HLHC (Fig. 1 and Supplementary Table S1 ). The G6P/OPPP shunt was estimated to be 7.0 and 7.1 µmol CO 2 g − 1 FW h − 1 in control and HLHC, respectively, contributing to 86% and 79% of the total R L in control and HLHC, respectively (Fig. 1 and Supplementary Table S1 ). TCA-associated reactions and fatty acid synthesis accounted for the remainder of R L . This included the partial counterbalance of cytosolic CO 2 release through oxidative decarboxylation reactions, with phosphoenolpyruvate (PEP) carboxylation to oxaloacetate OAA (1.1 and 2.1 µmol CO 2 g − 1 FW h − 1 for control and HLHC), offsetting CO 2 release from the decarboxylation of pyruvate to acetyl-CoA (0.8 and 1.7 µmol CO 2 g − 1 FW h − 1 for control and HLHC), α-ketoglutarate decarboxylation (0.8 and 1.7 µmol CO 2 g − 1 FW h − 1 for control and HLHC), and CO 2 release via oxidative decarboxylation of pyruvate, which facilitates the production of acetyl-CoA for fatty acid synthesis within the plastid (0.4 and 0.7 µmol CO 2 g − 1 FW h − 1 for control and HLHC) (Fig. 1 and Supplementary Table S1 ). Carbon partitioning shift An elevated proportion of carbon was directed towards starch in HLHC, with values of 0.24 ± 0.02 in control and 0.31 ± 0.02 in HLHC (Table 2 ). Conversely, the higher fraction directed to starch in HLHC was associated with a reduction in the carbon allocated to the ionic fraction (amino acids, organic acids, etc.), with values of 0.30 ± 0.04 in control and 0.20 ± 0.03 in HLHC (Table 2 ). The proportion of fixed carbon allocated to sucrose remained consistent between control and HLHC, with values of 0.46 ± 0.03 in control and 0.49 ± 0.03 in HLHC (Table 2 ). Table 2 Fractions of starch and sucrose measured by 14 C radioactivity in control and high light high CO 2 conditions (n = 6, ± SD). Significant differences between control and high light high CO 2 conditions are marked with asterisks (Student’s two tailed t-test, P ≤ 0.05). Fractions of starch and sucrose Control High light high CO 2 starch/total 0.24 ± 0.02 0.31 ± 0.02 * sucrose/total 0.46 ± 0.03 0.49 ± 0.03 ionic/total 0.30 ± 0.04 0.20 ± 0.03 * starch/sucrose 0.51 ± 0.06 0.62 ± 0.05 * Starch (µmol m − 2 s − 1 ) 3.4 ± 0.3 6.0 ± 0.8 * Sucrose (µmol m − 2 s − 1 ) 6.8 ± 0.4 9.6 ± 1.0 * Together, the synthesis of starch and sucrose contributed to roughly 70% of the overall net carbon fixation for control, but was increased significantly to 82% for HLHC. The ratio between starch and sucrose increased in HLHC, indicating a preference for carbon partitioning to starch over sucrose (Table 2 ). The synthesis rates for starch and sucrose were derived by multiplying the CO 2 assimilation rate by the ratio of starch or sucrose to total 14 C fixed (Table 2 ). The synthesis rates for starch were measured to be 3.4 ± 0.3 µmol m − 2 s − 1 for control and 6.0 ± 0.8 µmol m − 2 s − 1 for HLHC. Similarly, the synthesis rates for sucrose were 6.8 ± 0.4 µmol m − 2 s − 1 for control and 9.6 ± 1.0 µmol m − 2 s − 1 for HLHC, respectively. Triose phosphate partitioning and hexokinase activity Surprisingly, there was a significant increase in the partitioning of triose phosphate to the cytosol in HLHC, as evidenced by the increase in the export flux for dihydroxyacetone phosphate (DHAP) from the chloroplast to the cytosol, which rose from 37 to 59 µmol metabolite g − 1 FW hr − 1 in HLHC. The rate of the aldol condensation reaction, responsible for converting glyceraldehyde-3-phosphate (GAP) and DHAP to fructose-6-bisphosphate (FBP), increased from 18 to 28 µmol metabolite g − 1 FW hr − 1 in HLHC. This is a 55% increase in FBP synthesis even though the sucrose synthesis increase was just 41% indicating that FBP flux to sinks other than sucrose must have been present, likely an increase in hexose phosphate entering the G6P shunt. In contrast, the carbon recycled to the R L pool via hexokinase decreased from 2.8 to 1.7 µmol.metabolite.g − 1 .FW.hr − 1 in HLHC. Notably, the flux for the G6P/OPPP shunt remained unchanged at 7.0 and 7.1 µmol metabolite g − 1 FW hr − 1 in the control and HLHC, respectively. The percentage of carbon contributed by hexokinase to the R L pool decreased from 40–24% with the difference being made up by the increased flux from FBP. Discussion Impact of HLHC on photosynthesis and biomass High light and CO 2 caused a significant increase in A . The switch to high CO 2 caused the calculated CO 2 partial pressure in the chloroplast ( C c ) to increase. The increase in CO 2 reduced the ratio of v o to v c (Table 1 ) and this accounted for most if not all of the increase in A. To understand the underlying physiology, we used A/Ci curves of data previously reported 33 . Identifying the operating point on A/C c curves (Fig. 1, black arrows), can help assess how efficiently the components of photosynthesis are being used. At 400 Pa CO 2 , the operational point was very close to the cross over between V cmax and J rate limits, indicating these would be used at nearly their full capacity. In the higher CO 2, V cmax is in excess by 27%, and J is very near the junction with TPU, a syndrome that is often characterized by instability and rubisco deactivation 34,35 . CO 2 and light were increased only during the measurement, we expect that leaves grown in the higher CO 2 would adjust so that the operational point would be close to the confluence of the rubisco and RuBP regeneration rate limitations and slightly below TPU. This was shown by McClain, Cruz, Kramer and Sharkey 35 for adaptation to high CO 2 . Recently, Coast, Scafaro, Bramley, Taylor and Atkin 36 reported that wheat plants in which photosynthetic capacity increased by growth at increased temperature at night had operational points very near the confluence of rubisco and RuBP regeneration limitations and that TPU changed to always be about 20% greater that the operation point. Increased A , higher intercellular CO 2 ( C i ), lower stomatal conductance ( g ), and a reduced v o / v c ratio were observed in HLHC. Elevated CO 2 has been shown to enhance photosynthesis by increasing CO 2 assimilation rates, leading to higher plant growth and yield in many C 3 species 37,38 . This CO 2 fertilization effect can result in greater biomass accumulation and yield, as demonstrated in crops like wheat, rice, and soybeans 39,40 . Photosynthetic and ancillary metabolism In this study, we used metabolic flux analysis (MFA), a powerful method for assessing intracellular metabolic fluxes within living biological systems, to estimate R L and other metabolic fluxes under HLHC. MFA integrates computational models with experimental isotope labeling, providing a comprehensive understanding of functional metabolism by integrating factors such as enzyme expression, activity, and network structure 41–43 . Recent advancements in 13 CO 2 time-course labeling and computational modeling have made isotopically nonstationary metabolic flux analysis (INST-MFA) a potent tool for studying autotrophic carbon metabolism and estimating in vivo carbon fluxes 24,44 . CBC intermediates rapidly labeled over a 30-min period, reaching enrichments of 82%-94% in control and 90%-96% in HLHC, aligning with the well-established short half-lives of C 3 cycle intermediates 44–47 . FBP and G6P/F6P exhibited slower labeling compared to other CBC intermediates for both control and HLHC, likely due to their involvement in sucrose synthesis pathways outside the chloroplast, where labeling is diluted by hexokinase bringing in unlabeled carbon from the free glucose pool (plus fructose and possibly sucrose following invertase activity). HLHC had a substantial and significant impact on the fluxes of CBC, starch synthesis, and sucrose export. Carboxylation flux increased from 162 to 259 µmol metabolite g -1 FW hr − 1 , sucrose export flux increased from 5.3 to 10.3 µmol (hexose units) g -1 FW hr − 1 , while starch production increased from 10 to 15 µmol (hexose units) g -1 FW hr − 1 (Fig. 1). These results were consistent with a previous INST-MFA study for Arabidopsis thaliana acclimated in high light, showing increased fluxes of CBC, starch and sucrose synthesis, and sucrose export fluxes 44 . HLHC had a smaller but still significant effect on the fluxes attributed to TCA-associated reactions and fatty acid synthesis (Fig. 1). Phosphoenolpyruvate (PEP) carboxylation to oxaloacetate increased from 1.1 to 2.1 µmol CO 2 g − 1 FW h − 1 . Additionally, the rate of decarboxylation of pyruvate to acetyl-CoA for fatty acid synthesis increased from 0.8 to 1.7 µmol CO 2 g − 1 FW h − 1 . The α-ketoglutarate decarboxylation flux increased from 0.8 to 1.7 µmol CO 2 g − 1 FW h − 1 . The rate of oxidative decarboxylation of pyruvate for citrate synthesis increased from 0.4 to 0.7 µmol CO 2 g − 1 FW h − 1 (Fig. 1). Despite these heightened fluxes contributing to R L , they did not constitute the primary source, resulting in the total R L remaining unchanged in HLHC. All measured TCA cycle intermediates exhibited less than 5% labeling in 30 min (Fig. 2 a and Supplementary Fig. S3), consistent with previous findings in Arabidopsis, camelina, and tobacco 24,44,47,48 . This may be attributed to substantial vacuolar pools of organic acids with slow turnover rates, alongside low fluxes during photosynthesis through active (cytosolic and mitochondrial) pools of the same organic acids, which serve as TCA cycle intermediates. The metabolism of the TCA cycle and TCA-cycle-derived amino acids undergoes significant alterations in HLHC. Higher fluxes through the TCA cycle are observed in HLHC (Fig. 1), consistent with slightly elevated 13 C labeling for citrate and malate (Fig. 2 a, Supplementary Fig. S3). These results align with a previous metabolomics study on Arabidopsis thaliana , where TCA cycle intermediate levels increased under high light treatment 32,49 . Additionally, higher fluxes for aspartate and glutamate synthesis are noted in HLHC, correlating with faster and increased 13 C labeling for aspartate and glutamate. Alanine, resulting from the transamination of pyruvate, exhibits a higher rate of synthesis (Fig. 1) but slower fractional 13 C-labeling in HLHC (Fig. 2b3). This may be attributed to a larger inactive pool of alanine in HLHC, resulting in saturating 13 C-labeling kinetics. Sucrose glucosyl and fructosyl moieties exhibit faster and higher 13 C enrichment in HLHC(Fig. 2b2). In contrast, free glucose and fructose show slower and lower 13 C enrichment (Fig. 2b3), potentially due to the lower sucrose recycling flux in the cytosol in HLHC (Fig. 1). The flux for sucrose recycling in the cytosol decreased from 0.6 to 0.1 µmol CO 2 g − 1 FW h − 1 (Fig. 1). Consequently, the higher labeling from sucrose incorporates less into the glucose and fructose pool through sucrose recycling reactions. Another potential explanation is an increase in the pool sizes of glucose and fructose in HLHC, leading to an expansion of the inactive pool. Together, these findings imply that the plant maintains a fixed investment to buffer against transient loss of incoming light. This investment becomes a smaller proportion of overall carbon assimilation over time, naturally improving efficiency under HLHC. Photorespiration flux While HLHC affects the labeling rates of photorespiratory intermediates, the impact of daylength on 13 C incorporation into these intermediates demonstrated inconsistent trends: serine labeling was faster, glycine slower, and 2PG similar in HLHC compared to control (Fig. 2 ). These findings challenge the direct correlation between photorespiratory intermediate labeling kinetics and photorespiration rates, emphasizing the need for comprehensive compartmental information in 13 C MFA for accurate flux estimates. Current methodologies suggest using measured v o / v c values (0.30 ± 0.03 for control and 0.20 ± 0.01 for HLHC) from gas exchange as an input to the MFA model rather than deriving it as an output, ensuring reliability 25,48 . The decreased v o / v c aligns with the increase in CO 2 partial pressure from 27.8 to 39.2 Pa (Table 1 ). Polyexponential model fitting To statistically compare the labeling trajectories of the CBC and attribute these differences to different metabolic modules, we fitted control and HLHC labeling data to polyexponential models and compared their parameter estimates. Changes in the labeling rate of chloroplast CBC intermediates between control and HLHC are evident in Supplementary Table S2 . These changes suggest a decrease in labeling contribution from cytosolic hexose phosphates and/or turnover of vacuolar sugars under HLHC. Additionally, the observed increase in turnover rate aligns with MFA-estimated changes in the fast pool labeling constant, supporting the consistency of MFA modeling results. Furthermore, the rate of labeling contribution from cytosolic hexose phosphates and/or turnover of vacuolar sugars remains unchanged, consistent with the flux through the G6P/OPP shunt. Carbon partitioning dynamics The starch synthesis rate was higher in HLHC (Table 2 ), consistent with the faster 13 C labeling of the starch precursor ADPG, reaching 93% in control and 96% in HLHC (Fig. 2 a). Similarly, sucrose displayed a higher absolute synthesis rate in HLHC, aligning with the faster 13 C labeling of the sucrose precursor UDPG, measured at 79% in control and 88% in HLHC (Fig. 2 a). However, the increased starch-to-sucrose ratio (Table 2 ) in HLHC indicated a greater partitioning of carbon toward starch rather than sucrose. These findings are consistent with previous study 8 , which investigated how varying light intensity and CO 2 levels affect starch and sucrose synthesis in Phaseolus vulgaris (common bean) leaves. They found that both starch and sucrose synthesis displayed a linear relationship with CO 2 assimilation, responding to changes in both CO 2 levels or light intensity 8 . However, starch showed a steeper relationship compared to sucrose, suggesting that at HLHC, more carbon was allocated to starch than to sucrose. We also observed a decrease in the ionic fraction, dropping from 30% in the control condition to 20% in HLHC. This suggests that carbon either accumulated in intermediates of the carbon reduction or carbon oxidation cycles or that the carbon allocated towards amino acid production decreased under HLHC. This result is consistent with the findings of Sharkey et al. 8 , who observed that as photosynthetic rate increased, the ionic fraction decreased. With high intercellular CO 2 , stomata closed and led to a lower photorespiration rate potentially reducing the flow through the photorespiratory pool 8 . This may result in decreased carbon drainage into amino acids. Furthermore, a lower rate of photorespiration could lead to a reduced phosphate pool and an increase in the PGA pool, facilitating starch and sucrose formation while reducing carbon flow to amino acids 8 . R L The INST-MFA results revealed that the G6P/OPPP shunt significantly contributed to R L in both control and HLHC conditions, representing 86% and 79% of the total R L , respectively, in line with prior results 24,25 . Xu et al. 25 discussed that incomplete labeling of CBC can be accounted for by the reimport of unlabeled carbon via a cytosolic G6P/OPPP shunt. A stromal shunt would not account for the lack of complete labeling. The label in ADPG indicates that the stromal G6P pool labels to the same degree as CBC intermediates and so is not a source of unlabeled carbon while UDPG confirms that cytosolic G6P is significantly less labeled than CBC intermediates and so the cytosolic shunt is a source of unlabeled carbon. The cytosolic shunt is believed to be the only shunt in non-stressed leaves while both the cytosolic and stromal shunts may operate in stressed leaves 31 . Surprisingly, our study found no significant differences in both R L and fluxes through the G6P/OPPP shunt between control and HLHC (Fig. 1 and Supplementary Table S1 ). This finding is consistent with previous research 23 , which employed the DAT technique to estimate R L in paper birch and hybrid poplar and concluded that R L remains consistent under elevated CO 2 conditions. However, the flux of unlabeled carbon in free glucose through hexokinase into the cytosolic G6P pool was less in HLHC, which would reduce the flow of unlabeled carbon into the CBC. This may account for the greater degree of label in CBC intermediates in HLHC. Simplified model A simplified model based on that found in Sharkey et al. (2020) 31 was constructed and parameterized with data from the INST-MFA for the control and HLHC conditions (Supplemental Table S3). This made it easier to see that the lower level of labeling in the control was caused by several factors. Internal consistency could be checked using calculated versus measured values for the degree of label in UDP glucose. In the control condition, the measured value was 0.79 while the modeled value was 0.83. In HLHC the measured value was 0.88 while the modeled value was 0.87. The flux of carbon from free glucose, fructose, and (following invertase activity) sucrose, was 53% of net CO 2 fixation rate ( A ) for the control but only 40% for the HLHC. This explains part of the increased degree of labeling in the HLHC condition (93%) compared to the control condition (89%). The model also allowed calculation that 12 C was leaving the system as starch (etc.) and sucrose at a rate of 14% of A in the control condition but just 9% in the HLHC condition. Because 12 C was leaving the system at a slow rate it did not require very large inputs of 12 C to keep the degree of label from getting to 100%. Conclusions In the future, photosynthesis may be speeded up because of increasing CO 2 . This will affect flux through many ancillary pathways associated with photosynthesis, for example photorespiration. This work focused on CO 2 releasing reactions other than photorespiration, respiration in the light ( R L ). We found that the overall rate of R L was not changed in HLHC but that the source of G6P for the G6P/OPPP shunt, confirmed here to be responsible for most of R L , did change. Less free glucose was incorporated into cytosolic glucose in HLHC. In HLHC, the amount of G6P in the cytosol derived from triose phosphate export, and thus labeled to the same degree as CBC intermediates, was increased. This shift away from free glucose reduced the input of unlabeled carbon into the CBC causing the degree of label in the CBC after 30 min of 13 CO 2 feeding to be higher in HLHC. Methods Plant Growth and Labeling Conditions Camelina sativa plants (ecotype var. Calena ) were cultivated under a 16-hour light/8-hour dark cycle. The light intensity was maintained at 500 µmol m − 2 s − 1 , with temperatures set at 22°C and relative humidity at 50%. The plant substrate consisted of a mixture of 70% peat moss, 21% perlite, and 9% vermiculite (Suremix; Michigan Grower Products Inc., Galesburg, MI, USA). Additionally, plants were fertilized with 1/4 strength Hoagland's solution 50 . Statement of Seed Source and Plant Guidelines Camelina sativa wild-type seeds (ecotype var. Calena ) were obtained from the Heike Sederoff group at North Carolina State University. The seeds used here were not collected in the wild and are not of an endangered species. The authors hereby declare that the plant collection and use, as well as all methods, were carried out in accordance with all relevant guidelines. Gas Exchange and 13 CO 2 Labeling Experiments Gas exchange and 13 CO 2 labeling experiments followed established procedures 24 with slight modifications. Fully expanded leaves from 4-week-old plants were used for 13 CO 2 labeling. Measurements, including CO 2 assimilation rate, stomatal conductance, and other photosynthetic parameters, were conducted using a LI-COR 6800 portable photosynthesis system (LI-COR Biosciences, Lincoln, NE, USA). Plants were set under control conditions with a reference [CO 2 ] of 39 Pa, light intensity of 500 µmol m − 2 s − 1 , temperature of 22°C, and a water vapor pressure difference (VPD) of 1.0 kPa. High light high CO 2 conditions had a reference [CO 2 ] of 59 Pa, light intensity of 1500 µmol m − 2 s − 1 , temperature of 22°C, and a water vapor pressure difference (VPD) of 1.0 kPa. The 13 CO 2 labeling commenced after 20–30 minutes in both control and high light high CO 2 conditions to ensure a stable photosynthetic state. A pseudo-steady-state metabolism was assumed during the labeling period as the CO 2 source transitioned to 13 CO 2 while maintaining other parameters constant. Gas mixing utilized mass flow controllers (Alicat Scientific, Tucson AZ, USA) controlled by a custom-programmed Raspberry Pi touchscreen monitor (Raspberry Pi foundation). Labeled leaf samples were collected at time points of 0, 0.5, 1, 2, 3, 5, 7, 10, 15, and 30 minutes. Leaf freezing was achieved by spraying liquid nitrogen directly on the leaf surface. Sampling for different time points occurred randomly between 9:00 am and 4:00 pm, with one leaf sampled as a single biological replicate. Three biological replicates were collected for each time point, and all frozen leaf samples were stored at -80°C. A/Cc Curve Measurements and Parameter Calculation A/Cc curves were obtained using a LI-COR 6800 portable photosynthesis system (LI-COR Biosciences, Lincoln, NE, USA) under a light intensity of 500 µmol photon m − 2 s − 1 , leaf temperature of 22°C, and VPD of 1.0 kPa. The sequence of reference CO 2 partial pressures ranged from 5 to 150 Pa (Fig. 3 ). Photosynthetic parameters, including the maximum rubisco carboxylation rate ( V cmax ), the maximum attained rate of electron transport ( J ), respiration in the light ( R L ), mesophyll conductance to CO 2 transfer ( g m ), the rate of triose phosphate utilization ( TPU ), and the proportion of carbon exported from photorespiration as glycine ( α g ) or serine ( α s ), were estimated from the A/Cc curves using the R-script. The script details were described previously 51,52 and can be accessed at: https://github.com/poales/msuRACiFit . Polyexponential model fitting Polyexponential model fitting was performed as in Xu et al., 2022 25 , with technical details provided in Supplementary Methods. In Xu et al., 2022 25 , isotopic labeling data from an extended isotopic labeling time-course was used to demonstrate that the labeling of the CBC is best fit by a triexponential, or three process model. The three processes corresponded to ( 1 ) turnover of the CBC intermediates themselves, ( 2 ) influx of unlabeled carbon from cytosolic sugars via the G6P shunt, and ( 3 ) influx of unlabeled carbon from slow reintroduction of vacuolar sugars into the cytosol and then into the chloroplast via the glucose-6-phosphate shunt. Due to the shorter time-course of this study, we cannot accurately disambiguate processes ( 2 ) and ( 3 ); therefore, we have chosen to fit our data using a biexponential model, where the slow exponential term represents the cumulative effects of processes ( 2 ) and ( 3 ). This can be thought of as an approximation of a “true” triexponential model fit whose parameters cannot be accurately estimated due to data availability in the present study. Labeling data was fitted to a biexponential (E1) model using the curve_fit() function in SciPy. Briefly, when fitting such models to the labeling of metabolic intermediates, we interpret the coefficients of each exponential term as representing the proportion of the labeling signal contributed by the process described by that term (e.g. the turnover of cytosolic sugars), the rates the exponentials are raised to as the rate at which these respective pools are being labeled, and constants as inactive pools not labeled over the span of a time course study (e.g. vacuolar amino acids). Each optimization is initialized from 1,000 starting points selected by Latin Hypercube Sampling 53 to ensure a global best-fit is found. $$\begin{array}{c}{\varvec{\%}}^{12}C{\varvec{O}}_{2}\left(\varvec{t}\right)=A{\varvec{e}}^{-\varvec{b}\varvec{*}\varvec{t}}+C{\varvec{e}}^{-\varvec{d}\varvec{*}\varvec{t}}\#\left(\varvec{E}1\right)\end{array}$$ Metabolite Extraction and Mass Spectrometry Analyses Metabolites were extracted from rapidly frozen tissues following the protocol outlined previously 24 . Mass spectrometry analyses were conducted as previously detailed in publications 24,25,33 with minor modifications. The mass spectrometry (MS) parameters outlining transitions of measured metabolites during multiple reaction monitoring (MRM) with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and selected ion monitoring (SIM) with gas chromatography-mass spectrometry (GC-MS) can be found in Supplementary Table S4. Ion-Pair Chromatography – Tandem Mass Spectrometry (IPC-MS/MS) Analysis For the analysis of phosphorylated intermediates in the Calvin-Benson Cycle (CBC), ion-pair chromatography – tandem mass spectrometry (IPC-MS/MS) was performed using an ACQUITY UPLC pump system (Waters, Milford, MA, USA) coupled with a Waters XEVO TQ-S UPLC/MS/MS (Waters, Milford, MA, USA). Metabolites were separated on a 2.1×50 mm ACQUITY UPLC BEH C18 Column (Waters, Milford, MA, USA) at 40°C. The chromatographic separation utilized a multi-step gradient with mobile phase A (10 mM tributylamine in 5%(v/v) methanol) and mobile phase B (methanol): 0–1 min, 95 − 85% A; 1–6 min, 65 − 40% A; 6–7 min, 40 − 0% A; 7–8 min, 0% A; 8–9 min, 100% A, at a flow rate of 0.3 mL min − 1 . The source temperature was maintained at 120°C, and the desolvation temperature was set to 350°C. Nitrogen served as the sheath and auxiliary gas, with collision gas (argon) set to 1.1 mTorr. Gas flow for desolvation and cone was adjusted to 800 and 50 L/h, respectively. The scan time was 0.1 ms. Anion Exchange Chromatography – Tandem Mass Spectrometry (AEC-MS/MS) Analysis For the analysis of nucleotide sugars and additional phosphorylated intermediates (e.g., 2-phosphoglycolate (2PG), phosphoenolpyruvate), anion exchange chromatography – tandem mass spectrometry (AEC-MS/MS) was conducted using an ACQUITY UPLC pump system (Waters, Milford, MA, USA) coupled with a Xevo ACQUITY TQ Triple Quadrupole Detector (Waters, Milford, MA, USA). Metabolites were separated by an IonPac AS11 analytical column (2 × 250 mm, Dionex) equipped with an IonPac guard column AG11 (2 × 50 mm, Dionex) at a flow rate of 0.35 mL min − 1 . A multi-step gradient was employed with mobile phase A (0.5 mM KOH) and mobile phase B (75 mM KOH): 0 − 2 min, 100% A; 2–4 min, 100 − 93% A; 4–13 min, 93 − 60% A; 13–15 min, 0% A; 15–17 min, 100% A. To suppress the KOH concentration, a post-column anion self-regenerating suppressor (Dionex ADRS 600, Thermo Scientific, Waltham, MA, USA) was utilized, with a current of 50 mA and a flow rate of 3.5 mL min − 1 . Additionally, an IonPac ATC-3 Anion Trap Column (4 × 35 mm), conditioned with 2M KOH, was employed to eliminate contaminant ions from KOH solvents. Gas Chromatography-Mass Spectrometry (GC-MS) Analysis For the comprehensive analysis of amino acids, organic acids, and sugars, a gas chromatography-mass spectrometry (GC-MS) approach was employed using an Agilent 7890 GC system (Agilent, Santa Clara, CA, USA) coupled with an Agilent 5975C inert XL Mass Selective Detector (Agilent, Santa Clara, CA, USA). Prior to analysis, samples underwent a derivatization process involving methoxyamine hydrochloride dissolved in dry pyridine at room temperature overnight. Amino acids and organic acids were silylated to trimethylsilyl (TBDMS) derivatives, achieved by adding N-(tertbutyldimethylsilyl)-N-methyltrifluoroacetamide with 1% (w/v) tert-butyl-dimethylchlorosilane, and incubated at 60°C overnight. Sugars were silylated to trimethylsilyl (TMS) derivatives, accomplished by adding N, O-Bis (trimethylsilyl) trifluoroacetamide with 1% (w/v) trimethylchlorosilane, and incubated at 60°C overnight. Metabolite separation was performed on an Agilent VF5ms GC column (Agilent, Santa Clara, CA, USA). The inlet temperature and MS transfer line temperature were set at 230°C and 300°C, respectively. The oven temperature profile included an initial hold at 40°C for 1 minute, followed by a ramp at 40°C/min to 80°C, 10°C/min to 240°C, and 20°C/min until reaching 320°C, maintained for 5 minutes. Electron ionization (EI) was set at 70 eV, and the mass scan range covered 50–600 amu. Analysis of Mass Spectrometry Data The quantification of mass isotopologue distributions (MIDs) and determination of pool sizes were conducted following the protocol outlined previously 33 with minor adjustments. In the control condition, most of the MID data, excluding sugars, were obtained from a previous study 25 , while the data for the HLHC condition is newly generated in this study. LC-MS/MS data were obtained using MassLynx 4.0 (Agilent, Santa Clara, CA, USA), while GC-MS data were acquired with Agilent GC/MSD Chemstation (Agilent, Santa Clara, CA, USA). Metabolite identification relied on the comparison of retention time and mass-to-charge ratio (m/z) with authentic standards. To process both LC-MS and GC-MS data, conversion to MassLynx format and subsequent analysis using QuanLynx software were performed for peak detection and quantification. The calculation of MIDs for each metabolite, reflecting the incorporation of n 13 C or 2H atoms, was accomplished using the formula: $$\varvec{M}\varvec{I}\varvec{D}\varvec{n}=\frac{\varvec{M}\varvec{i}}{{\sum }_{\varvec{i}=0}^{\varvec{n}}\varvec{M}\varvec{i}}$$ Mi represents the isotopologue abundance for each metabolite, i ranges from 0 (no 13 C atoms) to n (all carbons labeled with 13 C), and n is the total number of carbon atoms in the compound. Experimental MIDs were adjusted for natural abundance using IsoCor 54 and FluxFix 55 software. The 13 C enrichment (E) is determined using the equation: $$\varvec{E}= \frac{{\sum }_{\varvec{i}=0}^{\varvec{n}}\varvec{M}\varvec{I}\varvec{D}\varvec{i}\times \varvec{i}}{\varvec{n}}$$ Isotopologue Network, Flux Determination, and Assessment of Flux Precision INST-MFA was conducted to estimate metabolic fluxes using the Isotopomer Network Compartmental Analysis software package (INCA2.2, http://mfa.vueinnovations.com , Vanderbilt University 56 ), employing the metabolic network model established in prior work 25,33 . A comprehensive list of reactions and corresponding abbreviations is provided in Supplementary Table S5, while the stoichiometry of reactions and atom transitions for each reaction is detailed in Supplementary Table S1 . The model's goodness of fit was evaluated through the sum-of-squared residuals (SSR), quantifying the overall difference between measured and simulated kinetics. The parameter continuation method 44 was employed to estimate 95% confidence intervals for both absolute and normalized fluxes of the best-fit models. The computational intensity of confidence interval determination was managed in parallel through a SLURM job scheduler, distributing tasks to numerous computer nodes within a high-performance computing cluster at the Institute for Cyber-Enabled Research at Michigan State University ( https://icer.msu.edu/ ). Starch Synthesis Rate and Fractions of Starch and Sucrose The partitioning of recently fixed carbon into starch and sucrose was determined through 14 C labeling experiments during steady-state photosynthetic assimilation, with slight modifications 8 . A mixture of 14 CO 2 gas and CO 2 -free air, controlled by mass flow controllers (Alicat Scientific, Tucson AZ, USA), was directed through the sample port on the back of a LI-6800 (LI-COR Biosciences, Lincoln, NE, USA). Conditions included a reference [CO 2 ] of 39 Pa, light intensity of 500 µmol m − 2 s − 1 , temperature of 22°C, and humidity for vapor pressure deficit (VPD) of 1.0 kPa. Each leaf underwent a 10-min pulse of 14 CO 2 at a concentration of 400 µL L − 1 . The 14 C-labeled leaf sample was promptly frozen in liquid nitrogen, and its fresh weight was measured. Frozen samples were stored at -80°C before extraction. Sampling occurred randomly between 9:00 am and 4:00 pm, with one leaf sampled at each time point, and six biological replicates collected for each time point. Each leaf sample was extracted with 0.5 mL formic acid solution (formic acid/ethanol 4:75, v/v). After centrifugation at 12,000 x g at 4°C for 10 min, half of the supernatant underwent radioactivity counting (total soluble fraction) using a 1450 Microbeta Trilux scintillation counter (PerkinElmer, Waltham, MA, USA). The other half passed through a cation-exchange resin (Dowex 50WX8 H + form) column (Sigma-Aldrich, St. Louis, MO, USA), followed by an anion exchange resin (Dowex 1X8 Cl- form) column (Sigma-Aldrich, St. Louis, MO, USA). The ionic fraction was calculated as the difference between the total soluble fraction and the neutral soluble fraction. The pellet, after washing and resuspension, underwent gelatinization and subsequent enzymatic digestion for starch measurement. The proportion of counts in the starch and neutral soluble fractions relative to total counts was calculated (Supplementary Table S6). Simplified model A simplified model, adapted from Sharkey et al. (2020) 31 , was parameterized using data from INST-MFA for both control and HLHC conditions (Supplementary Table S3). The model includes velocities expressed in terms of carbon atoms, adjusted as necessary to accommodate the number of carbon atoms per molecule and relative to net assimilation. Ratios representing the ratio of carbon-13 to carbon-12 in the molecule. The model provides a snapshot after 30 minutes of labeling to ensure saturation of short-term reactions. Detailed equations and values are provided in Supplementary Table S3. Declarations Acknowledgement The research received financial support from the Division of Chemical Sciences, Geosciences, and Biosciences, Office of Basic Energy Sciences at the U.S. Department of Energy, under Grants DE-FOA-0001650 and DE-FG02-91ER20021. TDS acknowledges partial salary support from MSU AgBioResearch. The authors are grateful to Dr. Berkley Walker (MSU) for insightful discussions, Ms. Emily Pawlowski and Mr. Cody Keilen (MSU Growth Chamber Facility) for their assistance with growth chamber operations and plant maintenance, and Dr. Daniel Jones, Dr. Tony Schilmiller, Dr. Lijun Chen, and Dr. Casey Johnny (MSU Mass Spectrometry and Metabolomics Core Facility) for their support in mass spectrometry methods. The team acknowledges the MSU Institute for Cyber-Enabled Research for providing access to high-performance computing clusters and services. Special thanks to Dr. Jamey Young for facilitating accessibility to INCA. Author Contributions: TDS, YSH, and YX conceived and designed the study. TDS analyzed A/Ci curves. YX performed the 13 C labeling experiments, mass spectrometry analyses, and INST-MFA. JAMK conducted the exponential decay analysis. SEW provided guidance and assistance to YX in the 14 CO 2 pulse-chase experiment. YX wrote the manuscript with contributions from all the authors. TDS serves as the corresponding author responsible for contact and ensuring communication. Data availability The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Competing interests The authors declare no competing interests. References IPCC. IPCC 2021 . Climate Change 2021: The Physical Science Basis. (2021). Roy, S. & Mathur, P. Delineating the mechanisms of elevated CO2 mediated growth, stress tolerance and phytohormonal regulation in plants. Plant Cell Rep. 40 , 1345–1365 (2021). Peter, R., Kuttippurath, J., Chakraborty, K. & Sunanda, N. Temporal evolution of mid-tropospheric CO 2 over the Indian Ocean. Atmos. Environ. 257 , 118475 (2021). IPCC. IPCC 2018. Glob. Warm. 1.5°C. An IPCC Spec. Rep. [...] (2018). Wuebbles, D. J. et al. Climate science special report: a sustained assessment activity of the US global change research program. US Glob. Chang. Res. Progr. Washington, DC, USA 669 , (2017). He, Y., Wang, K., Zhou, C. & Wild, M. A revisit of global dimming and brightening based on the sunshine duration. Geophys. Res. Lett. 45 , 4281–4289 (2018). Wild, M., Folini, D., Henschel, F., Fischer, N. & Müller, B. Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems. Sol. Energy 116 , 12–24 (2015). Sharkey, T. D., Berry, J. A. & Raschke, K. Starch and sucrose synthesis in phaseolus vulgaris as affected by light, CO 2 , and abscisic acid. Plant Physiol. 77 , 617–620 (1985). Tcherkez, G. et al. Leaf day respiration: low CO 2 flux but high significance for metabolism and carbon balance. New Phytologist at https://doi.org/10.1111/nph.14816 (2017). Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO 2 assimilation in leaves of C 3 species. Planta (1980) doi:10.1007/BF00386231. Pritchard, H. von Caemmerer S. 2000. Biochemical models of leaf photosynthesis. 165pp. Victoria, Australia: CSIRO Publishing. Ann. Bot. (2001) doi:10.1006/anbo.2000.1296. Wohlfahrt, G., Bahn, M., Haslwanter, A., Newesely, C. & Cernusca, A. Estimation of daytime ecosystem respiration to determine gross primary production of a mountain meadow. Agric. For. Meteorol. 130 , 13–25 (2005). Rogers, A. et al. A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol. 213 , 22–42 (2017). Tcherkez, G., Boex-Fontvieille, E., Mahé, A. & Hodges, M. Respiratory carbon fluxes in leaves. Curr. Opin. Plant Biol. 15 , 308–314 (2012). Laisk, A. K. Kinetics of photosynthesis and photorespiration of C 3 in plants. (1977). Atkin, O. K., Millar, A. H., Gardeström, P. & Day, D. A. Photosynthesis, Carbohydrate Metabolism and Respiration in Leaves of Higher Plants. in (2000). doi:10.1007/0-306-48137-5_7. Laisk, A. Prying into the green black-box. Photosynth. Res. 154 , 89–112 (2022). Kok, B. A critical consideration of the quantum yield of Chlorella photosynthesis. Enzymologia 13 , 1 (1948). Yin, X. et al. Using combined measurements of gas exchange and chlorophyll fluorescence to estimate parameters of a biochemical C 3 photosynthesis model: a critical appraisal and a new integrated approach applied to leaves in a wheat ( Triticum aestivum ) canopy. Plant. Cell Environ. 32 , 448–464 (2009). Yin, X., Sun, Z., Struik, P. C. & Gu, J. Evaluating a new method to estimate the rate of leaf respiration in the light by analysis of combined gas exchange and chlorophyll fluorescence measurements. J. Exp. Bot. 62 , 3489–3499 (2011). Stinziano, J. R. et al. The rapid A–Ci response: photosynthesis in the phenomic era . Plant. Cell Environ . 40 , 1256–1262 (2017). Taylor, S. H. & Long, S. P. Phenotyping photosynthesis on the limit–a critical examination of RACiR. New Phytol. 221 , 621–624 (2019). Schmiege, S. C., Sharkey, T. D., Walker, B., Hammer, J. & Way, D. A. Laisk measurements in the nonsteady state: Tests in plants exposed to warming and variable CO 2 concentrations. Plant Physiol. 193 , 1045–1057 (2023). Xu, Y., Fu, X., Sharkey, T. D., Shachar-Hill, Y. & Walker, B. J. The metabolic origins of non-photorespiratory CO 2 release during photosynthesis: A metabolic flux analysis. Plant Physiol. 186 , 297–314 (2021). Xu, Y., Wieloch, T., Kaste, J. A. M., Shachar-Hill, Y. & Sharkey, T. D. Reimport of carbon from cytosolic and vacuolar sugar pools into the Calvin–Benson cycle explains photosynthesis labeling anomalies. Proc. Natl. Acad. Sci. 119 , e2121531119 (2022). Tcherkez, G. et al. Short-term effects of CO 2 and O 2 on citrate metabolism in illuminated leaves. Plant, Cell Environ. (2012) doi:10.1111/j.1365-3040.2012.02550.x. Griffin, K. L. & Heskel, M. Breaking the cycle: how light, CO 2 and O 2 affect plant respiration. Plant Cell Env. 36 , 498–500 (2013). Tcherkez, G. & Atkin, O. K. Unravelling mechanisms and impacts of day respiration in plant leaves: an introduction to a virtual issue. New Phytologist vol. 230 5–10 at (2021). Williams, M. & Randall, D. D. Pyruvate dehydrogenase complex from chloroplasts of Pisum sativum L . Plant Physiol. (1979) doi:10.1104/pp.64.6.1099. Sharkey, T. D. & Weise, S. E. The glucose 6-phosphate shunt around the Calvin-Benson cycle. J. Exp. Bot. (2016) doi:10.1093/jxb/erv484. Sharkey, T. D., Preiser, A. L., Weraduwage, S. M. & Gog, L. Source of 12C in Calvin Benson cycle intermediates and isoprene emitted from plant leaves fed with 13 CO 2 . Biochem. J. (2020) doi:10.1042/bcj20200480. Xu, Y. & Fu, X. Reprogramming of plant central metabolism in response to abiotic stresses: a metabolomics view. Int. J. Mol. Sci. 23 , 5716 (2022). Xu, Y. et al. Daylength variation affects growth, photosynthesis, leaf metabolism, partitioning, and metabolic fluxes. Plant Physiol. 194 , 475–490 (2024). Sharkey, T. D., Seemann, J. R. & Berry, J. A. Regulation of ribulose-1, 5-bisphosphate carboxylase activity in response to changing partial pressure of O2 and light in Phaseolus vulgaris. Plant Physiol. 81 , 788–791 (1986). McClain, A. M., Cruz, J. A., Kramer, D. M. & Sharkey, T. D. The time course of acclimation to the stress of triose phosphate use limitation. Plant. Cell Environ. 46 , 64–75 (2023). Coast, O., Scafaro, A. P., Bramley, H., Taylor, N. L. & Atkin, O. K. Photosynthesis in newly developed leaves of heat-tolerant wheat acclimates to long-term nocturnal warming. J. Exp. Bot. 75 , 962–978 (2024). Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free‐air CO 2 enrichment (FACE): A meta‐analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO 2 . New Phytol. 165 , 351–372 (2005). Leakey, A. D. B. et al. Elevated CO 2 effects on plant carbon, nitrogen, and water relations: six important lessons from FACE. J. Exp. Bot. 60 , 2859–2876 (2009). Long, S. P., Ainsworth, E. A., Leakey, A. D. B., Nosberger, J. & Ort, D. R. Food for thought: lower-than-expected crop yield stimulation with rising CO 2 concentrations. Science (80-. ). 312 , 1918–1921 (2006). Kimball, B. A. Crop responses to elevated CO 2 and interactions with H2O, N, and temperature. Curr. Opin. Plant Biol. 31 , 36–43 (2016). Ratcliffe, R. G. & Shachar-Hill, Y. Measuring multiple fluxes through plant metabolic networks. Plant J. (2006) doi:10.1111/j.1365-313X.2005.02649.x. Stephanopoulos, G. N., Aristidou, A. a & Nielsen, J. Metabolic Engineering: Principles and Methodologies . Metabolic Engineering (1998). Li, T. et al. Re-Programing Glucose Catabolism in the Microalga Chlorella sorokiniana under Light Condition. Biomolecules 12 , 939 (2022). Ma, F., Jazmin, L. J., Young, J. D. & Allen, D. K. Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation. Proc. Natl. Acad. Sci. 111 , 16967–16972 (2014). Arrivault, S. et al. Use of reverse-phase liquid chromatography, linked to tandem mass spectrometry, to profile the Calvin cycle and other metabolic intermediates in Arabidopsis rosettes at different carbon dioxide concentrations. Plant J. 59 , 826–839 (2009). Hasunuma, T. et al. Metabolic turnover analysis by a combination of in vivo 13C-labelling from 13 CO 2 and metabolic profiling with CE-MS/MS reveals rate-limiting steps of the C 3 photosynthetic pathway in Nicotiana tabacum leaves. J. Exp. Bot. 61 , 1041–1051 (2010). Szecowka, M. et al. Metabolic Fluxes in an Illuminated Arabidopsis Rosette. Plant Cell 25 , 694–714 (2013). Fu, X., Gregory, L. M., Weise, S. E. & Walker, B. J. Integrated flux and pool size analysis in plant central metabolism reveals unique roles of glycine and serine during photorespiration. Nat. Plants 9 , 169–178 (2023). Xu, Y., Freund, D. M., Hegeman, A. D. & Cohen, J. D. Metabolic signatures of Arabidopsis thaliana abiotic stress responses elucidate patterns in stress priming, acclimation, and recovery. Stress Biol. (2022) doi:10.1007/s44154-022-00034-5. Hoagland, D. R. & Arnon, D. I. The water-culture method for growing plants without soil. Circ. Calif. Agric. Exp. Stn. 347 , (1950). Sharkey, T. D. What gas exchange data can tell us about photosynthesis. Plant Cell and Environment vol. 39 1161–1163 at https://doi.org/10.1111/pce.12641 (2016). Gregory, L. M. et al. The triose phosphate utilization limitation of photosynthetic rate: Out of global models but important for leaf models. Plant Cell and Environment vol. 44 3223–3226 at https://doi.org/10.1111/pce.14153 (2021). Virtanen, P. et al. Fundamental algorithms for scientific computing in python and SciPy 1.0 contributors. SciPy 1.0. Nat. Methods 17 , 261–272 (2020). Millard, P. et al. IsoCor: Isotope correction for high-resolution MS labeling experiments. Bioinformatics 35 , 4484–4487 (2019). Trefely, S., Ashwell, P. & Snyder, N. W. FluxFix: Automatic isotopologue normalization for metabolic tracer analysis. BMC Bioinformatics 17 , 1–8 (2016). Young, J. D. INCA: a computational platform for isotopically non-stationary metabolic flux analysis. Bioinformatics 30 , 1333-1335 (2014). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.pdf SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Jun, 2024 Reviews received at journal 06 Jun, 2024 Reviews received at journal 25 May, 2024 Reviewers agreed at journal 14 May, 2024 Reviewers agreed at journal 22 Apr, 2024 Reviewers invited by journal 12 Apr, 2024 Editor assigned by journal 18 Mar, 2024 Editor invited by journal 15 Mar, 2024 Submission checks completed at journal 15 Mar, 2024 First submitted to journal 27 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3995199","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":280784825,"identity":"1dc0692b-6425-4538-a70e-ef00bcb1f240","order_by":0,"name":"Yuan Xu","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Xu","suffix":""},{"id":280784826,"identity":"9cb38463-3b9a-4bad-b8cb-50268d697852","order_by":1,"name":"Joshua Kaste","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Kaste","suffix":""},{"id":280784827,"identity":"c79cfb3c-05cb-48fe-88c7-66d926b416a8","order_by":2,"name":"Sean Weise","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Sean","middleName":"","lastName":"Weise","suffix":""},{"id":280784828,"identity":"b03e93ed-467f-4991-b0ef-9d9e6faac50f","order_by":3,"name":"Yair Shachar-Hill","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Yair","middleName":"","lastName":"Shachar-Hill","suffix":""},{"id":280784829,"identity":"84a8baf0-25a3-4c8b-8212-82a0e93dd4ff","order_by":4,"name":"Thomas Sharkey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYFACHiA+wMDAD+ExE6MBqkWygWQtBgeI1WLPf/bg54ozNombrx1/JsFQYZ3YQNAWibxkyTM30hK33c4xk2A4k06MFh4DyYYPh0Fa2CQY2w4ToYX/jPHPhg//EzfPTn8mwfiPGC0MOWaSDTcOJG6QTjCTYGwgRsuNHDPLhjPJxjNu5xhbJBxLNyaohb3/jPHNhmN2sv2z0x/e+FBjLUtQCww4glUmEKscBOxJUTwKRsEoGAUjDAAAA9tBGrXvracAAAAASUVORK5CYII=","orcid":"","institution":"Michigan State University","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Sharkey","suffix":""}],"badges":[],"createdAt":"2024-02-28 00:59:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3995199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3995199/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-88574-4","type":"published","date":"2025-03-11T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52991098,"identity":"6b5e0a0d-a9ac-477e-8284-d99cb8a213f7","added_by":"auto","created_at":"2024-03-19 12:14:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":356422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentral carbon metabolic fluxes in photosynthetic \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCamelina sativa\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e leaves under control (a) and high light high CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e conditions (b).\u003c/strong\u003e Fluxes, indicated by variable arrow width, are presented as numbers. Comparison between control and high light high CO\u003csub\u003e2\u003c/sub\u003e fluxes is based on 95% confidence intervals by parameter continuation analysis, with significant differences highlighted by orange arrows. Flux units: μmol.metabolite.g\u003csup\u003e-1\u003c/sup\u003e.FW.hr\u003csup\u003e−1\u003c/sup\u003e. The model network is compartmentalized into cytosol (“.c”), chloroplast (“.p”), mitochondrion (“.m”), and vacuole (“.v”). See supplementary Table S5 for abbreviations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3995199/v1/b381d4873733f763ed67744b.png"},{"id":52991100,"identity":"a5851227-3dce-48ad-974f-d5e3dfa6d0a8","added_by":"auto","created_at":"2024-03-19 12:14:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) Heatmap of\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e 13\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eC enrichment of measured metabolites in plants grown in control and high light high CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e conditions in a 0-30 min labeling experiment. \u003c/strong\u003eThe color bar from blue to red represents the value of \u003csup\u003e13\u003c/sup\u003eC enrichment from low to high. \u003cstrong\u003e(b)\u003c/strong\u003e \u003csup\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eC enrichment of key metabolites in plants grown in control and high light high CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e conditions in a 0-30 min labeling experiment\u003c/strong\u003e (n=3, ± standard deviation). See supplementary Table S5 for abbreviations.\u003cstrong\u003e \u003c/strong\u003e(b1) Metabolites that demonstrate faster labeling in HLHC at early time points, plateauing to a level similar to the control condition by 30 minutes. (b2) Metabolites that exhibit faster and higher \u003csup\u003e13\u003c/sup\u003eC label incorporation under HLHC conditions. (b3) Metabolites that exhibit slower and lower \u003csup\u003e13\u003c/sup\u003eC label incorporation under HLHC conditions. The x-axis represents the time of labeling (in minutes), while the y-axis represents the fraction of \u003csup\u003e13\u003c/sup\u003eC enrichment. Nominal masses of unlabeled metabolites are shown in parentheses.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3995199/v1/073e44fae569feed1af5b923.png"},{"id":52991099,"identity":"f0e93229-50c6-4cf2-84cc-fb010216c8f6","added_by":"auto","created_at":"2024-03-19 12:14:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAveraged \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cstrong\u003e data fitting points for four leaves and rate determining capacities determined by curve fitting. \u003c/strong\u003eData from four leaves were fit with the fitting routine available in Sharkey et al 2016 \u003csup\u003e51\u003c/sup\u003e or from the authors. This provided data for seven fitting parameters: Rubisco potential activity,\u0026nbsp; \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ecmax\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, \u003c/em\u003eRuBP regeneration\u003cem\u003e, \u003c/em\u003eoften taken to be determined by electron transport activity\u003cem\u003e, J\u003c/em\u003e;\u003cem\u003e \u003c/em\u003etriose phosphate use limitation, reflecting how fast fixed CO\u003csub\u003e2\u003c/sub\u003e can be turned into end products, \u003cem\u003eTPU\u003c/em\u003e;\u003cem\u003e \u003c/em\u003eRespiration in the light, any CO\u003csub\u003e2\u003c/sub\u003e released during photosynthesis other than through photorespiration, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e; mesophyll conductance to CO\u003csub\u003e2\u003c/sub\u003e diffusion\u003cem\u003e, g\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e; glycolate carbon that leaves photorespiration as glycine\u003cem\u003e a\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e;\u003cem\u003e \u003c/em\u003eglycolate carbon that leaves photorespiration as serine\u003cem\u003e, a\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e.\u003c/em\u003e The averages of these fitted parameters were used to generate an averaged \u003cem\u003eA/C\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e curve (black circles). The solid black arrow indicates the operating \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e under normal light and CO\u003csub\u003e2\u003c/sub\u003e conditions while the dashed black arrow indicates the \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e at the high light and CO\u003csub\u003e2\u003c/sub\u003e operational point. At normal light and CO\u003csub\u003e2,\u003c/sub\u003e the plants were operating very close to the crossover point where both \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ecmax\u003c/em\u003e\u003c/sub\u003e\u003csub\u003e \u003c/sub\u003eand \u003cem\u003eJ\u003c/em\u003e determine the rate. The middle \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ecmax\u003c/em\u003e\u003c/sub\u003e line, deactivated 1, assumed the rubisco activation state was 86% (chosen to allow the \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ecmax\u003c/em\u003e\u003c/sub\u003e line, to meet the operational point). The operating point at high light and high CO\u003csub\u003e2 \u003c/sub\u003e(dashed arrow)\u003csub\u003e \u003c/sub\u003eis very close to the junction of \u003cem\u003eJ\u003c/em\u003e and \u003cem\u003eTPU\u003c/em\u003e. Rubisco activation state had to be reduced to 73% to match the photosynthetic rate at the operating point in high light and CO\u003csub\u003e2\u003c/sub\u003e. This is shown in the right-most \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ecmax\u003c/em\u003e\u003c/sub\u003e line labeled Deactivated 2.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3995199/v1/1266402d9ae691472266d8ea.png"},{"id":78689000,"identity":"882878b9-a5e8-455b-9ea3-42f163c6da1a","added_by":"auto","created_at":"2025-03-17 16:09:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2172763,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3995199/v1/2be6b8b4-a63d-4c80-919d-c9c352111ae1.pdf"},{"id":52991102,"identity":"6a80e450-0709-4d51-8537-f0087107a494","added_by":"auto","created_at":"2024-03-19 12:14:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":644045,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3995199/v1/b41ef6d6db387b2d4842e9f5.pdf"},{"id":52991101,"identity":"511c297b-56f4-42ce-8205-c68ded501f2c","added_by":"auto","created_at":"2024-03-19 12:14:28","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":217303,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3995199/v1/00c2ceff08755dddbba44144.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effects of photosynthetic rate on respiration in light, starch/sucrose partitioning, and other metabolic fluxes within photosynthesis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe atmospheric concentration of CO\u003csub\u003e2\u003c/sub\u003e is rapidly increasing due to human activities, such as fossil fuel combustion and deforestation \u003csup\u003e1\u003c/sup\u003e, driving global climate change and impacting various ecosystems \u003csup\u003e2,3\u003c/sup\u003e. CO\u003csub\u003e2\u003c/sub\u003e levels have exceeded 420 parts per million (mole fraction ppm) as of 2023, a sharp rise from pre-industrial levels of around 280 ppm \u003csup\u003e4\u003c/sup\u003e. Projections suggest CO\u003csub\u003e2\u003c/sub\u003e concentrations could reach 670 ppm (under RCP 6.0) or even 936 ppm (under RCP 8.5) by the end of the century \u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, solar radiation reaching Earth's surface is expected to increase, a phenomenon known as global brightening, following a period of global dimming from the 1950s to the 1980s. This trend, driven by reduced air pollution and changes in cloud cover, has been observed in regions like Europe \u003csup\u003e6\u003c/sup\u003e. Climate models project significant increases in solar radiation in parts of Europe, North America, and Southeast China \u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs a result, future conditions are likely to involve high light and high CO\u003csub\u003e2\u003c/sub\u003e levels (HLHC), impacting plant metabolism. Although increased light and CO\u003csub\u003e2\u003c/sub\u003e can enhance photosynthesis \u003csup\u003e8\u003c/sup\u003e, their effects on specific metabolic pathways associated with photosynthesis remain unclear.\u003c/p\u003e \u003cp\u003eIt is expected that HLHC will stimulate photosynthesis but inhibit photorespiration. Less clear is how HLHC will affect respiration in the light (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e). This CO\u003csub\u003e2\u003c/sub\u003e release during photosynthesis represents a small yet critical CO\u003csub\u003e2\u003c/sub\u003e flux from the leaf \u003csup\u003e9\u003c/sup\u003e and is pivotal in photosynthesis biochemical models \u003csup\u003e10,11\u003c/sup\u003e. Understanding \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e is essential for accurate carbon balance estimates \u003csup\u003e12,13\u003c/sup\u003e but remains challenging because of the simultaneous CO\u003csub\u003e2\u003c/sub\u003e uptake by photosynthesis and CO\u003csub\u003e2\u003c/sub\u003e release by photorespiration \u003csup\u003e9,14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTraditionally, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e is measured through gas exchange methods such as Laisk, Kok, and Yin methods. The Laisk method measures net CO\u003csub\u003e2\u003c/sub\u003e assimilation (\u003cem\u003eA\u003c/em\u003e) in response to changes in intercellular CO\u003csub\u003e2\u003c/sub\u003e concentrations (\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eA\u003c/span\u003e/\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e curves) at low CO\u003csub\u003e2\u003c/sub\u003e to estimate \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eΓ\u003c/em\u003e* \u003csup\u003e15\u0026ndash;17\u003c/sup\u003e. Kok and Yin methods estimate \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e from the response of \u003cem\u003eA\u003c/em\u003e to low levels of irradiance, with Yin accounting for declining photosystem II electron transport efficiency (\u003cem\u003eΦ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e) with light \u003csup\u003e18\u0026ndash;20\u003c/sup\u003e. Recent advancements like the dynamic assimilation technique (DAT) offer faster and more efficient methods for \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e measurements based on nonsteady-state assumptions and CO\u003csub\u003e2\u003c/sub\u003e mass balance principles \u003csup\u003e21\u0026ndash;23\u003c/sup\u003e. However, these methods all measure at very low photosynthetic rates near or below the compensation point. Accurately measuring \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e under conditions of HLHC has been a challenge.\u003c/p\u003e \u003cp\u003eTo estimate \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e in HLHC we have reported using isotopically nonstationary metabolic flux analysis (INST-MFA). Our results indicate that \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e results in large measure from the oxidative pentose phosphate pathway (OPPP) in the cytosol forming a shunt from glucose 6-phosphate (G6P) to ribulose 5-phosphate (Ru5P) bypassing the plastidial non-oxidative pentose phosphate pathway reactions of the Calvin-Benson Cycle \u003csup\u003e24,25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address this, our study used gas exchange techniques, \u003csup\u003e13\u003c/sup\u003eC and \u003csup\u003e14\u003c/sup\u003eC isotope labeling, and INST-MFA to precisely measure metabolic fluxes within photosynthesis, revealing intricate adjustments in plant metabolism under HLHC conditions. Gas exchange indicated that the high CO\u003csub\u003e2\u003c/sub\u003e component of HLHC was nearly entirely responsible for the increased photosynthetic rate observed. The effect of HLHC on starch/sucrose ratio was determined by \u003csup\u003e14\u003c/sup\u003eC feeding. The rate of \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e was unchanged by HLHC but the source of the glucose 6-phosphate to begin the OPPP shunt changed in HLHC.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic fluxes of central metabolism\u003c/h2\u003e \u003cp\u003eMetabolic fluxes of central metabolism were globally assessed for differences between control and HLHC conditions through a comprehensive, model-based analysis of isotope labeling dynamics provided by INST-MFA (Fig.\u0026nbsp;1). INST-MFA was performed using a previously developed reaction network model integrating the Calvin-Benson Cycle (CBC), photorespiration, the tricarboxylic acid (TCA) cycle, the G6P/OPPP shunt, cytosolic and vacuolar sugar pools, and biosynthesis pathways for starch, sucrose, amino acids, and intracellular metabolic transport. Constraints were applied based on gas exchange measurements, with the net photosynthetic rate recorded as 15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 \u0026micro;mol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e for control and 22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 \u0026micro;mol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e for HLHC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The oxygenation to carboxylation rate ratio by rubisco (\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e) was constrained to 0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 for control and 0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 for HLHC based on gas exchange measurement (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePhotosynthesis and\u003c/b\u003e \u003cb\u003eA\u003c/b\u003e\u003cb\u003e/\u003c/b\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e \u003cb\u003ecurve parameters for plants assayed in control (n\u0026thinsp;=\u0026thinsp;11, \u0026plusmn; SD) and high light high CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(n\u0026thinsp;=\u0026thinsp;8, \u0026plusmn; SD) conditions.\u003c/b\u003e Significant differences between control and high light high CO\u003csub\u003e2\u003c/sub\u003e conditions are marked with asterisks (Student\u0026rsquo;s two tailed t-test, P\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh light high CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eg\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e labeling experiment collected data at 10 time points, analyzing 43 fragment ions using LC-MS/MS and GC-MS. While various intermediates within CBC, photorespiration, starch, and sucrose biosynthesis pathways exhibited significant \u003csup\u003e13\u003c/sup\u003eC labeling, the TCA cycle intermediates analyzed, along with most amino acids derived from them, displayed minimal labeling within the 30-min time frame (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). A gradual transition of mass isotopologue distributions (MIDs) towards heavier isotopologues was noted over time. The fitted labeling kinetics for most metabolites were found to be in good agreement with the measured MIDs (Supplementary Figs. S1, S2, and S3). The degree of this fit was quantitatively determined by the sum-of-squared residuals (SSR), which assesses the total variance between the measured and simulated kinetics, with an emphasis on minimizing this discrepancy to refine the flux solution \u003csup\u003e44\u003c/sup\u003e. The χ2 goodness-of-fit test accepted SSR values of 825 and 566 for the control and HLHC models, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHLHC influenced most CBC intermediates (3-phosphoglycerate, PGA; the sum of glyceraldehyde 3-phosphate and dihydroxyacetone phosphate, GAP/DHAP; the sum of G6P and fructose 6-phosphate, F6P; the sum of ribose 5-phosphate and ribulose 5-phosphate R5P/Ru5P), exhibiting faster labeling initially but similar levels to the control after 30 min (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb1). In photorespiratory intermediates, HLHC led to varied effects: serine labeling increased, glycine decreased, and 2-phosphoglycolate remained unchanged compared to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb1). Regarding sugars, HLHC showed inconsistent trends: sucrose glucosyl and fructosyl moieties increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb2), while glucose and fructose decreased in \u003csup\u003e13\u003c/sup\u003eC label incorporation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb3). The labeling kinetics of TCA cycle intermediates and amino acids were significantly altered in HLHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Citrate and malate exhibited higher labeling in HLHC (2% each at 30 min) compared to the control (0% and 1%). Threonine, glutamine, and glutamate from the TCA cycle intermediates displayed slower labeling, with 3%, 4%, and 6% enrichment in HLHC, respectively, versus 0%, 0%, and 1% in the control at 30 min. Alanine achieved 34% enrichment in HLHC compared to 22% in the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb3), while aspartate reached 53% compared to 57% at 30 min (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCharacterization of photosynthesis and\u003c/b\u003e \u003cb\u003eA/Ci\u003c/b\u003e \u003cb\u003ecurve fitting\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUnder HLHC, we observed an increase in \u003cem\u003eA\u003c/em\u003e, a higher intercellular CO\u003csub\u003e2\u003c/sub\u003e (\u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) concentration, lower stomatal conductance (\u003cem\u003eg\u003c/em\u003e), and a reduced \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e ratio. At normal light and CO\u003csub\u003e2\u003c/sub\u003e, photosynthesis operated (solid arrow in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) very near the point where RuBP regeneration and rubisco activity co-limit the photosynthetic rate. This confluence of limitations occurred at 84% of the potential rate of triose phosphate use (TPU) capacity and so TPU was unlikely to have any control over the rate at normal light and CO\u003csub\u003e2\u003c/sub\u003e. When both light and CO\u003csub\u003e2\u003c/sub\u003e were increased, photosynthesis increased as expected. Photosynthesis increased along the RuBP-regeneration limited line (blue), indicating that the effect was mostly, if not entirely, explained by the increase in CO\u003csub\u003e2\u003c/sub\u003e; the increased light was only of secondary importance, if at all. The ratio of \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eO\u003c/em\u003e\u003c/sub\u003e to \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e decreased with increasing CO\u003csub\u003e2\u003c/sub\u003e from 0.30 to 0.20 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Photosynthesis approached the TPU limitation at high light and CO\u003csub\u003e2\u003c/sub\u003e as shown by the dashed black arrow indicating the operating point. The operating point was very close to the confluence of the RuBP regeneration line and TPU limitation line.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRespiration in the light\u003c/h2\u003e \u003cp\u003eQuantitative analysis of metabolic fluxes was conducted to determine the rates of non-photorespiratory CO\u003csub\u003e2\u003c/sub\u003e release from reactions contributing to \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e (Fig.\u0026nbsp;1 and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The reaction network model considered three main processes of \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e, including decarboxylation reactions in the TCA cycle \u003csup\u003e26\u0026ndash;28\u003c/sup\u003e, pyruvate decarboxylation for fatty acid synthesis \u003csup\u003e29\u003c/sup\u003e, and the OPPP \u003csup\u003e24,30\u0026ndash;32\u003c/sup\u003e. \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e was calculated as the aggregate of estimated non-photorespiratory fluxes producing CO\u003csub\u003e2\u003c/sub\u003e, amounting to 8.1 umol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the control condition and 9.0 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in HLHC (Fig.\u0026nbsp;1 and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe G6P/OPPP shunt was estimated to be 7.0 and 7.1 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in control and HLHC, respectively, contributing to 86% and 79% of the total \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e in control and HLHC, respectively (Fig.\u0026nbsp;1 and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). TCA-associated reactions and fatty acid synthesis accounted for the remainder of \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e. This included the partial counterbalance of cytosolic CO\u003csub\u003e2\u003c/sub\u003e release through oxidative decarboxylation reactions, with phosphoenolpyruvate (PEP) carboxylation to oxaloacetate OAA (1.1 and 2.1 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for control and HLHC), offsetting CO\u003csub\u003e2\u003c/sub\u003e release from the decarboxylation of pyruvate to acetyl-CoA (0.8 and 1.7 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for control and HLHC), α-ketoglutarate decarboxylation (0.8 and 1.7 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for control and HLHC), and CO\u003csub\u003e2\u003c/sub\u003e release via oxidative decarboxylation of pyruvate, which facilitates the production of acetyl-CoA for fatty acid synthesis within the plastid (0.4 and 0.7 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for control and HLHC) (Fig.\u0026nbsp;1 and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCarbon partitioning shift\u003c/h2\u003e \u003cp\u003eAn elevated proportion of carbon was directed towards starch in HLHC, with values of 0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 in control and 0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 in HLHC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Conversely, the higher fraction directed to starch in HLHC was associated with a reduction in the carbon allocated to the ionic fraction (amino acids, organic acids, etc.), with values of 0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 in control and 0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 in HLHC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The proportion of fixed carbon allocated to sucrose remained consistent between control and HLHC, with values of 0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 in control and 0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 in HLHC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eFractions of starch and sucrose measured by\u003c/b\u003e \u003csup\u003e\u003cb\u003e14\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC radioactivity in control and high light high CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003econditions (n\u0026thinsp;=\u0026thinsp;6, \u0026plusmn; SD).\u003c/b\u003e Significant differences between control and high light high CO\u003csub\u003e2\u003c/sub\u003e conditions are marked with asterisks (Student\u0026rsquo;s two tailed t-test, P\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFractions of starch and sucrose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh light high CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estarch/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esucrose/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eionic/total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estarch/sucrose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStarch (\u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSucrose (\u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTogether, the synthesis of starch and sucrose contributed to roughly 70% of the overall net carbon fixation for control, but was increased significantly to 82% for HLHC. The ratio between starch and sucrose increased in HLHC, indicating a preference for carbon partitioning to starch over sucrose (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The synthesis rates for starch and sucrose were derived by multiplying the CO\u003csub\u003e2\u003c/sub\u003e assimilation rate by the ratio of starch or sucrose to total \u003csup\u003e14\u003c/sup\u003eC fixed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The synthesis rates for starch were measured to be 3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for control and 6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for HLHC. Similarly, the synthesis rates for sucrose were 6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for control and 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for HLHC, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTriose phosphate partitioning and hexokinase activity\u003c/h2\u003e \u003cp\u003eSurprisingly, there was a significant increase in the partitioning of triose phosphate to the cytosol in HLHC, as evidenced by the increase in the export flux for dihydroxyacetone phosphate (DHAP) from the chloroplast to the cytosol, which rose from 37 to 59 \u0026micro;mol metabolite g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in HLHC. The rate of the aldol condensation reaction, responsible for converting glyceraldehyde-3-phosphate (GAP) and DHAP to fructose-6-bisphosphate (FBP), increased from 18 to 28 \u0026micro;mol metabolite g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in HLHC. This is a 55% increase in FBP synthesis even though the sucrose synthesis increase was just 41% indicating that FBP flux to sinks other than sucrose must have been present, likely an increase in hexose phosphate entering the G6P shunt.\u003c/p\u003e \u003cp\u003eIn contrast, the carbon recycled to the \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e pool via hexokinase decreased from 2.8 to 1.7 \u0026micro;mol.metabolite.g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.FW.hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in HLHC. Notably, the flux for the G6P/OPPP shunt remained unchanged at 7.0 and 7.1 \u0026micro;mol metabolite g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the control and HLHC, respectively. The percentage of carbon contributed by hexokinase to the \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e pool decreased from 40\u0026ndash;24% with the difference being made up by the increased flux from FBP.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImpact of HLHC on photosynthesis and biomass\u003c/h2\u003e \u003cp\u003eHigh light and CO\u003csub\u003e2\u003c/sub\u003e caused a significant increase in \u003cem\u003eA\u003c/em\u003e. The switch to high CO\u003csub\u003e2\u003c/sub\u003e caused the calculated CO\u003csub\u003e2\u003c/sub\u003e partial pressure in the chloroplast (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e) to increase. The increase in CO\u003csub\u003e2\u003c/sub\u003e reduced the ratio of \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e to \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and this accounted for most if not all of the increase in \u003cem\u003eA.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTo understand the underlying physiology, we used \u003cem\u003eA/Ci\u003c/em\u003e curves of data previously reported \u003csup\u003e33\u003c/sup\u003e. Identifying the operating point on \u003cem\u003eA/C\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e curves (Fig.\u0026nbsp;1, black arrows), can help assess how efficiently the components of photosynthesis are being used. At 400 Pa CO\u003csub\u003e2\u003c/sub\u003e, the operational point was very close to the cross over between \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ecmax\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eJ\u003c/em\u003e rate limits, indicating these would be used at nearly their full capacity. In the higher CO\u003csub\u003e2,\u003c/sub\u003e \u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ecmax\u003c/em\u003e\u003c/sub\u003e is in excess by 27%, and \u003cem\u003eJ\u003c/em\u003e is very near the junction with TPU, a syndrome that is often characterized by instability and rubisco deactivation \u003csup\u003e34,35\u003c/sup\u003e. CO\u003csub\u003e2\u003c/sub\u003e and light were increased only during the measurement, we expect that leaves grown in the higher CO\u003csub\u003e2\u003c/sub\u003e would adjust so that the operational point would be close to the confluence of the rubisco and RuBP regeneration rate limitations and slightly below TPU. This was shown by McClain, Cruz, Kramer and Sharkey \u003csup\u003e35\u003c/sup\u003e for adaptation to high CO\u003csub\u003e2\u003c/sub\u003e. Recently, Coast, Scafaro, Bramley, Taylor and Atkin \u003csup\u003e36\u003c/sup\u003e reported that wheat plants in which photosynthetic capacity increased by growth at increased temperature at night had operational points very near the confluence of rubisco and RuBP regeneration limitations and that TPU changed to always be about 20% greater that the operation point.\u003c/p\u003e \u003cp\u003eIncreased \u003cem\u003eA\u003c/em\u003e, higher intercellular CO\u003csub\u003e2\u003c/sub\u003e (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e), lower stomatal conductance (\u003cem\u003eg\u003c/em\u003e), and a reduced \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e ratio were observed in HLHC. Elevated CO\u003csub\u003e2\u003c/sub\u003e has been shown to enhance photosynthesis by increasing CO\u003csub\u003e2\u003c/sub\u003e assimilation rates, leading to higher plant growth and yield in many C\u003csub\u003e3\u003c/sub\u003e species \u003csup\u003e37,38\u003c/sup\u003e. This CO\u003csub\u003e2\u003c/sub\u003e fertilization effect can result in greater biomass accumulation and yield, as demonstrated in crops like wheat, rice, and soybeans \u003csup\u003e39,40\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePhotosynthetic and ancillary metabolism\u003c/h2\u003e \u003cp\u003eIn this study, we used metabolic flux analysis (MFA), a powerful method for assessing intracellular metabolic fluxes within living biological systems, to estimate \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e and other metabolic fluxes under HLHC. MFA integrates computational models with experimental isotope labeling, providing a comprehensive understanding of functional metabolism by integrating factors such as enzyme expression, activity, and network structure\u003csup\u003e41\u0026ndash;43\u003c/sup\u003e. Recent advancements in \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e time-course labeling and computational modeling have made isotopically nonstationary metabolic flux analysis (INST-MFA) a potent tool for studying autotrophic carbon metabolism and estimating in vivo carbon fluxes \u003csup\u003e24,44\u003c/sup\u003e. CBC intermediates rapidly labeled over a 30-min period, reaching enrichments of 82%-94% in control and 90%-96% in HLHC, aligning with the well-established short half-lives of C\u003csub\u003e3\u003c/sub\u003e cycle intermediates \u003csup\u003e44\u0026ndash;47\u003c/sup\u003e. FBP and G6P/F6P exhibited slower labeling compared to other CBC intermediates for both control and HLHC, likely due to their involvement in sucrose synthesis pathways outside the chloroplast, where labeling is diluted by hexokinase bringing in unlabeled carbon from the free glucose pool (plus fructose and possibly sucrose following invertase activity).\u003c/p\u003e \u003cp\u003eHLHC had a substantial and significant impact on the fluxes of CBC, starch synthesis, and sucrose export. Carboxylation flux increased from 162 to 259 \u0026micro;mol metabolite g\u003csup\u003e-1\u003c/sup\u003e FW hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, sucrose export flux increased from 5.3 to 10.3 \u0026micro;mol (hexose units) g\u003csup\u003e-1\u003c/sup\u003e FW hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while starch production increased from 10 to 15 \u0026micro;mol (hexose units) g\u003csup\u003e-1\u003c/sup\u003e FW hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;1). These results were consistent with a previous INST-MFA study for \u003cem\u003eArabidopsis thaliana\u003c/em\u003e acclimated in high light, showing increased fluxes of CBC, starch and sucrose synthesis, and sucrose export fluxes \u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHLHC had a smaller but still significant effect on the fluxes attributed to TCA-associated reactions and fatty acid synthesis (Fig.\u0026nbsp;1). Phosphoenolpyruvate (PEP) carboxylation to oxaloacetate increased from 1.1 to 2.1 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Additionally, the rate of decarboxylation of pyruvate to acetyl-CoA for fatty acid synthesis increased from 0.8 to 1.7 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The α-ketoglutarate decarboxylation flux increased from 0.8 to 1.7 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The rate of oxidative decarboxylation of pyruvate for citrate synthesis increased from 0.4 to 0.7 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;1). Despite these heightened fluxes contributing to \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e, they did not constitute the primary source, resulting in the total \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e remaining unchanged in HLHC.\u003c/p\u003e \u003cp\u003eAll measured TCA cycle intermediates exhibited less than 5% labeling in 30 min (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Supplementary Fig. S3), consistent with previous findings in Arabidopsis, camelina, and tobacco \u003csup\u003e24,44,47,48\u003c/sup\u003e. This may be attributed to substantial vacuolar pools of organic acids with slow turnover rates, alongside low fluxes during photosynthesis through active (cytosolic and mitochondrial) pools of the same organic acids, which serve as TCA cycle intermediates. The metabolism of the TCA cycle and TCA-cycle-derived amino acids undergoes significant alterations in HLHC. Higher fluxes through the TCA cycle are observed in HLHC (Fig.\u0026nbsp;1), consistent with slightly elevated \u003csup\u003e13\u003c/sup\u003eC labeling for citrate and malate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplementary Fig. S3). These results align with a previous metabolomics study on \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, where TCA cycle intermediate levels increased under high light treatment \u003csup\u003e32,49\u003c/sup\u003e. Additionally, higher fluxes for aspartate and glutamate synthesis are noted in HLHC, correlating with faster and increased \u003csup\u003e13\u003c/sup\u003eC labeling for aspartate and glutamate. Alanine, resulting from the transamination of pyruvate, exhibits a higher rate of synthesis (Fig.\u0026nbsp;1) but slower fractional \u003csup\u003e13\u003c/sup\u003eC-labeling in HLHC (Fig.\u0026nbsp;2b3). This may be attributed to a larger inactive pool of alanine in HLHC, resulting in saturating \u003csup\u003e13\u003c/sup\u003eC-labeling kinetics.\u003c/p\u003e \u003cp\u003eSucrose glucosyl and fructosyl moieties exhibit faster and higher \u003csup\u003e13\u003c/sup\u003eC enrichment in HLHC(Fig.\u0026nbsp;2b2). In contrast, free glucose and fructose show slower and lower \u003csup\u003e13\u003c/sup\u003eC enrichment (Fig.\u0026nbsp;2b3), potentially due to the lower sucrose recycling flux in the cytosol in HLHC (Fig.\u0026nbsp;1). The flux for sucrose recycling in the cytosol decreased from 0.6 to 0.1 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;1). Consequently, the higher labeling from sucrose incorporates less into the glucose and fructose pool through sucrose recycling reactions. Another potential explanation is an increase in the pool sizes of glucose and fructose in HLHC, leading to an expansion of the inactive pool. Together, these findings imply that the plant maintains a fixed investment to buffer against transient loss of incoming light. This investment becomes a smaller proportion of overall carbon assimilation over time, naturally improving efficiency under HLHC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePhotorespiration flux\u003c/h2\u003e \u003cp\u003eWhile HLHC affects the labeling rates of photorespiratory intermediates, the impact of daylength on \u003csup\u003e13\u003c/sup\u003eC incorporation into these intermediates demonstrated inconsistent trends: serine labeling was faster, glycine slower, and 2PG similar in HLHC compared to control (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings challenge the direct correlation between photorespiratory intermediate labeling kinetics and photorespiration rates, emphasizing the need for comprehensive compartmental information in \u003csup\u003e13\u003c/sup\u003eC MFA for accurate flux estimates. Current methodologies suggest using measured \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e values (0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 for control and 0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 for HLHC) from gas exchange as an input to the MFA model rather than deriving it as an output, ensuring reliability \u003csup\u003e25,48\u003c/sup\u003e. The decreased \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e aligns with the increase in CO\u003csub\u003e2\u003c/sub\u003e partial pressure from 27.8 to 39.2 Pa (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePolyexponential model fitting\u003c/h2\u003e \u003cp\u003eTo statistically compare the labeling trajectories of the CBC and attribute these differences to different metabolic modules, we fitted control and HLHC labeling data to polyexponential models and compared their parameter estimates. Changes in the labeling rate of chloroplast CBC intermediates between control and HLHC are evident in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. These changes suggest a decrease in labeling contribution from cytosolic hexose phosphates and/or turnover of vacuolar sugars under HLHC. Additionally, the observed increase in turnover rate aligns with MFA-estimated changes in the fast pool labeling constant, supporting the consistency of MFA modeling results. Furthermore, the rate of labeling contribution from cytosolic hexose phosphates and/or turnover of vacuolar sugars remains unchanged, consistent with the flux through the G6P/OPP shunt.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCarbon partitioning dynamics\u003c/h2\u003e \u003cp\u003eThe starch synthesis rate was higher in HLHC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), consistent with the faster \u003csup\u003e13\u003c/sup\u003eC labeling of the starch precursor ADPG, reaching 93% in control and 96% in HLHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Similarly, sucrose displayed a higher absolute synthesis rate in HLHC, aligning with the faster \u003csup\u003e13\u003c/sup\u003eC labeling of the sucrose precursor UDPG, measured at 79% in control and 88% in HLHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). However, the increased starch-to-sucrose ratio (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in HLHC indicated a greater partitioning of carbon toward starch rather than sucrose. These findings are consistent with previous study \u003csup\u003e8\u003c/sup\u003e, which investigated how varying light intensity and CO\u003csub\u003e2\u003c/sub\u003e levels affect starch and sucrose synthesis in \u003cem\u003ePhaseolus vulgaris\u003c/em\u003e (common bean) leaves. They found that both starch and sucrose synthesis displayed a linear relationship with CO\u003csub\u003e2\u003c/sub\u003e assimilation, responding to changes in both CO\u003csub\u003e2\u003c/sub\u003e levels or light intensity \u003csup\u003e8\u003c/sup\u003e. However, starch showed a steeper relationship compared to sucrose, suggesting that at HLHC, more carbon was allocated to starch than to sucrose.\u003c/p\u003e \u003cp\u003eWe also observed a decrease in the ionic fraction, dropping from 30% in the control condition to 20% in HLHC. This suggests that carbon either accumulated in intermediates of the carbon reduction or carbon oxidation cycles or that the carbon allocated towards amino acid production decreased under HLHC. This result is consistent with the findings of Sharkey et al.\u003csup\u003e8\u003c/sup\u003e, who observed that as photosynthetic rate increased, the ionic fraction decreased. With high intercellular CO\u003csub\u003e2\u003c/sub\u003e, stomata closed and led to a lower photorespiration rate potentially reducing the flow through the photorespiratory pool \u003csup\u003e8\u003c/sup\u003e. This may result in decreased carbon drainage into amino acids. Furthermore, a lower rate of photorespiration could lead to a reduced phosphate pool and an increase in the PGA pool, facilitating starch and sucrose formation while reducing carbon flow to amino acids \u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eR\u003csub\u003eL\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eThe INST-MFA results revealed that the G6P/OPPP shunt significantly contributed to \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e in both control and HLHC conditions, representing 86% and 79% of the total \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e, respectively, in line with prior results \u003csup\u003e24,25\u003c/sup\u003e. Xu et al. \u003csup\u003e25\u003c/sup\u003e discussed that incomplete labeling of CBC can be accounted for by the reimport of unlabeled carbon via a cytosolic G6P/OPPP shunt. A stromal shunt would not account for the lack of complete labeling. The label in ADPG indicates that the stromal G6P pool labels to the same degree as CBC intermediates and so is not a source of unlabeled carbon while UDPG confirms that cytosolic G6P is significantly less labeled than CBC intermediates and so the cytosolic shunt is a source of unlabeled carbon. The cytosolic shunt is believed to be the only shunt in non-stressed leaves while both the cytosolic and stromal shunts may operate in stressed leaves \u003csup\u003e31\u003c/sup\u003e. Surprisingly, our study found no significant differences in both \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e and fluxes through the G6P/OPPP shunt between control and HLHC (Fig.\u0026nbsp;1 and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This finding is consistent with previous research\u003csup\u003e23\u003c/sup\u003e, which employed the DAT technique to estimate \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e in paper birch and hybrid poplar and concluded that \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e remains consistent under elevated CO\u003csub\u003e2\u003c/sub\u003e conditions. However, the flux of unlabeled carbon in free glucose through hexokinase into the cytosolic G6P pool was less in HLHC, which would reduce the flow of unlabeled carbon into the CBC. This may account for the greater degree of label in CBC intermediates in HLHC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSimplified model\u003c/h2\u003e \u003cp\u003eA simplified model based on that found in Sharkey et al. (2020)\u003csup\u003e31\u003c/sup\u003e was constructed and parameterized with data from the INST-MFA for the control and HLHC conditions (Supplemental Table S3). This made it easier to see that the lower level of labeling in the control was caused by several factors. Internal consistency could be checked using calculated versus measured values for the degree of label in UDP glucose. In the control condition, the measured value was 0.79 while the modeled value was 0.83. In HLHC the measured value was 0.88 while the modeled value was 0.87. The flux of carbon from free glucose, fructose, and (following invertase activity) sucrose, was 53% of net CO\u003csub\u003e2\u003c/sub\u003e fixation rate (\u003cem\u003eA\u003c/em\u003e) for the control but only 40% for the HLHC. This explains part of the increased degree of labeling in the HLHC condition (93%) compared to the control condition (89%). The model also allowed calculation that \u003csup\u003e12\u003c/sup\u003eC was leaving the system as starch (etc.) and sucrose at a rate of 14% of \u003cem\u003eA\u003c/em\u003e in the control condition but just 9% in the HLHC condition. Because \u003csup\u003e12\u003c/sup\u003eC was leaving the system at a slow rate it did not require very large inputs of \u003csup\u003e12\u003c/sup\u003eC to keep the degree of label from getting to 100%.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn the future, photosynthesis may be speeded up because of increasing CO\u003csub\u003e2\u003c/sub\u003e. This will affect flux through many ancillary pathways associated with photosynthesis, for example photorespiration. This work focused on CO\u003csub\u003e2\u003c/sub\u003e releasing reactions other than photorespiration, respiration in the light (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e). We found that the overall rate of \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e was not changed in HLHC but that the source of G6P for the G6P/OPPP shunt, confirmed here to be responsible for most of \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e, did change. Less free glucose was incorporated into cytosolic glucose in HLHC. In HLHC, the amount of G6P in the cytosol derived from triose phosphate export, and thus labeled to the same degree as CBC intermediates, was increased. This shift away from free glucose reduced the input of unlabeled carbon into the CBC causing the degree of label in the CBC after 30 min of \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e feeding to be higher in HLHC.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003ePlant Growth and Labeling Conditions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eCamelina sativa\u003c/em\u003e plants (ecotype \u003cem\u003evar. Calena\u003c/em\u003e) were cultivated under a 16-hour light/8-hour dark cycle. The light intensity was maintained at 500 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with temperatures set at 22\u0026deg;C and relative humidity at 50%. The plant substrate consisted of a mixture of 70% peat moss, 21% perlite, and 9% vermiculite (Suremix; Michigan Grower Products Inc., Galesburg, MI, USA). Additionally, plants were fertilized with 1/4 strength Hoagland's solution \u003csup\u003e50\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStatement of Seed Source and Plant Guidelines\u003c/h2\u003e \u003cp\u003e\u003cem\u003eCamelina sativa\u003c/em\u003e wild-type seeds (ecotype \u003cem\u003evar. Calena\u003c/em\u003e) were obtained from the Heike Sederoff group at North Carolina State University. The seeds used here were not collected in the wild and are not of an endangered species. The authors hereby declare that the plant collection and use, as well as all methods, were carried out in accordance with all relevant guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGas Exchange and \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e Labeling Experiments\u003c/h2\u003e \u003cp\u003eGas exchange and \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e labeling experiments followed established procedures \u003csup\u003e24\u003c/sup\u003e with slight modifications. Fully expanded leaves from 4-week-old plants were used for \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e labeling. Measurements, including CO\u003csub\u003e2\u003c/sub\u003e assimilation rate, stomatal conductance, and other photosynthetic parameters, were conducted using a LI-COR 6800 portable photosynthesis system (LI-COR Biosciences, Lincoln, NE, USA). Plants were set under control conditions with a reference [CO\u003csub\u003e2\u003c/sub\u003e] of 39 Pa, light intensity of 500 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, temperature of 22\u0026deg;C, and a water vapor pressure difference (VPD) of 1.0 kPa. High light high CO\u003csub\u003e2\u003c/sub\u003e conditions had a reference [CO\u003csub\u003e2\u003c/sub\u003e] of 59 Pa, light intensity of 1500 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, temperature of 22\u0026deg;C, and a water vapor pressure difference (VPD) of 1.0 kPa.\u003c/p\u003e \u003cp\u003eThe \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e labeling commenced after 20\u0026ndash;30 minutes in both control and high light high CO\u003csub\u003e2\u003c/sub\u003e conditions to ensure a stable photosynthetic state. A pseudo-steady-state metabolism was assumed during the labeling period as the CO\u003csub\u003e2\u003c/sub\u003e source transitioned to \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e while maintaining other parameters constant. Gas mixing utilized mass flow controllers (Alicat Scientific, Tucson AZ, USA) controlled by a custom-programmed Raspberry Pi touchscreen monitor (Raspberry Pi foundation). Labeled leaf samples were collected at time points of 0, 0.5, 1, 2, 3, 5, 7, 10, 15, and 30 minutes. Leaf freezing was achieved by spraying liquid nitrogen directly on the leaf surface. Sampling for different time points occurred randomly between 9:00 am and 4:00 pm, with one leaf sampled as a single biological replicate. Three biological replicates were collected for each time point, and all frozen leaf samples were stored at -80\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003cb\u003eA/Cc\u003c/b\u003e \u003cb\u003eCurve Measurements and Parameter Calculation\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eA/Cc\u003c/em\u003e curves were obtained using a LI-COR 6800 portable photosynthesis system (LI-COR Biosciences, Lincoln, NE, USA) under a light intensity of 500 \u0026micro;mol photon m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, leaf temperature of 22\u0026deg;C, and VPD of 1.0 kPa. The sequence of reference CO\u003csub\u003e2\u003c/sub\u003e partial pressures ranged from 5 to 150 Pa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Photosynthetic parameters, including the maximum rubisco carboxylation rate (\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003ecmax\u003c/em\u003e\u003c/sub\u003e), the maximum attained rate of electron transport (\u003cem\u003eJ\u003c/em\u003e), respiration in the light (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e), mesophyll conductance to CO\u003csub\u003e2\u003c/sub\u003e transfer (\u003cem\u003eg\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e), the rate of triose phosphate utilization (\u003cem\u003eTPU\u003c/em\u003e), and the proportion of carbon exported from photorespiration as glycine (\u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e) or serine (\u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e), were estimated from the \u003cem\u003eA/Cc\u003c/em\u003e curves using the R-script. The script details were described previously \u003csup\u003e51,52\u003c/sup\u003e and can be accessed at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/poales/msuRACiFit\u003c/span\u003e\u003cspan address=\"https://github.com/poales/msuRACiFit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePolyexponential model fitting\u003c/h2\u003e \u003cp\u003ePolyexponential model fitting was performed as in Xu et al., 2022\u003csup\u003e25\u003c/sup\u003e, with technical details provided in Supplementary Methods. In Xu et al., 2022 \u003csup\u003e25\u003c/sup\u003e, isotopic labeling data from an extended isotopic labeling time-course was used to demonstrate that the labeling of the CBC is best fit by a triexponential, or three process model. The three processes corresponded to (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) turnover of the CBC intermediates themselves, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) influx of unlabeled carbon from cytosolic sugars via the G6P shunt, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) influx of unlabeled carbon from slow reintroduction of vacuolar sugars into the cytosol and then into the chloroplast via the glucose-6-phosphate shunt. Due to the shorter time-course of this study, we cannot accurately disambiguate processes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e); therefore, we have chosen to fit our data using a biexponential model, where the slow exponential term represents the cumulative effects of processes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This can be thought of as an approximation of a \u0026ldquo;true\u0026rdquo; triexponential model fit whose parameters cannot be accurately estimated due to data availability in the present study.\u003c/p\u003e \u003cp\u003eLabeling data was fitted to a biexponential \u003cb\u003e(E1)\u003c/b\u003e model using the \u003cem\u003ecurve_fit()\u003c/em\u003e function in SciPy. Briefly, when fitting such models to the labeling of metabolic intermediates, we interpret the coefficients of each exponential term as representing the proportion of the labeling signal contributed by the process described by that term (e.g. the turnover of cytosolic sugars), the rates the exponentials are raised to as the rate at which these respective pools are being labeled, and constants as inactive pools not labeled over the span of a time course study (e.g. vacuolar amino acids). Each optimization is initialized from 1,000 starting points selected by Latin Hypercube Sampling \u003csup\u003e53\u003c/sup\u003e to ensure a global best-fit is found.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{\\varvec{\\%}}^{12}C{\\varvec{O}}_{2}\\left(\\varvec{t}\\right)=A{\\varvec{e}}^{-\\varvec{b}\\varvec{*}\\varvec{t}}+C{\\varvec{e}}^{-\\varvec{d}\\varvec{*}\\varvec{t}}\\#\\left(\\varvec{E}1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMetabolite Extraction and Mass Spectrometry Analyses\u003c/h2\u003e \u003cp\u003eMetabolites were extracted from rapidly frozen tissues following the protocol outlined previously \u003csup\u003e24\u003c/sup\u003e. Mass spectrometry analyses were conducted as previously detailed in publications\u003csup\u003e24,25,33\u003c/sup\u003e with minor modifications. The mass spectrometry (MS) parameters outlining transitions of measured metabolites during multiple reaction monitoring (MRM) with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and selected ion monitoring (SIM) with gas chromatography-mass spectrometry (GC-MS) can be found in Supplementary Table S4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eIon-Pair Chromatography \u0026ndash; Tandem Mass Spectrometry (IPC-MS/MS) Analysis\u003c/h2\u003e \u003cp\u003eFor the analysis of phosphorylated intermediates in the Calvin-Benson Cycle (CBC), ion-pair chromatography \u0026ndash; tandem mass spectrometry (IPC-MS/MS) was performed using an ACQUITY UPLC pump system (Waters, Milford, MA, USA) coupled with a Waters XEVO TQ-S UPLC/MS/MS (Waters, Milford, MA, USA). Metabolites were separated on a 2.1\u0026times;50 mm ACQUITY UPLC BEH C18 Column (Waters, Milford, MA, USA) at 40\u0026deg;C. The chromatographic separation utilized a multi-step gradient with mobile phase A (10 mM tributylamine in 5%(v/v) methanol) and mobile phase B (methanol): 0\u0026ndash;1 min, 95\u0026thinsp;\u0026minus;\u0026thinsp;85% A; 1\u0026ndash;6 min, 65\u0026thinsp;\u0026minus;\u0026thinsp;40% A; 6\u0026ndash;7 min, 40\u0026thinsp;\u0026minus;\u0026thinsp;0% A; 7\u0026ndash;8 min, 0% A; 8\u0026ndash;9 min, 100% A, at a flow rate of 0.3 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The source temperature was maintained at 120\u0026deg;C, and the desolvation temperature was set to 350\u0026deg;C. Nitrogen served as the sheath and auxiliary gas, with collision gas (argon) set to 1.1 mTorr. Gas flow for desolvation and cone was adjusted to 800 and 50 L/h, respectively. The scan time was 0.1 ms.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eAnion Exchange Chromatography \u0026ndash; Tandem Mass Spectrometry (AEC-MS/MS) Analysis\u003c/h2\u003e \u003cp\u003eFor the analysis of nucleotide sugars and additional phosphorylated intermediates (e.g., 2-phosphoglycolate (2PG), phosphoenolpyruvate), anion exchange chromatography \u0026ndash; tandem mass spectrometry (AEC-MS/MS) was conducted using an ACQUITY UPLC pump system (Waters, Milford, MA, USA) coupled with a Xevo ACQUITY TQ Triple Quadrupole Detector (Waters, Milford, MA, USA). Metabolites were separated by an IonPac AS11 analytical column (2 \u0026times; 250 mm, Dionex) equipped with an IonPac guard column AG11 (2 \u0026times; 50 mm, Dionex) at a flow rate of 0.35 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. A multi-step gradient was employed with mobile phase A (0.5 mM KOH) and mobile phase B (75 mM KOH): 0\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e min, 100% A; 2\u0026ndash;4 min, 100\u0026thinsp;\u0026minus;\u0026thinsp;93% A; 4\u0026ndash;13 min, 93\u0026thinsp;\u0026minus;\u0026thinsp;60% A; 13\u0026ndash;15 min, 0% A; 15\u0026ndash;17 min, 100% A. To suppress the KOH concentration, a post-column anion self-regenerating suppressor (Dionex ADRS 600, Thermo Scientific, Waltham, MA, USA) was utilized, with a current of 50 mA and a flow rate of 3.5 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Additionally, an IonPac ATC-3 Anion Trap Column (4 \u0026times; 35 mm), conditioned with 2M KOH, was employed to eliminate contaminant ions from KOH solvents.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGas Chromatography-Mass Spectrometry (GC-MS) Analysis\u003c/h2\u003e \u003cp\u003eFor the comprehensive analysis of amino acids, organic acids, and sugars, a gas chromatography-mass spectrometry (GC-MS) approach was employed using an Agilent 7890 GC system (Agilent, Santa Clara, CA, USA) coupled with an Agilent 5975C inert XL Mass Selective Detector (Agilent, Santa Clara, CA, USA). Prior to analysis, samples underwent a derivatization process involving methoxyamine hydrochloride dissolved in dry pyridine at room temperature overnight. Amino acids and organic acids were silylated to trimethylsilyl (TBDMS) derivatives, achieved by adding N-(tertbutyldimethylsilyl)-N-methyltrifluoroacetamide with 1% (w/v) tert-butyl-dimethylchlorosilane, and incubated at 60\u0026deg;C overnight. Sugars were silylated to trimethylsilyl (TMS) derivatives, accomplished by adding N, O-Bis (trimethylsilyl) trifluoroacetamide with 1% (w/v) trimethylchlorosilane, and incubated at 60\u0026deg;C overnight. Metabolite separation was performed on an Agilent VF5ms GC column (Agilent, Santa Clara, CA, USA). The inlet temperature and MS transfer line temperature were set at 230\u0026deg;C and 300\u0026deg;C, respectively. The oven temperature profile included an initial hold at 40\u0026deg;C for 1 minute, followed by a ramp at 40\u0026deg;C/min to 80\u0026deg;C, 10\u0026deg;C/min to 240\u0026deg;C, and 20\u0026deg;C/min until reaching 320\u0026deg;C, maintained for 5 minutes. Electron ionization (EI) was set at 70 eV, and the mass scan range covered 50\u0026ndash;600 amu.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eAnalysis of Mass Spectrometry Data\u003c/h2\u003e \u003cp\u003eThe quantification of mass isotopologue distributions (MIDs) and determination of pool sizes were conducted following the protocol outlined previously \u003csup\u003e33\u003c/sup\u003e with minor adjustments. In the control condition, most of the MID data, excluding sugars, were obtained from a previous study\u003csup\u003e25\u003c/sup\u003e, while the data for the HLHC condition is newly generated in this study. LC-MS/MS data were obtained using MassLynx 4.0 (Agilent, Santa Clara, CA, USA), while GC-MS data were acquired with Agilent GC/MSD Chemstation (Agilent, Santa Clara, CA, USA). Metabolite identification relied on the comparison of retention time and mass-to-charge ratio (m/z) with authentic standards.\u003c/p\u003e \u003cp\u003eTo process both LC-MS and GC-MS data, conversion to MassLynx format and subsequent analysis using QuanLynx software were performed for peak detection and quantification. The calculation of MIDs for each metabolite, reflecting the incorporation of n \u003csup\u003e13\u003c/sup\u003eC or 2H atoms, was accomplished using the formula:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\varvec{M}\\varvec{I}\\varvec{D}\\varvec{n}=\\frac{\\varvec{M}\\varvec{i}}{{\\sum }_{\\varvec{i}=0}^{\\varvec{n}}\\varvec{M}\\varvec{i}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eMi\u003c/em\u003e represents the isotopologue abundance for each metabolite, \u003cem\u003ei\u003c/em\u003e ranges from 0 (no \u003csup\u003e13\u003c/sup\u003eC atoms) to n (all carbons labeled with \u003csup\u003e13\u003c/sup\u003eC), and n is the total number of carbon atoms in the compound. Experimental MIDs were adjusted for natural abundance using IsoCor \u003csup\u003e54\u003c/sup\u003e and FluxFix \u003csup\u003e55\u003c/sup\u003e software. The \u003csup\u003e13\u003c/sup\u003eC enrichment (E) is determined using the equation:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\varvec{E}= \\frac{{\\sum }_{\\varvec{i}=0}^{\\varvec{n}}\\varvec{M}\\varvec{I}\\varvec{D}\\varvec{i}\\times \\varvec{i}}{\\varvec{n}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eIsotopologue Network, Flux Determination, and Assessment of Flux Precision\u003c/h2\u003e \u003cp\u003eINST-MFA was conducted to estimate metabolic fluxes using the Isotopomer Network Compartmental Analysis software package (INCA2.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mfa.vueinnovations.com\u003c/span\u003e\u003cspan address=\"http://mfa.vueinnovations.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Vanderbilt University \u003csup\u003e56\u003c/sup\u003e), employing the metabolic network model established in prior work \u003csup\u003e25,33\u003c/sup\u003e. A comprehensive list of reactions and corresponding abbreviations is provided in Supplementary Table S5, while the stoichiometry of reactions and atom transitions for each reaction is detailed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The model's goodness of fit was evaluated through the sum-of-squared residuals (SSR), quantifying the overall difference between measured and simulated kinetics. The parameter continuation method \u003csup\u003e44\u003c/sup\u003e was employed to estimate 95% confidence intervals for both absolute and normalized fluxes of the best-fit models. The computational intensity of confidence interval determination was managed in parallel through a SLURM job scheduler, distributing tasks to numerous computer nodes within a high-performance computing cluster at the Institute for Cyber-Enabled Research at Michigan State University (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icer.msu.edu/\u003c/span\u003e\u003cspan address=\"https://icer.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eStarch Synthesis Rate and Fractions of Starch and Sucrose\u003c/h2\u003e \u003cp\u003eThe partitioning of recently fixed carbon into starch and sucrose was determined through \u003csup\u003e14\u003c/sup\u003eC labeling experiments during steady-state photosynthetic assimilation, with slight modifications \u003csup\u003e8\u003c/sup\u003e. A mixture of \u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e gas and CO\u003csub\u003e2\u003c/sub\u003e-free air, controlled by mass flow controllers (Alicat Scientific, Tucson AZ, USA), was directed through the sample port on the back of a LI-6800 (LI-COR Biosciences, Lincoln, NE, USA). Conditions included a reference [CO\u003csub\u003e2\u003c/sub\u003e] of 39 Pa, light intensity of 500 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, temperature of 22\u0026deg;C, and humidity for vapor pressure deficit (VPD) of 1.0 kPa. Each leaf underwent a 10-min pulse of \u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e at a concentration of 400 \u0026micro;L L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The \u003csup\u003e14\u003c/sup\u003eC-labeled leaf sample was promptly frozen in liquid nitrogen, and its fresh weight was measured. Frozen samples were stored at -80\u0026deg;C before extraction. Sampling occurred randomly between 9:00 am and 4:00 pm, with one leaf sampled at each time point, and six biological replicates collected for each time point. Each leaf sample was extracted with 0.5 mL formic acid solution (formic acid/ethanol 4:75, v/v). After centrifugation at 12,000 x g at 4\u0026deg;C for 10 min, half of the supernatant underwent radioactivity counting (total soluble fraction) using a 1450 Microbeta Trilux scintillation counter (PerkinElmer, Waltham, MA, USA). The other half passed through a cation-exchange resin (Dowex 50WX8 H\u0026thinsp;+\u0026thinsp;form) column (Sigma-Aldrich, St. Louis, MO, USA), followed by an anion exchange resin (Dowex 1X8 Cl- form) column (Sigma-Aldrich, St. Louis, MO, USA). The ionic fraction was calculated as the difference between the total soluble fraction and the neutral soluble fraction. The pellet, after washing and resuspension, underwent gelatinization and subsequent enzymatic digestion for starch measurement. The proportion of counts in the starch and neutral soluble fractions relative to total counts was calculated (Supplementary Table S6).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eSimplified model\u003c/h2\u003e \u003cp\u003eA simplified model, adapted from Sharkey et al. (2020) \u003csup\u003e31\u003c/sup\u003e, was parameterized using data from INST-MFA for both control and HLHC conditions (Supplementary Table S3). The model includes velocities expressed in terms of carbon atoms, adjusted as necessary to accommodate the number of carbon atoms per molecule and relative to net assimilation. Ratios representing the ratio of carbon-13 to carbon-12 in the molecule. The model provides a snapshot after 30 minutes of labeling to ensure saturation of short-term reactions. Detailed equations and values are provided in Supplementary Table S3.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research received financial support from the Division of Chemical Sciences, Geosciences, and Biosciences, Office of Basic Energy Sciences at the U.S. Department of Energy, under Grants DE-FOA-0001650 and DE-FG02-91ER20021. TDS acknowledges partial salary support from MSU AgBioResearch. The authors are grateful to Dr. Berkley Walker (MSU) for insightful discussions, Ms. Emily Pawlowski and Mr. Cody Keilen (MSU Growth Chamber Facility) for their assistance with growth chamber operations and plant maintenance, and Dr. Daniel Jones, Dr. Tony Schilmiller, Dr. Lijun Chen, and Dr. Casey Johnny (MSU Mass Spectrometry and Metabolomics Core Facility) for their support in mass spectrometry methods. The team acknowledges the MSU Institute for Cyber-Enabled Research for providing access to high-performance computing clusters and services. Special thanks to Dr. Jamey Young for facilitating accessibility to INCA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTDS, YSH, and YX conceived and designed the study. TDS analyzed \u003cem\u003eA/Ci\u003c/em\u003e curves. YX performed the \u003csup\u003e13\u003c/sup\u003eC labeling experiments, mass spectrometry analyses, and INST-MFA. JAMK conducted the exponential decay analysis. SEW provided guidance and assistance to YX in the \u003csup\u003e14\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e pulse-chase experiment. YX wrote the manuscript with contributions from all the authors. TDS serves as the corresponding author responsible for contact and ensuring communication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIPCC. \u003cem\u003eIPCC 2021\u003c/em\u003e. \u003cem\u003eClimate Change 2021: The Physical Science Basis.\u003c/em\u003e (2021).\u003c/li\u003e\n\u003cli\u003eRoy, S. \u0026amp; Mathur, P. Delineating the mechanisms of elevated CO2 mediated growth, stress tolerance and phytohormonal regulation in plants. \u003cem\u003ePlant Cell Rep.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 1345\u0026ndash;1365 (2021).\u003c/li\u003e\n\u003cli\u003ePeter, R., Kuttippurath, J., Chakraborty, K. \u0026amp; Sunanda, N. Temporal evolution of mid-tropospheric CO\u003csub\u003e2\u003c/sub\u003e over the Indian Ocean. \u003cem\u003eAtmos. Environ.\u003c/em\u003e \u003cstrong\u003e257\u003c/strong\u003e, 118475 (2021).\u003c/li\u003e\n\u003cli\u003eIPCC. IPCC 2018. \u003cem\u003eGlob. Warm. 1.5\u0026deg;C. An IPCC Spec. Rep. [...]\u003c/em\u003e (2018).\u003c/li\u003e\n\u003cli\u003eWuebbles, D. J. \u003cem\u003eet al.\u003c/em\u003e Climate science special report: a sustained assessment activity of the US global change research program. \u003cem\u003eUS Glob. Chang. Res. Progr. Washington, DC, USA\u003c/em\u003e \u003cstrong\u003e669\u003c/strong\u003e, (2017).\u003c/li\u003e\n\u003cli\u003eHe, Y., Wang, K., Zhou, C. \u0026amp; Wild, M. A revisit of global dimming and brightening based on the sunshine duration. \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 4281\u0026ndash;4289 (2018).\u003c/li\u003e\n\u003cli\u003eWild, M., Folini, D., Henschel, F., Fischer, N. \u0026amp; M\u0026uuml;ller, B. Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems. \u003cem\u003eSol. Energy\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 12\u0026ndash;24 (2015).\u003c/li\u003e\n\u003cli\u003eSharkey, T. D., Berry, J. A. \u0026amp; Raschke, K. Starch and sucrose synthesis in \u003cem\u003ephaseolus vulgaris\u003c/em\u003e as affected by light, CO\u003csub\u003e2\u003c/sub\u003e, and abscisic acid. \u003cem\u003ePlant Physiol.\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, 617\u0026ndash;620 (1985).\u003c/li\u003e\n\u003cli\u003eTcherkez, G. \u003cem\u003eet al.\u003c/em\u003e Leaf day respiration: low CO\u003csub\u003e2\u003c/sub\u003e flux but high significance for metabolism and carbon balance. \u003cem\u003eNew Phytologist\u003c/em\u003e at https://doi.org/10.1111/nph.14816 (2017).\u003c/li\u003e\n\u003cli\u003eFarquhar, G. D., von Caemmerer, S. \u0026amp; Berry, J. A. A biochemical model of photosynthetic CO\u003csub\u003e2\u003c/sub\u003e assimilation in leaves of C\u003csub\u003e3\u003c/sub\u003e species. \u003cem\u003ePlanta\u003c/em\u003e (1980) doi:10.1007/BF00386231.\u003c/li\u003e\n\u003cli\u003ePritchard, H. von Caemmerer S. 2000. Biochemical models of leaf photosynthesis. 165pp. Victoria, Australia: CSIRO Publishing. \u003cem\u003eAnn. Bot.\u003c/em\u003e (2001) doi:10.1006/anbo.2000.1296.\u003c/li\u003e\n\u003cli\u003eWohlfahrt, G., Bahn, M., Haslwanter, A., Newesely, C. \u0026amp; Cernusca, A. Estimation of daytime ecosystem respiration to determine gross primary production of a mountain meadow. \u003cem\u003eAgric. For. Meteorol.\u003c/em\u003e \u003cstrong\u003e130\u003c/strong\u003e, 13\u0026ndash;25 (2005).\u003c/li\u003e\n\u003cli\u003eRogers, A. \u003cem\u003eet al.\u003c/em\u003e A roadmap for improving the representation of photosynthesis in Earth system models. \u003cem\u003eNew Phytol.\u003c/em\u003e \u003cstrong\u003e213\u003c/strong\u003e, 22\u0026ndash;42 (2017).\u003c/li\u003e\n\u003cli\u003eTcherkez, G., Boex-Fontvieille, E., Mah\u0026eacute;, A. \u0026amp; Hodges, M. Respiratory carbon fluxes in leaves. \u003cem\u003eCurr. Opin. Plant Biol.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 308\u0026ndash;314 (2012).\u003c/li\u003e\n\u003cli\u003eLaisk, A. K. Kinetics of photosynthesis and photorespiration of C\u003csub\u003e3\u003c/sub\u003e in plants. (1977).\u003c/li\u003e\n\u003cli\u003eAtkin, O. K., Millar, A. H., Gardestr\u0026ouml;m, P. \u0026amp; Day, D. A. Photosynthesis, Carbohydrate Metabolism and Respiration in Leaves of Higher Plants. in (2000). doi:10.1007/0-306-48137-5_7.\u003c/li\u003e\n\u003cli\u003eLaisk, A. Prying into the green black-box. \u003cem\u003ePhotosynth. Res.\u003c/em\u003e \u003cstrong\u003e154\u003c/strong\u003e, 89\u0026ndash;112 (2022).\u003c/li\u003e\n\u003cli\u003eKok, B. A critical consideration of the quantum yield of Chlorella photosynthesis. \u003cem\u003eEnzymologia\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1 (1948).\u003c/li\u003e\n\u003cli\u003eYin, X. \u003cem\u003eet al.\u003c/em\u003e Using combined measurements of gas exchange and chlorophyll fluorescence to estimate parameters of a biochemical C\u003csub\u003e3\u003c/sub\u003e photosynthesis model: a critical appraisal and a new integrated approach applied to leaves in a wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e) canopy. \u003cem\u003ePlant. Cell Environ.\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 448\u0026ndash;464 (2009).\u003c/li\u003e\n\u003cli\u003eYin, X., Sun, Z., Struik, P. C. \u0026amp; Gu, J. Evaluating a new method to estimate the rate of leaf respiration in the light by analysis of combined gas exchange and chlorophyll fluorescence measurements. \u003cem\u003eJ. Exp. Bot.\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 3489\u0026ndash;3499 (2011).\u003c/li\u003e\n\u003cli\u003eStinziano, J. R. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eThe rapid A\u0026ndash;Ci response: photosynthesis in the phenomic era\u003c/em\u003e. \u003cem\u003ePlant. Cell Environ\u003c/em\u003e. \u003cstrong\u003e40\u003c/strong\u003e, 1256\u0026ndash;1262 (2017).\u003c/li\u003e\n\u003cli\u003eTaylor, S. H. \u0026amp; Long, S. P. Phenotyping photosynthesis on the limit\u0026ndash;a critical examination of RACiR. \u003cem\u003eNew Phytol.\u003c/em\u003e \u003cstrong\u003e221\u003c/strong\u003e, 621\u0026ndash;624 (2019).\u003c/li\u003e\n\u003cli\u003eSchmiege, S. C., Sharkey, T. D., Walker, B., Hammer, J. \u0026amp; Way, D. A. Laisk measurements in the nonsteady state: Tests in plants exposed to warming and variable CO\u003csub\u003e2\u003c/sub\u003e concentrations. \u003cem\u003ePlant Physiol.\u003c/em\u003e \u003cstrong\u003e193\u003c/strong\u003e, 1045\u0026ndash;1057 (2023).\u003c/li\u003e\n\u003cli\u003eXu, Y., Fu, X., Sharkey, T. D., Shachar-Hill, Y. \u0026amp; Walker, B. J. The metabolic origins of non-photorespiratory CO\u003csub\u003e2\u003c/sub\u003e release during photosynthesis: A metabolic flux analysis. \u003cem\u003ePlant Physiol.\u003c/em\u003e \u003cstrong\u003e186\u003c/strong\u003e, 297\u0026ndash;314 (2021).\u003c/li\u003e\n\u003cli\u003eXu, Y., Wieloch, T., Kaste, J. A. M., Shachar-Hill, Y. \u0026amp; Sharkey, T. D. Reimport of carbon from cytosolic and vacuolar sugar pools into the Calvin\u0026ndash;Benson cycle explains photosynthesis labeling anomalies. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e119\u003c/strong\u003e, e2121531119 (2022).\u003c/li\u003e\n\u003cli\u003eTcherkez, G. \u003cem\u003eet al.\u003c/em\u003e Short-term effects of CO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e on citrate metabolism in illuminated leaves. \u003cem\u003ePlant, Cell Environ.\u003c/em\u003e (2012) doi:10.1111/j.1365-3040.2012.02550.x.\u003c/li\u003e\n\u003cli\u003eGriffin, K. L. \u0026amp; Heskel, M. Breaking the cycle: how light, CO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e affect plant respiration. \u003cem\u003ePlant Cell Env.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 498\u0026ndash;500 (2013).\u003c/li\u003e\n\u003cli\u003eTcherkez, G. \u0026amp; Atkin, O. K. Unravelling mechanisms and impacts of day respiration in plant leaves: an introduction to a virtual issue. \u003cem\u003eNew Phytologist\u003c/em\u003e vol. 230 5\u0026ndash;10 at (2021).\u003c/li\u003e\n\u003cli\u003eWilliams, M. \u0026amp; Randall, D. D. Pyruvate dehydrogenase complex from chloroplasts of \u003cem\u003ePisum sativum L\u003c/em\u003e . \u003cem\u003ePlant Physiol.\u003c/em\u003e (1979) doi:10.1104/pp.64.6.1099.\u003c/li\u003e\n\u003cli\u003eSharkey, T. D. \u0026amp; Weise, S. E. The glucose 6-phosphate shunt around the Calvin-Benson cycle. \u003cem\u003eJ. Exp. Bot.\u003c/em\u003e (2016) doi:10.1093/jxb/erv484.\u003c/li\u003e\n\u003cli\u003eSharkey, T. D., Preiser, A. L., Weraduwage, S. M. \u0026amp; Gog, L. Source of 12C in Calvin Benson cycle intermediates and isoprene emitted from plant leaves fed with \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e. \u003cem\u003eBiochem. J.\u003c/em\u003e (2020) doi:10.1042/bcj20200480.\u003c/li\u003e\n\u003cli\u003eXu, Y. \u0026amp; Fu, X. Reprogramming of plant central metabolism in response to abiotic stresses: a metabolomics view. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 5716 (2022).\u003c/li\u003e\n\u003cli\u003eXu, Y. \u003cem\u003eet al.\u003c/em\u003e Daylength variation affects growth, photosynthesis, leaf metabolism, partitioning, and metabolic fluxes. \u003cem\u003ePlant Physiol.\u003c/em\u003e \u003cstrong\u003e194\u003c/strong\u003e, 475\u0026ndash;490 (2024).\u003c/li\u003e\n\u003cli\u003eSharkey, T. D., Seemann, J. R. \u0026amp; Berry, J. A. Regulation of ribulose-1, 5-bisphosphate carboxylase activity in response to changing partial pressure of O2 and light in Phaseolus vulgaris. \u003cem\u003ePlant Physiol.\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 788\u0026ndash;791 (1986).\u003c/li\u003e\n\u003cli\u003eMcClain, A. M., Cruz, J. A., Kramer, D. M. \u0026amp; Sharkey, T. D. The time course of acclimation to the stress of triose phosphate use limitation. \u003cem\u003ePlant. Cell Environ.\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 64\u0026ndash;75 (2023).\u003c/li\u003e\n\u003cli\u003eCoast, O., Scafaro, A. P., Bramley, H., Taylor, N. L. \u0026amp; Atkin, O. K. Photosynthesis in newly developed leaves of heat-tolerant wheat acclimates to long-term nocturnal warming. \u003cem\u003eJ. Exp. Bot.\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 962\u0026ndash;978 (2024).\u003c/li\u003e\n\u003cli\u003eAinsworth, E. A. \u0026amp; Long, S. P. What have we learned from 15 years of free‐air CO\u003csub\u003e2\u003c/sub\u003e enrichment (FACE): A meta‐analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO\u003csub\u003e2\u003c/sub\u003e. \u003cem\u003eNew Phytol.\u003c/em\u003e \u003cstrong\u003e165\u003c/strong\u003e, 351\u0026ndash;372 (2005).\u003c/li\u003e\n\u003cli\u003eLeakey, A. D. B. \u003cem\u003eet al.\u003c/em\u003e Elevated CO\u003csub\u003e2\u003c/sub\u003e effects on plant carbon, nitrogen, and water relations: six important lessons from FACE. \u003cem\u003eJ. Exp. Bot.\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 2859\u0026ndash;2876 (2009).\u003c/li\u003e\n\u003cli\u003eLong, S. P., Ainsworth, E. A., Leakey, A. D. B., Nosberger, J. \u0026amp; Ort, D. R. Food for thought: lower-than-expected crop yield stimulation with rising CO\u003csub\u003e2\u003c/sub\u003e concentrations. \u003cem\u003eScience (80-. ).\u003c/em\u003e \u003cstrong\u003e312\u003c/strong\u003e, 1918\u0026ndash;1921 (2006).\u003c/li\u003e\n\u003cli\u003eKimball, B. A. Crop responses to elevated CO\u003csub\u003e2\u003c/sub\u003e and interactions with H2O, N, and temperature. \u003cem\u003eCurr. Opin. Plant Biol.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 36\u0026ndash;43 (2016).\u003c/li\u003e\n\u003cli\u003eRatcliffe, R. G. \u0026amp; Shachar-Hill, Y. Measuring multiple fluxes through plant metabolic networks. \u003cem\u003ePlant J.\u003c/em\u003e (2006) doi:10.1111/j.1365-313X.2005.02649.x.\u003c/li\u003e\n\u003cli\u003eStephanopoulos, G. N., Aristidou, A. a \u0026amp; Nielsen, J. \u003cem\u003eMetabolic Engineering: Principles and Methodologies\u003c/em\u003e. \u003cem\u003eMetabolic Engineering\u003c/em\u003e (1998).\u003c/li\u003e\n\u003cli\u003eLi, T. \u003cem\u003eet al.\u003c/em\u003e Re-Programing Glucose Catabolism in the Microalga Chlorella sorokiniana under Light Condition. \u003cem\u003eBiomolecules\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 939 (2022).\u003c/li\u003e\n\u003cli\u003eMa, F., Jazmin, L. J., Young, J. D. \u0026amp; Allen, D. K. Isotopically nonstationary 13C flux analysis of changes in Arabidopsis thaliana leaf metabolism due to high light acclimation. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 16967\u0026ndash;16972 (2014).\u003c/li\u003e\n\u003cli\u003eArrivault, S. \u003cem\u003eet al.\u003c/em\u003e Use of reverse-phase liquid chromatography, linked to tandem mass spectrometry, to profile the Calvin cycle and other metabolic intermediates in Arabidopsis rosettes at different carbon dioxide concentrations. \u003cem\u003ePlant J.\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 826\u0026ndash;839 (2009).\u003c/li\u003e\n\u003cli\u003eHasunuma, T. \u003cem\u003eet al.\u003c/em\u003e Metabolic turnover analysis by a combination of in vivo 13C-labelling from \u003csup\u003e13\u003c/sup\u003eCO\u003csub\u003e2\u003c/sub\u003e and metabolic profiling with CE-MS/MS reveals rate-limiting steps of the C\u003csub\u003e3\u003c/sub\u003e photosynthetic pathway in Nicotiana tabacum leaves. \u003cem\u003eJ. Exp. Bot.\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 1041\u0026ndash;1051 (2010).\u003c/li\u003e\n\u003cli\u003eSzecowka, M. \u003cem\u003eet al.\u003c/em\u003e Metabolic Fluxes in an Illuminated Arabidopsis Rosette. \u003cem\u003ePlant Cell\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 694\u0026ndash;714 (2013).\u003c/li\u003e\n\u003cli\u003eFu, X., Gregory, L. M., Weise, S. E. \u0026amp; Walker, B. J. Integrated flux and pool size analysis in plant central metabolism reveals unique roles of glycine and serine during photorespiration. \u003cem\u003eNat. Plants\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 169\u0026ndash;178 (2023).\u003c/li\u003e\n\u003cli\u003eXu, Y., Freund, D. M., Hegeman, A. D. \u0026amp; Cohen, J. D. Metabolic signatures of Arabidopsis thaliana abiotic stress responses elucidate patterns in stress priming, acclimation, and recovery. \u003cem\u003eStress Biol.\u003c/em\u003e (2022) doi:10.1007/s44154-022-00034-5.\u003c/li\u003e\n\u003cli\u003eHoagland, D. R. \u0026amp; Arnon, D. I. The water-culture method for growing plants without soil. \u003cem\u003eCirc. Calif. Agric. Exp. Stn.\u003c/em\u003e \u003cstrong\u003e347\u003c/strong\u003e, (1950).\u003c/li\u003e\n\u003cli\u003eSharkey, T. D. What gas exchange data can tell us about photosynthesis. \u003cem\u003ePlant Cell and Environment\u003c/em\u003e vol. 39 1161\u0026ndash;1163 at https://doi.org/10.1111/pce.12641 (2016).\u003c/li\u003e\n\u003cli\u003eGregory, L. M. \u003cem\u003eet al.\u003c/em\u003e The triose phosphate utilization limitation of photosynthetic rate: Out of global models but important for leaf models. \u003cem\u003ePlant Cell and Environment\u003c/em\u003e vol. 44 3223\u0026ndash;3226 at https://doi.org/10.1111/pce.14153 (2021).\u003c/li\u003e\n\u003cli\u003eVirtanen, P. \u003cem\u003eet al.\u003c/em\u003e Fundamental algorithms for scientific computing in python and SciPy 1.0 contributors. SciPy 1.0. \u003cem\u003eNat. Methods\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 261\u0026ndash;272 (2020).\u003c/li\u003e\n\u003cli\u003eMillard, P. \u003cem\u003eet al.\u003c/em\u003e IsoCor: Isotope correction for high-resolution MS labeling experiments. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 4484\u0026ndash;4487 (2019).\u003c/li\u003e\n\u003cli\u003eTrefely, S., Ashwell, P. \u0026amp; Snyder, N. W. FluxFix: Automatic isotopologue normalization for metabolic tracer analysis. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 1\u0026ndash;8 (2016).\u003c/li\u003e\n\u003cli\u003eYoung, J. D. INCA: a computational platform for isotopically non-stationary metabolic flux analysis. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 1333-1335 (2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"elevated CO2, high light, metabolic flux analysis, photosynthesis, plant central metabolism, starch/sucrose partitioning","lastPublishedDoi":"10.21203/rs.3.rs-3995199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3995199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the future, plants may encounter increased light and elevated CO\u003csub\u003e2\u003c/sub\u003e levels. How consequent alterations in photosynthetic rates will impact fluxes in photosynthetic carbon metabolism remains uncertain. Respiration in light (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e) is pivotal in plant carbon balance and a key parameter in photosynthesis models. Understanding the dynamics of photosynthetic metabolism and \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e under varying environmental conditions is essential for optimizing plant growth and agricultural productivity. However, measuring \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e under high light and high CO\u003csub\u003e2\u003c/sub\u003e (HLHC) conditions poses challenges using traditional gas exchange methods. In this study, we employed isotopically nonstationary metabolic flux analysis (INST-MFA) to estimate \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e and investigate photosynthetic carbon flux, unveiling nuanced adjustments in \u003cem\u003eCamelina sativa\u003c/em\u003e under HLHC. Despite numerous flux alterations in HLHC, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e remained stable. HLHC affects several factors influencing \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e, such as starch and sucrose partitioning, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e ratio, triose phosphate partitioning, and hexose kinase activity. Analysis of \u003cem\u003eA/C\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e curve operational points reveals that HLHC's major changes primarily stem from CO\u003csub\u003e2\u003c/sub\u003e suppressing photorespiration. Integration of these fluxes into a simplified model predicts changes in CBC labeling under HLHC. This study extends our prior discovery that incomplete CBC labeling is due to unlabeled carbon reimported during \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003eL\u003c/em\u003e\u003c/sub\u003e, offering insights into manipulating labeling through adjustments in photosynthetic rates.\u003c/p\u003e","manuscriptTitle":"The effects of photosynthetic rate on respiration in light, starch/sucrose partitioning, and other metabolic fluxes within photosynthesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 12:14:23","doi":"10.21203/rs.3.rs-3995199/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-11T03:44:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-06T19:34:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-26T03:29:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136772657530957159083451749243910054546","date":"2024-05-14T16:45:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6289b43e-2d3c-41a1-9d86-33abc93c89d9","date":"2024-04-22T18:43:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-12T09:27:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-18T23:24:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-15T08:48:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-15T08:44:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-28T00:50:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3424c05-e11b-4986-944c-b48b8bf25a36","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29563248,"name":"Biological sciences/Biochemistry"},{"id":29563249,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-03-17T16:02:41+00:00","versionOfRecord":{"articleIdentity":"rs-3995199","link":"https://doi.org/10.1038/s41598-025-88574-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-11 15:57:54","publishedOnDateReadable":"March 11th, 2025"},"versionCreatedAt":"2024-03-19 12:14:23","video":"","vorDoi":"10.1038/s41598-025-88574-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-88574-4","workflowStages":[]},"version":"v1","identity":"rs-3995199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3995199","identity":"rs-3995199","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}