A high throughput gas exchange screen for determining rates of photorespiration or regulation of C4 activity.

Large-scale research programmes seeking to characterize the C4 pathway have a requirement for a simple, high throughput screen that quantifies photorespiratory activity in C3 and C4 model systems. At present, approaches rely on model-fitting to assimilatory responses (A/C i curves, PSII quantum yield) or real-time carbon isotope discrimination, which are complicated and time-consuming. Here we present a method, and the associated theory, to determine the effectiveness of the C4 carboxylation, carbon concentration mechanism (CCM) by assessing the responsiveness of V O/V C, the ratio of RuBisCO oxygenase to carboxylase activity, upon transfer to low O2. This determination compares concurrent gas exchange and pulse-modulated chlorophyll fluorescence under ambient and low O2, using widely available equipment. Run time for the procedure can take as little as 6 minutes if plants are pre-adapted. The responsiveness of V O/V C is derived for typical C3 (tobacco, rice, wheat) and C4 (maize, Miscanthus, cleome) plants, and compared with full C3 and C4 model systems. We also undertake sensitivity analyses to determine the impact of R LIGHT (respiration in the light) and the effectiveness of the light saturating pulse used by fluorescence systems. The results show that the method can readily resolve variations in photorespiratory activity between C3 and C4 plants and could be used to rapidly screen large numbers of mutants or transformants in high throughput studies.

Introduction

In most photosynthetic organisms Ribulose Bisphosphate Carboxylase Oxygenase (RuBisCO) catalyses the first key step in carbon assimilation, reacting ribulose-1,5-bisphosphate with CO2 to produce two molecules of 3-phosphoglycerate (PGA). Oxygen competitively inhibits this reaction and leads to the synthesis of the 2-carbon compound phosphoglycollate, which is recycled to PGA (consuming ATP, and then NADPH) and CO2 by the photorespiratory cycle (Yoshimura ; Sage ). The result of photorespiration is a noticeable carbon loss and a consequent metabolic cost for carbon recapture and for the recycling of photorespiratory intermediates (Ehleringer and Pearcy, 1983; Pearcy and Ehleringer, 1984; Eckardt, 2005). Many plants have evolved strategies to reduce photorespiration by increasing the level of CO2 around RuBisCO, including both crassulacean acid metabolism (CAM) and the C4 photosynthetic pathway (Dodd ; Sage, 2004; Sage ; Osborne and Sack, 2012; Griffiths ; Owen and Griffiths, 2013). C4 photosynthesis is most often based on a two-celled carbon concentrating mechanism, where HCO3 – is first fixed into the four-carbon compound oxaloacetic acid (OAA) in the mesophyll by phosphoenolpyruvate carboxylase (PEPC). OAA is then reduced to malate or transaminated to aspartate and the resulting C4-(amino)acid is shuttled into the bundle sheath (BS), where it is decarboxylated, releasing CO2 for refixation by RuBisCO (Hibberd and Covshoff, 2010; Bellasio and Griffiths, 2014c).

Although the enzymes catalysing the core C4 carbon concentration mechanism (CCM) are well characterized (Kanai and Edwards, 1999), many of the genes responsible for the accompanying anatomical alterations or for generating and maintaining expression of the C4 cycle genes (Hibberd ; Langdale, 2011) have yet to be identified. One approach that is increasingly proving useful to identify candidate genes underlying the C4 pathway is comparative transcriptomics of samples either undergoing C3 or C4 photosynthesis (Bräutigam ; Gowik ; John ), or tissues in the process of inducing the full C4 system (Li ; Pick ; Chang ; Wang ). Because stable transformation of C4 species is typically time-consuming, introduction of RNA interference constructs via a transient Agrobacterium tumefaciens-based system would be very helpful in screening these candidates being generated from transcriptomics.

At present, techniques used to screen for mutants possessing defective, or enhanced CCM characteristics are time-consuming (Table 1). Analysing the response of assimilation (A) to decreasing CO2 concentration in the substomatal cavity (C i), as A/C i curves (Long and Bernacchi, 2003; Yin ) can take 45 minutes per replicate leaf, and an appropriate model, which may require a priori knowledge of species-specific limitations (Laisk and Edwards, 2000; von Caemmerer, 2000, 2013; Yin and Struik, 2009; Yin ; Yin ). 13C/12C discrimination during photosynthesis (Evans ) can also be used, and a comparison with stomatal conductance allows the internal mesophyll conductance, or extent of CCM or PEPC activity, to be resolved (Meyer ; Kromdijk ; Pengelly ; Bellasio and Griffiths, 2014a, b, c). However, this latter technique is sensitive, and requires either off-line sample preparation for mass spectrometric analyses or specialized laser equipment which is not readily available (Table 1).

Table 1.

Comparison between methods screening for activity of a functional CCM

Method	Advantages and limitations	Reference
Dry matter isotopic discrimination	*Specialized equipment*Integrates the isotopic signal throughout growth*Cannot resolve transient changes in assimilatory physiology	Cernusak et al. (2013)
On line isotopic discrimination	*Laser is no longer commercially available*Maintenance costs of isotope ratio mass spectrometer*Need of highly skilled operator*Difficult computation and parameterization	Evans et al. (1986); Bellasio and Griffiths (2014b); von Caemmerer et al. (2014)
A/C i curves	*Requires a priori knowledge of the limitations underpinning each part for the A/C i curve for correct model fitting*Result may depend on experimental routine	Long and Bernacchi (2003); Yin et al. (2009)
Gas exchange and fluorescence	*Requires initial response curve for parameterisation*Requires model fitting	Long and Bernacchi (2003); Martins et al. (2013)
O2 sensitivity of carboxylation efficiency	*Delicate experimental routine	Laisk et al. (2002); Yin et al. (2009)
Assimilation increase under low O2	*Ease of determination*Ignores the effect of changing O2 concentration on Y(II)	Sharkey (1988); Ripley et al. (2007)
Gas exchange and fluorescence	*Rapid (6 minutes)*Widely available equipment*Independent of leaf size*Ease of determination and calculation*Does not require fitting or parameterisation*Assessment under growth conditions	This study

In this paper we describe a novel method, and present the associated theory, to determine rates of photorespiration from instantaneous rates of RuBisCO carboxylation and oxygenation. The approach compares concurrent gas exchange and pulse-modulated chlorophyll fluorescence measurements under ambient and low O2. Under these non-photorespiratory conditions assimilation (A) increases, because RuBisCO competitive inhibition from O2 is reduced. In contrast, Y(II) decreases because the demand for NADPH associated with photorespiratory by-product cycling (and reduction) is lower, and cannot entirely be offset by the increase in A. The new method combines developments in approaches using gas exchange (Sharkey, 1988; Long and Bernacchi, 2003; Ripley ) and the quantitative interpretation of quantum yield (Yin , 2009, 2011b; Yin and Struik, 2009, 2012; Bellasio and Griffiths, 2014b). This new method can be performed with off-the-shelf commercial equipment, which is generally available in ecophysiology laboratories. The procedure takes as little as 6 minutes to perform if plants are pre-adapted, making it significantly faster than A/C i curves and potentially useful as a high-throughput approach for assessing C4 activity in mutant screens, the progeny from C3–C4 crosses or C3–C4 intermediates.

Materials and methods

Plants

Plants of Miscanthus (Miscanthus giganteus), cleome (Cleome gynandra), maize (Zea mays L.), wheat (Triticum aestivum L.), tobacco (Nicotiana tabacum L.), and rice (Oryza sativa L.) were grown at the Plant Growth Facility located at the University of Cambridge Botanic Garden in controlled environment growth rooms (Conviron Ltd, Winnipeg, Canada) set at 16h day length, temperature of 25 °C/23 °C (day/night), 40% relative humidity, and photosynthetic photon flux density (PPFD)=300 μmol m–2 s–1. Plants were manually watered daily, with particular care to avoid overwatering.

Gas exchange measurements with concurrent PSII yield

Measurements were performed with an infra-red gas analyser (IRGA, a LI6400XT, LI-cor, USA), fitted with a 6400–40 leaf chamber fluorometer. The IRGA was fed with CO2 (through the IRGA gas mixing unit) and ambient air. Gas flow was set at 150 μmol s–1. Reference CO2 was set at 200 μmol mol–1 (Figure 1 and Table 1) or set alternatively at 400, 300, 200, 150, 100, and 50 μmol s–1 (Figure 3). Block temperature was controlled at 35 °C. The fluorometer was set to multiphase pulse with factory setting, target intensity=10 and ramp depth=40% (Loriaux ). A portion of a light-adapted leaf was clamped in the cuvette. The leaf was allowed to reach stable photosynthetic conditions under PPFD=300 μmol m–2 s–1 (factory setting: 90% red, 10% blue). Photosynthesis was measured every 10 s for 30 s (the three values were then averaged) and a multiphase pulse was applied for the determination of Y(II). A humidified 2% O2/N2 gas (pre-mixed, BOC, Guilford, UK) was switched to supply the inlet of the IRGA. The gas was allowed to completely flush the cuvette (c. 6min). Photosynthesis was measured every 10 s for 30 s (the three values were then averaged) and a multiphase pulse was applied for the determination of Y(II). Light was turned off, the inlet was fed with ambient air, the reference CO2 was set at 500 μmol mol–1, similar to the lab CO2 concentration (c. 550 μmol mol–1) to minimize the errors caused by CO2 leakage (Boesgaard ), and flow was set to 40 μmol s–1. Once the cuvette had been flushed, and the signal stabilised (c. 5min), respiration was measured every 10 s for 2min (the values were then averaged). C a was not adjusted to account for changes in stomatal conductance or for the control of C i during this procedure. This avoided the need for IRGA recalibration as the Y(II) measurements are independent of C i. The measured A and Y(II) under low and ambient O2, together with an estimate of R LIGHT (see below), were used to determine RuBisCO rate of carboxylation (V C), RuBisCO rate of oxygenation (V O), and the rate of photorespiratory CO2 evolution in the light (F).

Fig. 1.

Summary of experimental approach. One representative dataset from C3 tobacco is presented. Once stable assimilatory conditions are reached, a first set of data are recorded (left hatched area). The background gas is then switched from ambient to 2% O2. After a suitable acclimation time to allow flushing of the cuvette and reacclimation (c. 6min), a second set of data are recorded (right hatched area). The response of assimilation (triangles) and Photosystem II yield Y(II) (squares) during the experiment are shown.

Fig. 3.

Sensitivity to errors in the determination of R LIGHT. True values were simulated by calculating equation 8 for R LIGHT=1 μmol m–2 s–1, V O/V C = 0.2, and Y(II)=0.65 at variable assimilation (A) values. Test values of V O/V C were then calculated by solving equation 8 at different values for R LIGHT: 2 μmol m–2 s–1 (+100%), 1.5 μmol m–2 s–1 (+50%), 1.2 μmol m–2 s–1 (+20%), 0.8 μmol m–2 s–1 (–20%), 0.5 μmol m–2 s–1 (–50%), 0 μmol m–2 s–1 (–100%, GA=A). The difference in V O/V C between the test minus the true value was expressed as relative to the true value.

Theory

RuBisCO catalyses two reactions: a carboxylase reaction whereby Ribulose BisPhosphate (RuBP) is carboxylated to form two molecules of phosphoglyceric acid (PGA), and an oxygenase reaction whereby RuBP is oxygenated to form one PGA and one glycollate molecule. Each carboxylase event requires 2 NADPH for the reduction of the 2 PGA molecules formed. Each oxygenase event requires 1 NADPH for the reduction of the PGA directly produced by RuBisCO, 0.5 NADPH to recycle glycollate, and 0.5 NADPH to reduce the PGA regenerated, which total 2 NADPH (Bellasio and Griffiths, 2014c). The overall NADPH demand, at steady-state, equals the total photosynthetic NADPH production rate J NADPH (Yin ; Yin and Struik, 2012):

Where J NADPH is the total NADPH produced for photosynthesis, V C is RuBisCO carboxylation rate, and V O is RuBisCO oxygenation rate. Notably, this reducing power requirement is the same for all types of photosynthesis, as active types of CCM require additional ATP but not NADPH. In line with von Caemmerer (2000) equation 1 assumes that PGA is entirely reduced, and therefore the small quantity of PGA consumed by respiration ( R LIGHT) is neglected, in fact under growth light irradiance 2V C+2V O>> R LIGHT, unless at very low irradiances, see equation 7 in Bellasio and Griffiths (2014c).

Although the carboxylation reaction of RuBisCO consumes CO2, the regeneration of glycollate releases 0.5 CO2 for each oxygenase catalytic event. CO2 is also produced by light respiration, a process which is active during photosynthesis to support basal metabolism. The net assimilation rate (A, which is the quantity measured through gas exchange) results from summing the CO2 consumed by RuBisCO, the CO2 produced by glycollate regeneration and the CO2 produced by respiration:

Where A is net CO2 assimilation, R LIGHT is respiration in the light and other variables were previously defined. Notably, this equation is universal for all types of photosynthesis (von Caemmerer, 2013).

For the definition of gross assimilation (GA = A + R LIGHT), equation 2 can be rearranged:

Equation 1 and 3 can be combined to give:

The rate of photorespiratory CO2 evolution, F can be calculated as: (von Caemmerer, 2013)

Under low O2, V O can be approximated to ≈0, hence, from equation 4:

Which is valid when V O≈0.

NADPH is produced through linear electron flow. Independently from where this reaction is located (e.g. in mesophyll cells), electrons are invariably extracted from water by PSII (Yin and Struik, 2012), therefore J NADPH is proportional to Y(II) (Yin and Struik, 2012). This allows J NADPH to be calculated under photorespiratory conditions using the information derived under non-photorespiratory conditions, and can be expressed as (Bellasio and Griffiths, 2014b):

Where J NADPH and Y(II) refer to ambient O2 conditions. Equation 7 has been validated in C3 and C4 plants (Yin , 2011b; Bellasio and Griffiths, 2014b, c) but it is worth noting that equation 7 is a mathematical simplification and holds true when: (i) photorespiration is negligible under non-photorespiratory conditions, which is a widely used simplification; (ii) R LIGHT does not vary between low and ambient O2—this is also a fair assumption because any O2 effect is generally negligible (Badger, 1985; Gupta ); (iii) the allocation to alternative sinks (non-assimilatory and non-photorespiratory) is proportional to Y(II). This is the normal case in C4 plants where the relationship between Y(II) and Y(CO 2) has a null intercept (Edwards and Baker, 1993). When that is not the case, for instance when the allocation to alternative sinks is constant, equation 7 would also hold true if the allocation to alternative sinks is small compared with Y(II). This is the normal case in C3 plants (Valentini ; Martins ). Should the allocation to alternative sinks be large, equation 7 would still hold true mathematically when is close to the unity. The implications for method accuracy are detailed in the discussion.

Equation 3, 4, 6, and 7 can be combined to obtain:

Which expresses the RuBisCO rate of oxygenation relative to carboxylation. The influence on the quality of R LIGHT estimate on V O/V C is described in the discussion, together with the other factors influencing the results.

Modelling C3 and C4 V O/V C

The data obtained for tobacco and maize were compared with a simulated V O/V C based on the validated von Caemmerer models for C3 and C4 photosynthesis. Briefly, for tobacco, the response of A to C i was modelled using the quadratic equation (Table 3, equation 9) proposed by Ethier and Livingston (2004), which takes into account mesophyll conductance to CO2. The CO2 concentration at the site of carboxylation C C was then calculated through the supply function of mesophyll (equation 10), and, finally V O/V C was simulated from the kinetic properties of RuBisCO and the ratio between C C and the O2 concentration at the site of carboxylation (equation 11). For maize (Table 4), firstly we simulated the responses of V P and A to decreasing C i, using the equations for the enzyme-limited model for C4 photosynthesis (equation 12 and 16, respectively). These were used to simulate the CO2 and O2 concentration in the bundle sheath (equation 13 and 14, respectively), the ratio of which, together with RuBisCO specificity, was used to simulate V O/V C (equation 15 and 17).

Table 3.

Model for C3 photosynthesis

Symbol	Definition/calculation	Equation	Values/Units/References
A	Net Assimilation A=− b + b^2−4ac2a where: a =−1g_m; b =(V_Cmax−R_LIGHT)g_m+C_i+K_C(1+OK_O); c =R_LIGHT(C_i+K_C(1+OK_O)) −V_Cmax(C_i−Γ^*)	(9)	Ethier and Livingston (2004)
C c	CO2 partial pressure at the site of carboxylation  C_c =C_i−Ag_m	(10)	μbar
C i	CO2 concentration in the intercellular spaces as calculated by the IRGA.		μmol mol–1 (Li-cor 6400 manual equation 1–18)
g m	Mesophyll conductance to CO2		0.25mol m–2 s–1 bar–1 (Ethier and Livingston, 2004)
K C	RuBisCO Michaelis-Menten constant for CO2		319.3 μbar (Ethier and Livingston, 2004)
K O	RuBisCO Michaelis-Menten constant for O2		277100 μbar (Ethier and Livingston, 2004)
O	O2 partial pressure at the site of carboxylation		200000 μbar
R LIGHT	Respiration in the light		0.63 μmol m–2 s–1
V Cmax	Maximum RuBisCO carboxylation rate		34.7 μmol m–2 s–1 (Ethier and Livingston, 2004)
V O /V C	V_OV_C=V_OmaxK_CV_CmaxK_O OC_C	(11)	equation 2.16 in (von Caemmerer, 2000)
V Omax	Maximum RuBisCO oxygenation rate		13.25 μmol m–2 s–1 (Ethier and Livingston, 2004)
Γ*	CO2 compensation point in absence of dark respiration		44 μbar

Table 4.

Model for C4 photosynthesis

Symbol	Definition/calculation	Equation	Values/Units/References
A	Net Assimilation A=− b − b^2−4ac2a where: a =1−αK_C0.047K_O ; b =−{(V_P−R_M+g_BSC_M)+(V_Cmax−R_LIGHT)+g_BSK_C(1+O_MK_O)+α0.047(γ^* V_Cmax+R_LIGHTK_CK_O)}; c =(V_Cmax−R_LIGHT)(V_P−R_M+g_BSC_M)−(V_Cmaxg_BSγ^*O_M+R_LIGHTg_BSK_C(1+O_MK_O))	(12)	Equation 4.21 in (von Caemmerer, 2000)
C BS	CO2 concentration in the bundle sheath C_BS =γ^*O_BS+K_C(1+O_BSK_O) A+R_LIGHTV_Cmax1− A+R_LIGHTV_Cmax	(13)	Equation 4.11 in (von Caemmerer, 2000)
C M	CO2 partial pressure in M (at the site of PEP carboxylation)  C_M =C_i		μbar
C i	CO2 concentration in the intercellular spaces as calculated by the IRGA		μbar
g BS	Bundle sheath conductance to CO2		0.005mol m2 s–1
K C	RuBisCO Michaelis-Menten constant for CO2		650 μbar (von Caemmerer, 2000)
K O	RuBisCO Michaelis-Menten constant for O2		450000 μbar (von Caemmerer, 2000)
K P	PEPC Michaelis-Menten constant		80 μbar (von Caemmerer, 2000)
O BS	O2 mol fraction in the bundle sheath cells (in air at equilibrium) O_BS=O_M+αA0.047g_BS	(14)	μmol mol–1 Equation 4.16 in (von Caemmerer, 2000)
O M	O2 partial pressure in the mesophyll cells (in air at equilibrium)		210000 μbar
R LIGHT	Respiration in the light, assumed to equal dark respiration
R M	Mesophyll non photorespiratory CO2 production in the light R M = 0.5 R LIGHT		μmol m–2 s–1 (von Caemmerer, 2000; Kromdijk et al., 2010; Ubierna et al., 2013)
V Cmax	Maximum RuBisCO carboxylation rate		60 μmol m–2 s–1 (von Caemmerer, 2000
V O /V C	V_OV_C=2 Γ^*C_BS	(15)	Equation 4.8 in (von Caemmerer, 2000)
V P	PEP Carboxylation rate V_P=C_MV_PmaxC_M+K_P	(16)	Equation 4.17 in (von Caemmerer, 2000)
V Pmax	Maximum PEPC carboxylation rate		120 μmol m–2 s–1 (von Caemmerer, 2000)
α	Fraction of PSII active in BS cells		0.15 (Edwards and Baker, 1993; von Caemmerer, 2000; Kromdijk et al., 2010)
γ*	Half of the reciprocal of the RuBisCO specificity		0.000193 (von Caemmerer, 2000)
Γ*	CO2 compensation point in absence of dark respiration  Γ^* =γ^*O_BS	(17)	Equation 4.9 in (von Caemmerer, 2000)

Results

Figure 1 displays a typical primary data profile for a C3 tobacco leaf, showing the interaction between steady state assimilation (A) and quantum yield of PSII, Y(II), during the transition from ambient to low O2 (21 to 2% O2), with hatched areas indicating the steady state conditions under which readings were taken to derive V O/V C. Under non-photorespiratory conditions, A increases because of the lower competitive inhibition of O2, whereas Y(II) decreases owing to the lower NADPH demand for photorespiratory by-product recycling and reduction. The experimental conditions were deliberately chosen to minimize reductions of quantum yield at saturating light (relatively low PPFD of 300 μmol m–2 s–1), and enhance photorespiratory responses to low O2 partial pressure (measurements at 200 μmol mol–1 CO2) (Fig. 1 and Table 2). Subsequently, V O/V C was measured on C3 tobacco and C4 maize using different CO2 concentrations in the reference gas: 400, 300, 200, 150, 100, and 50 μmol mol–1 (Fig. 2) and results were compared with simulated values of V O/V C generated with the validated von Caemmerer C3 and C4 models. To facilitate the comparison, data were plotted against the substomatal CO2 concentration C i. As expected, under decreasing C i, V O/V C becomes progressively higher in tobacco but it is only marginally affected in maize. The measured data track the trend and magnitude of the theoretical curves in C3, whereas we could not capture the theoretical increase in V O/V C expected when C i was close to zero. This may be due to errors in the determination of C i at very low stomatal conductance or to the simplifications used to resolve equation 7. Our data slightly underestimate V O/V C derived using pulsed of 13C enriched CO2 (Busch ), which, however, lay above the curve simulated with the von Caemmerer C3 model (see Fig. 2).

Table 2.

Example of variability within populations and between populations displayed by plants with different pathways of assimilation

V O/V C was measured on species (Miscanthus, Cleome gynandra, maize, wheat, tobacco, and rice) under photosynthetic photon flux density (PPFD) of 300 μmol m–2 s–1, and C a=200 μmol mol–1.

Population	n	Mean V O/V C	Standard deviation	Coefficient of variation
Miscanthus	7	0.0504	0.0091	18%
Cleome gynandra	5	0.0852	0.0046	5.4%
Maize	4	0.0435	0.0074	17%
Wheat	3	0.522	0.071	14%
Tobacco	4	0.533	0.030	5.5%
Rice	4	0.569	0.037	6.5%

Fig. 2.

VO/V C measured under different CO2 concentrations in the substomatal cavity (Ci), obtained by imposing reference CO2 concentrations of 400, 300, 200, 150, 100, and 50 μmol mol–1 for C3 tobacco (triangles) and C4 maize (squares). Data are compared with simulated V O/V C using the validated von Caemmerer C3 and C4 models (lines, see also Table 3 and 4). With decreasing C i, V O/V C gets progressively higher in tobacco but it is only marginally affected in maize, CO2 concentration can therefore be used to control the resolution of the method. All data shown, n=4.

Additional measurements were undertaken with the IRGA, including a recalibration procedure to account for the changing sensitivity to water vapour pressure after the transition to low O2, but stomatal conductance was reduced on average by 1% and internal CO2 concentration, C i, by 3 μmol mol–1 (data not shown). In the subsequent sections, primary data for V O/V C determinations using this new method (calculated from equation 4) are initially presented for three representatives of C3 and C4 species. We then undertake a systematic error analysis of the method, to include the impact of biological and environmental variables. These include physiological components (R LIGHT) and Fm′, as well as light intensity and CO2 concentration used during experimentation.

Variability between and within populations

Table 2 demonstrates that the method clearly discriminates between C4 species, possessing a functional CCM, and C3 species with higher rates of photorespiration. V O/V C ranged from 0.0435 to 0.0852 for the representative C4 species, with coefficients of variation ranging from c. 15% down to 5% in C. gynandra (Table 2). For the C3 species, V O/V C ranged from 0.522 to 0.569, with a low coefficient of variation in tobacco and rice around 6% (Table 2). The magnitude of the offset between C3 and C4 systems, if being used as a rapid screen, would allow changes in expression of C4 characteristics to be clearly resolved. Such an approach would then allow more detailed characterisation of selected transformants, C2, or C3–C4 intermediates to be undertaken.

Accuracy of R LIGHT estimates

To account for the extent that R LIGHT affected the measurement of V O/V C, a sensitivity analysis was used to determine how R LIGHT influences V O/V C (Fig. 3). To do so, equation 8 was calculated for a realistic dataset (R LIGHT=1 μmol m–2 s–1, V O/V C=0.2 and Y(II)=0.65) at variable assimilation values. Then, test values for V O/V C were calculated after R LIGHT was varied to 2 μmol m–2 s–1 (+100%), 1.5 μmol m–2 s–1 (+50%), 1.2 μmol m–2 s–1 (+20%), 0.8 μmol m–2 s–1 (–20%), 0.5 μmol m–2 s–1 (–50%), 0 μmol m–2 s–1 (–100%, GA=A). The deviation from the set V O/V C value (0.2) represented the effect of errors in the evaluation of R LIGHT on V O/V C. Figure 3 shows that V O/V C was relatively insensitive to R LIGHT: for assimilation rates higher than 4 μmol m–2 s–1, R LIGHT values which differed ± 50% resulted in an error lower than 4% in relative terms. R LIGHT overestimation resulted in a lower error than R LIGHT underestimation. For these reasons there is generally no need for a high quality estimate of R LIGHT.

Accuracy of Fm′ measurements

Equations 7 and 8 require the photochemical yield of PSII, Y(II). This is determined according to the formula of Genty (Genty ; Maxwell and Johnson, 2000; Kramer ), whereby Y(II) is calculated as the difference between the light-saturated chlorophyll fluorescence signal (Fm′) minus the chlorophyll fluorescence signal measured during photosynthesis (Fs), expressed as relative to Fm′. Key to this technique is achieving full saturation of PSII in the determination of Fm′ (Earl and Ennahli, 2004; Loriaux ; Harbinson, 2013; Loriaux ). Sub-saturating light pulses result in the underestimation of Fm′; however, the degree of underestimation depends not only on the saturating pulse spectra and intensity, but also on the species, the growth light intensity, and the light intensity used during the measurements (Earl and Ennahli, 2004).

Here, we show how a given Fm′ underestimation influences the values for V O/V C (Fig. 4). To do so, equation 8 was set to physiologically realistic conditions (R LIGHT=1 μmol m–2 s–1, V O/V C=0.2, and A=5 μmol m–2 s–1), at different Y(II) values. Underestimates of Fm′ were then introduced by multiplying the realistic Fm′ value by, successively, 0.99 (–1%), 0.98 (–2%), 0.97 (–3%), and 0.95 (–5%). The difference between the two values represented the effect of Fm′ underestimation on V O/V C. Figure 4 shows that V O/V C was sensitive to Fm′ underestimation; for instance the relative error of V O/V C was c. 20% when Y(II) was 0.15 and Fm′ was underestimated by 3%. The error increased hyperbolically at decreasing Y(II), and increased proportionally as the Fm′ underestimation was increased.

Fig. 4.

Sensitivity to errors in the determination of Fm′. True values were simulated by calculating equation 8 for R LIGHT=1 μmol m–2 s–1, V O/V C=0.2 and A=5 μmol m–2 s–1 at different Y(II) values. Test values of V O/V C were then calculated by solving equation 8 introducing increasing Fm′ underestimation: –1, –2, –3, and –5%. The difference in V O/V C between the test minus the true value was expressed as relative to the true value.

Light intensity and CO2 concentration used for experimentation

High light intensities (e.g. PPFD>1000 μmol m–2 s–1) result in a low PSII yield, which may potentially amplify the systematic error from any Fm′ underestimation (see above). Similarly, small Y(II) could potentially lead to V O/V C underestimation when the allocation to alternative sinks is significant (see description of equation 7). Further, high light conditions require longer timescales to reach stable photosynthetic conditions. On the other hand, depending on growth conditions, low light intensities (e.g. <100 μmol m–2 s–1) might lead to low assimilation rates, which could amplify the systematic errors in the estimation of R LIGHT (see Fig. 3 and above). For these reasons, intermediate light intensities represent the best solution, whereby Y(II) and A are both high. For instance, values at the top end of the linear region of the light response curve would be ideal. These generally correspond to the growth light intensity.

CO2 concentration in the cuvette (C a) can be used to manipulate photorespiration. Figure 2 shows the measured and predicted V O/V C of C3 and C4 plants under different CO2 concentrations. Because of the CCM, V O/V C is low in maize, even at low C i, whereas in wheat V O/V C increases hyperbolically at decreasing C i. This contrasting behaviour allows the resolution of the method to be manipulated by changing the CO2 concentration in the background gas. However, decreasing CO2 concentration is disadvantageous because: (i) low C i results in quenching of PSII yield, which may potentially amplify the systematic error determined by Fm′ underestimation (see above); at the same time (ii) low Y(II) would amplify the magnitude of V O/V C underestimation owing to the partitioning of Y(II) to alternative sinks (see description of equation 7); (iii) under low C a, more time is required to reach stable photosynthetic conditions, which result in lower throughput; (iv) low C a increases the driving force for diffusion from outside of the cuvette, which may constitute a potential source of error, especially when assimilation is low (Boesgaard ). For these reasons the optimal C a will depend on the purpose of the analysis, and on the desired resolution and speed.

Discussion

This method is based upon the difference in net assimilation (A) and photosystem II yield (Y(II)) observed when the gas supplied to an actively photosynthesizing leaf is switched from ambient O2 to low O2. The goal was to develop a relatively quick, readily available method, which could be used to screen large numbers of transformants, C3–C4, C2, or photorespiratory refixation variants (Busch et al 2013; Oakley ) in a given population of plants. The data show that the method readily distinguishes between V O/V C for typical C3 and C4 plants (Table 2), and, given the low coefficients of variation, should detect more subtle variations in C4 repression or activation within a screen. It would then be possible to subject plants identified in this way to a more detailed, conventional gas exchange or stable isotope screen, to identify contributory morphological, metabolic or genetic factors. In the subsequent discussion, we explore the theoretical and practical limitations underpinning the accuracy of the method, and improvements that could be instituted to enhance the outputs, if high sample throughput was not a primary limitation.

Other methods have been proposed to determine the contribution of photorespiration in vivo through gas exchange measurements. The method proposed by Ripley uses only the increase in assimilation under non-photorespiratory conditions, and therefore ignores the effect on Y(II). In our work we observed that Y(II) is generally influenced by changes in O2 concentration (Figure 1), even in C4 plants (see Fig. 2 in Bellasio and Griffiths, 2014b); therefore it is important to take into account the feedback from assimilation on photosystem II yield. Long and Bernacchi (Long and Bernacchi, 2003) proposed a comprehensive method to determine the partitioning of total electron transport rate between photorespiratory and assimilatory demand. Their protocol requires an initial light or A/Ci response so as to fit a linear relationship between quantum yield for CO2 fixation Y(CO 2) and quantum yield of photosystem II, Y(II).

In comparison, the simple method that we have proposed requires no previous parameterization, no curve fitting, and no knowledge of the underpinning physiology or biochemical constants. It is also independent of leaf area, as when deriving V O/V C from equation 8, both the numerator and the denominator are proportional to leaf area, a huge advantage for small or dissected leaves. The likelihood of triose phosphate limitation (Sharkey, 1988) is minimized under the relatively low light intensities and low C i, which are optimal for this protocol. The determination of V O/V C could take as little as c. 6min, although the complete routine was longer (c. 40min) as leaves were allowed to acclimate before measurement of both assimilation and dark respiration. Therefore, the run time can be minimized by measuring assimilation under growth conditions (e.g. at growth light intensity and CO2 concentration), and either measuring respiration after all plants have been collectively dark–adapted, or estimating it separately (see below).

Other factors affecting accuracy of V O/V C determination

As shown in Fig. 3, the estimation of R LIGHT is important when calculating gross assimilation using eqn. 8 (GA=A+R LIGHT) at low assimilation rates. R LIGHT can be determined with several methods; for instance, by linear regression of assimilation (A) versus irradiance (under very low irradiance e.g. <150 μmol photons m–2 s–1), by linear regression of A versus irradiance multiplied by Y(II) [under moderate irradiance, e.g. <400 μmol photons m–2 s–1 (Yin )], by non-linear regression [throughout the light response curve (Prioul and Chartier, 1977; Dougherty )] or assumed to equal dark respiration [e.g. (Kromdijk ; Ubierna )]. These methods do not necessarily yield the same R LIGHT values, and so, the degree of similarity between different R LIGHT estimates depends on the species and growth conditions. For instance, in Cocklebur (Xanthium strumarium L., Asteraceae), R LIGHT was significantly different from dark respiration (Tcherkez ), whereas in maize R LIGHT is generally non-significantly different from dark respiration (C. Bellasio, unpublished data). The most suitable method to estimate R LIGHT should therefore be evaluated on a case-by-case basis (see Bellasio and Griffiths, 2014a), and for a uniform population (e.g. one species or set of transformants in a growth chamber), R LIGHT could be estimated on a subset of individuals, with one of the methods described above. If dark respiration is used as a proxy, the quality of the estimate can be increased using large chambers and low flow rates. In a diverse population, R LIGHT could be estimated by measuring dark respiration on each individual plant after the measurements in the light.

As shown in Fig. 3, errors in the determination of Fm′ suggest that techniques such as the multiphase flash (Loriaux ), or initial checks to ensure that the saturating pulse is saturating (see Bellasio and Griffiths, 2014b) are normally appropriate for this method. However, the use of our method is possible without a multiphase flash. Firstly, the underestimation of Fm′ introduces a systematic error, i.e. comparable plants will normally show similar V O/V C (see Bellasio ), unless the extent of C4 or C2 activity has changed under these conditions. Thus, the precision and the resolution of the method, when comparing different phenotypes against a common genetic background, are not affected by a consistent underestimation of Fm′. Secondly, to improve accuracy, i.e. the capacity of the method to estimate the true V O/V C, other approaches could: (i) increase the saturating pulse intensity; (ii) reduce the distance between light source or fibre-optic probe and leaf (in some systems); (iii) decrease actinic light intensity (as shown in this study) to maximise Y(II); and (iv) CO2 concentration can be increased, in order to maximise Y(II).

IRGA recalibration, matching Y(II), C i, and consideration of mesophyll conductance

As mentioned in the results, a slight effect on stomatal conductance and C i (under low O2) could have been caused by not recalibrating the IRGA upon switching background gas (Bunce, 2002). Although that recalibration could have increased C i and g S accuracy (under low O2), this procedure is liable to introduce operator error and extend the time taken for measurements; further, there are theoretical reasons why we need not account for these processes while carrying out such a simple comparative screen. Firstly, the data used to calculate equation 8 are measured by the CO2 channel of the IRGA and the fluorometer, which are both unaffected by the background gas (Bunce, 2002). Secondly, the effect of C i on A (under low O2) is, for the greatest part, accounted by the feedback on Y(II).

Although C i decreases under low O2, there is a strong feedback between assimilation and Y(II), and therefore Y(II) decreases proportionally. In fact, the relationship between gross assimilation (or, better, between Y(CO 2), which is GA divided by PPFD) and Y(II) is strictly linear (Edwards and Baker, 1993; Valentini ; Martins ). In C4 plants, this linear relationship has generally a zero intercept, (Edwards and Baker, 1993); therefore, for C4 plants, there is no need for curve fitting and the relationship can be correctly estimated with a single point. In C3 systems this relationship is still linear but the intercept is, although generally small, not zero. The intercept, which is the magnitude of engagemant of alternative sinks, can be estimated by linear curve fitting, although several data points are required (Valentini ; Martins ). Using the complete fitting of the Y(CO 2)/Y(II) relationship, however, did not improve the estimate of V O/V C (data not shown): the complete curve fitting correctly estimates the intercept, but the datapoints are taken under conditions which differ from those under which V O/V C is measured.

Another way to improve the estimate of V O/V C would be to adjust C a under low O2 so as to match Y(II) measured under ambient O2 with Y(II) measured under low O2. Alternatively, C a could be manipulated to deliver C i under low O2, which matches that under ambient. The advantages would be that the measured data would then probably fit the predicted C3 and C4 models more precisely when C i is limiting (see Fig. 2, Tables 3 and 4). However, these operations do not improve the capacity to screen between C3 and C4 photosynthesis and the additional manipulations increase time and likelihood of errors. We also note that such improvements would allow this method to be used to calculate the CO2 concentration at the site of carboxylation (C C) in C3 plants through equation 11 (Table 3), as well as mesophyll conductance via equation 10, using C C, and the values for assimilation and C i measured under ambient conditions.

Conclusion

In this paper a simple method, and associated theory, have been presented, which allow the determination of both the oxygenation (V O) and carboxylation (V C) rate of RuBisCO and the rate of photorespiratory CO2 evolution (F) based on gas exchange and variable chlorophyll fluorescence under ambient and low O2. This may be of particular interest for high throughput screening to identify C4 mutants lacking a fully functional CCM, C2 variants, or populations of C3–C4 hybrids (Oakley ).