High Stomatal Conductance in the Tomato Flacca Mutant Allows for Faster Photosynthetic Induction

Abstract

Due to their slow movement and closure upon shade, partially closed stomata can be a substantial limitation to photosynthesis in variable light intensities. The abscisic acid deficient flacca mutant in tomato (Solanum lycopersicum) displays very high stomatal conductance (g_s). We aimed to determine to what extent this substantially increased g_s affects the rate of photosynthetic induction. Steady-state and dynamic photosynthesis characteristics were measured in flacca and wildtype leaves, by the use of simultaneous gas exchange and chlorophyll fluorometry. The steady-state response of photosynthesis to CO₂, maximum quantum efficiency of photosystem II photochemistry (F_v/F_m), as well as mesophyll conductance to CO₂ diffusion were not significantly different between genotypes, suggesting similar photosynthetic biochemistry, photoprotective capacity, and internal CO₂ permeability. When leaves adapted to shade (50 µmol m⁻² s⁻¹) at 400 µbar CO₂ partial pressure and high humidity (7 mbar leaf-to-air vapour pressure deficit, VPD) were exposed to high irradiance (1500 µmol m⁻² s⁻¹), photosynthetic induction was faster in flacca compared to wildtype leaves, and this was attributable to high initial g_s in flacca (~0.6 mol m⁻² s⁻¹): in flacca, the times to reach 50 (t₅₀) and 90% (t₉₀) of full photosynthetic induction were 91 and 46% of wildtype values, respectively. Low humidity (15 mbar VPD) reduced g_s and slowed down photosynthetic induction in the wildtype, while no change was observed in flacca; under low humidity, t₅₀ was 63% and t₉₀ was 36% of wildtype levels in flacca. Photosynthetic induction in low CO₂ partial pressure (200 µbar) increased g_s in the wildtype (but not in flacca), and revealed no differences in the rate of photosynthetic induction between genotypes. Effects of higher g_s in flacca were also visible in transients of photosystem II operating efficiency and non-photochemical quenching. Our results show that at ambient CO₂ partial pressure, wildtype g_s is a substantial limitation to the rate of photosynthetic induction, which flacca overcomes by keeping its stomata open at all times, and it does so at the cost of reduced water use efficiency.

Introduction

In the leaves of higher plants, stomata balance carbon uptake against water loss. They achieve this balance by dynamically regulating stomatal aperture in response to intrinsic and extrinsic factors. Typically, stomatal aperture decreases in low irradiance or darkness, and increases in high irradiance. Stomatal opening after sudden increases in irradiance is slow compared to changes in Calvin cycle metabolism (McAusland et al., 2016), with time constants in the range of 4 to 29 min (Vico et al., 2011). Due to the slow opening of stomata, the increase of stomatal conductance (g_s, mol m⁻² s⁻¹) from a low initial value is assumed to be one of the three main limitations of net photosynthesis rate (A, µmol m⁻² s⁻¹) in response to increases in irradiance (e.g., Urban et al., 2008; Kaiser et al., 2016; Li et al., 2016; Kaiser et al., 2017a; Zhang et al., 2018; Zhang et al., 2020). Given that solar irradiance incident on a leaf often fluctuates, these dynamic limitations of photosynthesis decrease photosynthetic irradiance use efficiency (Morales et al., 2018). Improving g_s, including its dynamics, is an attractive means with which to improve both irradiance and water use efficiency (Lawson and Blatt, 2014; Vialet-chabrand et al., 2017). Increases in the limitation imposed upon A by g_s can be identified via transient decreases of leaf internal CO₂ partial pressure (C_i, µbar). The other two main limitations during photosynthetic induction arise from slow rates of change in the activity of enzymes involved in ribulose-1,5-bisphosphate (RuBP) regeneration, and from slow activation of Rubisco (Pearcy et al., 1996; Way and Pearcy, 2012; Kaiser et al., 2015; Kaiser et al., 2018). These limitations occur additionally to the limitations at steady state due to, e.g., the rate of electron transport, Calvin cycle metabolism, sucrose metabolism, or mesophyll conductance (g_m, mol m⁻² s⁻¹).

Disentangling stomatal and other limitations during photosynthetic induction is difficult. Many treatments affecting g_s also affect other transient limitations of photosynthetic induction, such as leaf temperature (Kaiser et al., 2017a) and salt stress (Zhang et al., 2018). Similarly, some genetic mutations affecting g_s, such as the abscisic acid (ABA) deficient aba2-1 mutant in Arabidopsis thaliana, showed enhanced A/C_i responses compared to its wildtype, Col-0 (Kaiser et al., 2016), suggesting pleiotropic effects, which may confound effects of altered g_s. Also, models estimating transient stomatal limitation have often been based on linear – not curvilinear – A/C_i relationships (Woodrow and Mott, 1989; Tinoco-Ojanguren and Pearcy, 1993), or are based on steady-state A/C_i responses (Kaiser et al., 2017a). In fact, the maximum rate of carboxylation (V_cmax), for example, increases strongly during photosynthetic induction (Soleh et al., 2016; Taylor and Long, 2017). In another approach, the limitation during induction was attributed to g_s alone up to the point where A reached 95% of the steady-state value (McAusland et al., 2016), entirely ignoring limitations by RuBP regeneration and Rubisco activation kinetics. Tools to better separate stomatal from other limitations are thus warranted, and mutants or transformants with substantially altered stomatal characteristics but similar photosynthetic biochemistry (Raissig et al., 2017; Papanatsiou et al., 2019; Tomimatsu et al., 2019; Kimura et al., 2020; Yamori et al., 2020) can be counted among such tools.

The tomato (Solanum lycopersicum L.) flacca mutant has a 80% to 90% lower ABA content than its wildtype (Tal and Nevo, 1973; Sagi et al., 2002). Flacca leaves exhibit a very high g_s, without affecting the A/C_i response, suggesting that photosynthetic capacity is independent of ABA (Bradford et al., 1983). Lack of ABA has not been found to affect g_m in the aba-1 mutant of Nicotiana plumbaginifolia (Mizokami et al., 2015) but to our knowledge has not been determined in flacca. The aim of this study is to determine the dynamic limitations of photosynthetic induction due to g_s in tomato leaves. For this, steady-state and dynamic photosynthesis characteristics were measured in the ABA-deficient flacca mutant and its wildtype, by the use of simultaneous gas exchange and chlorophyll fluorometry.

Materials and Methods

Plant Material

Seeds of tomato cv. Rheinlands Ruhm wildtype (LA0535) and flacca (LA0673) were obtained from the Tomato Genetics Resource Center (University of California, Davis, USA). Seeds were germinated in stonewool plugs (Grodan, Roermond, NL). A week after sowing, they were transferred to stonewool cubes (10 cm × 10 cm × 7 cm; Grodan). Plants were grown in a climate chamber under a day/night cycle of 16/8 h (day/night), 20/18°C temperature, ambient CO₂ partial pressure, 70% relative air humidity, and 154 µmol m⁻² s⁻¹ photosynthetically active radiation (PAR; measured 10 cm above table height), which was provided by fluorescent tubes (Master TL-D 58W/840 Reflex Eco; Philips, Eindhoven, the Netherlands). Stonewool cubes were standing in a layer (height, 1–2 cm) of nutrient solution (Yara Benelux B.V., Vlaardingen, the Netherlands), which was replenished every 1 to 2 days and contained 12.4 mM NO₃⁻, 7.2 mM K⁺, 4.1 mM Ca²⁺, 3.3 mM SO₄²⁻, 1.8 mM Mg²⁺, 1.2 mM NH₄⁺, 1.1 mM PO₄³⁻, 30 μM BO₃³⁻, 25 μM Fe³⁺, 10 μM Mn²⁺, 5 μM Zn²⁺, 0.75 μM Cu⁺, and 0.5 μM MoO₄²⁻ (EC 2.1 dS m⁻¹, pH 5.5). Between 1 and 4 weeks after sowing, flacca plants were sprayed daily with a solution containing 10 μM ABA, 0.01% (w/v) Triton-X, and 0.1% (v/v) ethanol (Bradford et al., 1983), using commercially available horticultural hand sprayers. Wildtype plants were sprayed with a control solution containing 0.01% Triton-X and 0.1% ethanol. Untreated flacca plants are smaller and show much higher transpiration rates than the wildtype, together with leaf epinasty and strong root formation along the stem (Tal, 1966). Growing flacca with application of ABA causes plants to grow similarly well as the wildtype (Imber and Tal, 1970). When the application of ABA is stopped, flacca reverts to its mutant phenotype within days, including always-open stomata (Imber and Tal, 1970). All chemicals were purchased from Sigma (St. Louis, MO, USA).

Measurements

When plants were between 5 and 6 weeks old, the fourth leaf, counting from the bottom, was selected for measurements. ABA spraying was stopped seven days before the start of measurements, to allow the high g_s phenotype of flacca to reassert itself. Measurements were performed in a lab, using the LI-6400 XT photosynthesis system (LI-COR Biosciences, Lincoln, Nebraska, USA) equipped with a fluorescence chamber (leaf area: 2 cm²). Conditions inside the leaf chamber during measurements were: 25°C chamber temperature, 7 mbar leaf-to-air vapour pressure deficit (VPD; except when stated otherwise) and a flow rate of 500 µmol s⁻¹. Irradiance was provided by LEDs in a 90/10 red/blue irradiance mixture, with peak intensities at wavelengths of 635 and 465 nm, respectively. For all measurements, five plants per genotype were used (n = 5).

All measurements were performed on the same spot of a leaf, to reduce measurement noise caused by spatial variation (Lawson and Weyers, 1999; Matthews et al., 2017): (a) dark-adapted F_v/F_m, (b) A/PAR curves at 2% oxygen, (c) A/C_i curves at 2% oxygen, (d) A/C_i curves at 21% oxygen, (e-g) photosynthetic induction under three different environmental conditions (described below). While measurements a-d were performed in the same sequence, the order of photosynthetic induction measurements was randomized for each plant. Values of A were corrected for CO₂ leakage based on the manufacturers' suggestions. Measurements were started at 7:30 in the morning and took 8 to 9 h to complete per leaf.

Dark-Adapted F_v/F_m and Net CO₂ Exchange in Darkness

Leaves were dark-adapted for 20 minutes. Then, net CO₂ exchange in darkness (A_dark) was logged, after which a weak measuring beam was turned on to measure F_o. Then, F_m was determined by exposing the leaf to a single-pulse saturating flash of ~9.000 µmol m⁻² s⁻¹ intensity and 1-s duration. Dark-adapted F_v/F_m was calculated as F_v/F_m= (F_m − F_o)/F_m.

A/PAR Curves at 2% Oxygen

A gas mixture containing 2% oxygen and 98% nitrogen was fed to the inlet of the LI-6400 XT. Leaf external CO₂ partial pressure (C_a) was set to 2000 µbar, and irradiance was set to 200 µmol m⁻² s⁻¹. After reaching steady-state A, irradiance was decreased in steps of 150, 100, 70, 50, and 30 µmol m⁻² s⁻¹, and A was logged for 30 s after reaching steady-state A, at steps of 5 s. Values were later averaged at each step to reduce measurement noise.

A/C_i Curves at 2% Oxygen

A gas mixture containing 2% oxygen and 98% nitrogen was fed to the inlet of the LI-6400 XT. Irradiance was set to 1,500 µmol m⁻² s⁻¹, and C_a was set to 150 µbar. After reaching steady-state A, C_a was decreased in steps of 130, 100, 70, and 50 µbar. A and C_i were logged as described above. At each C_a, the infrared gas analysers were matched.

A/C_i Curves at 21% Oxygen

Irradiance was set to 1,500 µmol m⁻² s⁻¹, and C_a was set to 400 µbar. After reaching steady-state A, C_a was decreased in steps of 300, 200, 130, 100, 70 and 50 µbar. Then, C_a was raised to 400 µbar and after reaching steady-state A, C_a was increased in steps of 600, 750, 900, 1,100, 1,400, 1,700, and 2,000 µbar. A and C_i were logged after reaching steady-state (3–5 min per step) as described above. At each C_a, the infrared gas analysers were matched. Parameters describing maximum rate of carboxylation (V_cmax), rate of linear electron transport at the measuring irradiance (J₁₅₀₀) and triose phosphate utilization capacity (TPU) were determined using the excel solver tool by Sharkey (2016). Additionally, operating and maximal fluorescence in light-adapted leaves (F and F_m′, respectively) were determined at each C_a by using a multi-phase flash protocol (MPF; Loriaux et al., 2013). The maximum intensity of the MPF was ~9.000 µmol m⁻² s⁻¹, the durations of the three phases were 0.3, 0.7 and 0.4 s respectively, and the percentage decrease of flash intensity during phase two was 60%. These MPF settings were found to yield the most accurate results in pilot experiments (data not shown). Photosystem II operating efficiency (Φ_PSII) was calculated as Φ_PSII = (F_m′ − F)/F_m′. Mesophyll conductance (g_m) was determined following the variable J method proposed by Harley et al. (1992); the variables A, C_i, and Φ_PSII to calculate g_m were determined at a C_a of 400 µbar and an irradiance of 1500 µmol m⁻² s⁻¹. Parameters to calculate g_m, namely R_d, Γ* and s, were determined from A/C_i and A/PAR measurements at 2 and 21% oxygen following Yin et al. (2009).

Photosynthetic Induction

Leaves were adapted to 50 µmol m⁻² s⁻¹ until g_s was constant (40–60 minutes). Irradiance was then increased to 1500 µmol m⁻² s⁻¹ in a step change, and gas exchange values were logged every 2 s for 60 min. These measurements were performed at C_a and air humidity close to the plant’s growth conditions (400 µbar C_a, 7 mbar VPD), termed “control” hereafter. Photosynthetic induction was additionally assessed under two other conditions: “high VPD” (15 mbar) and “low CO₂” (200 µbar), keeping all other conditions the same. During photosynthetic induction, chlorophyll fluorescence was measured using a saturating MPF (described above) once every minute during the first ten minutes, and once every two minutes thereafter. Photosynthetic induction (PI, %) was calculated as a percentage of the total change between initial A (A_i) and final A (A_f) of each transient: PI = (A-A_i)/(A_f-A_i)*100. Intrinsic water use efficiency (WUE_i) was calculated as WUE_i = A/g_s. Non-photochemical quenching (NPQ) during photosynthetic induction was calculated as NPQ = (F_m − F_m′)/F_m′. The coefficient of photochemical quenching (qP) and the efficiency of open photosystem II traps (F_v′/F_m′) were calculated after Oxborough and Baker (1997), as qP = (F_m′ − F)/(F_m′ − F_o′) and F_v′/F_m′ = (F_m′ − F_o′)/F_m′, where F_o′ is minimal fluorescence from irradiance- adapted leaves. F_o′ was calculated after Oxborough and Baker (1997).

Statistical Analysis

All statistical tests were performed at P=0.05 as threshold for significance. Where appropriate, a two-sided Student’s t-test was used to determine significant differences between genotypes. For photosynthetic induction under different environmental conditions, a two-way analysis of variance (ANOVA) was performed, and interaction means were separated based on Fisher’s least significant difference test. Residuals were tested for normal distribution (Shapiro-Wilk test) and equal variances were assumed for treatment groups. If the requirement for normal distribution was not fulfilled, the procedure was repeated on log-transformed data. If after log transformation residuals still did not show normality, Kruskal-Wallis one-way ANOVA, considering six treatments (three environmental conditions times two genotypes), was performed using the original data. In case of significant treatment effects, Dunn’s test of multiple comparisons was performed to identify differences between the six treatments. All statistical tests were performed in Genstat (VSN international, Hempstead, UK) except for Dunn’s test, which was performed in R (R Core Team, 2020) using the dunn.test package (Dinno, 2017).

Results

Steady-State CO₂ and Irradiance Responses of Photosynthesis

Wildtype and flacca leaves showed very similar responses of A and Φ_PSII to C_i (Figure 1). In the CO₂ range 50 to 300 µbar, A increased near-linearly, then peaked at ~500 µbar and with further increases in C_i, A declined in both genotypes. Compared to A, Φ_PSII peaked at lower C_i (~300 µbar) and exhibited a stronger decline with further increases in C_i. Parameters describing photosynthetic capacity, i.e., V_cmax, J₁₅₀₀ and TPU, were not significantly different between genotypes (Figure 1, insert). Mesophyll conductance and its components were not significantly different between genotypes (except C_i, which was significantly greater in flacca, Figure 2C), although flacca tended to show greater values for A, J, R_d, and C_c (Figure 2). In dark-adapted leaves, A was −1.2 ± 0.1 µmol m⁻² s⁻¹ in wildtype and −1.9 ± 0.1 µmol m⁻² s⁻¹ in flacca leaves (p=0.008). At low irradiance (50 µmol m⁻² s⁻¹), on the other hand, A was similar between genotypes (Table 1).

Figure 1

Figure 2

Response of Photosynthetic Gas Exchange to a Stepwise Irradiance Increase

Next, we tested how gas exchange in wildtype and flacca leaves that had been adapted to low irradiance (50 µmol m⁻² s⁻¹) reacted to a stepwise increase to high irradiance (1500 µmol m⁻² s⁻¹). In wildtype leaves, the rate of photosynthetic induction was slower at high VPD, compared to the other two treatments (low CO₂ or high VPD; Figure 3A), while in flacca, there was no difference between control and high VPD treatments (Figure 3B). However, while in wildtype leaves the rate of photosynthetic induction was the same in the control and low CO₂ treatments, in flacca, induction at low CO₂ was slower than in the control treatment (Figures 3A, B). The flacca mutation had significant effects on the times to reach 50 (t₅₀) and 90% (t₉₀) of full photosynthetic induction: under control conditions, t₅₀ was 91% and t₉₀ was 46% of wildtype values in flacca, while under high VPD, t₅₀ was 63% and t₉₀ was 36% of wildtype values in flacca (Figure 4). Both indices were not significantly different between genotypes under low CO₂ (Figure 4). Transient A was higher in flacca than in wildtype leaves, and in both genotypes was slightly higher in control than in high VPD, as well as substantially reduced at low CO₂ (insets in Figures 3A, B; Table 1).

Figure 3

Figure 4

In flacca, A showed a small decrease between ~1.5 and 2.0 min after the stepwise increase in irradiance under control and high VPD conditions (Figure S1B). These dynamics are very similar to those previously seen in shade-adapted wildtype tomato leaves undergoing photosynthetic induction under high CO₂ partial pressure (Kaiser et al., 2017b), and in the present study were observed neither at low CO₂ (Figure S1B), nor in wildtype leaves (Figure S1A). The most likely explanation for this phenomenon is a transient mismatch between the rate of CO₂ fixation in the Calvin cycle and downstream sucrose metabolism, which would transiently limit the availability of free phosphate in the chloroplast (Prinsley and Leegood, 1986; Stitt and Grosse, 1988; Stitt and Quick, 1989).

During the complete trajectory of photosynthetic induction, stomatal conductance (g_s) in wildtype leaves was strongly increased by lowering CO₂, and substantially reduced by increasing VPD (Figure 3C; Table 1). In flacca, g_s was lower in the high VPD treatment compared to both other treatments during photosynthetic induction (Figure 3D; Table 1). Intriguingly, while the low irradiance adapted (initial) g_s in wildtype leaves reacted to the treatment levels in a predictable way, i.e., decreasing under high VPD and increasing under low CO₂ relative to control (Figure 3C), initial g_s in flacca did not (Figure 3D; Table 1). Also, g_s in flacca was substantially higher than that in wildtype leaves in all cases, as would be expected of an ABA mutant.

In control conditions, C_i in wildtype leaves showed an initial decrease in the first 10 minutes of photosynthetic induction, after which it gradually increased as stomata opened (Figure 3E). This decrease was exacerbated in the high VPD treatment. Even after partial recovery of C_i due to stomatal opening, C_i did not reach control values when approaching steady state (Figure 3E; Table 1). In flacca leaves, C_i decreased less strongly under both control and high VPD conditions, and did not increase much during the remainder of photosynthetic induction (Figure 3F). Also, C_i time courses in both of these treatments were virtually indistinguishable in flacca, which is explained by the diminished reduction of g_s under high VPD (Figure 3D). Under low CO₂, C_i was similar in wildtype and flacca, displaying only small decreases in the beginning of photosynthetic induction without subsequent recovery (Figures 3E, F).

Intrinsic water use efficiency (WUE_i) was roughly twice as high in wildtype compared to flacca leaves (Figures 3G, H). In the wildtype, a high VPD resulted in large increases (+31%), and a low CO₂ in large decreases (−56%), of WUE_i relative to control conditions (Figure 3G). In flacca, WUE_i was markedly reduced (−50%) under low CO₂ relative to the other two conditions (Figure 3H). While WUE_i showed strong dynamics in the first 30 minutes after exposure to high irradiance under high VPD and control conditions in the wildtype, it plateaued early after an initial increase (<5 min) in all other cases (Figures 3G, H).

Chlorophyll Fluorescence Dynamics During Photosynthetic Induction

In wildtype leaves, an initial rapid increase in Φ_PSII within the first ~8 min was followed by a slower, more gradual increase towards a steady state under control and high VPD conditions until ~40 min (Figure 5A). In flacca leaves in control and high VPD conditions, a gradual decrease was observed after the initial increase in Φ_PSII (Figure 5B). In both genotypes, Φ_PSII under low CO₂ stabilized quickly at lower values and then plateaued. Dark-adapted F_v/F_m was ~0.82 in both genotypes (n.s., Figure 5B, inset). A rapid increase in NPQ in the first five minutes was followed by a decrease until 10 to 20 min, which was followed by a slower increase until the final measurement after 60 minutes (Figures 5C, D). Under control and high VPD conditions, NPQ initially rose to much higher values in wildtype (1.6–1.7) compared to flacca leaves (1.4–1.5). The subsequent decrease to a local minimum showed a greater amplitude in wildtype (~0.1) compared to flacca leaves (~0.05). Under low CO₂, NPQ tended to be greater in both genotypes compared to the other treatments. The coefficient of photochemical quenching (qP) showed dynamics similar to those of Φ_PSII (Figures 5E, F). qP and Φ_PSII were highly correlated in all treatments (R² >0.99). The efficiency of open photosystem II traps (F_v′/F_m′) showed dynamics that were the inverse of those of NPQ (Figures 5G, H); NPQ and F_v′/F_m′ were highly correlated (R² >0.99). These correlations suggest that Φ_PSII dynamics were largely due to changes in qP rather than changes in F_v′/F_m′ or NPQ (Baker et al., 2007).

Figure 5

Stomatal Effects on Rate of Photosynthetic Induction

Next, we explored several ways to visualize the effects of (partially) closed stomata on photosynthetic induction. First, initial, low-irradiance adapted g_s was plotted against t₉₀ (Figure 6A). Across both genotypes, there was a consistent threshold-type relationship between initial g_s and t₉₀: at initial g_s <0.4 mol m⁻² s⁻¹, t₉₀ increased strongly with decreases in initial g_s, reaching values of ~15 min at an initial g_s of 0.11 mol m⁻² s⁻¹. At initial g_s >0.4 mol m⁻² s⁻¹, the value of t₉₀ (~5 min) was unaffected by further increases in initial g_s. Roughly, a similar threshold was visible between t₅₀ and initial g_s, as initial g_s <0.2 mol m⁻² s⁻¹ tended to increase t₅₀, while t₅₀ was unaffected by differences in initial g_s in the range 0.2 to 0.8 (Figure 6A, inset). Initial g_s versus t₅₀ or t₉₀ did not show a similar relationship at low CO₂ (Figure S2) and was therefore omitted from Figure 6A.

Figure 6

Photosynthesis integrated over the initial five minutes of photosynthetic induction scaled well with C_i integrated over the same period (Figure 6C). This suggests that A was affected by C_i, while the change in C_i was due to treatment and/or genotype effects on g_s. Finally, plotting J vs. gross photosynthesis rates (A_g, calculated as A plus R_d) produced very similar results between control and high VPD conditions in flacca leaves (Figure 6D), while in wildtype leaves A_g showed higher values for the same J in control compared to high VPD conditions (Figure 6B). Under low CO₂, both genotypes showed decreased A_g for a given J. Also, while in flacca leaves the plots of A_g vs. J were highly linear (Figure 6D), in wildtype leaves they showed an upwards curvature at higher A_g/J values, i.e., A_g increased more strongly than J (Figure 6B). This upwards curvature is indicative of an increase in the rate of carboxylation relative to the rate of oxygenation, most likely caused by an increase in C_i due to increased stomatal opening (Kaiser et al., 2017a; Zhang et al., 2018).

Discussion

In recent years, the dynamic responses of photosynthesis to fluctuating light, and their limitations, have received more attention. It is now widely recognised that increasing photosynthesis is a pathway to increasing crop productivity (Ort et al., 2015), that crops frequently encounter light intensity fluctuations (Kaiser et al., 2018), and that alleviating some of the limitations acting on photosynthesis transients can strongly increase biomass (Kromdijk et al., 2016). Indeed, research looking into reductions of limitations acting on dynamic photosynthesis currently receives a lot of attention (Tanaka et al., 2019; Acevedo-Siaca et al., 2020; Kimura et al., 2020; Yamori et al., 2020).

Greater Stomatal Conductance Increases the Rate of Photosynthetic Induction

Responses of A to C_i, as well as mesophyll conductance, were similar between the flacca mutant and the wildtype (Figures 1 and 2), while stomatal conductance was strongly enhanced in flacca leaves (Figures 3C, D). This confirms our hypothesis that the flacca mutant is a useful system for strongly reducing stomatal limitations and, via their reduction, better understanding their effects. This is similar to earlier reports using genotypes with “always-open” stomata (Tomimatsu and Tang, 2012; Tomimatsu et al., 2019; Kimura et al., 2020; Yamori et al., 2020). Our results further suggest that the faster photosynthetic induction observed in flacca compared to wildtype leaves under control and high VPD conditions (Figures 3A, B and 4) was indeed due to much higher stomatal conductance (difference between genotypes in initial g_s was ~0.4 mol m⁻² s⁻¹). Initial g_s in leaves adapted to darkness or shade strongly impacts on rates of photosynthetic induction upon illumination with high irradiance. It has been reported previously that parameters such as the time needed to reach 50 or 90% of full photosynthetic induction (t₅₀ and t₉₀, respectively) show a strong bimodal relationship with initial g_s (Valladares et al., 1997; Allen and Pearcy, 2000; Kaiser et al., 2016), similar as shown in the present study (Figure 6A).

The decrease in initial g_s in the wildtype upon high VPD (Figure 3C) translated into a marked decrease in C_i during photosynthetic induction (Figures 3E and 6C). This decrease was less strong in control conditions and was barely visible in flacca under control or high VPD (Figure 3F), as initial g_s in flacca did not react to high VPD (Figure 3D). The reduced transient availability of C_i decreased the rate of photosynthetic induction in the wildtype under high VPD (Figure 3A). This reduction, in turn, fed back on dynamic Φ_PSII (which was slowed down; first 15 minutes in Figure 5A), NPQ (which initially increased to higher levels and then relaxed less quickly than under control conditions; Figure 5C) and the relationship between gross photosynthesis and electron transport (Figure 6B). Unlike initial g_s, stomatal opening (difference between initial and final g_s) was not different between wildtype and flacca leaves under control (~0.4 mol m⁻² s⁻¹) and high VPD conditions (~0.3 mol m⁻² s⁻¹; Table 1).

Perhaps surprisingly, photosynthetic induction was not different between genotypes under low CO₂ partial pressure (Figures 3 and 4). This may be explained in two ways: firstly, initial g_s in the wildtype increased, from 0.22 mol m⁻² s⁻¹ at 400 µbar to 0.34 mol m⁻² s⁻¹ at 200 µbar, while that in flacca did not (Table 1). This g_s increase in wildtype leaves almost halved the difference in initial g_s between genotypes (0.4 → 0.24 mol m⁻² s⁻¹). Secondly, any positive effect that the remaining difference in initial g_s may have had on photosynthetic induction in flacca was probably additionally decreased by low CO₂ availability.

Initial g_s in Leaves Lacking ABA Does Not React to Low CO₂ or High VPD

A striking finding of the present study was that while g_s in wildtype leaves (as expected) increased upon reductions in CO₂ partial pressure (Figure 3C, Table 1), g_s in flacca was virtually unchanged (Figure 3D). This confirms that ABA is part of the CO₂ signalling pathway in stomatal regulation (reviewed in Engineer et al., 2016). Under high VPD (15 mbar), g_s in flacca leaves adapted to low irradiance was similar to that at low VPD (7 mbar; Figure 3D). In wildtype leaves, stomata again responded as expected, by reducing their aperture under increased VPD (Figure 3C). At high irradiance, however, g_s in flacca did respond to the increase in VPD, as its value was reduced by ~0.15 mol m⁻² s⁻¹ compared to that at 7 mbar (Table 1). While there is ongoing controversy about the role of ABA in stomatal sensing of humidity, Merilo et al. (2018) showed that a number of genotypes that are either ABA deficient or ABA insensitive closed their stomata when exposed to an increase in VPD (at 150–500 µmol m⁻² s⁻¹ PAR). The authors explained this phenomenon (which is at variance with Mcadam et al., 2015; McAdam et al., 2016) as most likely being a hydropassive response, resulting from much higher initial g_s in ABA mutants, and thus a greater drop in humidity in the substomatal cavity (Merilo et al., 2018).

Limitations of the Study

In this study, we only used one mutant (flacca, LA0673) to examine the effects of open stomata on the rate of photosynthetic induction. Ideally, a larger number of mutants, each resulting in differential g_s relative to its wildtype, should be used; this to make sure that the observed effects on photosynthetic induction were truly caused by g_s rather than some other putative effects of the flacca mutation. Secondly, during growth flacca plants were regularly sprayed with ABA, following the recommendations of Imber and Tal (1970). It may be that this ABA application triggered unwanted responses in the plants, although based on all results presented here it seems that photosynthesis was fully functional in flacca.

Conclusions and Outlook

The current study suggests that in wildtype leaves, g_s exerts a substantial limitation on non-steady state photosynthesis. The flacca mutant can partially overcome this limitation through stomata that remain open in low light, resulting in substantially reduced t₉₀ in ambient CO₂ partial pressure (t₉₀ was 36–46% in in flacca relative to wildtype values). Nevertheless, while flacca is a good system for testing (dynamic) stomatal limitation in the laboratory, breeding for a similar stomatal behaviour will not be useful for most crops (except possibly for production in wetland areas), as this improvement of photosynthesis in fluctuating light will come with a significant reduction in water use efficiency and a major fitness disadvantage. A more promising approach may be to improve stomatal responsiveness to light intensity fluctuations, as this can potentially increase both light and water use efficiency under fluctuating light intensities. Recent, promising examples of increased g_s responsiveness are the BLINK1 transformant (Papanatsiou et al., 2019) and the PATROL1 overexpressor (Kimura et al., 2020).

References

• 1

  Acevedo-SiacaL. G.CoeR.WangY.KromdijkJ.QuickW. P.LongS. P. (2020). Variation in photosynthetic induction between rice accessions and its potential for improving productivity. New Phytol.227, 1097–1108. doi: 10.1111/nph.16454. 10.1111/np.

  • CrossRef
  • Google Scholar

• 2

  AllenM. T.PearcyR. W. (2000). Stomatal behavior and photosynthetic performance under dynamic light regimes in a seasonally dry tropical rain forest. Oecologia122, 470–478. doi: 10.1007/s004420050968

  • CrossRef
  • Google Scholar

• 3

  BakerN. R.HarbinsonJ.KramerD. M. (2007). Determining the limitations and regulation of photosynthetic energy transduction in leaves. Plant Cell Environ.30, 1107–1125. doi: 10.1111/j.1365-3040.2007.01680.x

  • CrossRef
  • Google Scholar

• 4

  BradfordK. J.SharkeyT. D.FarquharG. D. (1983). Gas exchange, stomatal behavior, and delta13C values of the flacca tomato mutant in relation to abscisic acid. Plant Physiol.72, 245–250. doi: 10.1104/pp.72.1.245

  • CrossRef
  • Google Scholar

• 5

  DinnoA. (2017).Dunn’s Test of Multiple Comparisons Using Rank Sums. Available at: https://cran.r-project.org/web/packages/dunn.test/dunn.test.pdf (Accessed last accessed June 7, 2020).

  • Google Scholar

• 6

  EngineerC. B.Hashimoto-SugimotoM.NegiJ.Israelsson-NordströmM.Azoulay-ShemerT.RappelW.-J.et al. (2016). CO2 sensing and CO2 regulation of stomatal conductance: advances and open questions. Trends Plant Sci.21, 16–30. doi: 10.1016/j.tplants.2015.08.014

  • CrossRef
  • Google Scholar

• 7

  HarleyP. C.LoretoF.Di MarcoG.SharkeyT. D. (1992). Theoretical considerations when estimating the mesophyll conductance to CO2 flux by analysis of the response of photosynthesis to CO2. Plant Physiol.98, 1429–1436. doi: 10.1104/pp.98.4.1429

  • CrossRef
  • Google Scholar

• 8

  ImberD.TalM. (1970). Phenotypic reversion of flacca, a wilty mutant of tomato, by abscisic acid. Sci. (80-. ).169, 592–593. doi: 10.1126/science.169.3945.592

  • CrossRef
  • Google Scholar

• 9

  KaiserE.MoralesA.HarbinsonJ.KromdijkJ.HeuvelinkE.MarcelisL. F. M. (2015). Dynamic photosynthesis in different environmental conditions. J. Exp. Bot.66, 2415–2426. doi: 10.1093/jxb/eru406

  • CrossRef
  • Google Scholar

• 10

  KaiserE.MoralesA.HarbinsonJ.HeuvelinkE.PrinzenbergA. E.MarcelisL. F. M. (2016). Metabolic and diffusional limitations of photosynthesis in fluctuating irradiance in Arabidopsis thaliana. Sci. Rep.6 (31252), 1–13 .doi: 10.1038/srep31252. 10.1038/sr.

  • CrossRef
  • Google Scholar

• 11

  KaiserE.KromdijkJ.HarbinsonJ.HeuvelinkE.MarcelisL. F. M. (2017a). Photosynthetic induction and its diffusional, carboxylation and electron transport processes as affected by CO2 partial pressure, temperature, air humidity and blue irradiance. Ann. Bot.119, 191–205. doi: 10.1002/dvdy

  • CrossRef
  • Google Scholar

• 12

  KaiserE.ZhouD.HeuvelinkE.HarbinsonJ.MoralesA.MarcelisL. F. M. (2017b). Elevated CO2 increases photosynthesis in fluctuating irradiance regardless of photosynthetic induction state. J. Exp. Bot.68, 5629–5640. doi: 10.1093/jxb/erx357

  • CrossRef
  • Google Scholar

• 13

  KaiserE.MoralesA.HarbinsonJ. (2018). Fluctuating light takes crop photosynthesis on a rollercoaster ride. Plant Physiol.176, 977–989. doi: 10.1104/pp.17.01250

  • CrossRef
  • Google Scholar

• 14

  KimuraH.Hashimoto-SugimotoM.IbaK.TerashimaI.YamoriW. (2020). Improved stomatal opening enhances photosynthetic rate and biomass production in fluctuating light. J. Exp. Bot.71, 2339–2350. doi: 10.1093/jxb/eraa090

  • CrossRef
  • Google Scholar

• 15

  KromdijkJ.GlowackaK.LeonelliL.GabillyS. T.IwaiM.NiyogiK. K.et al. (2016). Improving photosynthesis and crop productivity by accelerating recovery from photoprotection. Sci. (80-.).354, 857–861. doi: 10.1126/science.aai8878

  • CrossRef
  • Google Scholar

• 16

  LawsonT.BlattM. R. (2014). Stomatal size, speed, and responsiveness impact on photosynthesis and water use efficiency. Plant Physiol.164, 1556–1570. doi: 10.1104/pp.114.237107

  • CrossRef
  • Google Scholar

• 17

  LawsonT.WeyersJ. (1999). Spatial and temporal variation in gas exchange over the lower surface of Phaseolus vulgaris L. primary leaves. J. Exp. Bot.50, 1381–1391. doi: 10.1093/jxb/50.337.1381

  • CrossRef
  • Google Scholar

• 18

  LiT.KromdijkJ.HeuvelinkE.van NoortF. R.KaiserE.MarcelisL. F. M. (2016). Effects of diffuse light on radiation use efficiency of two Anthurium cultivars depend on the response of stomatal conductance to dynamic light intensity. Front. Plant Sci.7, 56. doi: 10.3389/fpls.2016.00056

  • CrossRef
  • Google Scholar

• 19

  LoriauxS. D.AvensonT. J.WellesJ. M.McDermittD. K.EcklesR. D.RienscheB.et al. (2013). Closing in on maximum yield of chlorophyll fluorescence using a single multiphase flash of sub-saturating intensity. Plant Cell Environ.36, 1755–1570. doi: 10.1111/pce.12115

  • CrossRef
  • Google Scholar

• 20

  MatthewsJ. S. A.Vialet-ChabrandS. R. M.LawsonT. (2017). Diurnal variation in gas exchange: the balance between carbon fixation and water loss. Plant Physiol.174, 614–623. doi: 10.1104/pp.17.00152

  • CrossRef
  • Google Scholar

• 21

  McadamS. A. M.SussmilchF. C.BrodribbT. J.RossJ. J. (2015). Molecular characterization of a mutation affecting abscisic acid biosynthesis and consequently stomatal responses to humidity in an agriculturally important species. AoB Plants7, 1–11. doi: 10.1093/aobpla/plv091

  • CrossRef
  • Google Scholar

• 22

  McAdamS. A. M.SussmilchF. C.BrodribbT. J. (2016). Stomatal responses to vapour pressure deficit are regulated by high speed gene expression in angiosperms. Plant Cell Environ.39, 485–491. doi: 10.1111/pce.12633

  • CrossRef
  • Google Scholar

• 23

  McAuslandL.Vialet-ChabrandS.DaveyP.BakerN. R.BrendelO.LawsonT. (2016). Effects of kinetics of light-induced stomatal responses on photosynthesis and water-use efficiency. New Phytol.211, 1209–1220. doi: 10.1111/nph.14000

  • CrossRef
  • Google Scholar

• 24

  MeriloE.YarmolinskyD.JalakasP.ParikH.TulvaI.RasulovB.et al. (2018). Stomatal VPD response: there is more to the story than ABA. Plant Physiol.176, 851–864. doi: 10.1104/pp.17.00912

  • CrossRef
  • Google Scholar

• 25

  MizokamiY.NoguchiK.KojimaM.SakakibaraH.TerashimaI. (2015). Mesophyll conductance decreases in the wild type but not in an ABA-deficient mutant (aba1) of Nicotiana plumbaginifolia under drought conditions. Plant Cell Environ.38, 388–398. doi: 10.1111/pce.12394

  • CrossRef
  • Google Scholar

• 26

  MoralesA.KaiserE.YinX.HarbinsonJ.MolenaarJ.DrieverS. M.et al. (2018). Dynamic modelling of limitations on improving leaf CO2 assimilation under fluctuating irradiance. Plant Cell Environ.41, 589–604. doi: 10.1111/pce.13119

  • CrossRef
  • Google Scholar

• 27

  OrtD. R.MerchantS. S.AlricJ.BarkanA.BlankenshipR. E.BockR.et al. (2015). Redesigning photosynthesis to sustainably meet global food and bioenergy demand. Proc. Natl. Acad. Sci.112, 8529–8536. doi: 10.1073/pnas.1424031112

  • CrossRef
  • Google Scholar

• 28

  OxboroughK.BakerN. R. (1997). Resolving chlorophyll a fluorescence images of photosynthetic efficiency into photochemical and non-photochemical components - Calculation of qP and Fv’/Fm’ without measuring Fo’. Photosynth. Res.54, 135–142. doi: 10.1023/A:1005936823310

  • CrossRef
  • Google Scholar

• 29

  PapanatsiouM.PetersenJ.HendersonL.WangY.ChristieJ. M.BlattM. R. (2019). Optogenetic manipulation of stomatal kinetics improves carbon assimilation,water use, and growth. Sci. (80-. ).363, 1456–1459. doi: 10.1126/science.aaw0046

  • CrossRef
  • Google Scholar

• 30

  PearcyR. W.KrallJ. P.Sassenrath-ColeG. F. (1996). “Photosynthesis in fluctuating light environments,” in Photosynthesis and the Environment. Ed. BakerN. R. (Dordrecht, the Netherlands: Kluwer Academic), 321–346. doi: 10.1007/0-306-48135-9_13

  • CrossRef
  • Google Scholar

• 31

  PrinsleyR. T.LeegoodR. C. (1986). Factors affecting photosynthetic induction in spinach leaves. Biochim. Biophys. Acta849, 244–253. doi: 10.1016/0005-2728(86)90031-9

  • CrossRef
  • Google Scholar

• 32

  R Core Team. (2020). R: A language and environment for statistical computing. (Vienna, Austria: R Foundation for Statistical Computing). Available at: https://www.R-project.org/.

  • Google Scholar

• 33

  RaissigM. T.MatosJ. L.GilM. X. A.KornfeldA.BettadapurA.AbrashE.et al. (2017). Mobile MUTE specifies subsidiary cells to build physiologically improved grass stomata. Sci. (80-. ).355, 1215–1218. doi: 10.1126/science.aal3254

  • CrossRef
  • Google Scholar

• 34

  SagiM.ScazzocchioC.FluhrR. (2002). The absence of molybdenum cofactor sulfuration is the primary cause of the flacca phenotype in tomato plants. Plant J.31, 305–317. doi: 10.1046/j.1365-313X.2002.01363.x

  • CrossRef
  • Google Scholar

• 35

  SharkeyT. D. (2016). What gas exchange data can tell us about photosynthesis. Plant Cell Environ.39, 1161–1163. doi: 10.1111/pce.12641

  • CrossRef
  • Google Scholar

• 36

  SolehM. A.TanakaY.NomotoY.IwahashiY.NakashimaK.FukudaY.et al. (2016). Factors underlying genotypic differences in the induction of photosynthesis in soybean [Glycine max (L.) Merr.]. Plant Cell Environ.131, 305–315 .doi: 10.1111/pce.12674

  • CrossRef
  • Google Scholar

• 37

  StittM.GrosseH. (1988). Interactions between sucrose synthesis and CO2 fixation I. Secondary kinetics during photosynthetic induction are related to a delayed activation of sucrose synthesis. J. Plant Physiol.133, 129–137. doi: 10.1016/S0176-1617(88)80127-5

  • CrossRef
  • Google Scholar

• 38

  StittM.QuickW. P. (1989). Photosynthetic carbon partitioning: its regulation and posibilities for manipulation. Physiol. Plant77, 633–641. doi: 10.1111/j.1399-3054.1989.tb05402.x

  • CrossRef
  • Google Scholar

• 39

  TalM.NevoY. (1973). Abnormal stomatal behavior and root resistance, and hormonal imbalance in three wilty mutants of tomato. Biochem. Genet.8, 291–300. doi: 10.1007/BF00486182

  • CrossRef
  • Google Scholar

• 40

  TalM. (1966). Abnormal Stomatal Behavior in Wilty Mutants of Tomato. Plant Physiol.41, 1387–1391. doi: 10.1104/pp.41.8.1387

  • CrossRef
  • Google Scholar

• 41

  TanakaY.AdachiS.YamoriW. (2019). Natural genetic variation of the photosynthetic induction response to fluctuating light environment. Curr. Opin. Plant Biol.49, 52–59. doi: 10.1016/j.pbi.2019.04.010

  • CrossRef
  • Google Scholar

• 42

  TaylorS. H.LongS. P. (2017). Slow induction of photosynthesis on shade to sun transitions in wheat may cost at least 21% of productivity. Philos. Trans. R. Soc B372, 1–9. doi: 10.1098/rstb.2016.0543

  • CrossRef
  • Google Scholar

• 43

  Tinoco-OjangurenC.PearcyR. W. (1993). Stomatal dynamics and its importance to carbon gain in two rainforest Piper species. II. Stomatal versus biochemical limitations during photosynthetic induction. Oecologia94, 395–402.

  • Google Scholar

• 44

  TomimatsuH.TangY. (2012). Elevated CO2 differentially affects photosynthetic induction response in two Populus species with different stomatal behavior. Oecologia169, 869–878. doi: 10.1007/s00442-012-2256-5

  • CrossRef
  • Google Scholar

• 45

  TomimatsuH.SakataT.FukayamaH.TangY. (2019). Short-term effects of high CO2 accelerate photosynthetic induction in Populus koreana × trichocarpa with always-open stomata regardless of phenotypic changes in high CO2 growth conditions. Tree Physiol.39, 474–483. doi: 10.1093/treephys/tpy078

  • CrossRef
  • Google Scholar

• 46

  UrbanO.ŠprtováM.KošvancováM.LichtenthalerH. K.MarekM. V. (2008). Comparison of photosynthetic induction and transient limitations during the induction phase in young and mature leaves from three poplar clones. Tree Physiol.28, 1189–1197. doi: 10.1093/treephys/28.8.1189

  • CrossRef
  • Google Scholar

• 47

  ValladaresF.AllenM. T.PearcyR. W. (1997). Photosynthetic responses to dynamic light under field conditions in six tropical rainforest shrubs occuring along a light gradient. Oecologia111, 505–514. doi: 10.1007/s004420050264

  • CrossRef
  • Google Scholar

• 48

  Vialet-chabrandS. R. M.MatthewsJ. S. A.McAuslandL.BlattM. R.GriffithsH.LawsonT. (2017). Temporal dynamics of stomatal behavior: Modeling and implications for photosynthesis and water use. Plant Physiol.174, 603–613. doi: 10.1104/pp.17.00125

  • CrossRef
  • Google Scholar

• 49

  VicoG.ManzoniS.PalmrothS.KatulG. (2011). Effects of stomatal delays on the economics of leaf gas exchange under intermittent light regimes. New Phytol.192, 640–652. doi: 10.1111/j.1469-8137.2011.03847.x

  • CrossRef
  • Google Scholar

• 50

  WayD. A.PearcyR. W. (2012). Sunflecks in trees and forests: from photosynthetic physiology to global change biology. Tree Physiol.32, 1066–1081. doi: 10.1093/treephys/tps064

  • CrossRef
  • Google Scholar

• 51

  WoodrowI. E.MottK. A. (1989). Rate limitation of non-steady-state photosynthesis by Ribulose-1,5-bisphosphate Carboxylase in spinach. Aust. J. Plant Physiol.16, 487–500. doi: 10.1071/PP9890487

  • CrossRef
  • Google Scholar

• 52

  YamoriW.KusumiK.IbaK.TerashimaI. (2020). Increased stomatal conductance induces rapid changes to photosynthetic rate in response to naturally fluctuating light conditions in rice. Plant Cell Environ.43, 1230–1240. doi: 10.1111/pce.13725

  • CrossRef
  • Google Scholar

• 53

  YinX.StruikP. C.RomeroP.HarbinsonJ.EversJ. B.Van Der PuttenP. E. L.et al. (2009). Using combined measurements of gas exchange and chlorophyll fluorescence to estimate parameters of a biochemical C3 photosynthesis model: a critical appraisal and a new integrated approach applied to leaves in a wheat (Triticum aestivum) canopy. Plant Cell Environ.32, 448–464. doi: 10.1111/j.1365-3040.2009.01934.x

  • CrossRef
  • Google Scholar

• 54

  ZhangY.KaiserE.ZhangY.YangQ.LiT. (2018). Short-term salt stress strongly affects dynamic photosynthesis, but not steady-state photosynthesis, in tomato (Solanum lycopersicum). Environ. Exp. Bot.149, 109–119. doi: 10.1016/j.envexpbot.2018.02.014

  • CrossRef
  • Google Scholar

• 55

  ZhangY.KaiserE.MarcelisL. F. M.YangQ.LiT. (2020). Salt stress and fluctuating light have separate effects on photosynthetic acclimation, but interactively affect biomass. Plant Cell Environ.1–15. doi: 10.1111/pce.13810

  • CrossRef
  • Google Scholar