The coding sequences for CO and DGAT (S205A mutation; Winichayakul et al., 2013) were optimized for expression in rice and placed in a back-to-back orientation under the control of the rice CAB and RUBISCO small subunit promoters, respectively. For Agrobacterium-mediated transformation, the expression cassette was cloned into the pCAMBIA1300 binary vector, while for particle bombardment, the cassette was cloned into a pUC-based vector.

Transformed lines were generated from L. perenne callus induced from immature inflorescences and transformed by Agrobacterium-mediated transformation (DGAT + CO1-4) or particle bombardment (DGAT + CO5). Plants from Agrobacterium-mediated transformation were generated as per Bajaj et al. (2006) while plants from microprojectile bombardment were generated as per Altpeter et al. (2000).

In experiment 1, we examined multiple DGAT + CO lines, varying in DGAT + CO accumulation. Five DGAT + CO ryegrass lines were selected from three genetic backgrounds (i.e., two DGAT + CO lines were generated from an “Alto” cultivar individual and three DGAT + CO lines were generated from two “Impact” cultivar individuals) and propagated asexually via the production of clonal ramets as per Beechey-Gradwell et al. (2020). Each DGAT + CO line was then designated an arbitrary label, DGAT + CO1-5 [DGAT + CO5 was previously reported as either “HL” or “6205” by Beechey-Gradwell et al. (2020)]. To eliminate growth form or tiller age differences between ramets, all DGAT + CO lines, and respective non-transformed (NT) controls, underwent three rounds of propagation. During each round, five ramets of five tillers each were potted and grown for 4 weeks. All plants were grown in a controlled temperature room with ~600 μmol photons m⁻² s⁻¹ red/blue light provided by 600W NanaPro LED lights (LEDgrowlights, Hamilton, New Zealand), 20°C/15°C day/night temperature and 12 h photoperiod, with humidity uncontrolled and commonly fluctuating between 65 and 75% during the day and 80 and 90% at night. In Jan 2019, 40 × 5-tiller ramets were produced for each line, 10 of which were immediately harvested to confirm comparable starting weights (Supplementary Table S1). The remaining 30 ramets per line were transplanted into 1.3 L sand and grown for 3 weeks to establish a root system. During this “establishment phase,” pots were flushed thrice weekly with 100 ml of basal nutrient media described in Andrews et al. (1989) containing N as 2 mM KNO₃. Following the establishment phase, shoot material was harvested 5 cm above the sand and used to rank plants from smallest to largest. The five smallest and five largest plants per line were discarded and 10 of the remaining 20 plants per line were randomly selected and the remaining shoots (0–5 cm above pot surface) and roots were harvested, oven dried and weighed (post-establishment harvest). The remaining 10 plants per line were grown for another 3 weeks, with 4 mM NH₄NO₃ applied as described above, and harvested (final harvest). Relative growth rate was calculated as per Poorter (1989); RGR = (ln W₂–ln W₁)/(t₂–t₁) where W₁ = post-establishment dry weight, W₂ = final harvest dry weight, t₁ = day 22 and t₂ = day 43.

Experiments 2 and 3 provide a detailed analysis of a single transgenic high lipid line, DGAT + CO5, and the corresponding control, NT3. Experiment 2 relates to unreported leaf N and gas exchange data from plants grown at ambient CO₂ as part of a larger experiment described in Beechey-Gradwell et al. (2020). Plant growth conditions, preparation and establishment for experiment 2 was similar to experiment 1 [additional details provided in Beechey-Gradwell et al. (2020)], however, during the regrowth phase, different N treatments were introduced, and plants were regrown under one of five levels of NO₃⁻ (1–7.5 mM).

For experiment 3, in which we examined rubisco contents, mesophyll conductance and within-leaf N allocation (details below), an additional 12 ramets of DGAT + CO5 and NT3 were prepared and grown as per experiment 1 with two minor alterations; regrowth phase N supply was delivered as 5 mM NO₃⁻, and growth irradiance, provided by the above LEDs, had reduced slightly to ~550 μmol photons m⁻² s⁻¹.

Protein samples were prepared by collecting four fresh L. perenne leaf blades (approximately 2 cm long) in a 2-ml screw cap micro tube containing 150 μl of sterile H₂O, 200 μl of 2x protein loading buffer [1:2 diluted 4x lithium dodecyl sulphate (LDS) sample buffer (Life Technologies, Carlsbad, CA, United States)], 8 M urea, 5% (v/v) β-mercaptoethanol, and 0.2 M dithiothreitol and 40 μl of NUPAGE^™ sample reducing agent (NP0009, ThermoFisher Scientific, Waltham, MA, United States). The mixtures were homogenized using the Bead Ruptor 24 model (Omni International, Kennesaw, GA, United States). The samples were heated at 70°C for 10 min, centrifuged at 20,000 g for 30 s and collected for the soluble protein suspension. Equal quantities of proteins were determined and separated by SDS-PAGE (Mini-PROTEAN® TGX stain-free^™ precast gels; Bio-Rad, Hercules, CA, United States) and blotted onto Bio-Rad polyvinylidene difluoride (PVDF) membrane for the DGAT1-V5 immunoblotting. Equivalent amounts of proteins were separated on gradient 4–12% Bis-Tris gel (NUPAGE; Life Technologies) and blotted onto nitrocellulose membrane for the CO immunoblotting. Immunoblotting was performed as described previously in Winichayakul et al. (2013). Chemiluminescent activity was developed using WesternBright ECL spray (Advansta, Menlo Park, CA, United States) and visualized by ChemiDoc^™ imaging system (Bio-Rad Laboratories Inc.). Volume intensity of monomeric forms of the protein was quantified using Image Lab^™ software for PC version 5.2.1 (Bio-Rad Laboratories Inc.).

Gas exchange measurements for each experiment were completed 2–3 weeks after the post-establishment defoliation. Three tillers were selected per plant and the youngest fully expanded leaves (determined by the appearance of a leaf collar and selected to minimize the effects of self-shading) of each were simultaneously acclimated in the leaf chamber of either a LI-COR 6800 (experiments 1 and 3) or a LI-COR 6400 infrared gas exchange system (LI-COR Biosciences Ltd., Nebraska, United States; experiment 2) under the following conditions; 400 ppm CO₂, 70% relative humidity, 20°C and PAR of either 600, 1500 or 550 μmol photons m⁻² s⁻¹ red/blue light (for experiments 1, 2, and 3, respectively). After 20 min, net photosynthesis (A_area), stomatal conductance (g_s) and transpiration (E) were measured.

For experiment 3, mesophyll conductance (g_m) was calculated via the variable J method (Harley et al., 1992) using

where J was derived from chlorophyll fluorescence (quantum efficiency of PSII × PAR × PSII absorbance). PSII absorbance was assumed to be half of leaf absorbance (Pons et al., 2009), measured as Chl_A+B/(Chl_A+B + 76) as in Evans and Poorter (2001) where Chl_A+B is total chlorophyll per unit leaf area (the quantification of which is described below). J was then adjusted according to Pons et al. (2009) using the relationship between J derived via chlorophyll fluorescence and J derived via gas exchange (4 x gross assimilation) under non-photorespitory conditions. Accordingly, A-C_i curves were performed under photorespitory and non-photorespitory conditions as follows; ambient O₂ A-C_i curves were performed first using the chamber conditions described above and the following CO₂ concentrations; 400, 300, 200, 100, 50, 0, 400, 400, 400, 600, 700, 800, 900, 1000, and 1200. At each step, leaves were given 3 min to acclimate before data logging. The air supply was then immediately switched to 2% O₂ provided by supplementary gas (2% O₂ in N; BOC Limited, NSW, Australia) via the main console air inlet with a flow meter used to confirm positive air flow. Leaves were given an additional 30 min to acclimate to low O₂ before the A-C_i procedure was repeated. Rapid light response curves were used for determination of R_d via the Kok method (Kok, 1948), modified after Yin et al. (2011); the same ambient O₂ chamber conditions described above were used except leaves were acclimated under saturating PAR, 1500 μmol photons m⁻² s⁻¹, before dropping PAR to 0 across 10 steps with 5 min acclimation at each. R_d was then substituted into the regression equation of the initial A-C_i curve to solve for C_i*(Brooks and Farquhar, 1985). Γ* was solved simultaneously with g_m by substituting Γ* with C_i* + R_d/g_m into equation 1 (Warren, 2006). This delivered a single converging value for each g_m and Γ*. V_cmax was derived using the A-C_i analysis excel tool (Sharkey, 2016) with rate-limitation assigned using chlorophyll fluorescence (Sharkey, 2016; Supplementary Figure S1), and g_m and R_d fixed as determined above. Slow light-response curves were completed for determination of J_max (Sharkey, 2016). Again, the chamber conditions described above were used, however PAR increased from 0 to 1500 μmol photons m⁻² s⁻¹ across 10 steps with 30 min acclimation at each. J_max was then derived from the light response analysis of the Sharkey excel tool (Sharkey, 2016), with g_m and R_d fixed.

For all gas exchange measurements, bulk flow leaks were periodically checked by blowing on the leaf chamber. Diffusion leaks through the chamber gaskets were minimized by performing all low CO2 measurements with the Licor 6800, which displays a lower leak rate coefficient than the 6400XT. After all gas exchange analyses, leaves were then removed and photographed. Leaf area was calculated using GIMP 2.8.22 (GNU Image Manipulation Program¹). Leaves were then dried and weighed, and specific leaf area calculated as SLA = LA/DW and photosynthesis per unit leaf mass calculated as A_mass = SLA × A.

Leaf material was collected on the final day of the experiment, freeze dried and ground via bead mill. Ten milligram was sub-sampled per plant and from this, FA were extracted in hot methanolic HCl (modified after Browse et al., 1986). FA were quantified by GC-MS (QP 2010 SE, Shimadzu Corp., Kyoto, Japan) against an internal standard of 10 mg C15:0 and total FA were calculated as the sum of palmitic acid (16:0), palmitoleic acid (16:1), stearic acid (18:0), oleic acid (18:1), linoleic acid (18:2) and linolenic acid (18:3).

Total water-soluble carbohydrates (WSC) were analyzed using the anthrone method (Hedge and Hofreiter, 1962). Leaves were sampled at midday and immediately frozen in liquid nitrogen. Using 25 mg freeze-dried, ground leaf material, low molecular weight carbohydrates (LMW; including glucose, fructose, sucrose and some LMW fructans; Pollock and Jones, 1979) were twice extracted in 1 ml, 4:1 EtOH: H₂O at 65°C for 30 min, centrifuged and supernatant collected and combined at each extraction. High molecular weight carbohydrates (HMW; this fraction includes HMW fructans, the main storage sugar in L. perenne; Pollock and Jones, 1979) were then twice extracted in 1 ml H₂O at 65°C for 30 min, centrifuged and supernatant collected and combined at each extraction. The soluble carbohydrate extracts were mixed with anthrone reagent (Sigma-Aldrich, St Louis, MO, United States) for 25 min at 65°C, A₆₂₀ determined using a Versamax tunable plate reader (Molecular Devices Corporation, Sunnyvale, CA, United States) and compared to LMW and HMW standards, prepared using sucrose and inulin, respectively.

Total soluble protein was quantified according to Bradford (1976). Using 500 mg leaf FW ground in liquid N, soluble protein was twice extracted in 15 ml of 50 mM sodium phosphate buffer (pH 7) containing 5 mM DTT, centrifuged (4000 g, 15 min, 4°C) and 5 μl of the supernatant combined with 200 μl Bio-Rad protein assay dye (Bio-Rad, CA, United States). Absorbance was measured at 595 nm using bovine serum albumin (MP biomedical, Auckland, New Zealand) as the protein standard. Rubisco was determined according to Makino et al. (1986) with minor modifications. The total soluble protein extract (20 μl) was combined (1:1) with 2x Laemmli Buffer (Sigma S3401-10VL, St Louis, MO, United States) and heated at 95°C for 5 min. Protein samples were then separated by SDS-PAGE (Bio-Rad, 4–15% Mini-PROTEAN TGX Stain-free) for 30 min at 180 V and the resulting gels stained with 0.25% CBB-R dye in 40% methanol and 10% acetic acid solution overnight, and then rinsed repeatedly with 40% methanol and 10% acetic acid solution until the background was colorless. Large (52 kDa) and small (15 kDa) Rubisco subunits were excised from the gel and transferred into tubes with 0.75 ml of formamide and shaken at 50°C for 6 h. The absorbance of the formamide extracts was measured at 595 nm using the background gel as a blank and bovine serum albumin as the protein standard. N associated with soluble protein (N_S) and rubisco (N_R) was calculated assuming protein contains 16% N.

Using 200 mg leaf FW, ground in liquid N, chlorophylls were twice extracted in 10 ml 95% EtOH, then stored for 6–8 h in the dark with regular vortexing, and then centrifuged (4000 g, 15 min, 4°C). The supernatant was removed, diluted 2-fold and absorbance peaks measured using a Versamax tunable plate reader using the pathlength correction formula described in Warren (2008). Chlorophyll concentrations were determined from A₆₄₈ and A₆₆₄ using the formula described in Lichtenthaler (1987). N associated with pigment-protein complexes (N_P) was calculated assuming 37.3 mol N mol⁻¹ total Chl (Evans and Clarke, 2019).

Nitrogen associated with electron transport and ATP synthesis (collectively “bioenergetics”; N_E) was calculated indirectly from electron transport capacity [J_max; the calculation of which was described above and here adjusted to 25°C as per June et al. (2004)]. A linear correlation between cytochrome f (cyt f) content per unit leaf area and J_max at 25°C, of 155 mol electron mol⁻¹ cyt f s⁻¹ was assumed (Evans, 1987). Recently revised ratios of cyt f to the other components of electron transport and ATP synthesis were used to calculate an N_E cost of 10.86 mol N mmol⁻¹ cyt f (Evans and Clarke, 2019).

Total N concentration (N_mass) was determined on 200 mg of dried, ground samples using a CN elemental analyzer (Elementar VarioMax CN analyzer, Hanau, Germany). “Remaining” leaf N (N_O) was calculated as N_mass – N_S – N_P – N_E. Approximately 1 g fresh subsamples were weighed, oven-dried, and weighed again for converting the above FW measurements to DW basis, and then converted onto a leaf area basis by dividing by SLA.

All statistical analyses as well as normality and variance tests were performed using R version 3.3.3 (R Foundation, Vienna, Austria). For experiment 1, two-way factorial ANOVAs were used to evaluate the relationship between each of the following dependent variables: FA, WSC, gas exchange parameters, biomass, RGR, SLA and independent factors: genetic background (3 levels) and line (8 levels, i.e., 3 NT and 5 DGAT + CO). Tukey tests were used for post-hoc analysis. Kruskall-Wallis tests were used to evaluate the effect of DGAT + CO on non-normal variables, projected leaf area, and their values of p were adjusted using a Bonferroni correction. Linear regressions were used to evaluate the relationship between recombinant protein contents and FA. One-way ANOVAs were used to compare LMW and HMW carbohydrates of NT1-3 with Tukey tests used for post-hoc analysis. For experiment 2, a forward stepwise regression was used to evaluate the relationship between genotype (NT3 and DGAT + CO5), NO₃⁻ supply (treated as continuous) and dependent variables: A_sat, SLA, leaf N_mass, N_area, g_s and PNUE. Variables and interaction terms with a value of p < 0.05 were retained in the final model. Quadratic terms were tested in each of the models to account for non-linear responses to NO₃⁻ supply or leaf N. The same procedure was used for investigating the relationships between genotype, WSC, and leaf N content on photosynthesis. For experiment 3, NT3 and DGAT + CO5 were compared using a Student’s t-test or Wilcoxon rank sum test.

Five DGAT + CO lines were examined here, two (DGAT + CO1-2) transformed using an “Alto” cultivar individual (NT1), and three transformed using two “Grasslands Impact” cultivar individuals (DGAT + CO3-4 background, NT2; DGAT + CO5 background, NT3). For each DGAT + CO line, the presence of transgenic proteins was confirmed via SDS-page immunoblot analysis (Figure 1). All DGAT + CO lines displayed a significant increase in leaf fatty acids (FA), ranging from 118 to 174% of respective non-transformed (NT) controls (Figure 1). For the DGAT + CO lines, total leaf FA represented 4.8–5.5% of leaf dry weight (DW), whereas NT controls ranged from 2.9 to 4% of leaf DW (Table 1). The relative increase in total FA for each line, compared to respective NT control, strongly correlated with DGAT accumulation (r² = 0.82, p = 0.03), but was not statistically significant for CO (r² = 0.67, p > 0.05). The composition of FA was significantly altered by DGAT + CO expression, with all lines exhibiting a significant increase in C18:1 and C18:2 and a decrease in C16:0, C16:1, and C18:3 (Table 1).

Leaf low molecular weight carbohydrates and HMW were significantly lower in DGAT + CO3-5, compared to respective NT controls (Figure 2), resulting in a reduction in total leaf WSC of 57–69% (Figure 2). In contrast, there were no statistical differences in LMW, HMW, or total WSC between DGAT + CO1, DGAT + CO2, and the NT1 control (Figure 2). Both LMW and HMW carbohydrates were significantly lower for NT1 compared to both NT2 and NT3 and for NT2 compared to NT3 (p < 0.01).

Of the five DGAT + CO lines examined here, two (DGAT + CO1 and DGAT + CO2) showed no significant difference in gas exchange or biomass, compared to their respective NT control (Figure 3; Table 2) In contrast, DGAT + CO3-5 were between 59 and 82% larger than their respective NT controls at the final harvest, displaying a significant increase in shoot, root and total plant DW (Table 2). Differences in establishment growth (i.e., growth in the 3 weeks following propagation) explained some of the total growth difference for these lines (Supplementary Table S1); however, the relative growth rate (RGR) between the post-establishment harvest (3 weeks after propagation) and final harvest (6 weeks after propagation) was also significantly greater for DGAT + CO3-5, compared to respective controls (Figure 3). SLA was significantly greater for DGAT + CO5 compared to NT3, but not for DGAT + CO1-4 (Table 2). Regardless, DGAT + CO3-5 all displayed a significant increase in projected total leaf area (leaf DW × SLA), compared to respective NT controls (Table 2).

DGAT + CO3-5 all displayed a significant increase in net photosynthesis (A_area; Figure 3), transpiration (E) and stomatal conductance (g_s; Table 2), compared to respective NT controls. Similarly, DGAT + CO3-5 exhibited significantly greater leaf N_mass than controls (Figure 4), whereas no difference in gas exchange or N_mass was identified between DGAT + CO1-2 and NT1 (Figure 4). When N was expressed on a leaf area basis (N_area), there was no significant difference between DGAT + CO1-4 and respective controls, but there was a significant decrease in N_area for DGAT + CO5 relative to NT3 (Figure 4). A significant increase in PNUE (A_area/N_area) was observed for DGAT + CO3-5 (Figure 4), which was not observed for DGAT + CO1-2. Net photosynthesis correlated negatively with foliar carbohydrates for those plants derived from NT2 (NT2 and DGAT3-4; r² = 0.3; p < 0.001) and NT3 (NT3 and DGAT + CO5; r² = 0.7; p < 0.001), but not NT1 (NT1 and DGAT + CO1-2; p = 0.4; Figure 5).

DGAT + CO5 had a greater SLA than NT3 at all levels of NO₃⁻ supply (Genotype effect, p < 0.001). A_sat was comparable between DGAT + CO5 and NT3 at 1-3 mM NO₃⁻ supply but significantly greater for DGAT + CO5 at 5–7.5 mM NO₃⁻ supply (Genotype x concentration interaction, p < 0.01; Figure 6B). Stomatal conductance (g_s) was unaffected by NO₃⁻ supply and was consistently greater for DGAT + CO5 than for NT3 (Genotype effect, p < 0.001; Figure 6E). DGAT + CO5 had a greater leaf N_mass than NT3 and this difference became progressively larger with increasing NO₃⁻ supply (Genotype × concentration interaction, p < 0.001; Figure 6C). However, N_area was greater for NT3 than for DGAT + CO5 from 1 to 5 mM NO₃⁻ supply and was similar for the two genotypes at the 7.5 mM NO₃⁻ supply (Genotype × concentration interaction, p < 0.05; Figure 6D). DGAT + CO5 exhibited a greater PNUE than NT3 across the entire NO₃⁻ supply range (Genotype effect, p < 0.001; Figure 6F).

Photosynthesis and leaf N correlated positively for NT3 and DGAT + CO5, regardless of whether measurements were expressed on a mass or area basis (Figure 7). The slope of the relationship between A_mass and leaf N_mass was, however, steeper for DGAT + CO5 than for NT3 across much of the leaf N_mass range observed (Genotype × N_mass interaction, p < 0.05; Figure 7). A_mass exhibited a saturating response to high leaf N_mass for DGAT + CO5 (Quadratic N_mass effect, p < 0.01; Figure 7). A_area (per unit leaf area) exhibited an even stronger saturating response to N_area beyond approximately 1.25 gN.m⁻² for both DGAT + CO 5 and NT3 (Quadratic N_area effect, p < 0.001; Figure 7).

DGAT + CO5 displayed a significant decrease in total soluble protein and chlorophyll per unit leaf area, compared to NT3 (Table 3). In contrast, rubisco per unit leaf area did not significantly differ for DGAT + CO5 and NT3 (Table 3). DGAT + CO5 and NT3 invested similar proportions of leaf N into rubisco (N_R/N) and pigment-protein complexes (N_P/N; Figure 8), while DGAT + CO5 invested a significantly greater proportion of leaf N to bioenergetics (N_E/N) than NT3 (Figure 8). Due primarily to this increase in N_E/N, investment in “photosynthetic-N” × [(N_R + N_P + N_E)/N] was significantly greater for DGAT + CO5 than for NT3 (Figure 8). The proportion of N invested in non-rubisco soluble protein (N_S-N_R)/N did not differ for DGAT + CO5 and NT3 (Figure 8) while investment in all remaining pools (N_O/N) was significantly lower for DGAT + CO5 than for NT3 (Figure 8).

There was no significant difference in intercellular CO₂ concentrations (C_i) measured between DGAT + CO5 and NT3. However, DGAT + CO5 exhibited a 39% increase in mesophyll conductance, compared to NT3 (Table 3). As such, chloroplastic CO₂ concentrations (C_c) were 5% greater, and the CO₂ drawdown from substomatal cavities to chloroplasts (C_i-C_c) was 24% lower for DGAT + CO5, compared to NT3 (Table 3). The carboxylation efficiency (CE; initial slope of A-C_i response), the quantum efficiency of PSII (φPSII) and J_max were all significantly greater for DGAT + CO5, whereas Ci*, Γ*, and V_cmax, did not significantly differ between DGAT + CO5 and NT3 (Table 3).

Regulation of photosynthetic capacity is determined by, among other things, the availability of carbon (source strength) relative to the demand for carbon (sink strength; (Arp, 1991; Paul and Foyer, 2001; Ainsworth et al., 2004) and sugar plays a key role in signaling carbon availability (Paul and Driscoll, 1997; Roitsch, 1999; Iglesias et al., 2002; Ainsworth and Bush, 2011; Ribeiro et al., 2017). Beechey-Gradwell et al. (2020) postulated that DGAT + CO lipid sinks may elevate the demand for carbon which could induce physiological and morphological changes which promote carbon capture (i.e., increased photosynthesis and SLA). Consistent with this hypothesis, we identified a significant negative correlation between WSC and photosynthesis in L. perenne leaves (Figure 5) and only those three lines with the largest FA increases and a significant reduction in leaf WSC (DGAT + CO3-5; Figure 2) displayed an increase in net photosynthesis and PNUE (Figure 3). Below, we discuss those leaf-level physiological changes which delivered increased photosynthesis and PNUE following DGAT + CO lipid sink accumulation.

A_area and PNUE are determined by a range of factors including the amount of light absorbed, the rate of CO₂ transfer from the atmosphere to carboxylation sites, the proportion of N invested in photosynthesis, the fraction of photosynthetic-N devoted to the most rate-limiting photosynthetic processes, the specific activity and activation state of rubisco, and differences in respiration in the light (Poorter and Evans, 1998). Photosynthetic rate under growth PAR and CO₂ conditions (600 μmol photons m-² s⁻¹ and 415 ppm CO₂, respectively) appeared at the intersection of “rubisco-limited” and “RuBP-limited” in L. perenne (Supplementary Figure S1). Interestingly, neither rubisco content per unit leaf area nor N_R/N significantly differed between NT3 and DGAT + CO5 (Table 3) and given the identical genetic backgrounds of these lines, rubisco likely had identical kinetic properties. However, both stomatal (g_s) and mesophyll (g_m) conductance were higher for DGAT + CO5 compared to NT3 (Tables 2, 3), collectively delivering a 5% increase in C_c at ambient CO₂ (Table 3). Moreover, when g_m values were fixed in an A-C_i model (Sharkey et al., 2007), NT3 and DGAT + CO5 exhibited no significant difference in V_cmax (Table 3), suggesting enhanced g_m could account for the higher DGAT + CO5 carboxylation efficiency (CE; Table 3). Changes in g_m following sink capacity manipulation have previously been reported for rice (Detmann et al., 2012) and various legumes (Sugiura et al., 2018, 2020). Increased g_m may explain the reduced rubisco oxygenation to carboxylation ratio (V_o/V_c) previously reported for DGAT + CO L. perenne (Beechey-Gradwell et al., 2020) and contribute to the enhanced A_area and PNUE identified here.

RuBP-regeneration limited photosynthetic rate is typically attributed to insufficient electron transport (J). This can be alleviated by reducing photorespiration and its associated ATP costs, possibly achieved via increased g_m. Alternatively, RuBP-regeneration limited photosynthesis could be enhanced with increases in the enzyme complexes that perform photosynthetic electron transport. In this study, thylakoid membrane-associated N was divided into two components, light harvesting (N_P) and electron transport plus ATP synthesis (collectively “bioenergetics”; N_E). We estimated cyt f and N_E indirectly, assuming that NT3 and DGAT + CO5 shared the same fixed relationship between J_max, cyt f and N_E (Evans and Clarke, 2019). Under this assumption, DGAT + CO5 exhibited 73% higher N_E/N than did NT3 (Figure 8), which could account for most (64%) of the difference in total photosynthetic N (N_R + N_P + N_E) between the genotypes. However, available estimates of the N cost of bioenergetics vary and are highly sensitive to the amount of ATP synthase assumed (Evans and Clarke, 2019). For this reason, we additionally calculated the N_E/N difference for DGAT + CO5 and NT3 by substituting an older, more conservative N_E cost of 8.85 mol N mmol⁻¹ cyt f (Evans and Seemann, 1989) for DGAT + CO5 (c.f. 10.86 mol N mmol⁻¹ cyt f for NT3). This did not alter the conclusion that DGAT + CO5 had a higher N_E/N than did NT3 (46%; p < 0.001).

Changes in chlorophyll content also present an opportunity to improve PNUE (Slattery et al., 2017). Crop plants “overinvest” in N_P under high light (Evans and Poorter, 2001), and one proposed strategy to engineer higher A_area is to reduce chlorophyll in order to “free up” N for more rate-limiting processes (Slattery et al., 2017). In experiment 3, N_P/N did not significantly differ for DGAT + CO5 and NT3 (Figure 8); however, DGAT + CO5 exhibited 15% lower Chl_A+B per unit leaf area than did NT3 (Table 3). Lower Chl_A+B penalizes light absorption which can reduce A_area at low irradiance but has less effect near saturating irradiance. Additionally, increases in light absorption (α) per unit of additional Chl_A+B diminish as Chl_A+B approaches 400 μmol m⁻² (Evans and Poorter, 2001), values similar to that reported here (Table 3). For this reason, estimated absorptance (α) was only 2% lower for DGAT + CO5 than NT3, while estimated absorptance per chlorophyll molecule (α/Chl_A+B) was 14% higher. Assuming the same pigment-protein stoichiometry for NT3 and DGAT + CO5 leaves (37.3 mol N mol⁻¹ Chl_A+B, as in Evans and Clarke, 2019), spreading chlorophyll over a greater leaf area would be expected to reduce the N cost of light harvesting and increase PNUE. However, pigment-protein stoichiometry and, therefore, the N cost of light harvesting vary naturally. For example, an increase in Chl_A:B during acclimation to high irradiance slightly increases the protein cost (and therefore N) of complexing pigments (Leong and Anderson, 1984; Evans and Seemann, 1989). DGAT + CO5 also exhibited a 10% higher Chl_A:B than NT3 (Figures 5, 6), perhaps indicative of a higher N cost of light harvesting which could partially offset the positive ΔPNUE due to higher α/Chl_A+B.

In experiment 1, only the three lines transformed from cultivar “Impact” (DGAT + CO3-5) displayed a reduction in leaf WSC content and an increase in photosynthesis and growth, whereas the two “Alto” cultivar transformed lines (DGAT + CO1-2) did not. The reason for this was unclear; either carbon allocation into lipids was too low in DGAT + CO1-2, or the response to DGAT + CO differs depending on the L. perenne cultivar used for transformation. It is worth noting that compared to either “Impact” control line (NT2-3), the “Alto” conrol line (NT1) displayed a lower leaf WSC content and a greater photosynthesis and growth rate. It may be that carbon utilization (e.g., translocation) was already high in the “Alto” background and there was little remaining capacity to enhance photosynthesis or growth via a new sink. This highlights an important consideration regarding the utility of engineered carbon sinks to improve photosynthesis. Benefits are likely to depend upon factors which influence the overall balance of activity between source leaves and various sinks throughout the plant, and thus may depend on environmental conditions, species, cultivar, or developmental stage. Crop species vary in both the capacity to accumulate carbohydrates in leaf cellular compartments (Chu et al., 2020) and in the sensitivity of photosynthesis to feedback regulation by carbohydrates (Sugiura et al., 2018). Thus, assessment of DGAT + CO photosynthesis in a range of plant backgrounds is needed to understand the broader applicability of our findings.

It is well-established that major carbon sinks in the form of reproductive structures and storage organs can influence the photosynthetic traits of source leaves (Ainsworth et al., 2004; Sugiura et al., 2015), but mature leaves themselves consist of various metabolic and structural sinks which compete for carbon (Vanhercke et al., 2019b). Manipulating leaf sink capacity though metabolic engineering may enhance photosynthesis if carbon-rich compounds can accumulate without triggering evolved carbon-sensing mechanisms (Paul and Eastmond, 2020). Encapsulated TAG appears to be capable of such an effect and, providing this energy-dense sink does not create excessive competition for carbon (Mitchell et al., 2020), an increase in net assimilation can be achieved (Beechey-Gradwell et al., 2020). Could other soluble and polymeric compounds (sugar derivatives, polysaccharides, proteins, or entirely novel bio-products such as vitamins, drugs, or plastics) beengineered to circumvent feedback inhibition? Creating an efficient carbon sink in metabolically active leaves is complex (Sweetlove et al., 2017). Introduced pathways must interfere minimally with desirable endogenous processes, and end-products should be metabolically inert or compartmentalized appropriately (Morandini, 2013). Futile cycles of synthesis and hydrolysis should be avoided (Winichayakul et al., 2013), and synthesis would ideally be turned on late in development when adequate source capacity has been established (Morandini, 2013). Despite this complexity, a growing range of options exists for fine-tuning the spatial and temporal synthesis of novel molecules in photosynthetic organisms (Sweetlove et al., 2017). A range of strategies by which leaf sink capacity might be enhanced remain to be explored, which could help to maintain photo-assimilate utilization and therefore maximize the photosynthetic potential of future crops.

The DGAT+CO ryegrass material examined in this study was generated with funding from DairyNZ, PGG Wrightson Seeds and Grasslanz Technology. The research conducted here, including all experimental designs and analyses was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.