Plant Cell. 2008 January; 20(1): 75–87

Guard cells play a critical role in plant growth and development by optimizing gas exchange under variable environments.

Plant Cell. 2004 May; 16(5): 1143–1162

Asc and DHA MeasurementsAsc was measured as described (Foyer et al., 1983). Apoplastic levels were obtained essentially as described (Pignocchi et al., 2003) in which leaves from 8-week-old tobacco were collected, the midvein removed, and 2-cm2 pieces vacuum infiltrated at −70 kPa with chilled 10 mM citrate, pH 3.0, for 10 min. Leaves were blotted dry, rolled, and centrifuged in a prechilled syringe at 2000 rpm for 10 min at 4°C. An equal volume of 2.5 M HClO4 (for the ascorbate assay) or 10% metaphoric acid (for the GSH assay) was added to the apoplastic wash fluid. Guard cells were isolated as described (Kruse et al., 1989). For whole leaf assays, leaves were ground in 2.5 M HClO4 and centrifuged at 13,000 rpm for 10 min. Two volumes of 1.25 M Na2CO3 were added to the supernatant, and after centrifugation, 100 μL was added to 895 μL 100 mM K2HPO4/KH2PO4, pH 5.6. Asc was determined by the change in absorbence at 265 nm after the addition of 0.25 units of ascorbate oxidase. The total amount of reduced and oxidized ascorbic acid (i.e., Asc and DHA) was determined by reducing DHA to Asc (in a reaction containing 100 mM K2HPO4/KH2PO4, pH 6.5, 2 mM GSH, and 0.1 μg recombinant wheat DHAR protein incubated at 25°C for 20 min) before measuring Asc. The amount of DHA was determined as the difference between these two assays. The level of GSH and GSSG was determined as described (Shimaoka et al., 2000).

PLoS Biol. 2006 October; 4(10): e312.

Dynamic Modeling Our model differs from previous models employed in the life sciences in the following fundamental aspects. First, we have reconstructed the signaling network from inferred indirect relationships and pathways as opposed to direct interactions; in graph theoretical terminology, we found the minimal network consistent with a set of reachability relationships. This network predicts the existence of numerous additional signal mediators (intermediary nodes), all of which could be targets of regulation. Second, the network obtained is significantly more complex than those usually modeled in a dynamic fashion. We bridge the incompleteness of regulatory knowledge and the absence of quantitative dose-response relationships for the vast majority of the interactions in the network by employing qualitative and stochastic dynamic modeling previously applied only in the context of gene regulatory networks [53]. Mathematical models of stomatal behavior in response to environmental change have been studied for decades [63,64]. However, no mathematical model has been formulated that integrates the multitude of recent experimental findings concerning the molecular signaling network of guard cells. Boolean modeling has been used to describe aspects of plant development such as specification of floral organs [65], and there are a handful of reports describing Boolean models of light and pathogen-, and light by carbon-regulated gene expression [66–68]. Use of a qualitative modeling framework for signaling networks is justified by the observation that signaling networks maintain their function even when faced with fluctuations in components and reaction rates [69]. Our model uses experimental evidence concerning the effects of gene knockouts and pharmacological interventions for inferring the downstream targets of the corresponding gene products and the sign of the regulatory effect on these targets. However, use of this information does not guarantee that the dynamic model will reproduce the dynamic outcome of the knockout or intervention. Indeed, all model ingredients (node states, transfer functions) refer to the node (component) level, and there is no explicit control over pathway-level effects. Moreover, the combinatorial transfer functions we employed are, to varying extents, conjectures, informed by the best available experimental information (see Text S1). Finally, in the absence of detailed knowledge of the timing of each process and of the baseline (resting) activity of each component, we deliberately sample timescales and initial conditions randomly. Thus, an agreement between experimental and theoretical results of node disruptions is not inherent, and would provide a validation of the model. The accuracy of our model is indeed supported by its congruency with experimental observation at multiple levels. At the pathway level, our model captures, for example, the inhibition of ABA-induced ROS production in both ost1 mutants and atrboh mutants [12,19,21] and the block of ABA-induced stomatal closure in a dominant-positive atRAC1 mutant [22]. In our model, as in experiments, ABA-induced NO production is abolished in either nos single or nia12 double mutants [13,18]. Moreover, the model reproduces the outcome that ABA can induce cytosolic K+ decrease by K+ efflux through the alternative potassium channel KAP, even when ABA-induced NO production leads to the inhibition of the outwardly-rectifying (KOUT) channel [70]. At the level of whole stomatal physiology, our model captures the findings that anion efflux [35,71] and actin cytoskeleton reorganization [22] are essential to ABA-induced stomatal closure. The importance of other components such as PA, PLD, S1P, GPA1, KOUT, pHc in stomatal closure control [8,20,31,43,58,72], and the ABA hypersensitivity conferred by elimination of signaling through ABI1 [28,29], are also reproduced. Our model is also consistent with the observation that transgenic plants with low PLC expression still display ABA sensitivity [73]. The fact that our model accords well with experimental results suggests that the inferences and assumptions made are correct overall, and enables us to use the model to make predictions about situations that have yet to be put to experimental test. For example, the model predicts that disruption of all Ca2+ ATPases will cause increased ABA sensitivity, a phenomenon difficult to address experimentally due to the large family of calcium ATPases expressed in Arabidopsis guard cells (unpublished data). Most of the multiple perturbation results presented in Figure 5 and Table 2 also represent predictions, as very few of them have been tested experimentally. Results from our model can now be used by experimentalists to prioritize which of the multitude of possible double and triple knockout combinations should be studied first in wet bench experiments. Most importantly, our model makes novel predictions concerning the relative importance of certain regulatory elements. We predict three essential components whose elimination completely blocks ABA-induced stomatal closure: membrane depolarization, anion efflux, and actin cytoskeleton reorganization. Seven components are predicted to dramatically affect the extent and stability of ABA-induced stomatal closure: pHc control, PLD, PA, SphK, S1P, G protein signaling (GPA1), and K+ efflux. Five additional components, namely increase of cytosolic Ca2+, Atrboh, ROS, the Ca2+ ATPase(s), and ABI1, are predicted to affect the speed of ABA-induced stomatal closure. Note that a change in stomatal response rate may have significant repercussions, as some stimuli to which guard cells respond fluctuate on the order of seconds [74,75]. Thus our model predicts two qualitatively different realizations of a partial response to ABA: fluctuations in individual responses (leading to a reduced steady-state sensitivity at the population level), and delayed response. These predictions provide targets on which further experimental analysis should focus. Six of the 13 key positive regulators, namely increase of cytosolic Ca2+, depolarization, elevation of pHc, ROS, anion efflux, and K+ efflux through outwardly rectifying K+ channels, can be considered as network hubs [45], as they are in the set of ten highest degree (most interactive) nodes. Other nodes whose disruption leads to reduced ABA sensitivity, namely SphK, S1P, GPA1, PLD, and PA, are part of the ABA → PA path. While they are not highly connected themselves, their disruption leads to upregulation of the inhibitor ABI1, thus decreasing the efficiency of ABA-induced stomatal closure. Similarly, the node representing actin reorganization has a low degree. Thus the intuitive prediction, suggested by studies in yeast gene knockouts [76,77], that there would be a consistent positive correlation between a node's degree and its dynamic importance, is not supported here, providing another example of how dynamic modeling can reveal insights difficult to achieve by less formal methods. This lack of correlation has also been found in the context of other complex networks [78]. Comparing Figure 3 and Figure 6C, one can notice a similar heterogeneity in the measured stomatal aperture size distributions and the theoretical distribution of the cumulative probability of closure in the case of multiple node disruptions. While apparently unconnected, there is a link between the two types of heterogeneity. Due to stochastic effects on gene and protein expression, it is possible that in a real environment not all components of the ABA signal transduction network are fully functional. Therefore, even genetically identical populations of guard cells may be heterogeneous at the regulatory and functional level, and may respond to ABA in slightly different ways. In this case, the heterogeneity in double and triple disruption simulations provides an explanation for the observed heterogeneity in the experimentally normal response: the latter is actually a mixture of responses from genetically highly similar but functionally nonidentical guard cells.

PLoS Biol. 2006 October; 4(10): e312.

Network Synthesis and Path Analysis Logical organization of large-scale data sets is an important challenge in systems biology; our model provides such organization for one guard cell signaling system. As summarized in Table S1, we have organized and formalized the large amount of information that has been gathered on ABA induction of stomatal closure from individual experiments. This information has been used to reconstruct the ABA signaling network (Figure 2). Figure 2 uses different types of edges (lines) to depict activation and inhibition, and also uses different edge colors to indicate whether the information was derived from our model species, Arabidopsis, or from another plant species. Different types of nodes (metabolic enzymes, signaling proteins, transporters, and small molecules) are also color coded. An advantage of our method of network construction over other methods such as those used in Science's Signal Transduction Knowledge Environment (STKE) connection maps [61] is the inclusion of intermediate nodes when direct physical interactions between two components have not been demonstrated. As is evident from Figure 2, network synthesis organizes complex information sets in a form such that the collective components and their relationships are readily accessible. From such analysis, new relationships are implied and new predictions can be made that would be difficult to derive from less formal analysis. For example, building the network allows one to “see” inferred edges that are not evident from the disparate literature reports. One example is the path from S1P to ABI1 through PLD. Separate literature reports indicate that PLDα null mutants show increased transpiration, that PLDα1 physically interacts with GPA1, that S1P promotion of stomatal closure is reduced in gpa1 mutants, that PLD catalyses the production of PA, and that recessive abi1 mutants are hypersensitive to ABA. Network inference allows one to represent all this information as the S1P → GPA1 → PLD → PA—| ABI1—| closure path, and make the prediction that ABA inhibition of ABI1 phosphatase activity will be impaired in sphingosine kinase mutants unable to produce S1P. Another prediction that can be derived from our network analysis is a remarkable redundancy of ABA signaling, as there are eight paths that emanate from ABA in Figure 2 and, based on current knowledge (though see below) these paths are initially independent. The prediction of redundancy is consistent with previous, less formal analyses [62]. The integrated guard cell signal transduction network (which includes the ABA signal transduction network) has been proposed as an example of a robust scale-free network [62]. To classify a network as scale-free, one needs to determine the degree (the number of edges, representing interactions/regulatory relationships) of each node, and to calculate the distribution of node degrees (denoted degree distribution) [45,46]. Scale-free networks, characterized by a degree distribution described by a power law, retain their connectivity in the face of random node disruptions, but break down when the highest-degree nodes (the so-called hubs) are lost [46]. While the guard cell network may ultimately prove to be scale-free, the network is not sufficiently large at present to verify the existence of a power-law degree distribution; thus, the analogy with scale-free networks cannot be rigorously satisfied.

PLoS Biol. 2006 October; 4(10): e312.

Figure 1 Illustration of the Inference Rules Used in Network Reconstruction (1) If A → B and C → process (A → B), where A → B is not a biochemical reaction such as an enzyme catalyzed reaction or protein-protein/small molecule interaction, we assume that C is acting on an intermediary node (IN) of the A–B pathway. (2) If A → B, A → C, and C → process (A → B), where A → B is not a direct interaction, the most parsimonious explanation is that C is a member of the A–B pathway, i.e. A → C → B. (3) If A —| B and C —| process (A —| B), where A —| B is not a direct interaction, we assume that C is inhibiting an intermediary node (IN) of the A–B pathway. Note that A→ IN —| B is the only logically consistent representation of the A–B pathway. PLoS Biol. 2006 October; 4(10): e312.

PLoS Biol. 2006 October; 4(10): e312.

Figure 2 Current Knowledge of Guard Cell ABA Signaling The color of the nodes represents their function: enzymes are shown in red, signal transduction proteins are green, membrane transport–related nodes are blue, and secondary messengers and small molecules are orange. Small black filled circles represent putative intermediary nodes mediating indirect regulatory interactions. Arrowheads represent activation, and short perpendicular bars indicate inhibition. Light blue lines denote interactions derived from species other than Arabidopsis; dashed light-blue lines denote inferred negative feedback loops on pHc and S1P. Nodes involved in the same metabolic pathway or protein complex are bordered by a gray box; only those arrows that point into or out of the box signify information flow (signal transduction). The full names of network components corresponding to each abbreviated node label are: ABA, abscisic acid; ABI1/2, protein phosphatase 2C ABI1/2; ABH1, mRNA cap binding protein; Actin, actin cytoskeleton reorganization; ADPRc, ADP ribose cyclase; AGB1, heterotrimeric G protein β component; AnionEM, anion efflux at the plasma membrane; Arg, arginine; AtPP2C, protein phosphatase 2C; Atrboh, NADPH oxidase; CaIM, Ca2+ influx across the plasma membrane; Ca2+ ATPase, Ca2+ ATPases and Ca2+/H+ antiporters responsible for Ca2+ efflux from the cytosol; Ca2+c, cytosolic Ca2+ increase; cADPR, cyclic ADP-ribose; cGMP, cyclic GMP; CIS, Ca2+ influx to the cytosol from intracellular stores; DAG, diacylglycerol; Depolar, plasma membrane depolarization; ERA1, farnesyl transferase ERA1; GC, guanyl cyclase; GCR1, putative G protein–coupled receptor; GPA1, heterotrimeric G protein α subunit; GTP, guanosine 5′-triphosphate; H+ ATPase, H+ ATPase at the plasma membrane; InsPK, inositol polyphosphate kinase; InsP3, inositol-1,4,5-trisphosphate; InsP6, inositol hexakisphosphate; KAP, K+ efflux through rapidly activating K+ channels (AP channels) at the plasma membrane; KEV, K+ efflux from the vacuole to the cytosol; KOUT, K+ efflux through slowly activating outwardly-rectifying K+ channels at the plasma membrane; NAD+, nicotinamide adenine dinucleotide; NADPH, nicotinamide adenine dinucleotide phosphate; NOS, Nitric oxide synthase; NIA12, Nitrate reductase; NO, Nitric oxide; OST1, protein kinase open stomata 1; PA, phosphatidic acid; PC, phosphatidyl choline; PEPC, phosphoenolpyruvate carboxylase; PIP2, phosphatidylinositol 4,5-bisphosphate; PLC, phospholipase C; PLD, phospholipase D; RAC1, small GTPase RAC1; RCN1, protein phosphatase 2A; ROP2, small GTPase ROP2; ROP10, small GTPase ROP10; ROS, reactive oxygen species; SphK, sphingosine kinase; S1P, sphingosine-1-phosphate. PLoS Biol. 2006 October; 4(10): e312.

PLoS Biol. 2006 October; 4(10): e312.

If A → B and C → process (A → B), where A → B is not a biochemical reaction such as an enzyme catalyzed reaction or protein–protein/small molecule interaction, we assume that C is acting on an intermediary node (IN) of the A–B pathway. This IN could be an intermediate protein complex, protein–small molecule complex, or multiple complexes (see Figure 1, panel 1). For example, ABA → closure, and NO synthase (NOS) → process (ABA → closure); therefore, ABA → IN → closure, NOS → IN. If A → B is a direct process such as a biochemical reaction or a protein–protein interaction, we assume that C → process (A → B) corresponds to C → A → B. A → B and C → process (A → B) can be transformed to A → C → B if A → C is also documented. This means that the simplest explanation is to identify the putative intermediary node with C. For example, ABA → NOS, and NOS → process (ABA → NO) are experimentally verified and NOS is an enzyme producing NO, therefore, we infer ABA → NOS → NO (see Figure 1, panel 2). A rule similar to rule 1 applies to inhibitory interactions (denoted by —|); however, in the case of A —| B, and C —| process (A —| B), the logically correct representation is: A → IN —| B, C —| IN (see Figure 1, panel 3). The above rules constitute a heuristic algorithm for first expanding the network wherever the experimental relationships are known to be indirect, and second, minimizing the uncertainty of the network by filtering synonymous relationships. Mathematically, this algorithm is related to the problem of finding the minimum transitive reduction of a graph (i.e., for finding the sparsest subgraph with the same reachability relationships as the original) [14]; however, it differs from previously used algorithms by the fact that the edges can have one of two signs (activating and inhibitory), and edges corresponding to direct interactions are maintained.

Plant Cell. 2004 May; 16(5): 1143–1162

Leaf Water Loss AssayExpanded leaves were detached from well-watered plants and immediately weighed. Water loss from the detached leaves held at room temperature was followed by determining leaf weight every 5 min for 40 min. Four leaves from separate plants were used. The rate of water loss was plotted as the loss in leaf weight over time. In situ rates of CO2 assimilation, transpiration, and stomatal conductance were measured with the TPS-1 portable photosynthesis system (PP Systems, Haverhill, MA). Every other leaf on a plant was measured. Sequence data from this article have been deposited with the EMBL/GenBank data libraries under accession numbers AY074784 and AY074787.

Plant Cell. 2004 May; 16(5): 1143–1162

RT-PCR AnalysisTotal nucleic acid was isolated from whole leaves and guard cell protoplasts, and the DNA was removed using RQ1 RNase free DNase I (Promega, Madison, WI). The absence of DNA in the samples was confirmed after saturating PCR (38 cycles) with actin-specific primers. One microgram of total RNA was used for cDNA synthesis using Omniscript RT (Qiagen, Valencia, CA) with oligo(dT)20 as the primer. PCR reactions contained 1× buffer, 2 μL of the reverse transcription reaction, and 1.5 μg of forward and reverse primers in a total reaction volume of 25 μL using the following conditions: 94°C/5 min; 94°C/50 s, annealing temperature (see below)/50 s; 72°C/1 min, 72°C/5 min. PCR products were visualized on ethidium bromide–stained 1.2% agarose gels. Primers used were as follows: actin (X63603) (annealing temperature, 52°C), forward, 5′-CGCGAAAAGATGACTCAAATC-3′ and reverse, 5′-AGATCCTTTCTGATATCCACG-3′; CAT (U93244) (annealing temperature, 55°C), forward, 5′-CGGATACCTGAGCGTGTTGTTCATG-3′ and reverse, 5′-GTGATTATTGTGATGAGCACAC-3′; MDHAR (BQ842867) (annealing temperature, 55°C), forward, 5′-ACTTCAAATAGCCGTTTTTAATCCA-3′ and reverse, 5′-AGTTGAACATGTTGATCATTCTC-3′; FeSOD (M55090) (annealing temperature, 53°C), forward, 5′-TGCTTTGGAGCCTCATATGAG-3′ and reverse, 5′-AAGTCCAGATAGTAAGCATGC-3′; tobacco DHAR (AY074787) (annealing temperature, 55°C), forward, 5′-AATTGGATCCCTGATTCTGATGT-3′ and reverse, 5′-GCGAAACAACGGGATTATAATTATG-3′; wheat DHAR (AY074784) (annealing temperature, 59°C), forward, 5′-AATTGGATCCCTGATTCTGATGT-3′ and reverse, 5′-GGATCCAGGGGCTTACGGGTTCACTTTC-3′; to detect wheat and tobacco DHAR (annealing temperature, 59°C), forward, 5′-AATTGGATCCCTGATTCTGATGT-3′ and reverse, 5′-AGATGGTA(G/C)AG(C/T)TTCGGAGCCA-3′.

Plant Cell. 2004 May; 16(5): 1143–1162

Stomatal MeasurementsStomatal bioassay experiments were performed as described (Pei et al., 1997). Abaxial epidermis strips were loaded onto glass slides with 100 μL of buffer A (50 mM KCl and 10 mM Mes, pH 6.1). After staining with 0.2% toluidine blue O for 20 s, the sample was rinsed twice with distilled water and imaged using a compound microscope (Leica, Wetzlar, Germany). For each sample, the percentage of stomata that were open (defined as having a width >1 μm) was determined from at least 400 stomata. The width and length of the opening (i.e., aperture) of only those stomata that were open were measured and used to calculate the average stomatal aperture (width/length). The width and length of at least 30 stomatal pores were measured. The width and length of open stomatal apertures also were used to calculate the stomatal aperture area [π × (width/2) × (length/2)], which together with the percentage of stomata that remained open, were used to calculate the total open stomatal area per unit leaf area containing 100 stomata [i.e., (average stomatal aperture area) × (percentage of stomata that remained open) × 100]. Induction of stomatal closure by ABA and H2O2 was investigated using epidermal strips that were first incubated in CO2-free buffer A for 2 h at 22 to 25°C under a photon flux density of 200 μmol m−2 s−) to promote stomatal opening. ABA (50 μM final concentration, dissolved in 95% ethanol, with equal volume of ethanol used as a control) or H2O2 (1 mM final concentration) was added to the buffer. After the indicated time, samples were stained with toluidine blue O for image recording or loaded with fluorescence dye to determine the production of H2O2.

Plant Cell. 2004 May; 16(5): 1143–1162

Determination of Leaf Water Potential and Leaf Water ContentLeaf water potential was determined at 10 am and 2 pm using a pressure chamber (Model 3000; Soilmoisture Equipment, Santa Barbara, CA) as described by Gómez-Cadenas et al. (1996). Data are presented as the average and standard deviation of six replicate leaves. Leaf water content was measured at 10 am and 2 pm. Leaf fresh weight was measured immediately, and their water saturated weight was determined after allowing the leaf to absorb water until saturation. Leaf dry weight was measured after drying at 80°C. The difference in water weight between fresh and water-saturated leaves was then calculated in the morning and afternoon and expressed on a leaf dry weight basis. Data are presented as the average and standard deviation of six replicate leaves.

PLoS Biol. 2006 October; 4(10): e312.

Limitations of the Current Analysis Network topology. Our graph reconstruction is incomplete, as new signaling molecules will certainly be discovered. Novel nodes may give identity to the intermediary nodes that our model currently incorporates. Discovery of a new interaction among known nodes could simplify the graph by reducing (apparent) redundancy. For example, if it is found that GPA1 → OST1, the simplest interpretation of the ABA → ROS pathway becomes ABA → GPA1 → OST1 → ROS, and the graph loses one edge and an alternative pathway. As an effect, the graph's robustness will be attenuated. Among likely candidates for network reduction are the components currently situated immediately downstream of ABA because, in the absence of information about guard cell ABA receptors [94], we assumed that ABA independently regulates eight components. It is also possible that a newly found interaction will not change the existing edges, but only add a new edge. A newly added positive regulation edge will further increase the redundancy of signaling and correspondingly its robustness. Newly added inhibitory edges could possibly damage the network's robustness if they affect the main positive regulators of the network, especially anion channels and membrane depolarization. For example, experimental evidence indicates that abi1 abi2 double recessive mutants are more sensitive to ABA-induced stomatal closure than abi1 or abi2 single recessive mutants [29], suggesting that ABI1 and ABI2 act synergistically. Due to limited experimental evidence, we do not explicitly incorporate ABI2, but an independent inhibitory effect of ABI2 would diminish ABA signaling. While it is difficult to estimate the changes in our conclusions due to future knowledge gain, we can gauge the robustness of our results by randomly deleting entries in Table S1 or rewiring edges of Figure 2 (see Texts S2 and S3). We find that most of the predicted important nodes are documented in more than one entry, and more than one entry needs to be removed from the database before the topology of the network related to that node changes (Text S2). Random rewiring of up to four edge pairs shows that the dynamics of our current network is moderately resilient to minor topology changes (Text S3 and Figure S1). Dynamic model. In our dynamic model we do not place restrictions on the relative timing of individual interactions but sample all possible updates randomly. This approach reflects our lack of knowledge concerning the relative reaction speeds as well as possible environmental noise. The significance of our current results is the prediction that whatever the timing is, given the current topology of regulatory relationships in the network, the most essential regulators will not change. Our approach can be iteratively refined when experimental results on the strength and timing of individual interactions become available. For example, we can combine Boolean regulation with continuous synthesis and degradation of small molecules or signal transduction proteins [95,96] as kinetic (rate) data emerge. Our model considers the response of individual guard cell pairs to the local ABA signal; however, there is recent evidence of a synchronized oscillatory behavior of stomatal apertures over spatially extended patches in response to a decrease in humidity [97]. Our model can be extended to incorporate cell-to-cell signaling and spatial aspects by including extracellular regulators when information about them becomes available (see [51]). Node disruptions. A knockout may either deprive the system of an essential signaling element (the gene itself), or it may “set” the entire system into a different state (e.g., by affecting the baseline expression of other, seemingly unrelated signaling elements). Our analysis and current experimental data only address the former. Because of this caveat, in some ways rapid pharmacological inhibition may actually have a more specific effect on the cell than gene

Plant Cell. 2004 May; 16(5): 1143–1162

Guard Cell IsolationGuard cells and guard cell protoplasts were isolated essentially as described (Kruse et al., 1989). Pieces collected from expanding leaves of 8-week-old plants were transferred to a blender jar in 100 mL of homogenization buffer (10% Ficoll, 5 mM CaCl2, and 0.1% polyvinylpyrrolidone 40) and homogenized at high speed for 1 to 2 min. Epidermal fragments were collected using a nylon mesh (220 μm) and rinsed thoroughly with water. To isolate guard cell protoplasts, the epidermal fragments were incubated in 40 mL of digestion solution (0.25 M d-mannitol, 0.7% cellulysin [Behring Diagnostics, La Jolla, CA], 1 mM CaCl2, 0.1% polyvinylpyrrolidone 40, and 1 μg/mL of pepstatin A, pH 5.5) with shaking for 45 min, collected on nylon mesh, rinsed with 0.25 M mannitol and 1 mM CaCl2, and incubated in fresh enzyme solution for an additional 45 min. Guard cell protoplasts were then released within 3 h in a 50-mL solution of 0.35 M mannitol, 1 mM CaCl2, 1% cellulase Onozuka R-10 (Yakult Honsha, Tokyo, Japan), 0.01% pectolyase Y-23 (Seishin Pharmaceutical, Tokyo, Japan), and 1 μg/mL of pepstatin A, pH 5.5. Epidermal debris were removed using nylon mesh (30 μm), and the protoplasts were collected by centrifugation at 60g for 7 min and were washed three times with 0.35 M mannitol and 1 mM CaCl2.

Plant Cell. 2004 May; 16(5): 1143–1162

Enzyme AssaysDHAR activity was assayed essentially as described (Hossain and Asada, 1984). Tobacco leaves were ground in extraction buffer (50 mM Tris-HCl, pH 7.4, 100 mM NaCl, 2 mM EDTA, and 1 mM MgCl2), and soluble protein was obtained after a 5-min centrifugation at 13,000 rpm. DHAR was assayed from an equal amount of protein as described (Bradford, 1976) in 50 mM K2HPO4/KH2PO4, pH 6.5, 0.5 mM DHA, and 1 mM GSH and its activity followed by an increase in absorbance at 265 nm. MDHAR (10−8 mol NAPDH oxidized/min/mg protein), SOD (units to inhibit nitroblue tetrazolium photoreduction by 50%), CAT (10−6 mol H2O2 reduced/min/mg protein), GR (10−8 mol NAPDH oxidized/min/mg protein), and APX (10−8 mol Asc oxidized/min/mg protein) activities were determined as described (Gainnopolitis and Pies, 1977; Aebi, 1984; de Pinto et al., 2000). Ascorbate oxidase activity present in apoplastic wash fluid (obtained as described below) was determined from the decrease in A265 (extinction coefficient of 14 mM−1cm−1) at 25°C in a reaction mixture containing 0.1 M sodium phosphate, pH 5.6, 0.5 mM EDTA, and 100 μM Asc as described (Pignocchi et al., 2003).

Plant Physiol. 2007 February; 143(2): 1068–1077.

Shielding of the Leaf with Perforated Adhesive FoilTo shield the entire leaf except circular areas of 0.8 mm, thin polyethylene plastic foil (20 μm) was punched with a 0.8-mm syringe needle, which was ground squarely and sharpened. The correct size of the holes was confirmed microscopically after attachment to the leaf. Very thin double-sided adhesive tape (Pritt permanent) was used to attach the foil to the leaf, allowing only the epidermis inside the circular holes to transpire. In some cases, the foil was not attached firmly to the epidermis at the edge of the punched hole, leaving a gap between leaf and foil. These stomata were excluded from the experiments. Up to six holes with a distance of at least 10 mm were punched into a foil in one experiment. The foil was at first attached provisionally in its final position to select stomata located inside the holes. The foil was then removed to observe a control response to a decrease in air humidity of selected stomata. Thereafter, the foil was attached firmly in the same position as before to measure the humidity response of the sample of stomata on the next day. In some experiments, the foil was carefully removed afterward and another control response was measured on the next day to test for permanent damage due to experimental treatment.