Abstract

Light intensity and atmospheric CO2 partial pressure are two environmental signals known to regulate stomatal numbers. It has previously been shown that if a mature Arabidopsis leaf is supplied with either elevated CO2 (750 ppm instead of ambient at 370 ppm) or reduced light levels (50 μmol m−2 s−1 instead of 250 μmol m−2 s−1), the young, developing leaves that are not receiving the treatment grow with a stomatal density as if they were exposed to the treatment. But the signal(s) that it is believed is generated in the mature leaves and transmitted to developing leaves are largely unknown. Photosynthetic rates of treated, mature Arabidopsis leaves increased in elevated CO2 and decreased when shaded, as would be expected. Similarly, the levels of sugars (glucose, fructose, and sucrose) in the treated mature leaves increased in elevated CO2 and decreased with shade treatment. The levels of sugar in developing leaves were also measured and it was found that they mirrored this result even though they were not receiving the shade or elevated CO2 treatment. To investigate the effect of these treatments on global gene expression patterns, transcriptomics analysis was carried out using Affymetrix, 22K, and ATH1 arrays. Total RNA was extracted from the developing leaves after the mature leaves had received either the ambient control treatment, the elevated CO2 treatment, or the shade treatment, or both elevated CO2 and shade treatments for 2, 4, 12, 24, 48, or 96 h. The experiment was replicated four times. Two other experiments were also conducted, one to compare and contrast gene expression in response to plants grown at elevated CO2 and the other to look at the effect of these treatments on the mature leaf. The data were analysed and 915 genes from the untreated, signalled leaves were identified as having expression levels affected by the shade treatment. These genes were then compared with those whose transcript abundance was affected by the shade treatment in the mature treated leaves (1181 genes) and with 220 putative ‘stomatal signalling’ genes previously identified from studies of the yoda mutant. The results of these experiments and how they relate to environmental signalling are discussed, as well as possible mechanisms for systemic signalling.

Introduction

Stomata develop from meristemoids, which are located in the developing epidermal cell layer of plant leaves. The meristemoid produces, directly or through a number of asymmetric cell divisions, a guard mother cell, the immediate stomatal precursor cell. An equal division of the guard mother cell then leads to the formation of two guard cells, which, in conjunction with additional neighbouring epidermal cells, constitute the stomatal complex (Serna and Fenoll, 1997) that regulates the aperture of a stomatal pore.

The stomatal complex represents a group of highly specialized cell types in the epidermal cell layer of several higher plant organs including leaves, stems, and reproductive structures (Willmer and Fricker, 1996). This complex controls gas exchange between the plant and the atmosphere. Proper control of pore size is critical for optimal plant performance. The guard cells of the stomatal complex sense and integrate many different exogenous signals (including CO2, light and water deficit) as well as endogenous signals (like phytohormone concentrations) in order to balance CO2 uptake versus water loss in continually changing environmental conditions (Schroeder et al., 2001) and these signalling pathways are likely to be integrated into more complex scale-free networks rather than independent stand-alone pathways (Hetherington and Woodward, 2003).

The observation that increases in atmospheric CO2 concentration also affect stomatal numbers was the first piece of in situ evidence for a developmental response to the increased CO2 emissions during the industrial era (Woodward, 1987). Changes in stomatal development, recorded as changes in stomatal density (stomatal numbers per unit area), have also provided unique information about the CO2 environment during leaf development which is recorded in fossils over geological time scales (Beerling and Woodward, 1997). A critical feature of the developmental stomatal response to the environment is that the environmental signal is detected by mature leaves and then signalled systemically to developing leaves. This has been shown to occur for both CO2 concentration and light intensity in Arabidopsis (Lake et al., 2001) and for light intensity in tobacco (Thomas et al., 2004). This detection of environmental signals by mature parts of the plant ensures that the most accurate measurement of the environment around the plant is determined by a wholly unfurled and mature leaf, rather than by (or in addition to) the developing leaf growing in a less representative environment (Larcher, 1995). As well as CO2 and light, the same mature leaves also detect a range of independent environmental challenges such as pathogens, wounding, ethylene, and low temperatures that are also signalled systemically and have been reviewed elsewhere (Shinozaki et al., 2003; Alonso and Stepanova, 2004; Braam, 2005; Glazebrook, 2005). More general and rapid systemic signals between leaves have also been shown in Arabidopsis that involve the production of hydrogen peroxide within the vasculature in response to excess light (Karpinski et al., 1999), but these responses have not been demonstrated to target developing tissue and subsequent leaf development.

A number of insights have been made with respect to the pathways regulating stomatal development. For example, a mitogen-activated protein kinase kinase kinase (MAPKKK) called YODA has been identified as a key intermediary in stomatal formation, with mutation of YODA in yoda mutant plants leading to ectopic stomatal formation, and expression of a constitutively active form of YODA (ΔN-YODA) resulting in the generation of leaves lacking stomata (Bergmann et al., 2004). With respect to patterning of stomata across the epidermal surface, TMM (the mutation of which leads to the formation of stomatal clusters) has been shown to encode a leucine-rich repeat-like receptor (LRR), suggesting that the protein acts as a component of a receptor for some unknown signal (Nadeau and Sack, 2002). It has been suggested that this signal may be peptide-based as another gene involved in stomatal patterning, SDD1, encodes a putative subtilisin-like serine protease (Berger and Altmann, 2000). Such a protease may well be involved in signal processing. Fatty acid-derived signals have also been implicated in the environmental modulation of stomatal frequency as the HIC (high carbon dioxide) gene, which encodes a putative 3-ketoacyl CoA synthase (KCS) believed to be involved in the synthesis of cuticle waxes, affects the response of stomatal development to changing CO2 concentration in Arabidopsis (Gray et al., 2000). When the expression of HIC is reduced, plants respond to elevated CO2 with a large increase in stomatal density (stomatal numbers per unit area) and index (stomatal numbers as the fraction of all epidermal cells). This contrasts with decreases in stomatal density and index for wild-type plants grown at elevated CO2. A model linking these components of stomatal development has recently been proposed (Gray and Hetherington, 2004) but the signals that are perceived and passed to these downstream components still remain unknown.

More recently, a family of three ERECTA (ER) genes that encode leucine-rich repeat-receptor-like kinases have been shown to regulate two important steps in stomatal development (Shpak et al., 2005): Firstly, the specification of stomatal cell fate by influencing the initial decision of a protodermal cell to undertake an asymmetric division to form a meristemoid cell. Secondly, the decision of the meristemoid terminally to differentiate into a guard mother cell. Interestingly, the three genes were shown to give rise to independent phenotypes and to act synergistically to control stomatal development. TMM was also shown to interact with ER and the authors speculated that the two putative classes of membrane-bound receptor-like proteins may interact to form heterodimers that are inactive and prevent signalling. One interesting aspect of this work is that the single er mutant or the double mutants er-er1 and er-er2 all led to increased numbers of stomatal cells without the formation of stomatal clusters, whereas the triple mutant er-er1-er2 formed very large clusters of stomata (Shpak et al., 2005). Systemic signalling also regulates stomatal numbers without the formation of clusters and this could indicate an interaction with the ER gene family and signalling pathway.

The roles of phytohormones (abscisic acid, auxin, brassinosteroids, cytokinins, ethylene, gibberellins, jasmonates) in regulating responses to environmental stimuli have been known for some time (Taiz and Zeiger, 2002). Recently, studies of mutants have begun to elucidate the mechanisms by which phytohormones integrate with biochemical signal transduction pathways to regulate plant processes (Gray, 2004), but there are still many gaps in our knowledge with respect to receptors, signalling and integration of signalling pathways. Other signalling molecules, such as nutrients, Ca2+ and sugars, have also been implicated (Lake et al., 2002). It is likely that the CO2 response controlling stomatal development in Arabidopsis is affected by input from systemic and local responses to other environmental challenges such as light, wounding, fungal infection, ethylene, and defoliation. The stomatal development response to CO2 has also been shown to be restored in mutants that are insensitive to these challenges (Lake et al., 2002).

There are several other possible sources of systemic signals; leaves exposed to high atmospheric CO2 concentrations typically increase their rates of carbohydrate production. Under these conditions, carbohydrate accumulation is known to regulate the expression of photosynthetic genes such as ribulose-1,5-bisphosphate carboxylase/oxygenase and this has been proposed as a sugar-sensing mechanism (Sheen et al., 1999). In addition, sugar signalling is frequently found to affect/interact with other hormonal signalling pathways mentioned earlier (Rolland and Sheen, 2005). High atmospheric CO2 treatments and low light intensity treatments generally induce stomatal closure and hence reduce transpiration rates (Taiz and Zeiger, 2002). This might also affect the delivery of hormones and sugars that are transported to leaves via the transpiration stream (Pons et al., 2001).

The hypothesis was that the mature leaves would respond to these treatments at the physiological, biochemical, and molecular level and that it would be possible to detect biochemical and molecular changes in the untreated developing leaves and gain some insights into the environmental signalling network that modulates stomatal development. A cuvette system was used (Lake et al., 2001) where the mature leaves of an Arabidopsis plant were enclosed and the growing environment could be manipulated independently of the developing central rosette leaves that were exposed to the environment of the growth chamber. Ambient air and air containing elevated levels of CO2 were supplied and a neutral density filter was used as a shade treatment to reduce the irradiance received by the mature leaves while the developing leaves received ambient air and were fully illuminated. Photosynthesis, carbohydrate content, and chlorophyll fluorescence were measured and a large-scale transcriptomics experiment was conducted to assess the impact of these treatments on global Arabidopsis gene expression, not only in the mature, treated leaves but also in the untreated, developing leaves over a 4-d period.

Materials and methods

Plant growth

Seeds of Arabidopsis thaliana (ecotype Columbia, Col-0) were purchased from Lehle Seeds (Texas, USA) and were germinated in Humax multi-purpose compost (Humax Horticulture Ltd., Cumbria, UK). The seedlings and subsequent plants were grown in two, side-by-side, constant temperature rooms; both kept at 20 °C, with lights at 250 μmol m−2 s−1 supplied from cool fluorescent tubes (TLD 58W/840 Reflex, Philips, Netherlands), 10 h photoperiod starting at 09.00 h and relative humidity at 50%. Plants were watered every second day. The ambient room had a CO2 level of 370 ppm and the elevated room next door had a CO2 level of 750 ppm supplied from a CO2 cylinder (BOC Edwards, Surry, UK) attached to a Tylan mass flow controller (Munich, Germany) and filtered through a column of potassium permanganate to oxidize volatile organic compounds such as ethylene. After 7 d the germinated seedlings were pricked out into 8×13 cell plug trays (LBS horticulture, Lancs, UK) and grown for a further 2 weeks before potting up into 10 cm pots. The plants then had the first half of the cuvette system added (Lake et al., 2001) which was the lid of a standard, 9 cm Petri dish with a 2.5 cm hole cut in the middle and a foam ring added around it. This can be seen in Fig. 1. The foam (West Tapes, Sheffield, UK) rings were cut from a 2.5 m roll that was 25 cm wide and 3 mm thick using two punches so that the internal diameter was 2.5 cm and the external diameter was 3.5 cm. The plant continued growing through the hole and the mature leaves spread out over the foam disc. After 4 weeks, when the plants were at growth stage 3.50 (Boyes et al., 2001), leaves 5 to 13 were enclosed using the lower part of an upturned Petri dish that had a 2.5 cm hole and two foam rings attached. The two halves of the Petri dish were then reunited so that the sets of foam discs met in the middle to isolate the mature leaves whilst the young developing leaves could grow out of the top. The upper and lower parts of the cuvette system were then attached to each other and sealed with black insulation tape. The upper part of the cuvette system had four 0.5 cm holes drilled into them and four sets of plastic tubing were glued in place so that there was one gas inlet and three outlets (Fig. 1). Continual air was supplied from an oil-free compressor (Machine Mart, Sheffield, UK) with a 35 l reserve, using 4 mm silicone tubing (Portex, SLS, Nottingham, UK) that was then passed through water to humidify it and then was split into two lines of tubing to two manifolds (Air Accessories, Sheffield, UK) that supplied six flow-meters (K1100, Solartron Mobrey, Berks., UK) each, that allowed the air to be supplied at a controlled rate of 500 ml min−1. A duplicate air flow system was set up in an adjacent room maintained at 750 ppm CO2 and delivered to the ambient room via tubing. This meant that 12 plants in cuvettes could be supplied with CO2 at 370 ppm and 12 plants could be supplied with CO2 at 750 ppm that was then externally exhausted from the growth room. The CO2 levels in each room were continually monitored using an IRGA (LCA3, ADC, Herts., UK) and were found to be constant even when the elevated CO2 was being pumped into the treatment cuvettes.

Fig. 1.

The systemic signalling experimental design showing the mature leaves of 4-week-old Arabidopsis plants enclosed within the cuvette system (A). The developing leaves can be seen growing out of the top of the cuvette (B). The target developing leaves are marked with acrylic paint and can be seen at 0 h (C) at the start of the experiment and after 24 h (D), 48 h (E), and 96 h (F). The time points for 2, 4, and 12 h are not shown and the scale in millimetres is visible. The chlorophyll fluorescence experimental set-up (G) and examples of the difference in photosynthetic efficiency (

\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{m}}\)
) in the mature and developing leaves can be seen at 0 h (H) and after 8 h (I). The parameter images (H, I) are representative and were constructed from images saved during the 8 h experiment. The same palette (visible bottom right in I) was used for each image and the range of values represented by the palette is given.

Fig. 1.

The systemic signalling experimental design showing the mature leaves of 4-week-old Arabidopsis plants enclosed within the cuvette system (A). The developing leaves can be seen growing out of the top of the cuvette (B). The target developing leaves are marked with acrylic paint and can be seen at 0 h (C) at the start of the experiment and after 24 h (D), 48 h (E), and 96 h (F). The time points for 2, 4, and 12 h are not shown and the scale in millimetres is visible. The chlorophyll fluorescence experimental set-up (G) and examples of the difference in photosynthetic efficiency (

\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{m}}\)
) in the mature and developing leaves can be seen at 0 h (H) and after 8 h (I). The parameter images (H, I) are representative and were constructed from images saved during the 8 h experiment. The same palette (visible bottom right in I) was used for each image and the range of values represented by the palette is given.

The target developing leaves (insertions 16–19, see Fig. 1) were marked with a dot of white acrylic paint before the mature leaves were sealed into the cuvette at 2 h after the start of the photoperiod (10.00 h). The plants were then left in the cuvette for 24 h to acclimatize and during this time all 24 plants were continually supplied with ambient air containing 370 ppm CO2. 24 h later the target developing leaves from four plants were harvested and immediately frozen in liquid N2 (zero h control samples). Ten of the plants were switched over to air containing 750 ppm CO2 and the remaining ten plants received air containing ambient CO2. Each of these groups was then further subdivided into two groups of five plants with half receiving a shade treatment which consisted of a 12 cm square piece of neutral density filter (Cat. No. 210, 0.6ND, Lee Filters, Hampshire, UK), that had had a 3.5 cm hole punched out of the middle, and was overlaid on top of the cuvette so that it shaded the mature leaves inside the cuvette but not the central developing, target leaves (Fig. 1). These leaves were then harvested 2 h later at 12.00 h and pooled before being frozen in liquid N2 as quickly as possible. This procedure was repeated over and over, each with another batch of 24 plants, to gain samples for 4, 12, 24, 48, and 96 h treatments and then further repeated until the whole experiment was then independently replicated four times. A further set of leaf samples was collected from control mature leaves (leaves enclosed within the cuvette for 24 h without treatment) and leaves subject to a further 24 h of treatment. These too were immediately harvested and frozen. This experiment was independently replicated twice for all four treatments.

Carbohydrate analysis

Carbohydrates were assayed from the leaf samples using a modified method of Caporn et al. (1999) as previously described by Baxter et al. (2003).

Gas exchange measurement

Gas exchange measurements were carried out using a portable IRGA (LCA3, ADC, Herts. UK). The CO2 concentration in the air was measured before entering the cuvette and tubing was attached to the outlets of the cuvette assembly to measure the CO2 concentration in the air leaving the cuvette. Measurements were made three times on three different cuvettes for each treatment between 11.00 h and 13.00 h. Leaf area was determined from digital photographs using imaging software (Image Pro Plus, Media Cybernetics, Berks, UK).

Stomatal counting

The effect of the treatments on stomatal numbers were determined from dental putty impressions (No.4600, President Plus, Coltene Whaledent, Altstätten, Switzerland) once the developing leaves had grown and become fully expanded. Nail polish was applied to the dental imprints to obtain a replica of the leaf surface and a light microscope (×200) was used to count the number of stomata and epidermal cells per mm2. The number of stomatal and epidermal cells were counted in 10 fields of view from the three marked leaves of five individual plants for each treatment.

Chlorophyll fluorescence

The experiments detailed earlier were re-created inside a chlorophyll fluorescence imaging system (FluorImager, Technologica Ltd., Colchester, UK). The aim was to quantify the treatment effects of elevated CO2 concentration or reduced light intensity on the enclosed mature and untreated, developing leaves. Images of chlorophyll fluorescence parameters were obtained using this system that has been described extensively elsewhere (Barbagallo et al., 2003; Oxborough, 2004; von Caemmerer et al., 2004). Two chlorophyll fluorescence parameters were calculated from the images to assess whether changes in photosynthesis of the mature leaves resulted in a change in photosynthesis of the developing leaves.

\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{m}}\)
is defined as the PSII photochemical efficiency (an indicator of photosynthetic efficiency),
\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{v}}\)
as the PSII photochemical factor (relating the maximum PSII photochemical efficiency, given by
\(F{^\prime}_{\mathrm{v}}/F{^\prime}_{\mathrm{m}}\)
and the actual PSII photochemical efficiency, given by
\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{m}}\)
and is affected by the proportion of PSII reaction centres in the oxidized state.

Transcriptomics

Total RNA was extracted from the frozen leaf samples using the Qiagen RNeasy kit (Qiagen, Crawley, UK) and each extract was analysed on an Agilent 2100 Bioanalyser (Agilent Technologies UK., Wokingham, UK) to verify lack of rRNA degradation and used in a RT-PCR reaction with Actin2 primers, before being submitted to NASC (http://affymetrix.arabidopsis.info/) for hybridization to the ATH1 Affymetrix gene chip arrays. The procedures used for the Affymetrix data production, collection and analysis (Craigon et al., 2004) were performed as described in the documentation, provided by NASC (http://affymetrix.arabidopsis.info). The data from 112 Affymetrix hybridization experiments have been made publicly available and downloadable from the NASC web-site at the following URLs:

The data were deposited in October 2004 and were later distributed on Affywatch CDs 24–26 (NASC Affymetrix data subscription service).

Gene chip data analysis

Hybridization data from the transcriptomics experiments were imported directly into GeneSpring software (ver 7.0, Agilent Technologies, Wokingham, UK). The 112 data sets were normalized to the 50th percentile (per chip) and to the median (per gene) and the data were first analysed using the PCA tool and conditions set with all genes and all 112 data sets included. The data were also normalized per chip to the 50th percentile and per gene to the control samples (24 h ambient for the mature leaf data and the respective ambient control at each time point for the developing leaf data). The data were then filtered using four different filters: (i) by flags removing genes that were absent in any replicate (no signal detected); (ii) by expression level to remove those genes that were deemed to be unchanging between log values 0.8 and 1.2 (>1.5-fold difference); (iii) by a word filter to remove the Affymetrix control genes (AFFX); and (iv) by confidence using a one-sample t-test and a P-value cut-off of 0.05 so that any gene with a P-value of 0.05 or less when compared to the normalized control was regarded as statistically significant, i.e. up- or down-regulated compared to the expression baseline of 1. (All genes were centred on 1 after normalization). To determine those genes that were statistically, differentially expressed between groups of samples, GeneSpring's ANOVA statistical analysis function (a Welch t-test) was applied to the filtered data set.

Results

Leaf development

Mature leaf insertions, 5 to 13, were enclosed in a plastic cuvette at 4 weeks (Fig. 1A, B). These leaves were exposed to elevated CO2 concentration or were shaded to reduce light intensity, or were subject to both treatments. The control leaves were exposed to the same ambient conditions as the developing leaves. Developing leaf insertions, 16–19, which were not inside the cuvette, were marked with a dot of acrylic paint (Fig. 1C) at 0 h, when they were 1–3 mm in length and their growth was monitored. The growth of leaves after 2, 4, and 12 h was minimal and hence are not shown. After 24 h the leaves had grown to 2–5 mm in length (Fig. 1D), after 48 h the leaves had grown to 3–8 mm in length (Fig. 1E) and after 96 h they had grown to 6–12 mm (Fig. 1F). The shade treatment of mature leaves was found to slow the growth of developing leaves (leaf length after 4 d was reduced by 26%) whereas treatment of mature leaves with elevated CO2 caused the developing leaves to grow slightly faster (leaf length after 4 d was increased by 18%).

Stomatal counting

Plants were set up in the cuvette system and grown continuously in the treatments until the marked developing leaves reached full expansion (2 weeks); these were then harvested and the stomata counted. All of the results in Fig. 2 are for developing or mature-treated leaves where the mature leaves were maintained in ambient conditions (A), given a shade treatment (AS), or exposed to increased CO2 concentration (EA).

Fig. 2.

Arabidopsis stomatal density (A) and photosynthetic rate (B) in response to ambient (A or AA), shade (AS), or elevated CO2 (EA) treatments on abaxial (abx) or adaxial (adx) leaf surfaces. Total sugar content in mature (C) or developing/signalled (D) leaves in response to the signalling treatments. Chlorophyll fluorescence parameters of mature (E, H) and developing leaves (F, I) to assess photosynthetic performance and efficiency in response to the treatments applied to mature leaves. Arrows denote at what point the treatments were started. Data are the means of three replicates ±SE.

Fig. 2.

Arabidopsis stomatal density (A) and photosynthetic rate (B) in response to ambient (A or AA), shade (AS), or elevated CO2 (EA) treatments on abaxial (abx) or adaxial (adx) leaf surfaces. Total sugar content in mature (C) or developing/signalled (D) leaves in response to the signalling treatments. Chlorophyll fluorescence parameters of mature (E, H) and developing leaves (F, I) to assess photosynthetic performance and efficiency in response to the treatments applied to mature leaves. Arrows denote at what point the treatments were started. Data are the means of three replicates ±SE.

Figure 2A shows data for stomatal density; as reported previously increasing the CO2 concentration of mature leaves decreased the stomatal density of the developing leaves on both the adaxial and abaxial leaf surfaces (data not shown). Similarly the shade treatment also caused a marked reduction in the stomatal density of the unshaded developing leaves (Fig. 2A).

Gas exchange

When mature leaves were enclosed in the cuvette system (Fig. 1A, B) and treated with ambient CO2 and ambient light (treatment A), the photosynthetic rate was found to be 9.32 μmol m−2 s−1 (Fig. 2B). When a shade treatment (AS, which reduced the light intensity from 250 μmol m−2 s−1 to 50 μmol m−2 s−1) was applied, this immediately reduced photosynthesis to 1.98 μmol m−2 s−1. When air containing elevated CO2 was supplied (treatment EA) to the cuvette system, the photosynthetic rate was increased to 13.25 μmol m−2 s−1 (Fig. 2B).

Carbohydrate content of mature and developing leaves

Mature leaves that were enclosed in the cuvettes and supplied with air containing elevated CO2 (AE) contained more soluble carbohydrates, especially after 2–4 d, than those receiving ambient CO2 (A) (see Fig. 2C). By contrast, leaves that were shaded (AS) contained significantly less soluble carbohydrates. When the sugar content of the developing, untreated leaves was measured, the same trend of higher sugar concentrations in the plants where the mature leaves were receiving elevated CO2 was seen within 2 h of treatment when compared with the ambient control (Fig. 2D). The plants where the mature leaves were shaded had similar amounts of sugars as the ambient control in the developing leaves except at 4 h after treatment when significantly reduced levels of sugars were recorded (Fig. 2D).

Chlorophyll fluorescence

Images of chlorophyll fluorescence were recorded to determine fluorescence quenching parameters (Barbagallo et al., 2003). Using a FluorImager (Fig. 1G) images of

\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{m}}\)
for a plant in the cuvette system receiving ambient air can be seen in Fig. 1H, I. When a shade treatment was applied to the mature leaves,
\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{m}}\)
increased in these leaves (Fig. 1I). This effect can be seen more clearly in Fig. 2E–I, where there is a clear increase in
\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{m}}\)
and also in
\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{v}}\)
in the shaded mature leaves, relative to the ambient control and a decrease in
\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{v}}\)
in response to elevated CO2 concentration (Fig. 2E, H). By contrast,
\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{v}}\)
and
\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{m}}\)
were unchanged in the untreated, developing leaves for both elevated CO2 and shade treatment of mature leaves (Fig. 2F, I).

Microarray data analysis

Over 2000 plants and 200 RNA extractions were performed to generate samples with replicates that were hybridized to 112 Affymetrix gene chip arrays. The data were imported into the GeneSpring data analysis package and, after the initial standard normalization, the data were subjected to a principal components analysis (PCA) (data not shown). It was clear from the PCA (Fig. 3) that the data from the mature leaf samples group vary differently from the developing leaf samples. It was also clear that the time of sampling, independent of treatment, was an important parameter perhaps reflecting a circadian rhythm. For the purposes of this study the focus was only on those genes whose expression changed in response to the shade treatment; there is insufficient space within the article to present the data concerning the effects of CO2 and the combination of shade and CO2; these will be the subject of a future article. The focus was also on those genes whose expressions were altered in mature leaves that received the shade treatment directly (treated mature) and genes whose expressions were altered in developing leaves that did not receive the shade treatment (signalled leaves).

Fig. 3.

Principal components analysis of data obtained from hybridization experiments with 112 Affymetrix microarray chips and mature and developing leaf extracts over the period of 96 h after shade and elevated CO2 treatments were applied to mature leaves. The PCA plot was generated using GeneSpring v. 7.0.

Fig. 3.

Principal components analysis of data obtained from hybridization experiments with 112 Affymetrix microarray chips and mature and developing leaf extracts over the period of 96 h after shade and elevated CO2 treatments were applied to mature leaves. The PCA plot was generated using GeneSpring v. 7.0.

Genes that showed high variation in expression between replicates or were not present in all of the 112 samples were removed and this reduced the number of genes to 16 397 in signalled leaves and 14 972 genes in treated mature leaves. The data were then filtered to remove those genes whose expression level changed up or down less than 1.5-fold of the control treatment at each respective time point; this further reduced the number of genes to 12 837 in signalled leaves and 10 719 in treated mature leaves. Lastly, the data were analysed using a t-test to identify those genes whose expression changed and had a low P-value score (up to 0.05 in at least 1 condition and no multiple testing corrections) which left 11 397 genes in signalled leaves and 4163 genes in treated mature leaves. A second t-test was then performed using the Benjamini and Hochberg false discovery rate and using ‘variances not assumed equal’ along with a Tukey post hoc test and this returned 915 genes that change expression in signalled leaves and 1181 in treated mature leaves that were statistically significantly different between the ambient and shade treatments.

Functional classification and candidate signalling genes

The two groups of genes that showed at least a 1.5-fold change in expression level in at least one of the time points following shading of the mature leaves were classified according to their putative functions. This was based on their classification in the Munich Information Centre for Protein Sequence (MIPS) database to determine whether untreated developing leaves differed in their gene expression patterns compared with the treated mature leaves. Figure 4A and B show a summary of the functional characterization of those transcripts identified as having altered expression patterns in treated mature and untreated developing leaves. The data show that, where gene function can be assigned, the majority of transcripts where expression was altered encode proteins that were associated with protein turnover and metabolism in the shaded mature leaves and metabolism and cell wall/development in the developing, untreated leaves. Seven-fold more genes involved in photosynthesis were altered in the mature leaves compared with the untreated developing leaves. The remaining categories were quite similar with the exception that 5% of the genes in the mature leaves were predicted to be involved in translation compared with only 1% in the developing leaves.

Fig. 4.

A pie-chart comparison of predicted functional distribution of shade-regulated genes in treated mature (A) and untreated developing (B) leaves. Distribution of mature and developing leaf profiles among 12 major classes was performed using the MIPs database (http://mips.gsf.de/proj/thal/db/tables/tables_func_frame.html). (C) Venn diagram to compare and contrast the 1181 shade-responsive genes present in the mature leaf and the 915 shade-responsive genes present in the signalled leaves. 20 897 remaining genes were filtered out as they were not consistently expressed or not responsive to the treatment and were not included in the analysis. (D) Venn diagram to compare and contrast the shade-responsive genes from the mature and developing leaves with the 220 YODA-related genes thought to be important in controlling stomatal fate (Bergmann et al., 2004). The Venn diagrams were generated using GeneSpring v. 7.0.

Fig. 4.

A pie-chart comparison of predicted functional distribution of shade-regulated genes in treated mature (A) and untreated developing (B) leaves. Distribution of mature and developing leaf profiles among 12 major classes was performed using the MIPs database (http://mips.gsf.de/proj/thal/db/tables/tables_func_frame.html). (C) Venn diagram to compare and contrast the 1181 shade-responsive genes present in the mature leaf and the 915 shade-responsive genes present in the signalled leaves. 20 897 remaining genes were filtered out as they were not consistently expressed or not responsive to the treatment and were not included in the analysis. (D) Venn diagram to compare and contrast the shade-responsive genes from the mature and developing leaves with the 220 YODA-related genes thought to be important in controlling stomatal fate (Bergmann et al., 2004). The Venn diagrams were generated using GeneSpring v. 7.0.

When the two sets of gene lists (i.e. genes with altered expression by the shading of mature leaves) were compared and contrasted using a Venn diagram in Fig. 4C, there were 998 genes specific to the shaded mature leaves and 732 genes that were specific to the signalled developing leaves, with 183 genes common to both. Examples from these three groups of genes can be seen in Table 1. There were several suitable candidate genes in the ‘treated-mature’ list that might be involved in signalling to developing leaves, for example, eight auxin-related genes, one brassinosteroid-related gene, one gibberellin-related gene, a putative mitogen activated protein kinase kinase (MAPKK), and a putative sugar transporter. Examination of the untreated developing leaf gene list (Table 1) shows there were several interesting candidate genes that might interact with a signal from the mature leaves and these include CER2, EIN3, and SDD1.

Table 1.

Examples of gene annotations from the 1181 treated mature leaf and 915 untreated developing leaf shade-responsive gene lists generated by analysis of the microarray experiments


Mature leaf only
 
 
Both
 
 
Signalled leaves only
 
 
Description
 
AGI
 
Description
 
AGI
 
Description
 
AGI
 
Auxin transport At2g01420 Carbonic anhydrase At3g08510 Phospholipase C At1g58180 
Auxin responsive 7 At3g23050 Light regulated protein At3g26740 CER2 At4g24510 
Auxin response factor At1g19220 Auxin-regulated At2g33830 EIN3 At3g20770 
Auxin PIN7 protein At1g23080 Senescence protein At3g22550 SDD1 At1g04110 
Auxin regulatory protein At4g39400 SEN1 protein At4g35770 Expansin At1g66350 
Auxin PIN3 protein At1g70940 Zinc finger protein At5g22920 GA3 signalling At3g29030 
Auxin ARF1 protein At2g46530 Calnexin 1 At5g61790 SDD1-like At1g01900 
Auxin protein IAA1 At4g14560 Glucose transporter At1g11260 GA3 SPINDLY At3g11540 
MAPKK At4g08500 Myb T.F. protein At1g22640 MAPK17 At2g01450 
Brassinosteroid 1 At4g39400 Protein phosphatase At2g25620 Scarecrow T.F. At1g21450 
Sugar transporter At2g48020 Amino acid transport At2g40420 Sugar transporter At4g04750 
Gibberellin signalling At1g14920 Neoxanthin protein At4g19170 Auxin protein At3g07760 
Aquaporin PIP1;5 At4g23400 Drought protein At1g56280 Dof protein At5g60850 
Aquaporin TIP2;1
 
At3g16240
 
EIN3 related 1
 
At2g27050
 
Aquaporin TIP2;2
 
At4g17340
 

Mature leaf only
 
 
Both
 
 
Signalled leaves only
 
 
Description
 
AGI
 
Description
 
AGI
 
Description
 
AGI
 
Auxin transport At2g01420 Carbonic anhydrase At3g08510 Phospholipase C At1g58180 
Auxin responsive 7 At3g23050 Light regulated protein At3g26740 CER2 At4g24510 
Auxin response factor At1g19220 Auxin-regulated At2g33830 EIN3 At3g20770 
Auxin PIN7 protein At1g23080 Senescence protein At3g22550 SDD1 At1g04110 
Auxin regulatory protein At4g39400 SEN1 protein At4g35770 Expansin At1g66350 
Auxin PIN3 protein At1g70940 Zinc finger protein At5g22920 GA3 signalling At3g29030 
Auxin ARF1 protein At2g46530 Calnexin 1 At5g61790 SDD1-like At1g01900 
Auxin protein IAA1 At4g14560 Glucose transporter At1g11260 GA3 SPINDLY At3g11540 
MAPKK At4g08500 Myb T.F. protein At1g22640 MAPK17 At2g01450 
Brassinosteroid 1 At4g39400 Protein phosphatase At2g25620 Scarecrow T.F. At1g21450 
Sugar transporter At2g48020 Amino acid transport At2g40420 Sugar transporter At4g04750 
Gibberellin signalling At1g14920 Neoxanthin protein At4g19170 Auxin protein At3g07760 
Aquaporin PIP1;5 At4g23400 Drought protein At1g56280 Dof protein At5g60850 
Aquaporin TIP2;1
 
At3g16240
 
EIN3 related 1
 
At2g27050
 
Aquaporin TIP2;2
 
At4g17340
 

Those genes in bold show statistically significant increases in expression and those not in bold show statistically significant decreases in expression.

The inclusion of SDD1 and the closely related, SDD1-like (At1g01900), in the list of ‘signalled’ genes that respond in developing leaves to the shade treatment in the mature leaves is interesting and fits in with previously proposed models of stomatal development in developing Arabidopsis leaves (Bergmann, 2004). There was a need to find out if any other genes that have been implicated in signalling stomatal development were present in the two lists. Recently, a microarray experiment was carried out using a mutant as controlling stomatal development and patterning and encoding a putative MAPKKK called YODA (Bergmann et al., 2004). 220 genes were identified that changed expression pattern in opposing directions in the mutant yoda (excess stomata) and plants expressing the ΔN-YODA protein (no stomata). The list of these 220 YODA-related genes imported into GeneSpring are compared with the two gene lists described earlier (Table 1), again using a Venn diagram (Fig. 4D). Twelve of the YODA-related genes were specific to the mature leaf list, nine were specific to the untreated developing leaf list, and six were common to both (Table 2).

Table 2.

Results of the microarray data analysis to show the distribution of YODA-related genes within the 1181 mature and 915 signalled leaf genes that respond to shade


Yoda mature leaf only
 
 
Yoda both
 
 
Yoda signalled leaves only
 
 
Description
 
AGI
 
Description
 
AGI
 
Description
 
AGI
 
Kinase At1g21920 AP2 domain protein At1g79700 SDD1 At1g04110 
Auxin protein At1g29510 Glycine- rich protein At2g05540 Gibberellin 3-β-hydroxylase At1g15550 
Unknown At1g47400 Serine peptidase At2g22980 Histone protein At2g18050 
Lysine decarboxylase At2g28300 Zinc finger protein At2g25900 Tropinine reductase At2g29290 
Unknown At2g38210 Unknown At4g19160 Protein kinase At2g41820 
PEPC At2g42600 E2 ubiquitin protein At5g41700 Acyl-CoA synthase At2g47240 
bZIP T.F. At3g58120   Glycosyl hydrolase 36 At3g57520 
Glycosyl hydrolase 19 At3g61060   DHFR At5g42800 
Peroxidase At4g01700   Unknown At5g54970  
Unknown At4g08390     
Unknown At5g02020     
Arabinogalactan
 
At5g44130
 

 

 

 

 

Yoda mature leaf only
 
 
Yoda both
 
 
Yoda signalled leaves only
 
 
Description
 
AGI
 
Description
 
AGI
 
Description
 
AGI
 
Kinase At1g21920 AP2 domain protein At1g79700 SDD1 At1g04110 
Auxin protein At1g29510 Glycine- rich protein At2g05540 Gibberellin 3-β-hydroxylase At1g15550 
Unknown At1g47400 Serine peptidase At2g22980 Histone protein At2g18050 
Lysine decarboxylase At2g28300 Zinc finger protein At2g25900 Tropinine reductase At2g29290 
Unknown At2g38210 Unknown At4g19160 Protein kinase At2g41820 
PEPC At2g42600 E2 ubiquitin protein At5g41700 Acyl-CoA synthase At2g47240 
bZIP T.F. At3g58120   Glycosyl hydrolase 36 At3g57520 
Glycosyl hydrolase 19 At3g61060   DHFR At5g42800 
Peroxidase At4g01700   Unknown At5g54970  
Unknown At4g08390     
Unknown At5g02020     
Arabinogalactan
 
At5g44130
 

 

 

 

 

Discussion

The search for a systemic signal

Stomatal density and index generally decrease with decreasing light intensity. It has previously been shown that shading of only mature Arabidopsis leaves also leads to untreated developing leaves growing as if they have received the shade treatment with a lower stomatal density/index when compared with control plants (Lake et al., 2001). It has been shown that this also occurs on both surfaces of the developing leaf in the shade signalling system used here. These results suggest that signals triggered by a change in irradiance are transmitted from mature to young leaves where they act to regulate stomatal development (Lake et al., 2001; Thomas et al., 2004). The results also indicate that the effect of changing CO2 concentration (on the mature leaves) has an effect on stomatal development in young leaves and that the effect interacts directly with the light signal (data not shown), suggesting an interdependence of the respective signalling mechanisms.

These CO2 and light treatments caused large changes to the gas exchange parameters and photosynthetic rates of the mature leaves enclosed within the treatment cuvette when compared with ambient controls. The reduction in photosynthesis correlated well with the reduction in light intensity caused by the shade treatment (Fig. 2B) whilst the increase in response to elevated CO2 was also as expected. The rates of gas exchange observed for these plants are within the range of values previously published (Lake, 2004).

When the total soluble sugar content of the enclosed, treated, mature leaves was measured there was a marked decrease in the level of sugar in response to the shade treatment even after 2 h and the trend continued throughout the 96 h time-course (Fig. 2C). Increased sugar concentrations (sucrose, glucose, and fructose) were measured in mature leaves in response to the elevated CO2 treatment as expected with increased photosynthesis. Altered sucrose concentrations are likely to directly impact the rate of sugar export. This may at least partly explain why the developing leaves grew at different rates in response to the two treatments. This was further supported by the measurement of the sugar content of the untreated developing leaves (Fig. 2D) which were much higher (even after 2 h) in the plants where the mature leaves had been supplied with elevated CO2 up to 24 h. The trend was less obvious where the mature leaves had been shaded, but a clear decrease could be seen at 4 h. There was therefore a direct effect of this study's treatments to mature leaves on the sugar status and growth rate of the developing leaves concomitant with altered rates of phloem transport between the mature and developing leaves.

Chlorophyll fluorescence parameters were also measured in the mature and developing leaves to determine if any of the treatments were causing changes in photosynthesis of the developing leaves that could not be enclosed in the measuring cuvette of this laboratory's gas-exchange equipment (Fig. 2E–I). Chlorophyll fluorescence imaging gave the ability to assess both the enclosed mature leaves and the exposed developing leaves simultaneously during the treatment period. Chlorophyll fluorescence, measures of photosynthetic efficiency (

\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{m}}\)
and
\(F{^\prime}_{\mathrm{q}}/F{^\prime}_{\mathrm{v}}\)
), increased in the mature leaves in response to shade, suggesting decreased reduction of PSII reaction centres and reduced levels of non-photosynthetic energy dissipation, as expected (Fig. 2E, H). The developing leaves, however, showed no change, suggesting that there was no photosynthetic response to the treatments of the mature leaves (Fig. 2F, I). So, clearly, the treatments are causing changes in photosynthesis, sugar content, and chlorophyll fluorescence parameters of the mature leaf and the end result of that is a change in stomatal numbers and sugar content of the untreated developing leaves. It is likely therefore that the differences in sugar content may be one source of the signal being passed from mature to developing leaves. However, it should be noted that increased CO2 and shade have opposite effects on the sugar concentration of the mature leaves yet both treatments lead to a decline in stomatal numbers of the developing leaves.

Can transcriptomics help identify signalling candidates?

It is clear from the explosion of transcriptomics data in recent years that characterizing an organism's transcriptomic response to a treatment is a powerful way of identifying many new genes that respond to a specific stimuli. Treatments such as circadian rhythms (Harmer et al., 2000), nitrate (Wang et al., 2003), shade (Devlin et al., 2003), sucrose starvation (Contento et al., 2004), pathogens (Eulgem et al., 2004), senescence (Buchanan-Wollaston et al., 2005), and hormones such as abscisic acid (Leonhardt et al., 2004), auxin and brassinosteroids (Goda et al., 2004) have been used to identify candidate genes that might be involved in these processes. Transcriptomic approaches have also been used to characterize those differences that a mutation in a specific gene can cause in the wider transcriptome, as recently shown for the yoda mutant (Bergmann et al., 2004). These experiments, however, all focus on changes in gene expression occurring directly in the tissues that were receiving the treatment or carried the mutation. The responses of a plant's transcriptome have been measured not only in the mature leaves receiving the shade and elevated CO2 treatments but also in the untreated developing leaves over the course of 4 d with the aim of identifying genes that may be involved in the systemic signalling of environmental stimuli.

Initial observations of the microarray data showed that it would easily be possible to discriminate between the mature leaf samples and the signalled leaves using PCA analysis (Fig. 3). PCA has previously been shown to be a useful tool for revealing subtle differences in large metabolic datasets (Overy et al., 2005). It was also found that time was a significant principal component that could be used to distinguish the treatments in the time-course. Given that circadian rhythms have already been shown to influence gene expression significantly (Harmer et al., 2000), it was necessary to include an untreated control at each time point and that the data were normalized to these control time points rather than a zero h time point.

After the data were filtered and analysed statistically, 1181 genes were identified from the mature leaf experiments that were differentially expressed between the ambient and shade treatments. 915 genes were differentially expressed between the ambient and shade treatments in the untreated developing leaves. Those genes whose expression was altered by shading mature leaves were grouped into functional classes (Fig. 4A, B). The classifications of both sets of affected genes are quite similar but there are several differences, for example, the mature leaf set has seven photosynthesis genes that form part of the metabolism category, but the signalled leaves show only one. This is perhaps not surprising as it has already been shown (by chlorophyll fluorescence, Fig. 2) that the signalled leaves do not show a photosynthetic response when the mature leaves were treated. There were also more differences in genes involved in protein turnover and translation in the mature leaves compared with the developing leaves and this may be related to the changes in gene expression in response to the treatments, as opposed to the untreated developing leaves. The developing leaves have a larger proportion of affected genes involved in cell wall synthesis and cell development which reflect the different stage of growth and development.

The Venn diagram (Fig. 4C) identifies 998 genes with expression patterns specifically affected in the treated mature leaves, 732 in the untreated developing leaves and 183 genes in both. A high proportion of auxin signalling genes; for example, PIN3, PIN7, and IAA1 (Table 1) were observed for mature leaves. There was also a putative MAPKK in the list of genes affected in mature leaves. This may be of interest as a MAPK signalling pathway is implicated in stomatal development (Bergmann et al., 2004). There were also components of the brassinosteroid (BRI1) and gibberellin (GA, GAI) signalling pathways that were affected. Many of the genes in this list have previously been shown to be regulated by shade (Devlin et al., 2003) and this convergence of light and hormone signalling might be particularly interesting when looking for candidates that might be involved in environmental signalling from mature to developing leaves.

From a list of known genes specifically affected in untreated developing leaves (Table I) it is interesting to note the presence of EIN3 and SDD1 as well as components of the GA signalling pathway. These two sets of genes were compared with the 220 genes that contained 109 up-regulated genes in yoda and down-regulated in ΔN-YODA and 111 up-regulated genes in ΔN-YODA and down-regulated in yoda that were hypothesized to be closely involved in guard cell specification and function (Bergmann et al., 2004). The Venn diagram (Fig. 4) showed that 12 of these genes were present in the treated mature leaf gene list and nine were present in the untreated developing leaf gene list and six were present in both gene lists (Table 2). Again auxin and GA signalling related genes are present as well as putative transcription factors, kinases and glycosyl hydrolases but one of the most interesting findings was the presence of SDD1. SDD1 encodes a putative subtilisin-like serine protease and mutations in SDD1 lead to elevated stomatal density and frequent violations of the 1-cell spacing rule that normally prevents individual stomata developing next to each other (Nadeau and Sack, 2003). The full role that SDD1 plays is unknown, but it is thought to act in a common signalling pathway (along with TMM, YODA, and ERECTA proteins) to cleave a ligand that is perceived by TMM and a co-receptor kinase (Nadeau and Sack, 2003). In these results SDD1 and the closely related SDD1-like (At1g01900) are both down-regulated in the signalled leaves in response to the shade treatment of mature leaves (data not shown). These two genes share 43% identity at the amino acid level and out of the 56 Arabidopsis subtilisin-like genes are the most closely related (see http://web.uni-frankfurt.de/fb15/botanik/mcb/AFGN/altmann.htm) and have been renamed AtSBT1.1 (SDD1-like) and AtSBT1.2 (SDD1). The subtilisins have diverse roles in plant development and have been implicated in epidermal development in Arabidopsis embryos (Tanaka et al., 2001) and in auxin-induced root hair formation (Xie et al., 2000). The photosynthetic performance of sdd1 has been evaluated (Schluter et al., 2003) and they found that the mutant accumulated 30% more starch and sugar than wild-type under increasing light but conclude that SDD1 is not responsible for environmentally mediated regulation of stomatal density. These preliminary results suggest that SDD1 expression is affected in developing leaves by shading mature leaves.

In summary, this study of a systemic signalling system has suggested that both sugars and hormones may play a part in systemic signalling but that further work is required to discover the precise mechanism. It has recently been shown that leaf morphogenesis can be controlled by microRNAs (Palatnik et al., 2003) and these have been implicated in auxin signalling (Kidner and Martienssen, 2005). Ten per cent of the 915 genes identified as having altered expression in treated developing leaves and 70% of the forty putative transcription factors from this gene list contain high homology microRNA binding domains as identified on the Arabidopsis small RNA web site (http://asrp.cgrb.oregonstate.edu/). It has also been demonstrated that an mRNA member of the GRAS gene family, a negative regulator of gibberellic acid responses, is delivered by the phloem from developing leaves to leaf meristems and alters subsequent leaf development. This suggests possible mRNA candidates as systemic signallers. Furthermore, this mRNA did not affect all newly developed leaves but rather was position-dependent in grafting experiments; increasing distance from the source of mRNA reduced both the quantity of mRNA arriving at the meristem and efficacy in altering leaf development (Haywood et al., 2005). This type of system seems appropriate in ensuring that only signals from ‘nearby’ mature leaves are effective in this systemic signalling pathway and provides a mechanism whereby developing leaves are optimized for performance under the prevailing environmental conditions.

*
Present address: Department of Biology, University of York, PO Box 373, York YO10 5YW, UK.
Abbreviations: CO2, carbon dioxide; IRGA, infrared gas analyser; PCA, principal components analysis; ppm, parts per million; NASC, Nottingham Arabidopsis Stock Centre.

The authors would like to thank NASC for carrying out the Affymetrix microarray hybridization experiments, Dr Steven Rolfe for help with the chlorophyll fluorescence image analysis, and undergraduate students Michael Hahn, Jonathon Freer, and Daniel Hungerford, for help with the growing of Arabidopsis plants, and Dr Bela Tiwari and the bioinformatics team at CEH, Oxford for help with data anlysis and access to GeneSpring through the NERC Genomics Thematic Programme. This work was funded by NERC and BBSRC grants to JE Gray, WP Quick and FI Woodward.

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