Short title: Computational analysis of leaf energetics ... · 43 metabolism with full stoichiometric accounting of energy production and consumption. Different 44 scenarios of energy

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Short title: Computational analysis of leaf energetics 1 2 Corresponding Authors: 3 Lee J. Sweetlove 4 Professor of Plant Sciences, University of Oxford 5 Department of Plant Sciences, South Parks Road 6 Oxford, OX1 3RB, UK 7 [email protected] 8 9 10 R. George Ratcliffe 11 Professor and Head of Department of Plant Sciences, University of Oxford 12 Department of Plant Sciences, South Parks Road 13 Oxford, OX1 3RB, UK 14 [email protected] 15 16 17 Article title: Leaf energy balance requires mitochondrial respiration and export of 18 chloroplast NADPH in the light 19 20 Sanu Shameer1, R. George Ratcliffe1 and Lee J Sweetlove1 21 22 1 Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK 23 24 One-sentence summary: Metabolic modelling reveals why mitochondrial respiration and chloroplast 25 NAD(P)H export are required in illuminated leaves. 26 27 Author contributions: SS, RGR and LJS co-conceived the research and co-wrote the paper. SS and LJS 28 constructed and analysed the model 29 30 Funding: ERA-CAPS ('Simultaneous manipulation of source and sink metabolism for improved crop 31 yield'; BO 1482/18-1 | FE 552/33-1 | RE 1351/2-1 | SW 122/2-1) 32 33 34 35

36 ABSTRACT 37 Key aspects of leaf mitochondrial metabolism in the light remain unresolved. For example, there is 38 debate about the relative importance of exporting reducing equivalents from mitochondria for the 39 peroxisomal steps of photorespiration versus oxidation of NADH to generate ATP by oxidative 40 phosphorylation. Here, we address this and explore energetic coupling between organelles in the 41 light using a diel flux balance analysis model. The model included >600 reactions of central 42 metabolism with full stoichiometric accounting of energy production and consumption. Different 43 scenarios of energy availability (light intensity) and demand (source leaf versus a growing leaf) were 44 considered and the model was constrained by the non-linear relationship between light and CO2 45 assimilation rate. The analysis demonstrated that the chloroplast can theoretically generate 46 sufficient ATP to satisfy the energy requirements of the rest of the cell in addition to its own. 47 However, this requires unrealistic high light use efficiency and, in practice, the availability of 48 chloroplast-derived ATP is limited by chloroplast energy dissipation systems, such as non-49 photochemical quenching, and the capacity of the chloroplast ATP export shuttles. Given these 50

Plant Physiology Preview. Published on June 18, 2019, as DOI:10.1104/pp.19.00624

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limitations, substantial mitochondrial ATP synthesis is required to fulfil cytosolic ATP requirements, 51 with only minimal, or zero, export of mitochondrial reducing equivalents. The analysis also revealed 52 the importance of exporting reducing equivalents from chloroplasts to sustain photorespiration. 53 Hence, the chloroplast malate valve and triose phosphate - 3-phosphoglycerate shuttle are 54 predicted to have important metabolic roles, in addition to their more commonly discussed 55 contribution to the avoidance of photo-oxidative stress. 56 57 INTRODUCTION 58 The role of mitochondria in leaves in the light has long been a matter of debate (Nunes-Nesi et al., 59 2008; Dahal et al., 2017; Tcherkez et al., 2017; O’Leary et al., 2018). This is in part because 60 photosynthesis dominates the energetics of a leaf during the day, but also because the biochemistry 61 of leaf mitochondria during the day departs substantially from the conventional tricarboxylic acid 62 (TCA)-cycle-coupled-to-oxidative-phosphorylation mode that is the norm for non-photosynthetic 63 cells (Sweetlove et al., 2010; Millar et al., 2011; Tcherkez et al., 2012; O’Leary et al., 2018). Flux 64 through the complete set of reactions of the TCA cycle is largely absent in the light due to allosteric 65 inhibition of the pyruvate dehydrogenase enzyme (Tovar-Méndez et al., 2003). Instead, in C3 leaves, 66 the main source of NADH within leaf mitochondria during the day is from the oxidation of 67 photorespiratory glycine by the enzyme glycine decarboxylase. Within the photorespiratory cycle, 68 stoichiometrically equal amounts of NADH are generated by mitochondrial glycine decarboxylase 69 and consumed by peroxisomal hydroxypyruvate reductase (Bauwe et al., 2010). This had led to the 70 suggestion that all of the mitochondrial NADH generated by glycine decarboxylase would be 71 transferred to the peroxisome using a malate-oxaloacetate (OAA) metabolite shuttle system (Fig. 1). 72 Consistent with this, studies of Arabidopsis knockout mutants of the mitochondrial isoforms of 73 malate dehydrogenase (MDH) show they have pronounced growth defects under low CO2 conditions 74 that promote photorespiration (Tomaz et al., 2010; Lindén et al., 2016). However, the stoichiometric 75 equivalence within the photorespiratory cycle in terms of the respective production and 76 consumption of NADH in the mitochondrion and peroxisome belies the complexity of the process by 77 which reducing power is balanced across the metabolic system, including across subcellular 78 compartments. Indeed, within the photorespiratory cycle itself, additional reducing power is 79 required in the chloroplast to re-assimilate released ammonium. 80 81 Use of inhibitors of the mitochondrial ATP synthase and glycine decarboxylase suggests that a 82 proportion of the NADH from glycine oxidation is oxidised via the mitochondrial respiratory chain to 83 generate ATP by oxidative phosphorylation (Gardeström and Igamberdiev, 2016). For example, the 84 addition of oligomycin to barley protoplasts decreased the ATP:ADP ratio in both mitochondria and 85 cytosol whereas the chloroplast ATP:ADP ratio was unchanged (Gardeström and Wigge, 1988; 86 Krömer and Heldt, 1991; Wigge et al., 1993). Based on experiments of this kind, it has been 87 suggested that between 50–75% of the NADH from glycine oxidation is used for mitochondrial ATP 88 synthesis (Krömer, 1995), in distinct contrast to the argument that all of the NADH is exported to 89 maintain NADH balance within the photorespiratory cycle. 90 91 The apparent discrepancy between the Arabidopsis MDH mutants and the barley protoplast 92 experiments may be explained partly by the different experimental systems used. But the actual 93 balance between export of mitochondrial NADH and its oxidation for mitochondrial ATP synthesis is 94 likely to depend strongly on the overall energetic balance of the cell. Of crucial importance will be 95 the photosynthetic photon flux density (PPFD) experienced by the leaf in relation to the flux of the 96 Calvin-Benson-Bassham (CBB) cycle and other ATP/NAD(P)H-consuming metabolism in the 97 chloroplast. If excess energy is available, then the chloroplast can contribute to the peroxisomal 98 demand for NADH and the cytosolic demand for ATP, exporting NADH via a malate-OAA shuttle or 99 other dicarboxylate transporters (often referred to as the ‘malate valve’) (Selinski and Scheibe, 2018) 100 and both NADH and ATP via a triose-phosphate-3-phosphoglycerate (TP-3PGA) shuttle (Taniguchi 101



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and Miyake, 2012) – see Fig. 1. Recent analysis of in vivo cytosolic ATP levels in Arabidopsis 102 cotyledons using a fluorescent protein sensor are consistent with some export of chloroplast ATP 103 even under a relatively low PPFD of 296 mol m2 s-1 (Voon et al., 2018). The sensor response when 104 oligomycin was added was also consistent with the earlier barley protoplast studies, suggesting that 105 mitochondria contribute substantially to the cytosolic ATP pool. 106 107 However, none of these experiments measure the transfer fluxes for ATP and reducing equivalents 108 between organelles and the cytosol, and so quantitative conclusions about the relative importance 109 of different routes cannot be drawn. Moreover, although the studies all impose slightly different 110 light conditions, there has been no systematic investigation of the relative importance of the 111 different routes under different energetic states. Given that the energetic balance will depend not 112 only on the PPFD incident on the leaf, but also on the varying demands for ATP and reducing power 113 in the different subcellular compartments, this is a complex issue. Hundreds of reactions draw on 114 the ATP and NAD(P)H pools in the different subcellular compartments and the net energy balance of 115 the leaf is hence a system-level property of the metabolic network operating at steady state. 116 117 Computational models are powerful tools for understanding such system-level properties with two 118 main approaches being used to model metabolism, namely kinetic models and stoichiometric 119 models. Kinetic models capture the response of enzymes, transporters and electron transport chains 120 to their substrates and effectors, and they provide a powerful predictive tool for analysing the 121 response of the system to variable conditions (Baghalian et al., 2014). For leaf metabolism, this 122 approach has mainly focused on photosynthesis and associated processes in the chloroplast 123 (Rohwer, 2012). However, the large number of parameters in these models and the challenge of 124 solving the large system of ordinary differential equations generally limit the number of metabolic 125 steps that can be included. Although recent kinetic models of photosynthesis have been expanded 126 to include processes beyond photosynthesis, including, for example, the chloroplast costs of 127 photorespiration, starch synthesis and nitrate reduction (Zhu et al., 2013; Morales et al., 2018), 128 these models do not include a complete account of the energy demands beyond the chloroplast. 129 Therefore, these models do not accurately address the energetic interactions between the 130 chloroplast and the rest of the cell. 131 132 In contrast, a second modelling approach, flux balance analysis (FBA), is readily scaled to incorporate 133 the entire metabolic system of a cell (Sweetlove and Ratcliffe, 2011; Shi and Schwender, 2016; 134 Gomes de Oliveira Dal’Molin and Nielsen, 2018). This approach simplifies the mathematical 135 representation of the system by considering only the stoichiometry of the metabolic reactions and 136 uses experimental constraints and an optimisation objective to make predictions about flux 137 distributions in the metabolic network at steady state (Nikoloski et al., 2015). This approach has 138 been proven capable of making quantitatively realistic predictions of plant metabolic behaviour 139 (Basler et al., 2018; Moreira et al., 2018). A number of studies have applied FBA to C3 leaf 140 metabolism (de Oliveira Dal’Molin et al., 2010; Poolman et al., 2013; Arnold and Nikoloski, 2014; 141 Cheung et al., 2014; Poolman et al., 2014; Cheung et al., 2015; Lakshmanan et al., 2015; de Oliveira 142 Dal’Molin et al., 2016), but the specific questions of the role of mitochondria in the light and the 143 energetic coupling between subcellular compartments have not been explicitly considered. 144 145 An earlier and simplified stoichiometric model of leaf metabolism did consider the balance between 146 mitochondrial respiration and photosynthesis (Buckley and Adams, 2011). However, the study 147 focused specifically on respiratory CO2 release and the simplicity of the model meant that the energy 148 demands of the leaf were not fully accounted for (for example the costs associated with subcellular 149 transport of metabolites and ions). Moreover, the central question that we are asking, specifically 150 what is the fate of mitochondrial photorespiratory NADH, cannot be predictively explored in the 151 Buckley and Adams model because the fraction of NADH retained in the mitochondrion is a defined 152



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parameter rather than a prediction of the model. Hence, the aim of this work was to analyse a 153 stoichiometric model of leaf metabolism using a diel FBA framework with a specific focus on 154 metabolic and energetic interactions between chloroplasts and mitochondria in the context of the 155 energetic balancing of the system as a whole under scenarios of different energy availability or 156 utilisation. 157 158 RESULTS 159 160 Modelling framework and set-up 161 The engagement of mitochondrial ATP synthesis in a leaf in the light is likely to depend upon the 162 balance between the available light energy (PPFD) and energy demand. To assess this, we set up a 163 diel FBA model of primary metabolism of an Arabidopsis leaf (Shameer et al., 2018) to operate at 164 different light intensities. The charge- and proton-balanced model accounted for all reactions 165 required for autotrophic synthesis of the main components of biomass (cell wall, lipid, protein and 166 nucleotides). As a flux balance model, total production of energy (ATP) and reducing power (NADH) 167 must be balanced by their consumption. This will include the multitude of reactions and intracellular 168 transport steps required to generate a defined metabolic output, as well as maintenance costs. The 169 model contains full stochiometric descriptions of the electron transport chains of chloroplasts and 170 mitochondria, including alternative modes such as cyclic photophosphorylation and uncoupled 171 mitochondrial respiration (although these alternative modes were not constrained to specific 172 values). A diagrammatic representation of the model is shown in Fig. 2. The figure is also available in 173 its original cytoscape format as Supplemental Fig. S1 and this format is fully searchable by 174 metabolite or reaction name. The basic configuration of the model accounting for the day and night 175 phases of leaf metabolism and the constraints applied are shown in Fig. 3A. The model was free to 176 choose from a range of storage compounds that can be accumulated in either day or night and then 177 released in the complementary temporal phase. Two types of leaf were considered and were 178 constrained by defining the nature of the metabolic output. First, a mature source leaf, where the 179 output of the model was export of sugars and amino acids to the phloem in relative proportions 180 found in Arabidopsis phloem sap (Wilkinson and Douglas, 2003); and secondly, a growing leaf, where 181 the output of the model was synthesis of biomass components for growth of new leaf tissue as 182 defined by experimental measurements and described in the AraGEM model (Gomes de Oliveira 183 Dal’Molin et al., 2015). 184 185 To capture the non-linear response of photosynthesis to light, the model was constrained to 186 experimental measurements of the relationship between PPFD and net CO2 assimilation rate for 187 Arabidopsis (Donahue et al., 1997). This was achieved by varying the metabolic output of the model 188 until the net CO2 uptake flux predicted by the model matched the experimental net CO2 assimilation 189 rate (Fig. 3B). To account for light lost by reflectance and transmission, the amount of light available 190 to the model was set to be 90% of the PPFD (Zhu et al., 2010). The objective function of the FBA 191 optimization problem was to minimize the sum of all fluxes. The fluxes in an FBA solution are not 192 necessarily uniquely defined and this limitation was addressed by performing flux-variability analysis 193 (FVA) (Mahadevan and Schilling, 2003). Conclusions have only been drawn where the FVA range was 194 less than 10% of the flux value. All code and associated model files, including all constraints applied 195 to the model are available at https://github.com/ljs1002/Shameer-et-al-Role-of-mitochondria-in-C3-196 leaf-during-the-day 197 198 Experimental rates of photosynthesis are achievable in diel FBA models of both source and 199 growing leaves 200 Surprisingly, in both the source- and growing-leaf models, initial simulations revealed that it is 201 possible to achieve the experimentally constrained net CO2 assimilation rate without any flux 202 through the mitochondrial electron transport chain or mitochondrial ATP synthase. To examine the 203



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influence of the energy balance of the system on this result, we ran the model over a range of light 204 inputs (Fig. 4 A, B). The source leaf model was able to generate its output (export of sugars and 205 amino acids to the phloem) without requiring mitochondrial ATP synthesis as long as the light 206 intensity was 200 mol s-1 m-2or greater (Fig. 4A). For the growing leaf, a higher light intensity of 400 207 mol m2 s-1 was required before mitochondrial ATP synthesis was dispensable (Fig. 4B). The higher 208 light intensity is understandable because biosynthesis of biomass components requires more energy 209 than synthesis of sugars and amino acid for phloem export (as can be seen from the lower molar 210 output of biomass per unit light compared to that of sugars and amino acids – compare Figs 4A and 211 4B). In the growing leaf model at light intensities where daytime mitochondrial ATP synthesis was 212 required, there was a complex relationship between its flux and light intensity that is difficult to 213 intuitively rationalize, but likely has its origins in the interaction between a linear increase in energy 214 (light) input and a non-linear increase in CO2 assimilation and model output. The results 215 demonstrate that, provided sufficient light energy is available, daytime mitochondrial ATP synthesis 216 is not necessarily required in either source or growing leaves. 217 218 In the absence of mitochondrial ATP synthesis, high rates of export of ATP from the chloroplast 219 are required to satisfy the demand for cytosolic ATP. 220 To examine how the model meets the demand for cytosolic ATP in the absence of mitochondrial ATP 221 synthesis, we inspected the fluxes of the organellar metabolite shuttles (Fig. 1) in the source leaf 222 model with a light input of 200 mol s-1 m-2 (Fig. 4C). All predicted fluxes and flux variability ranges 223 from this model are shown in Supplemental Data S1. It can be seen that even at this low light 224 intensity there is excess energy in the chloroplast such that the majority of the cytosolic ATP 225 demand of a source leaf can be met from exported chloroplast ATP (Fig. 4C). In this simulation, the 226 export of ATP occurs mainly by the PEP-pyruvate shuttle with a much smaller export of ATP and 227 NADPH via the TP-3PGA shuttle. The peroxisomal requirement for NADH is met from the NADH 228 generated by mitochondrial glycine decarboxylase, with the NADH transferred to the peroxisome by 229 the action of mitochondrial and peroxisomal malate dehydrogenase and malate-OAA shuttles. As a 230 result, there is no NADH available for mitochondrial ATP synthesis. In contrast, in the model of a 231 growing leaf at the same light intensity (Fig. 4D), a portion of the mitochondrial NADH from glycine 232 decarboxylase is used for mitochondrial ATP synthesis. Because this reduces the amount of 233 mitochondrial NADH that can be transferred to the peroxisome, the chloroplast malate valve 234 becomes operational to export NADPH (as malate) from the chloroplast to enable the peroxisomal 235 NADH requirement to be met, with the chloroplast meeting 21% of the peroxisomal NADH demand. 236 This demonstrates the flexible and inter-dependent nature of the energy exchanges between 237 organelles and how these are dependent upon the overall energy status of the system. 238 239 The capacity of the chloroplast ATP shuttles dictates the use of mitochondrial respiration to meet 240 cytosolic ATP demands 241 Although it is known that the chloroplast can use metabolite shuttles to export ATP, especially in 242 conditions of energy excess (Gardeström and Igamberdiev, 2016), the metabolite shuttles that allow 243 export of chloroplastic ATP may lack the capacity to support the ATP export fluxes shown in Fig 4. 244 We therefore constrained the upper limit of the shuttles transporting ATP according to experimental 245 estimates of the maximal catalytic activities of the relevant enzymes. The chloroplast PEP-pyruvate 246 shuttle requires the activity of pyruvate orthophosphate dikinase to convert pyruvate to PEP in the 247 chloroplast (Fig. 1). This enzyme generally has a low activity in C3 leaves, and in Arabidopsis leaves, a 248 value of 0.9 ± 0.2 mol mg chlorophyll-1 h-1 has been reported for the maximal catalytic activity 249 (Ishimaru et al., 1997). This converts to 0.034 mol m-2 s-1 which was set as the upper limit for this 250 reaction flux in the model (for details of the conversion factors used, see Methods). Similarly, data 251 for the maximal catalytic activities of phosphorylating NAD-GAPDH (93 mol m-2 s-1; (Gibon et al., 252 2004)) and non-phosphorylating NADP-GAPDH (0.33 mol m-2 s-1; (Rius et al., 2006)) in Arabidopsis 253 leaves were used to constrain the upper limit of the chloroplast TP-3PGA shuttle. 254



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255 When these constraints were applied to a source leaf model with an incident PPFD of 200 mol s-1 256 m2, the predicted pattern of ATP and NADPH exchanges between organelles changed markedly 257 compared to those shown in Fig. 4. The full set of predicted fluxes is shown in Supplemental Data S2 258 and key fluxes are illustrated schematically in Fig. 5. The chloroplast PEP-pyruvate shuttle now 259 shows negligible flux, a result that is consistent with experimental observations. For example, 260 analysis of transgenic tobacco with reduced cytosolic pyruvate kinase showed that the main role of 261 this enzyme in leaves is associated with nocturnal respiration (Grodzinski et al., 1999). Moreover, 262 isotope labelling experiments in cocklebur leaves suggest that cytosolic PEP-pyruvate 263 interconversion in the light is in the wrong direction for chloroplast ATP export (Tcherkez et al., 264 2011). As an alternative to the chloroplast PEP-pyruvate shuttle, the constrained model uses the 265 chloroplast TP-3PGA shuttle to export excess chloroplast ATP to the cytosol (Fig. 5). However, 266 because this shuttle also leads to stoichiometric export of NAD(P)H, there is a limit to the amount of 267 ATP that can be exported in this way in the balanced system. In the constrained model, the flux 268 through the TP-3PGA shuttle is sufficient to provide the majority of the NADH required in the 269 peroxisome (Fig. 5). However, because this flux provides insufficient ATP to meet the cytosolic 270 demand, a substantial flux through mitochondrial ATP synthase was predicted (Fig. 5). Note that in 271 this model solution, the cytosolic phosphorylating NADP-GAPDH reaction carries zero flux. This 272 reaction would allow the export of ATP and NAD(P)H by the chloroplast TP-3PGA shuttle to be 273 partially uncoupled, causing the ratio of NAD(P)H to ATP exported to be increased. However, in the 274 scenario being modelled, the requirement is for more ATP than NAD(P)H to be exported, hence the 275 non-phosphorylating NADP-GAPDH reaction, which generates only NADPH and carries no flux. 276 Moreover, the maximum rate of the reaction from experimental data (0.33 mol m-2 s-1) means that 277 it cannot have a major bearing on the overall exchange of energy and reducing power between 278 compartments in this system. The conclusion from this analysis is that capacity limits in the 279 chloroplast ATP exporting system lead to the use of the mitochondrial respiratory chain to generate 280 sufficient ATP to meet cytosolic demands in a source leaf. This conclusion also holds for a growing 281 leaf (Supplemental Data S2). 282 283 To assess the validity of this conclusion, we looked at experimental data in which components of the 284 chloroplast TP-3PGA shuttle had been genetically manipulated. The three components are the triose 285 phosphate translocase (TPT), cytosolic GAPDH and cytosolic phosphoglycerate kinase (PGK). Of 286 these, manipulations of the TPT are not particularly informative because the TPT is also the principal 287 route of carbon export from the chloroplast and reduction in TPT leads to a major compensatory 288 alteration in carbon flows between starch and sucrose as a result (Häusler et al., 1998; Heineke et 289 al., 1994; Walters et al., 2004). However, mutant and transgenic plants in which the amount of the 290 other two components have been suppressed have less dramatic phenotypes and provide a more 291 suitable basis on which to assess the validity of the model prediction. Based on the results from the 292 model, we predict that reduction in the capacity of the chloroplast TP-3PGA shuttle would restrict 293 the supply of ATP to the cytosol. Further, we suggest that this is unlikely to be compensated by 294 increased mitochondrial ATP synthesis because the model shows that when ATP export from the 295 chloroplast is constrained, almost all of the available intramitochondrial NADH (from glycine 296 decarboxylase) is already being used for ATP synthesis (Fig. 4). The only way that mitochondrial ATP 297 synthesis could be further increased would be to import additional reductant into the mitochondria. 298 In the light this is unlikely to occur to any significant extent via pyruvate import because of inhibition 299 of pyruvate dehydrogenase (Tovar-Mendez et al., 2003) and is also unlikely via import of carboxylic 300 acids such as malate or citrate because of the redox poise of mitochondria in relation to the cytosol 301 (Igamberdiev and Gardeström, 2003). Hence, we would expect that suppression of cytosolic GAPDH 302 or PGK would lead to reduced cytosolic (and possibly total cellular) ATP levels and reduced rates of 303 sucrose synthesis. The latter might manifest as lower daytime sucrose levels and possibly slower 304



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growth, but could also be compensated for by increased starch accumulation and increased sucrose 305 export at night. 306 307 Consistent with this, Arabidopsis lines with decreased cytosolic GAPDH are slow growing and have 308 decreased levels of ATP in illuminated leaves (Rius et al., 2008). These lines also showed decreased 309 leaf respiration which is not consistent with our prediction. However, respiration rates in leaves can 310 only be measured in the dark and the measured rates are probably more reflective of nocturnal 311 metabolic modes where GADPH will be required to supply mitochondria with respiratory substrate 312 as pyruvate via glycolysis. Measurements in the GAPDH mutant lines were made 15 min after the 313 light was switched off and this is sufficient time for the chloroplast ATP supply to be depleted and 314 the ATP export capacity of the chloroplast to become irrelevant. Results from Arabidopsis knockouts 315 of the PGK3 gene encoding cytosolic PGK are also consistent with our predictions: these lines had 316 reduced growth but also reduced leaf sucrose and higher starch levels than WT (Rosa-Telléz et al., 317 2018). 318 319 Ultimately, the most direct test of the model predictions about the extent of mitochondrial ATP 320 synthesis in the light would be to measure the rate of respiration in illuminated leaves. 321 Unfortunately, there is no current method to do this due to the complication of photosynthetic gas 322 exchange occurring simultaneously with respiratory gas exchange. 323 324 Parsimonious use of light energy may explain the use of mitochondrial respiration during the day 325 Given that the chloroplast has an excess of energy, even at low light levels (Fig. 4), the question 326 arises as to why Arabidopsis leaves do not invest in a greater capacity of the chloroplast ATP 327 exporting metabolite shuttles to allow excess chloroplast ATP to be utilized in the cytosol. We have 328 already shown that the extent to which cytosolic ATP demand is met by the chloroplast and 329 mitochondrion is dependent on the overall energy balance of the system (Fig. 4). We therefore 330 looked again at how we deal with the variable light input into the model. The only reactions in the 331 model that can use the incoming photons are photosystem I and photosystem II (PSI & PSII) in the 332 chloroplast. Hence, according to the FBA problem, the sum of the fluxes of PSI and PSII must equal 333 the incident PPFD. In the source leaf model when PPFD is 200 mol s-1 m-2, this leads to an excess of 334 both ATP and NADPH being produced in the chloroplast in relation to the constrained amount of CO2 335 being fixed. Some of this excess energy is exported from the chloroplast, as shown in Figs. 4 and 5, 336 while the rest is dissipated using metabolic cycles ((Cheung et al., 2015); Supplemental Data S1 & 337 S2). 338 339 In practice, there are also non-photochemical quenching (NPQ) mechanisms that function to 340 dissipate excess light energy before it reaches the photosystems (Müller et al., 2001). NPQ functions 341 to prevent photooxidative stress (Li et al., 2009) and is activated by feedbacks from the chloroplast 342 reduction state and ATP demand (Ruban et al., 2012). In effect, NPQ serves to balance the 343 photosynthetic production of NADPH and ATP with the energy demand of the chloroplast. If the 344 balance is perfect, there will be little excess chloroplast NADPH or ATP, and the rest of the metabolic 345 system may therefore be required to be parsimonious with its use of reducing power and energy. 346 347 To assess this, we changed the way that we set the light input into the model. Rather than forcing all 348 of the incident light to be used for photosynthesis, we instead constrained the model so that the 349 incident PPFD represented an upper bound on the summed PSII and PSI fluxes, leaving the model to 350 use less light if this better satisfied the optimization objective function. Given that the optimization 351 objective is to minimize the sum of all fluxes to achieve a set metabolic output flux and the 352 utilization of light fluxes will contribute significantly to this flux sum, the model will likely use less 353 light than the maximum PPFD, in order to match photosynthetic ATP and NADPH production rates 354 with the total demand of the system for energy and reducing power. This indirectly mimics the 355



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operation of NPQ. The full set of flux predictions for this simulation are provided in Supplemental 356 Data S3. Note that in this simulation, no constraints were placed on the chloroplast ATP-export 357 shuttles and hence, apart from light utilization, it is comparable to that of Fig. 4 and Supplemental 358 Data S1. 359 360 As can be seen in Fig. 6A, only a fraction of the incident light is required by the photosystems in 361 order to meet the net CO2 assimilation constraint, with 42% of the incident light being used at a 362 PPFD of 200 mol s-1 m-2, falling to 23% at a PPFD of 1500 mol s-1 m-2. These predictions are broadly 363 in line with experimental measurements for Arabidopsis (Kalituho et al., 2006; Khanal et al., 2017). 364 Note that the light used in these simulations was not necessarily the theoretical minimum. 365 Comparison of the light used against those obtained when the model was re-run with minimization 366 of light use as the objective function revealed that slightly greater than the minimal amount of light 367 was used (1–4% greater) at PPFDs of greater than 200 mol s-1 m-2 (Supplemental Table S1). 368 Interestingly, above 200 mol s-1 m-2, the model began to use cyclic electron transport in the 369 chloroplast with the fraction of cyclic electron transport relative to linear electron transport rising 370 from 4–11% as light increased (Supplemental Table S2). Mitochondrial ATP synthesis was required 371 throughout the light range (Fig. 6B) suggesting that this is important to achieve the minimal sum of 372 fluxes and low light use while meeting the CO2 uptake and model output constraints. To test this, the 373 same simulation was run with mitochondrial ATP synthesis constrained to zero flux. This resulted in 374 a light utilization rate by the photosystems of 94.7 mol s-1 m-2, 13% more than the 83.9 mol s-1 m-2 375 used when mitochondrial ATP synthesis was operative. Hence, the operation of mitochondrial ATP 376 synthesis to satisfy the cytosolic ATP demand results in a more energy-use efficient state of the leaf 377 metabolic system. 378 379 Export of energy of reducing equivalents and ATP from the chloroplast is predicted even at low 380 light intensities 381 The fluxes obtained from the model at an incident PPFD of 200 mol s-1 m-2 but with no constraint on 382 the minimum amount of light used and no constraints on mitochondrial ATP synthesis or any of the 383 inter-compartmental ATP/reducing equivalent shuttles, revealed that even when only a small 384 proportion of the incident photons are being utilized by the photosystems, there is still some export 385 of reducing power and ATP from the chloroplast (Fig. 6C). The flux through the chloroplast malate 386 valve was dominant in this scenario, providing reducing equivalents in the form of malate to the 387 peroxisome and to the mitochondrion, the latter to support a high rate of mitochondrial ATP 388 synthesis (Fig. 6C). A smaller export of ATP and reducing power from the chloroplast occurred via 389 the TP-3PGA shuttle (Fig. 6C) and there was no flux through the chloroplast PEP-pyruvate shuttle. 390 Both chloroplast ATP-exporting shuttles were operating below experimentally estimated maximal 391 capacities (see ‘Limits to the capacity of chloroplast ATP shuttles necessitate mitochondrial 392 respiration to meet cytosolic ATP demands’ section). However, the flux of the chloroplast malate 393 valve (1.81 mol m-2 s-1) exceeded the upper flux limit of 0.75 mol m-2 s-1 estimated by kinetic 394 modelling (Fridlyand et al., 1998). When the upper bound of the chloroplast malate-OAA transporter 395 in our model was constrained to this lower value, additional flux through the TP-3PGA shuttle was 396 observed (Fig. 6D) (although still below the experimental maximum value) to provide the balance of 397 reducing equivalents for the peroxisome and the balance of the cytosolic ATP demand. 398 399 The fact that export of chloroplast ATP and reducing equivalents occurred even when light utilization 400 was being minimized by the model suggests that this energy export is necessary to enable the 401 experimentally constrained amount of carbon fixed to be converted to sugars and amino acids and 402 exported to the phloem. This is consistent with the observation that export of chloroplast ATP and 403 reducing equivalents occurs in all of the various simulations we have described, including that of a 404 growing leaf. Export of chloroplast ATP is dispensable, with the model still able to generate a 405 feasible solution when all chloroplast ATP shuttles were constrained to zero flux (Supplemental Data 406



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S4). This is unsurprising given the relatively small ATP export flux shown in Fig. 6C. However, if all 407 possible routes of NAD(P)H export from the chloroplast were blocked in the model, then the model 408 displayed lower light use efficiency, using 48% of the incident 200 mol s-1 m-2 PPFD compared to 409 42% without the NAD(P)H chloroplast export constraint (Supplemental data S5). This was because 410 the model was forced to use a proportion of fixed photoassimilate as a respiratory substrate in the 411 mitochondrion: starch accumulation was increased by 5% to allow high rates of vacuolar citrate 412 storage at night (~5 fold higher) which was then transferred to the mitochondrion during the day 413 where the action of isocitrate dehydrogenase generated NADH for respiration. The predicted rates 414 of citrate accumulation, an order of magnitude lower than the rate of starch accumulation (moles 415 citrate per night as moles hexose equivalents per day) are unrealistically high. 416 417 DISCUSSION 418 419 The role of mitochondria in the light 420 The modelling analysis described here provides a system-level view of energy balancing across the 421 leaf metabolic network and between subcellular compartments. As such, it provides insight into the 422 integrated role of mitochondria in leaves in the light. Given the capacity limits on the various 423 shuttles that permit export of reducing equivalents and ATP from the chloroplast, the analysis 424 predicts that mitochondrial ATP synthesis allows the leaf to meet the demand for ATP in the cytosol 425 (Fig. 5). This conclusion applies to both source leaves and to growing leaves that are actively 426 synthesizing new biomass. As a consequence, the majority of the mitochondrial NADH generated by 427 glycine oxidation is used for ATP biosynthesis by oxidative phosphorylation, and only a relatively 428 minor proportion is exported from the mitochondrion as malate to supply the NADH for the 429 peroxisomal reactions of the photorespiratory pathway. Hence, our analysis suggests that the main 430 function of leaf mitochondria during the day is to use photorespiratory glycine as a respiratory 431 substrate to generate ATP, with only a minor role in supplying peroxisomal NADH. This contrasts 432 with conclusions drawn from experimental analyses of Arabidopsis mutants deficient in one or both 433 isoforms of mitochondrial malate dehydrogenase (required to generate mitochondrial malate for 434 export) where a combination of strong reduction in growth exacerbated by low CO2 and an 435 alteration in abundance and labelling of glycine and serine at low CO2 emphasized the role of 436 mitochondria in supplying reducing equivalents for the peroxisome to sustain photorespiration 437 (Tomaz et al., 2010; Lindén et al., 2016). However, the experimental studies did not quantify the rate 438 of mitochondrial malate export and so conclusions about the magnitude of this flux relative to the 439 rate of mitochondrial glycine oxidation and oxidative phosphorylation are not possible. It is 440 important to note that the predicted flux of mitochondrial malate export in our model when 441 chloroplast ATP export was constrained was not zero (Fig. 5) and even a small imbalance in the 442 photorespiratory pathway can lead to rapid photorespiratory metabolite accumulation that is likely 443 to inhibit growth and metabolism (Timm et al., 2016). Our prediction that mitochondrial NADH from 444 glycine oxidation is predominantly used for ATP synthesis corroborates experimental studies using 445 respiratory inhibitors (Gardeström and Igamberdiev, 2016) and in vivo fluorescent sensors (Voon et 446 al., 2018). 447 448 Flexible energetic coupling between organelles 449 The flux maps of Figs. 4 to 6 show rather different flux modes responsible for providing energy and 450 reducing equivalents to the cytosol and peroxisome. In these flux modes, the chloroplast and 451 mitochondria make markedly different contributions, and different shuttle systems are used to 452 export organellar ATP and/or reducing equivalents to the cytosol. Yet in each of these scenarios, the 453 same rate of CO2 assimilation and the same rate of export of sugars and amino acids to the phloem 454 was achieved (see Supplemental Data S1-S4). This demonstrates that these different flux modes are 455 equivalent in terms of carbon-use efficiency. Hence, it is stoichiometrically possible for 456 mitochondrial respiration in the light to be entirely dispensed with, although this requires higher 457



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rates of export of ATP from the chloroplast than is likely possible in the plant given capacity limits of 458 the chloroplast ATP export shuttles. 459 460 The leaf has more energy than it needs, even at low light intensities 461 One of the more surprising observations that emerged from this quantitative analysis of energy 462 utilization in leaves is that even at a low incident light intensity of 200 mol s-1 m-2 there is 463 substantially more energy available than is required for the leaf to generate experimentally 464 observed rates of CO2 assimilation and to utilize the fixed carbon for synthesis of sucrose and amino 465 acids for export to the phloem. Our analysis shows that this is connected to the use of efficient 466 methods of energy metabolism within the model: in simulations in which minimal amounts of light 467 were used, the model used mitochondrial ATP synthesis to meet cytosolic ATP demands (Fig. 6). The 468 contribution of mitochondrial respiration to high energy use efficiency can be explained by the 469 superior stoichiometry of the process, yielding approximately twice as much ATP per photon 470 compared to linear electron transport in the chloroplast (Kramer and Evans, 2011b). As a result, at a 471 PPFD of 200 mol s-1 m-2 the model needs less than half the available light energy to be absorbed by 472 the photosystems. Indeed, in real leaves, much of the incident light is dissipated before it reaches 473 the photosystems by NPQ as is apparent from experimental measurements in a range of plants 474 (Demmig-Adams et al., 2008; Khanal et al., 2017). Typically, the fraction of light energy dissipated by 475 NPQ increases as PPFD increases, reflected in our model by a decrease in the fraction of light used as 476 PPFD increases (Fig. 6). Consequently, much of the focus has been on the photoprotective role of 477 NPQ at high light. Less attention has been paid to the fact that NPQ continues even at low light 478 (Demmig-Adams et al., 2008; Khanal et al., 2017). It has been suggested that NPQ is necessary 479 because of sink limitation; i.e. the capacity of the system to utilize photochemical energy for growth 480 of sink tissues is less than the energy produced if 100% of the incident light is used for 481 photosynthesis (Adams et al., 2013). Our analysis suggests that this is not the whole story, because 482 even if all of the experimentally constrained assimilated carbon is used for export of sugars and 483 amino acids to the phloem, the model does not require 100% of the incident light energy. From this 484 work it is not possible to say whether use of energy-efficient mitochondrial respiration drives 485 dissipation of more light energy by NPQ or vice versa. But the apparent importance of metabolic 486 energy efficiency in our models, even when light energy is in excess, may explain the impact of 487 transgenic introduction of more energy-efficient bypasses of photorespiration on overall plant 488 productivity (South et al., 2019). 489 490 The metabolic importance of export of chloroplast reducing equivalents 491 Whereas mitochondrial respiration is stoichiometrically dispensable with little impact on the flux 492 distribution, the same is not true for chloroplast export of reducing equivalents, without which 493 unrealistically high rates of nocturnal citrate accumulation are required to sustain mitochondrial 494 respiration. This can be rationalized as follows: when chloroplast export of ATP is below the capacity 495 limits of the export shuttles, mitochondrial ATP synthesis is required to meet the cytosolic ATP 496 demand; the stoichiometrically balanced production and consumption of NADH in the 497 photorespiratory cycle means that if NADH from mitochondrial oxidation of photorespiratory glycine 498 is used for mitochondrial ATP synthesis, then another source of NADH must be used to sustain the 499 peroxisomal hydroxypyruvate reductase reaction. The chloroplast is the only net source of reducing 500 equivalents in the system. Hence, if mitochondrial ATP synthesis is active, there must be an 501 increased rate of export of reducing equivalents from the chloroplast during the day, or an increased 502 rate of starch accumulation to provide reducing equivalents (citrate was predicted) from the night 503 phase. The prevailing view in the literature is that export of chloroplast reducing equivalents during 504 the day mainly occurs in conditions of excess light energy and functions to regulate the 505 NADPH/NADP ratio in the chloroplast stroma to avoid photo-oxidative stress (Taniguchi and Miyake, 506 2012; Selinski and Scheibe, 2018). The analysis here demonstrates that export of chloroplast 507 reducing equivalents during the day is an important component of the energy balance of the whole 508



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system and not just the chloroplast, allowing the metabolic demands of ATP in the cytosol and NADH 509 in the peroxisome to be met in an efficient manner. This places the malate valve and other shuttle 510 systems capable of exporting chloroplast NADPH to the cytosol as central players for achieving a 511 balanced leaf metabolism and illustrate the degree of energetic coupling between organelles in 512 leaves. 513 514 Limits of the current approach 515 Although the FBA modelling presented here demonstrates how a stoichiometric model can provide 516 insight into system-level properties of metabolic networks such as energy balancing, this type of 517 modelling does not incorporate regulation at the enzyme/protein or metabolite level. Despite this, 518 FBA can generate remarkably realistic flux predictions, (Williams et al., 2010; Chen et al., 2011; 519 Cheung et al., 2013) and this is partly because regulation at the enzyme level (i.e. 520 feedback/feedforward regulation by allosteric effectors) acts to maintain metabolite steady state, 521 which is a core constraint of FBA. Nevertheless, some details of the flux predictions from FBA may be 522 incorrect as they are not captured in this indirect way. For example, the direction of flux through 523 near-equilibrium reactions, such as malate dehydrogenase, is strongly influenced by the 524 concentrations of the reactants and coenzymes involved. Thus, the direction of the malate-525 oxaloacetate shuttle that transfers reducing equivalents between the mitochondria and cytosol is 526 dependent on the respective NADH:NAD ratios in the mitochondrial matrix and cytosol. Because of 527 the steady state constraint, FBA models are blind to these ratios and such shuttles are free to run in 528 either direction in our model. Constraints could be added on shuttle directionality but this would 529 require accurate data on subcellular NADH:NAD ratios in the different scenarios being measured and 530 this is hard data to acquire. 531 532 Another example of the influence of regulatory mechanisms is cyclic versus linear electron transport 533 in the chloroplast. In many of the solutions presented here, photosynthetic electron transport is 534 exclusively linear (i.e. equal fluxes of the PSII and PSI reactions), even though our model is capable of 535 cyclic electron transport (Cheung et al., 2015). Cyclic electron transport is considered to be an 536 important mechanism by which the ratio NAD(P)H:ATP is adjusted to match demand (Finazzi and 537 Johnson, 2016). In practice, the fraction of cyclic electron transport is very low under non-stress 538 conditions (Morales et al., 2018) and so this will have little quantitative impact on the simulations 539 presented here. The one scenario in which cyclic electron transport is used in the simulations we 540 analysed was when incident PPFD was greater than 200 mol s-1 m-2 in a model free to use less than 541 the incident PPFD and with the constraints of using the metabolic output to attain an experimental 542 rate of net CO2 assimilation, while minimizing the sum of fluxes of the system. The complex nature of 543 these constraints makes it difficult to intuitively understand why different energy rebalancing 544 methods are chosen by the model. 545 546 Another issue is that FBA tends not to choose parallel routes to achieve the same end. Hence, our 547 model prefers to dissipate excess reducing power in the chloroplast using the xanthophyll cycle 548 rather than using, for example, uncoupled mitochondrial respiration (Vanlerberghe et al., 2016; 549 Dahal et al., 2017). To accurately predict the relative engagement of the variety of energy 550 rebalancing systems used in a leaf, one would have to capture the regulatory mechanisms that 551 respond to system-level readouts such as energy and redox poise in different subcellular 552 compartments. And ultimately, this is a matter of enzyme/transporter kinetics and their effect on 553 the concentrations of ATP/ADP and NAD(P)H/NAD(P)+ in different subcellular compartments. For 554 example, the rate of NADH generation by glycine oxidation in C3 plants can exceed the capacity at 555 which it can be oxidized in the cytochrome branch of the respiratory chain and this triggers NADH 556 oxidation via the uncoupled alternative respiratory chain (Igamberdiev et al., 1997). Various kinetic 557 models have included aspects of this regulation in the chloroplast in order to predict the 558 engagement of cyclic electron transport, non-photochemical quenching, and other electron sinks 559



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such as the water-water cycle, malate valve and nitrate reduction (Riznichenko et al., 2009; Zaks et 560 al., 2012; Matuszyńska et al., 2016; Matuszyńska and Ebenhöh, 2018). But these models lack 561 consideration of extra-chloroplastic metabolism that, as our FBA modelling demonstrates, can have 562 a major bearing on the overall energy balance of the system and hence the engagement of energy-563 balancing regulatory mechanisms. Ultimately, this modelling problem might be solved by coupling 564 kinetic models that capture essential regulatory aspects of the system with stoichiometric-FBA 565 models that can make predictions of flux distributions in large metabolic networks. This is extremely 566 challenging because tight coupling of the two approaches renders the FBA problem non-linear, 567 calling for new approaches for analysis of more loosely coupled models, which is beyond the scope 568 of the current study. 569 570 In the shorter term, some of these aspects might be better captured indirectly in FBA models by the 571 use of additional data-based constraints. For example, as comprehensive, quantitative proteomic 572 datasets for plants become more widely available, it will be possible to systematically introduce 573 capacity constraints on enzymes and transporters (when Kcat is known) which may generate more 574 precisely defined flows of energy and reductant between subcellular compartments (Davidi and 575 Milo, 2017). Additionally, systematic accounting of reaction thermodynamics can also help 576 (Hamilton et al., 2013), but this will only substantially improve the models if it can be combined with 577 measurements of in vivo mass action ratios which remains challenging in eukaryotes due to 578 subcellular compartmentation. 579 580 Summary 581 The diel FBA framework presented here allows leaf metabolic network fluxes to be predicted for 582 different scenarios of energy availability (light intensity versus CO2 assimilation) and utilization 583 (source leaf versus growing leaf). The analysis revealed that capacity limits of metabolite shuttles for 584 export of chloroplast ATP mean that a substantial proportion of NADH generated by 585 photorespiratory glycine oxidation must be respired by mitochondria to generate sufficient ATP to 586 meet cytosolic demands. This, in turn, requires that reducing equivalents are exported from the 587 chloroplast in order to meet the peroxisomal demand for NADH to keep the photorespiratory cycle 588 running. This analysis provides a new metabolic perspective for the role of the malate valve and 589 other chloroplast NADPH-exporting shuttles and emphasizes the importance of mitochondrial 590 respiration in illuminated leaves. 591 592 MATERIALS AND METHODS 593 594 Computational modelling 595 A stoichiometric model of central metabolism “PlantCoreMetabolism_v1_2” (Shameer et al., 2018) 596 was used. The model is available as an xml file in SBML format at https://github.com/ljs1002/ 597 Shameer-et-al-Role-of-mitochondria-in-C3-leaf-during-the-day. FBA problems were set up and 598 solved using custom Python 2 scripts, the COBRAPy package (Ebrahim et al., 2013) and the CPLEX 599 solver. All code required to reproduce the results in this paper are available as a series of Jupyter 600 notebook files at https://github.com/ljs1002/Shameer-et-al-Role-of-mitochondria-in-C3-leaf-during-601 the-day. A list of constraints common to all simulations is given in Supplemental Information S1. 602 603 Unit conversion factors for experimentally measured enzyme capacity constraints 604 In some simulations, constraints were applied to the upper bounds of reactions that are components 605 of the metabolite shuttles that export ATP and/or reducing equivalents from the chloroplast. 606 Experimental data for the maximal catalytic capacity of these enzymes was available in units of nmol 607 min-1 g fresh weight-1 or nmol min-1 mg protein-1, in contrast to the flux unit of the model which was 608 mol s-1 m-2. To convert, the following data were used: specific leaf area of Arabidopsis ~ 50 m2 kg 609 dry weight-1 (i.e. 1 g dry weight = 0.05 m2) (Weraduwage et al., 2015); Arabidopsis dry weight:fresh 610



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weight ratio = 0.088 (i.e. 1 g dry weight = 11.4 g fresh weight) (Arnold and Nikoloski, 2014). Hence, 611 11.4 g fresh weight = 0.05m2; 1 g fresh weight = 0.0043 m2. Therefore, 1 nmol min-1 g fresh weight-1 612 = 0.00387597 mmol s-1 m-2 (1/0.0043/1000/60). To convert from per mg protein to per g fresh 613 weight, we used a value of 5.7 mg protein per g fresh weight of Arabidopsis rosette leaves (Strand et 614 al., 2000). 615 616 SUPPLEMENTAL DATA 617 618 619 Supplemental Table S1. Comparison of light used by the photosystems at different incident light 620 intensities (PPFD mol s-1 m-2) for two modelling scenarios. 621 622 Supplemental Table S2. Predicted relative use of linear- versus cyclic-electron transport in the 623 chloroplast at different incident light intensities (PPFD). 624 625 Supplemental Information S1. List of constraints common to all simulations 626 627 Supplemental Dataset S1. Predicted fluxes and flux variability analysis in source- and growing-leaf 628 diel FBA models with a light input of 200 µmol s-1 m-2 and constrained to achieve an experimentally 629 measured CO2 assimilation rate. 630 631 Supplemental Dataset S2. Predicted fluxes and flux variability analysis in source- and growing-leaf 632 diel FBA models with a light input of 200 µmol s-1 m-2 and constrained to achieve an experimentally 633 measured CO2 assimilation rate plus additional constraints on the upper bounds of reactions of the 634 chloroplast ATP-exporting triose phosphate-3-phosphoglycerate shuttle and phosphoenolpyruvate-635 pyruvate shuttle to match experimental measurements of the maximal catalytic capacity of the 636 respective enzymes. 637 638 Supplemental Dataset S3. Predicted fluxes and flux variability analysis in source- and growing-leaf 639 diel FBA models with a light input of 200 µmol s-1 m-2 and constrained to achieve an experimentally 640 measured CO2 assimilation rate. 641 642 Supplemental Dataset S4. Predicted fluxes and flux variability analysis in a source leaf diel FBA 643 models with a light input of 200 µmol s-1 m-2 and constrained to achieve an experimentally measured 644 CO2 assimilation rate. 645 646 Supplemental Dataset S5. Predicted fluxes and flux variability analysis in a source leaf diel FBA model 647 with a light input of 200 µmol s-1 m-2 and constrained to achieve an experimentally measured CO2 648 assimilation rate. 649 650 Supplemental Data F1. Cytoscape format version of the model diagram shown in Fig. 2. 651 652 653 Acknowledgements 654 Funding from ERA-CAPS ('Simultaneous manipulation of source and sink metabolism for improved 655 crop yield'; BO 1482/18-1 | FE 552/33-1 | RE 1351/2-1 | SW 122/2-1) is acknowledged. 656 657 FIGURE LEGENDS 658 659 Figure 1. Schematic showing the principal routes by which ATP and NAD(P)H can be generated and 660 moved between organelles in the leaf model. Purple lines indicate sites of generation. Blue lines 661



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indicate mechanisms to move ATP and / or NAD(P)H between organelles and the cytosol. Each is 662 numbered as follows: 1, chloroplast malate valve to shuttle NAD(P)H from chloroplast to the cytosol; 663 2a, triose-phosphate-3PGA shuttle to export NADH and ATP from chloroplast to the cytosol (using 664 cytosolic phosphorylating glyceraldehyde 3-phosphate dehydrogenase and phosphoglycerate 665 kinase); 2b, triose-phosphate-3PGA shuttle to export NADPH from chloroplast to the cytosol (using 666 cytosolic non-phosphorylating glyceraldehyde 3-phosphate dehydrogenase); 3, PEP-pyruvate shuttle 667 to export ATP from the chloroplast to the cytosol; 4, the mitochondrial malate valve to shuttle NADH 668 from the mitochondrion to the cytosol; 5 malate-oxaloacetate shuttle to transfer cytosolic reducing 669 equivalents as malate to the peroxisome; 6 mitochondrial adenylate nucleotide translocase to 670 export ATP from the mitochondrion to the cytosol; 7 import of ATP into the chloroplast via the 671 plastidial nucleotide transporter, NTT. Note that the NTT plays a role in energising the chloroplast at 672 night but is not thought to be important during in the illuminated leaf (Flügge et al., 2011) and was 673 therefore constrained to zero flux during the day in the FBA model. Abbreviations: 1,3BPG, 1,3-674 bisphosphoglycerate; 3PGA, 3-phosphoglycerate; CBB, Calvin-Benson-Bassham; cETC, chloroplast 675 electron transport chain; GA3P, glyceraldehyde 3-phosphate; Gly, glycine; mETC, mitochondrial 676 electron transport chain; Mal, malate; OAA, oxaloacetic acid; PEP, phosphoenolpyruvate; PYR, 677 pyruvate; Ser, serine. 678 679 Figure 2. Diagrammatic representation of the metabolites and reactions present in the model. 680 Metabolites and cofactors involved in large number of reactions (such as CO2, H2O, ATP, ADP, Pi) are 681 omitted for the sake of clarity. Metabolites are represented by coloured circles and reactions by grey 682 diamonds. The metabolite circle colour corresponds to its subcellular localization (blue, cytosol; red, 683 mitochondria; green, plastid; orange, peroxisome; purple, vacuole; grey, apoplast; yellow, 684 environment; black represents model outputs). In this bipartite graph, the nodes are reactions and 685 metabolites and the edges are reaction-metabolite associations. Reaction and metabolite names 686 follow the conventions used in the PlantCyc metabolic pathways database (Schläpfer et al., 2017). 687 688 Figure 3. Schematic showing the set-up and simulation procedure for a leaf diel FBA metabolic 689 model. A, Constraints that were applied to the model. Blue arrows indicate input constraints. Green 690 arrows indicate output constraints. Metabolites in the dashed box are those that can be stored in 691 one phase of the model (day or night) and passed to the other for utilisation. B, Procedure used to 692 simulate leaf metabolism accounting for the non-linear relationship between photosynthetic rate 693 and light intensity. Data shown were extracted from Donahue et al., 1997. Abbreviations: cit, citrate; 694 fum, fumarate; M, maintenance; mal, malate; suc, sucrose 695 696 Figure 4. Daytime energy shuttling between organelles in models of source and growing leaves. A 697 and C, Source leaf. B and D, Growing leaf. A and B, Flux of mitochondrial ATP synthase (M ATPase; 698 green) and diel model output (blue) at a range of light intensities (PPFD). C and D, Schematic of key 699 fluxes transferring ATP or NAD(P)H between subcellular compartments at a PPFD of 200 mol s-1 m-2 700 (indicated by the yellow ‘lightning bolt’ arrow). The widths of the arrows are scaled relative to the 701 flux as indicated under the diagram. Blue arrows are fluxes that involve ATP and green arrows are 702 those that involve NAD(P)H. For more details of the metabolite shuttles, see Fig. 2. Abbreviations are 703 as in Fig. 2 and: ANT, adenine nucleotide translocase; GDC, glycine decarboxylase; tp-pga, triose 704 phosphate - 3-phosphoglycerate. 705 706 Figure 5. Daytime energy shuttling between organelles in a model of a source leaf at a PPFD of 200 707 mol s-1 m-2 s-1 with the upper bounds of the chloroplast ATP exporting metabolite shuttles 708 constrained to experimental values of the relevant enzyme maximum catalytic activities. Features of 709 the schematic are as in Fig. 4 and abbreviations are as in Figs. 2 & 4. 710 711 Figure 6. Daytime energy shuttling between organelles in a model of a source leaf in which light 712



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utilization by the chloroplast photosystems is permitted to be less than the incident PPFD. A, The 713 photons used by the photosystems at a range of incident PPFD values. B, Daytime mitochondrial ATP 714 synthase flux at a range of incident PPFD values. C, Schematic of the principal fluxes transferring ATP 715 or NAD(P)H between subcellular compartments at an incident PPFD of 200 mol m-2 s-1 and no 716 constraints on chloroplast metabolite shuttles. D, As in C but with a constraint of 0.75 mol m-2 s-1 on 717 the maximum flux of the chloroplast malate-OAA shuttle (Fridlyand et al., 1998). Features of the 718 schematic are as in Fig. 4 and abbreviations are as in Figs. 2 & 4. 719 720 721 722 723 724 References 725 Adams WW, Muller O, Cohu CM, Demmig-Adams B (2013) May photoinhibition be a consequence, 726

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Chloroplast Peroxisome MitochondrionGly

Ser

Mal

OAA

Mal

OAA

NAD+NADH

Mal OAA

NAD+ NADH

Gly

SerOH-PyrGlycerate

GlyoxylateGlycolate

NADHNAD+

mETC

cETC

NADPH ATP

thylakoid

PYR

ATPADP

PEP

PYRPEP

ATPAMP GA3PGA3P

3PGAATP

ADP1,3BPG

NADHNAD+

3PGA

NADHNAD+

Mal

OAA

Mal

OAA

NADP+

NADPH

CBB cycle

1

2a

3

4

ADPATP

ADP

ATP

6

Figure 1. Schematic showing the principal routes by which ATP and NAD(P)H can be generated and moved between organelles in the leaf model. Purple lines indicate sites of generation. Blue lines indicate mechanisms to move ATP and / or NAD(P)H between organelles and the cytosol. Each is numbered as follows: 1, chloroplast malate valve to shuttle NAD(P)H from chloroplast to the cytosol; 2a, triose-phosphate-3PGA shuttle to export NADH and ATP from chloroplast to the cytosol (using cytosolic phosphorylating glyceraldehyde 3-phosphate dehydrogenase and phosphoglycerate kinase); 2b, triose-phosphate-3PGA shuttle to export NADPH from chloroplast to the cytosol (using cytosolic non-phosphorylating glyceraldehyde 3-phosphate dehydrogenase); 3, PEP-pyruvate shuttle to export ATP from the chloroplast to the cytosol; 4, the mitochondrial malate valve to shuttle NADH from the mitochondrion to the cytosol; 5 malate-oxaloacetate shuttle to transfer cytosolic reducing equivalents as malate to the peroxisome; 6 mitochondrial adenylate nucleotide translocase to export ATP from the mitochondrion to the cytosol; 7 import of ATP into the chloroplast via the plastidial nucleotide transporter, NTT. Note that the NTT plays a role in energising the chloroplast at night but is not thought to be important during in the illuminated leaf (Flügge et al., 2011) and was therefore constrained to zero flux during the day in the FBA model. Abbreviations: 1,3BPG, 1,3-bisphosphoglycerate; 3PGA, 3-phosphoglycerate; CBB, Calvin-Benson-Bassham; cETC, chloroplast electron transport chain; GA3P, glyceraldehyde 3-phosphate; Gly, glycine; mETC, mitochondrial electron transport chain; Mal, malate; OAA, oxaloacetic acid; PEP, phosphoenolpyruvate; PYR, pyruvate; Ser, serine.

2bNADP+

NADPH

5

2PG

Glycolate

Glycerate

ATPADP+Pi

ATP7

ADP+Pi

NAD(P)+NAD(P)H

ADP

ATP



Figure 2. Diagrammatic representation of the metabolites and reactions present in the model. Metabolites and cofactors involved in large number of reactions (such as CO2, H2O, ATP, ADP, Pi) are omitted for the sake of clarity. Metabolites are represented by coloured circles and reactions by grey diamonds. The metabolite circle colour corresponds to its subcellular localization (blue, cytosol; red, mitochondria; green, plastid; orange, peroxisome; purple, vacuole; grey, apoplast; yellow, environment; black represents model outputs). In this bipartite graph, the nodes are reactions and metabolites and the edges are reaction-metabolite associations. Reaction and metabolite names follow the conventions used in the PlantCyc metabolic pathways database (Schläpfer et al., 2017).



Figure 3. Schematic showing the set-up and simulation procedure for a leaf diel FBA metabolic model. A, Constraints that were applied to the model. Blue arrows indicate input constraints. Green arrows indicate output constraints. Metabolites in the dashed box are those that can be stored in one phase of the model (day or night) and passed to the other for utilisation. B, Procedure used to simulate leaf metabolism accounting for the non-linear relationship between photosynthetic rate and light intensity. Data shown were extracted from Donahue et al., 1997. Abbreviations: cit, citrate; fum, fumarate; M, maintenance; mal, malate; suc, sucrose

Day Night starch

suc

cit

fum

mal

NO3-

photons

CO2

O2

CO2

O2

AAs

M cost M cost

NO3-

3:2

3 : 1

biomass

OR

source

leaf

growing

leaf

phloem suc & AAs

Net

CO

2 a

ssim

ilation

(mm

ol m

-2 s

-1)

PPFD (mmol m-2 s-1)

A. Model constraints B. Workflow for flux balance analysis

Step 1: analyse

experimental

data to obtain the

net CO2

exchange rate at

a given light

intensity.

Step 2: Run the model multiple times

(photon input constrained to the value

specified above; objective function minimize

sum of fluxes), each time with a different set

value for the model output. Identify the

output value that gives a net CO2 uptake by

the model during the day that matches the

experimental value.

Step 3. Run the model with the output value

constrained to that identified in Step 2 with

photon input and objective value as in Step

2. Solve model using minimization of sum of

fluxes as the optimization objective

MO

DE

L O

UT

PU

T



mal-o

aa

shuttle

mETC

GDC

peroxisome

chloroplast mitochondrion

mal-o

aa

shuttle

mETC

GDC

CYTOSOLIC ATP DEMAND

peroxisome


pep-pyr

shuttle pep-pyr

shuttle

NAD(P)H ATP 2 mmol m2 s-1 2 mmol m2 s-1

mal-o

aa

shuttle

tp-p

ga

shuttle

source leaf growing leaf

200


A B

C D

Figure 4. Daytime energy shuttling between organelles in models of source and growing leaves. A and C, Source leaf. B and D, Growing leaf. A and B, Flux of mitochondrial ATP synthase (M ATPase; green) and diel model output (blue) at a range of light intensities (PPFD). C and D, Schematic of key fluxes transferring ATP or NAD(P)H between subcellular compartments at a PPFD of 200 mmol s-1 m-2 (indicated by the yellow ‘lightning bolt’ arrow). The widths of the arrows are scaled relative to the flux as indicated under the diagram. Blue arrows are fluxes that involve ATP and green arrows are those that involve NAD(P)H. For more details of the metabolite shuttles, see Fig. 2. Abbreviations are as in Fig. 2 and: ANT, adenine nucleotide translocase; GDC, glycine decarboxylase; tp-pga, triose phosphate - 3-phosphoglycerate.

200

mal-oaa

shuttle

mal-o

aa

shuttle

mal-oaa

shuttle

AN

T

tp-p

ga

shuttle

AN

T



mal-o

aa

shuttle

AN

T

mETC

GDC

peroxisome


NADH ATP 2 mmol m2 s-1 2 mmol m2 s-1

tp-p

ga

shuttle

200


Figure 5. Daytime energy shuttling between organelles in a model of a source leaf at a PPFD of 200 mmol s-1 m-2 s-1 with the upper bounds of the chloroplast ATP exporting metabolite shuttles constrained to experimental values of the relevant enzyme maximum catalytic activities. Features of the schematic are as in Fig. 4 and abbreviations are as in Figs. 2 & 4.

mal-o

aa

shuttle

mal-oaa

shuttle

pep-pyr

shuttle



mal-o

aa

shuttle

AN

T

mETC

GDC

peroxisome


pep-pyr

shuttle


Daytim

e m

itochon

dri

al A

TP

synth

ase flu

x (m

mol m

2 s

-1)

% o

f pho

tos u

sed for

pho

tochem

istr

y

mal-o

aa

shuttle

AN

T

mETC

GDC

peroxisome


pep-pyr

shuttle


mal-o

aa

shuttle

A B

C D

Figure 6. Daytime energy shuttling between organelles in a model of a source leaf in which light utilization by the chloroplast photosystems is permitted to be less than the incident PPFD. A, The photons used by the photosystems at a range of incident PPFD values. B, Daytime mitochondrial ATP synthase flux at a range of incident PPFD values. C, Schematic of the principal fluxes transferring ATP or NAD(P)H between subcellular compartments at an incident PPFD of 200 mmol m-2 s-1 and no constraints on chloroplast metabolite shuttles. D, As in C but with a constraint of 0.75 mmol m-2 s-1 on the maximum flux of the chloroplast malate-OAA shuttle (Fridlyand et al., 1998). Features of the schematic are as in Fig. 4 and abbreviations are as in Figs. 2 & 4.

mal-oaa

shuttle

tp-p

ga

shuttle

200 200

mal-o

aa

shuttle

tp-p

ga

shuttle

mal-oaa

shuttle

NADH ATP 2 mmol m2 s-1 2 mmol m2 s-1



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https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Arnold&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=Bottom-up metabolic reconstruction of arabidopsis and its application to determining the metabolic costs of enzyme production.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

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https://www.ncbi.nlm.nih.gov/pubmed?term=Baghalian K%2C Hajirezaei M%2DR%2C Schreiber F %282014%29 Plant metabolic modeling%3A achieving new insight into metabolism and metabolic engineering%2E Plant Cell 26%3A 3847%26%23x&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Baghalian&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Baghalian&as_ylo=2014&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Basler G%2C Fernie AR%2C Nikoloski Z %282018%29 Advances in metabolic flux analysis toward genome%2Dscale profiling of higher organisms%2E Biosci Rep 38%3A BSR&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Basler&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=Advances in metabolic flux analysis toward genome-scale profiling of higher organisms.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=Advances in metabolic flux analysis toward genome-scale profiling of higher organisms.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Basler&as_ylo=2018&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Bauwe H%2C Hagemann M%2C Fernie AR %282010%29 Photorespiration%3A players%2C partners and origin%2E Trends Plant Sci&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Bauwe&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=Photorespiration: players, partners and origin&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=Photorespiration: players, partners and origin&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Bauwe&as_ylo=2010&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Buckley TN%2C Adams MA %282011%29 An analytical model of non%2Dphotorespiratory CO2 release in the light and dark in leaves of C3 species based on stoichiometric flux balance%2E Plant Cell Environ 34%3A 89%26%23x&dopt=abstract

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https://www.ncbi.nlm.nih.gov/pubmed?term=de Oliveira Dal%27Molin CG%2C Orellana C%2C Gebbie L%2C Steen J%2C Hodson MP%2C Chrysanthopoulos P%2C Plan MR%2C McQualter R%2C Palfreyman RW%2C Nielsen LK %282016%29 Metabolic reconstruction of Setaria italica%3A a systems biology approach for integrating tissue%2Dspecific omics and pathway analysis of bioenergy grasses%2E Front Plant Sci&dopt=abstract

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https://www.ncbi.nlm.nih.gov/pubmed?term=Donahue RA%2C Poulson ME%2C Edwards GE %281997%29 A method for measuring whole plant photosynthesis in Arabidopsis thaliana%2E Photosynth Res 52%3A 263%26%23x&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Donahue&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=A method for measuring whole plant photosynthesis in Arabidopsis thaliana.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=A method for measuring whole plant photosynthesis in Arabidopsis thaliana.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Donahue&as_ylo=1997&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Ebrahim A%2C Lerman JA%2C Palsson BO%2C Hyduke DR %282013%29 COBRApy%3A COnstraints%2DBased Reconstruction and Analysis for Python%2E BMC Syst Biol&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Ebrahim&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=COBRApy: COnstraints-Based Reconstruction and Analysis for Python.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

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https://www.ncbi.nlm.nih.gov/pubmed?term=Finazzi G%2C Johnson GN %282016%29 Cyclic electron flow%3A facts and hypotheses%2EPhosyn Res&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Finazzi&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=Cyclic electron flow: facts and hypotheses&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=Cyclic electron flow: facts and hypotheses&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Finazzi&as_ylo=2016&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Fl�gge UI%2C H�usler RE%2C Ludewig F%2C Gierth M %282011%29 The role of transporters in supplying energy to plant plastids%2E J Exp Bot&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Flügge&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=The role of transporters in supplying energy to plant plastids&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=The role of transporters in supplying energy to plant plastids&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Flügge&as_ylo=2011&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Fridlyand LE%2C Backhausen JE%2C Scheibe R %281998%29 Flux control of the malate valve in leaf cells%2E 349%3A 290%26%23x&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Fridlyand&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=Flux control of the malate valve in leaf cells.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=Flux control of the malate valve in leaf cells.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Fridlyand&as_ylo=1998&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Gardestr�m P%2C Igamberdiev AU %282016%29 The origin of cytosolic ATP in photosynthetic cells%2E Physiol Plant 157%3A 367%26%23x&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Gardeström&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=The origin of cytosolic ATP in photosynthetic cells.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=The origin of cytosolic ATP in photosynthetic cells.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Gardeström&as_ylo=2016&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Gardestr�m P%2C Wigge B %281988%29 Influence of photorespiration on ATP%2FADP Ratios in the chloroplasts%2C mitochondria%2C and cytosol%2C studied by rapid fractionation of barley %28Hordeum vulgare%29 protoplasts%2E Plant Physiol&dopt=abstract

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https://scholar.google.com/scholar?as_q=Influence of photorespiration on ATP/ADP Ratios in the chloroplasts, mitochondria, and cytosol, studied by rapid fractionation of barley (Hordeum vulgare) protoplasts&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Gardeström&as_ylo=1988&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Gibon Y%2C Blaesing OE%2C Hannemann J%2C Carillo P%2C Hohne M%2C Hendriks JH%2C Palacios N%2C Cross J%2C Selbig J%2C Stitt M %282004%29 A robot%2Dbased platform to measure multiple enzyme activities in arabidopsis using a set of cycling assays%3A comparison of changes of enzyme activities and transcript levels during diurnal cycles and in prolonged darkness%2E Plant Cell 16%3A 3304%26%23x&dopt=abstract

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https://scholar.google.com/scholar?as_q=A robot-based platform to measure multiple enzyme activities in arabidopsis using a set of cycling assays: comparison of changes of enzyme activities and transcript levels during diurnal cycles and in prolonged darkness.&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=A robot-based platform to measure multiple enzyme activities in arabidopsis using a set of cycling assays: comparison of changes of enzyme activities and transcript levels during diurnal cycles and in prolonged darkness.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Gibon&as_ylo=2004&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Gomes de Oliveira Dal%27Molin C%2C Nielsen LK %282018%29 Plant genome%2Dscale reconstruction%3A from single cell to multi%2Dtissue modelling and omics analyses%2E Curr Opin Biotechnol 49%3A 42%26%23x&dopt=abstract

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https://scholar.google.com/scholar?as_q=Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses.&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Gomes&as_ylo=2018&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Gomes de Oliveira Dal%27Molin C%2C Quek L%2DE%2C Saa PA%2C Nielsen LK %282015%29 A multi%2Dtissue genome%2Dscale metabolic modeling framework for the analysis of whole plant systems%2E Front Plant Sci&dopt=abstract

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https://www.ncbi.nlm.nih.gov/pubmed?term=Grodzinski B%2C Jiao J%2C Knowles VL%2C Plaxton WC %281999%29 Photosynthesis and carbon partitioning in transgenic tobacco plants deficient in leaf cytosolic pyruvate kinase%2E Plant Physiol&dopt=abstract

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https://scholar.google.com/scholar?as_q=Photosynthesis and carbon partitioning in transgenic tobacco plants deficient in leaf cytosolic pyruvate kinase&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=Photosynthesis and carbon partitioning in transgenic tobacco plants deficient in leaf cytosolic pyruvate kinase&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Grodzinski&as_ylo=1999&as_allsubj=all&hl=en&c2coff=1

https://www.ncbi.nlm.nih.gov/pubmed?term=Hamilton JJ%2C Dwivedi V%2C Reed JL %282013%29 Quantitative assessment of thermodynamic constraints on the solution space of genome%2Dscale metabolic models%2E Biophys J&dopt=abstract

https://scholar.google.com/scholar?as_q=&num=10&as_occt=any&as_sauthors=Hamilton&hl=en&c2coff=1

https://scholar.google.com/scholar?as_q=Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models&num=10&btnG=Search+Scholar&as_epq=&as_oq=&as_eq=&as_occt=any&as_publication=&as_yhi=&as_allsubj=all&hl=en&lr=&c2coff=1

https://scholar.google.com/scholar?as_q=Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models&num=10&btnG=Search+Scholar&as_occt=any&as_sauthors=Hamilton&as_ylo=2013&as_allsubj=all&hl=en&c2coff=1

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Short title: Computational analysis of leaf energetics ... · 43 metabolism with full stoichiometric accounting of energy production and consumption. Different 44 scenarios of energy

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