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

doi:10.21203/rs.3.rs-3995199/v1

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

https://doi.org/10.21203/rs.3.rs-3995199/v1

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In the future, plants may encounter increased light and elevated CO₂ 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 CO₂ (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 CO₂ 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

The atmospheric concentration of CO₂ 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. CO₂ 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 CO₂ 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 CO₂ levels (HLHC), impacting plant metabolism. Although increased light and CO₂ 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 CO₂ release during photosynthesis represents a small yet critical CO₂ 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 CO₂ uptake by photosynthesis and CO₂ 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 CO₂ assimilation (A) in response to changes in intercellular CO₂ concentrations (A/C_i curves) at low CO₂ 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 (Φ₂) 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 CO₂ 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, ¹³C and ¹⁴C 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 CO₂ component of HLHC was nearly entirely responsible for the increased photosynthetic rate observed. The effect of HLHC on starch/sucrose ratio was determined by ¹⁴C 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.

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 CO₂ conditions are marked with asterisks (Student’s two tailed t-test, P ≤ 0.05).
Parameters	Unit	Control	High light high CO₂
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 ¹³CO₂ 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 ¹³C labeling, the TCA cycle intermediates analyzed, along with most amino acids derived from them, displayed minimal labeling within the 30-min time frame (Fig. 2a). 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. 2b1). In photorespiratory intermediates, HLHC led to varied effects: serine labeling increased, glycine decreased, and 2-phosphoglycolate remained unchanged compared to the control (Fig. 2a and Fig. 2b1). Regarding sugars, HLHC showed inconsistent trends: sucrose glucosyl and fructosyl moieties increased (Fig. 2b2), while glucose and fructose decreased in ¹³C label incorporation (Fig. 2b3). 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. 2a and Fig. 2b3), while aspartate reached 53% compared to 57% at 30 min (Fig. 2a and Fig. 2b2).

Characterization of photosynthesis and A/Ci curve fitting

Under HLHC, we observed an increase in A, a higher intercellular CO₂ (c_i) concentration, lower stomatal conductance (g), and a reduced v_o/v_c ratio. At normal light and CO₂, 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 CO₂. When both light and CO₂ 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 CO₂; the increased light was only of secondary importance, if at all. The ratio of v_O to v_C decreased with increasing CO₂ from 0.30 to 0.20 (Table 1). Photosynthesis approached the TPU limitation at high light and CO₂ 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 CO₂ 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 CO₂, amounting to 8.1 umol CO₂ g^− 1 FW h^− 1 in the control condition and 9.0 µmol CO₂ 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 CO₂ 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 CO₂ release through oxidative decarboxylation reactions, with phosphoenolpyruvate (PEP) carboxylation to oxaloacetate OAA (1.1 and 2.1 µmol CO₂ g^− 1 FW h^− 1 for control and HLHC), offsetting CO₂ release from the decarboxylation of pyruvate to acetyl-CoA (0.8 and 1.7 µmol CO₂ g^− 1 FW h^− 1 for control and HLHC), α-ketoglutarate decarboxylation (0.8 and 1.7 µmol CO₂ g^− 1 FW h^− 1 for control and HLHC), and CO₂ 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 CO₂ 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 CO₂ conditions are marked with asterisks (Student’s two tailed t-test, P ≤ 0.05).
Fractions of starch and sucrose	Control	High light high CO₂
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 CO₂ assimilation rate by the ratio of starch or sucrose to total ¹⁴C 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.

Impact of HLHC on photosynthesis and biomass

High light and CO₂ caused a significant increase in A. The switch to high CO₂ caused the calculated CO₂ partial pressure in the chloroplast (C_c) to increase. The increase in CO₂ 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 CO₂, 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. CO₂ and light were increased only during the measurement, we expect that leaves grown in the higher CO₂ 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 CO₂. 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 CO₂ (C_i), lower stomatal conductance (g), and a reduced v_o/v_c ratio were observed in HLHC. Elevated CO₂ has been shown to enhance photosynthesis by increasing CO₂ assimilation rates, leading to higher plant growth and yield in many C₃ species ^37,38. This CO₂ 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 ¹³CO₂ 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 C₃ 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 CO₂ 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 CO₂ g^− 1 FW h^− 1. The α-ketoglutarate decarboxylation flux increased from 0.8 to 1.7 µmol CO₂ g^− 1 FW h^− 1. The rate of oxidative decarboxylation of pyruvate for citrate synthesis increased from 0.4 to 0.7 µmol CO₂ 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. 2a 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 ¹³C labeling for citrate and malate (Fig. 2a, 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 ¹³C labeling for aspartate and glutamate. Alanine, resulting from the transamination of pyruvate, exhibits a higher rate of synthesis (Fig. 1) but slower fractional ¹³C-labeling in HLHC (Fig. 2b3). This may be attributed to a larger inactive pool of alanine in HLHC, resulting in saturating ¹³C-labeling kinetics.

Sucrose glucosyl and fructosyl moieties exhibit faster and higher ¹³C enrichment in HLHC(Fig. 2b2). In contrast, free glucose and fructose show slower and lower ¹³C 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 CO₂ 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 ¹³C 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 ¹³C 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 CO₂ 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 ¹³C labeling of the starch precursor ADPG, reaching 93% in control and 96% in HLHC (Fig. 2a). Similarly, sucrose displayed a higher absolute synthesis rate in HLHC, aligning with the faster ¹³C labeling of the sucrose precursor UDPG, measured at 79% in control and 88% in HLHC (Fig. 2a). 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 CO₂ 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 CO₂ assimilation, responding to changes in both CO₂ 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 CO₂, 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 research²³, which employed the DAT technique to estimate R_L in paper birch and hybrid poplar and concluded that R_L remains consistent under elevated CO₂ 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 CO₂ 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 ¹²C 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 ¹²C was leaving the system at a slow rate it did not require very large inputs of ¹²C to keep the degree of label from getting to 100%.

In the future, photosynthesis may be speeded up because of increasing CO₂. This will affect flux through many ancillary pathways associated with photosynthesis, for example photorespiration. This work focused on CO₂ 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 ¹³CO₂ feeding to be higher in HLHC.

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 ¹³CO₂ Labeling Experiments

Gas exchange and ¹³CO₂ labeling experiments followed established procedures ²⁴ with slight modifications. Fully expanded leaves from 4-week-old plants were used for ¹³CO₂ labeling. Measurements, including CO₂ 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 [CO₂] 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 CO₂ conditions had a reference [CO₂] 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 ¹³CO₂ labeling commenced after 20–30 minutes in both control and high light high CO₂ conditions to ensure a stable photosynthetic state. A pseudo-steady-state metabolism was assumed during the labeling period as the CO₂ source transitioned to ¹³CO₂ 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 CO₂ 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 CO₂ 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., 2022²⁵, 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 study²⁵, 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 ¹³C 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 ¹³C atoms) to n (all carbons labeled with ¹³C), 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 ¹³C 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 ¹⁴C labeling experiments during steady-state photosynthetic assimilation, with slight modifications ⁸. A mixture of ¹⁴CO₂ gas and CO₂-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 [CO₂] 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 ¹⁴CO₂ at a concentration of 400 µL L^− 1. The ¹⁴C-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.

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 ¹³C labeling experiments, mass spectrometry analyses, and INST-MFA. JAMK conducted the exponential decay analysis. SEW provided guidance and assistance to YX in the ¹⁴CO₂ 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.

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 CO₂ 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, CO₂, and abscisic acid. Plant Physiol. 77, 617–620 (1985).
Tcherkez, G. et al. Leaf day respiration: low CO₂ 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 CO₂ assimilation in leaves of C₃ 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 C₃ 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 C₃ 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 CO₂ 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 CO₂ 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 CO₂ and O₂ 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, CO₂ and O₂ 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 ¹³CO₂. 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 CO₂ enrichment (FACE): A meta‐analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO₂. New Phytol. 165, 351–372 (2005).
Leakey, A. D. B. et al. Elevated CO₂ 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 CO₂ concentrations. Science (80-. ). 312, 1918–1921 (2006).
Kimball, B. A. Crop responses to elevated CO₂ 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 ¹³CO₂ and metabolic profiling with CE-MS/MS reveals rate-limiting steps of the C₃ 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).

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You are reading this latest preprint version

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

Status:

Version 1

Abstract

Figures

Introduction

Results

Metabolic fluxes of central metabolism

Respiration in the light

Carbon partitioning shift

Triose phosphate partitioning and hexokinase activity

Discussion

Impact of HLHC on photosynthesis and biomass

Photosynthetic and ancillary metabolism

Photorespiration flux

Polyexponential model fitting

Carbon partitioning dynamics

R_L

Simplified model

Conclusions

Methods

Plant Growth and Labeling Conditions

Statement of Seed Source and Plant Guidelines

Gas Exchange and ¹³CO₂ Labeling Experiments

Polyexponential model fitting

Metabolite Extraction and Mass Spectrometry Analyses

Ion-Pair Chromatography – Tandem Mass Spectrometry (IPC-MS/MS) Analysis

Anion Exchange Chromatography – Tandem Mass Spectrometry (AEC-MS/MS) Analysis

Gas Chromatography-Mass Spectrometry (GC-MS) Analysis

Analysis of Mass Spectrometry Data

Isotopologue Network, Flux Determination, and Assessment of Flux Precision

Starch Synthesis Rate and Fractions of Starch and Sucrose

Simplified model

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

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

Status:

Version 1

Abstract

Figures

Introduction

Results

Metabolic fluxes of central metabolism

Respiration in the light

Carbon partitioning shift

Triose phosphate partitioning and hexokinase activity

Discussion

Impact of HLHC on photosynthesis and biomass

Photosynthetic and ancillary metabolism

Photorespiration flux

Polyexponential model fitting

Carbon partitioning dynamics

RL

Simplified model

Conclusions

Methods

Plant Growth and Labeling Conditions

Statement of Seed Source and Plant Guidelines

Gas Exchange and 13CO2 Labeling Experiments

Polyexponential model fitting

Metabolite Extraction and Mass Spectrometry Analyses

Ion-Pair Chromatography – Tandem Mass Spectrometry (IPC-MS/MS) Analysis

Anion Exchange Chromatography – Tandem Mass Spectrometry (AEC-MS/MS) Analysis

Gas Chromatography-Mass Spectrometry (GC-MS) Analysis

Analysis of Mass Spectrometry Data

Isotopologue Network, Flux Determination, and Assessment of Flux Precision

Starch Synthesis Rate and Fractions of Starch and Sucrose

Simplified model

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

R_L

Gas Exchange and ¹³CO₂ Labeling Experiments