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Keywords:

  • network analysis;
  • photosynthesis;
  • ROS;
  • systems biology

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

The heat shock response continues to be layered with additional complexity as interactions and crosstalk among heat shock proteins (HSPs), the reactive oxygen network and hormonal signalling are discovered. However, comparative analyses exploring variation in each of these processes among species remain relatively unexplored. In controlled environment experiments, photosynthetic response curves were conducted from 22 to 42 °C and indicated that temperature optimum of light-saturated photosynthesis was greater for Glycine max relative to Arabidopsis thaliana or Populus trichocarpa. Transcript profiles were taken at defined states along the temperature response curves, and inferred pathway analysis revealed species-specific variation in the abiotic stress and the minor carbohydrate raffinose/galactinol pathways. A weighted gene co-expression network approach was used to group individual genes into network modules linking biochemical measures of the antioxidant system to leaf-level photosynthesis among P. trichocarpa, G. max and A. thaliana. Network-enabled results revealed an expansion in the G. max HSP17 protein family and divergence in the regulation of the antioxidant and heat shock modules relative to P. trichocarpa and A. thaliana. These results indicate that although the heat shock response is highly conserved, there is considerable species-specific variation in its regulation.


INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Plants respond to elevated temperatures by eliciting the heat shock response characterized by the rapid accumulation of heat shock proteins (HSPs: Lindquist 1986). This response is considered to be highly conserved and ubiquitous among biological organisms (although see Bosch et al. 1988; Hofmann et al. 2000) and is often characterized by conceptual models (e.g. Morimoto 1998) and more recently with mechanistic biochemical models (yeast, Kurata et al. 2003; human, Proctor et al. 2005) where HSPs and heat shock factors (HSFs) exist as multimeric complexes under optimal conditions. During exposure to heat, HSPs dissociate to chaperone client proteins, while HSFs trimerize to induce expression of HSPs. While such models are useful to explain observed patterns of heat shock responses in mammalian systems, they do not capture or reflect the considerable species-specific variation known to exist in Planta. For example, in tomato, HSFA1 acts as the master switch to activate HSFA2, which induces the expression of small HSPs (sHSPs), HSP70 and HSP101 (Tripp, Mishra & Scharf 2009). In contrast, Arabidopsis HSFA2 is not regulated by either HSFA1a or HSFA1b under heat, but a number of other stress- and non-stress-related genes are (Busch, Wunderlich & Schoffl 2005).

Further complicating the interpretation of the heat shock pathways among plant species is the widespread phenomenon of genome duplication resulting in the presence of large gene families encoding HSPs (e.g. 14 members of HSP70 in Arabidopsis) with potentially redundant or non-heat shock roles (discussed in Larkindale, Mishkind & Vierling 2005; Tonsor et al. 2008). Out of the five HSP families (HSP100/ClpB, HSP90, HSP70/DnaK, HSP60/GroEL, sHSPs), there is still no specific role for HSP60, HSP70 and HSP90 in plant survival to heat (Kotak et al. 2007). Although recent work by Salvucci (2008) implicates chaperonin-60 (cpn60 beta), the chloroplast GroEL homolog, in stabilizing ribulose 1·5-bisphosphate carboxylase/oxygenase (Rubisco) activase at high temperatures.

In addition to HSP and HSF activation, a number of studies have demonstrated the importance of reactive oxygen species (ROS) scavenging as a protective mechanism against heat alone (Larkindale & Knight 2002; Larkindale & Huang 2004), and in combination with light (Suzuki & Mittler 2006). The production of ROS, such as superoxide radicals, hydroxyl radicals and hydrogen peroxide (H2O2), is known to occur at photosystem I (PSI), photosystem II (PSII) and throughout the Calvin–Benson cycle (Kim & Portis 2004; Asada 2006), demonstrating the potential vulnerability of the photosynthetic apparatus to oxidation and membrane damage. Therefore, it is not surprising that ROS signalling and scavenging are necessary components for acquiring thermotolerance following a mild heat pretreatment (Larkindale et al. 2005). In fact, crosstalk between the antioxidant system and the heat shock response has been suggested (Pnueli et al. 2003; Suzuki & Mittler 2006; Kotak et al. 2007), and heat shock elements have been identified in the promoter region of the Arabidopsis peroxidase-scavenging enzyme APX1 (Pnueli et al. 2003). Furthermore, H2O2 induces the expression of AtHSFA2, a key regulator of APX2 (Nishizawa et al. 2006). Such studies have led Davletova et al. (2005) to hypothesize that some Arabidopsis HSFs (e.g. HSFA4a and HSFA8) act as ROS sensors to orchestrate a highly regulated system between the heat shock response and ROS signalling and scavenging pathways.

Consistent with the heat shock response, it appears that the antioxidant system also exhibits considerable species-specific variation. Dudai et al. (2008) conducted a broad survey of total antioxidant capacity and phenolics on 47 species indigenous to the Dead Sea area. Using 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging, they estimated the antioxidant activity to vary ∼300-fold among species. Within-species variation in the antioxidant system has also been reported. Using wheat genotypes exposed to heat, Dash & Mohanty (2002) found that heat tolerance, as estimated from photosynthetic properties, correlated with ascorbate peroxidase and catalase enzyme activities. Likewise, Almeselmani et al. (2006) attribute increased activities of various antioxidant enzymes with increased chlorophyll and lower membrane damage of wheat genotypes under heat. Similar results were observed using thermo-sensitive and -insensitive genotypes of cotton, leading Snider, Oosterhuis & Kawakami (2010) to conclude that the maintenance of adequate antioxidant enzyme pools is an innate mechanism for coping with rapid leaf temperature changes.

These studies suggest that the maintenance of protein homeostasis through the function of HSPs and ROS-scavenging mechanisms has a profound effect on plant performance and productivity in response to heat stress. However, the variation and conservation between these pathways among species, and how they interact to influence plant physiological performance under heat are not well defined. From this perspective, a genome-wide survey among diverse species would aid in the identification and comparison of transcriptional regulatory mechanisms underlying cellular protective mechanisms. In a prior study (Weston et al. 2008), we used a weighted gene co-expression network analysis to interrogate the Arabidopsis abiotic stress transcriptome. In this study, we modify this global approach to link heat shock-induced transcriptional networks to biochemistry underlying the antioxidant system and leaf-level photosynthesis to build a system-level comparative analysis among three species with contrasting life forms. The woody perennial Populus trichocarpa, annual legume Glycine max and the annual forb Arabidopsis thaliana were compared to address two main questions: (1) What components of the heat shock response have diversified among species? (2) What is the relationship between the ROS and heat shock cellular protective networks with leaf-level photosynthetic decline under heat? Our results indicate that there is species-specific variation in the expression of the abiotic stress and raffinose/galactinol pathways, variation in the HSP17 gene family, and regulation of the antioxidant and heat shock modules during temperature response curves.

MATERIALS AND METHODS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Plant growth and growth conditions

Seeds of A. thaliana (ecotype Col-0) were imbibed at 4 °C with distilled water for 4 d to facilitate uniform germination. The seeds were resuspended in 0.05% agarose after the cold treatment and planted in pots containing Fafard 3B mix (Conrad Fafard, Agawam, MA, USA). Soybeans (G. max cv.Williams 82) were grown in pots containing Fafard 3B soil. Both Arabidopsis and soybean plants were grown in one Conviron BDW80 walk-in growth chamber (Controlled Environments Ltd., Winnipeg, Manitoba, Canada) under the following growth conditions: 22 °C, 60% relative humidity (RH), a 10 h photoperiod and 350 µmol m−2 s−1 light intensity. Dormant cuttings of black cottonwood (P. trichocarpa clone 93-968) averaging ∼0.5 cm diameter, 25 cm length, and two to three buds were rooted using 0.3% IBA and grown in an additional Conviron BDW80 walk-in growth chamber under the same settings as Arabidopsis and soybean except the photoperiod was extended to 18 h. The plants were routinely moved between chambers to minimize potential chamber effect on growth. The plants were watered as needed and fertilized weekly according to species-specific requirements: 250 ppm N for poplar and soybean (1 tsp gallon−1 20–20–20, Jack's Professional, J.R. Peters Inc., Allentown, PA, USA) and 125 ppm N for Arabidopsis (1 tsp gallon−1 10–10–10; Vigoro, St. Louis, MO, USA).

Measurement of leaf gas exchange

To determine the optimal and inhibitory temperatures for each species, measurements of photosynthesis were taken using a Li-6400 Photosynthesis System (Li-Cor Biosciences, Lincoln, NE, USA). Measurements were made on the most recently fully expanded leaf for all species to minimize the developmental differences among species. For all three species, data were collected at 380 µmol mol−1 CO2 (ambient cuvette concentration), a flow of ∼500 mL min−1 and RH between 60 and 75% [vapour pressure deficit (VPD) was kept between 1 and 2.3 kPa for poplar and soybean, and between 1 and 1.75 kPa for Arabidopsis]. The leaf chamber contained a sealed leaf area of 2 cm2 using the chlorophyll fluorescent cuvette. Measurements for poplar and soybean were taken with a photosynthetic photon flux density (PPFD) of 1000 µmol m−2 s−1, whereas Arabidopsis received 800 µmol m−2 s−1. Previous experiments confirmed that photosynthesis was light saturated at these levels of PPFD. On measurement days, the plants were left in the dark until experimentation where they were placed in a temperature-controlled growth chamber at 22 °C (daytime growth temperature), 60% RH and a light intensity of 350 µmol m−2 s−1 for 1 h to reduce the potential consequences of transient decreases in inorganic phosphate concentration and PSII efficiency on CO2 assimlation (Bernacchi et al. 2005). Gas exchange and chlorophyll fluorescence parameters were calculated as previously described (Weston & Bauerle 2007; Weston et al. 2007). From these data, photosynthetic temperature optimum, 20% inhibition and 30% inhibition from temperature optimum were calculated for each species and used to standardize physiological state to sampling regime for subsequent gene expression and biochemical assays.

Measurement of ROS production and total antioxidant capacity from leaf samples

Samples from the first fully expanded leaf for each species were frozen in liquid nitrogen and stored at −80 °C until further use. For estimating H2O2 production, 100 mg of frozen sample was extracted in 500 µL cold leaf extraction buffer [10 mm Tris–HCl (pH 7.3), 1 mm ethylenediaminetetraacetic acid (EDTA), 20% glycerol, 2 mm dithiothreitol (DTT) and 1 mm phenylmethylsulphonyl fluoride (PMSF), all from EMD, Gibbstown, NJ, USA], the extract was centrifuged at 13 000 rpm for 10 min at 4 °C and the supernatant was used for all downstream measurements. H2O2 production was assayed by adding 10 mm 2′,7′-dichlorodihydrofluoriscein-diacetate (H2DCF-DA; Calbiochem, San Diego, CA, USA) to 10 µL of the extract. Oxidation of H2DCF-DA to fluorescent 2′,7′-dichlorofluoriscein (DCF) was measured using a Fluoroscan Ascent microplate fluorometer (Thermo Scientific, Waltham, MA, USA). Each sample was measured in three technical replicates. The fluorescence data were normalized by fresh weight. The total antioxidant capacity was estimated by oxygen radical antioxidant capacity (ORAC) assay as previously described (Gillespie, Chae & Ainsworth 2007) using fluorescein (Sigma-Aldrich, St Louis, MO, USA) decay area curves created from a microplate fluorometer.

Enzyme activity assays

For measuring total peroxidase (PEROX), glutathione reductase (GR) and superoxide dismutase (SOD) activities, samples from the first fully expanded leaves were homogenized by grinding in cold leaf extraction buffer [100 mm tricine, 1 mm EDTA, 20% (v/v) glycerol and 2 mm DTT] with 1% polyvinylpolypyrrolidone (PVPP) (w/w). Total PEROX and GR activities were estimated as previously described (Colville & Smirnoff 2008). SOD activity was estimated by SOD assay kit-WST (Dojindo Molecular Technologies, Rockville, MD, USA) using the manufacturer's protocol. All the assays were performed in 96-well plate format using a SpectraMax Plus384 microplate reader (Molecular Devices, Sunnyvale, CA, USA) equipped with SoftMax Pro 5.3 software (Molecular Devices).

RNA isolation, labelling and microarray hybridization

Samples from the first fully expanded leaf of all three plant species were harvested at defined states determined from temperature response curves, frozen in liquid nitrogen, ground using a mortar and pestle and frozen at −80 °C until further use. Total RNA was isolated from 100 mg of tissue using an RNeasy Mini Kit (Qiagen, Valencia, CA, USA) according to the manufacturer's instructions. An on-column DNase 1 treatment (Sigma-Aldrich) was used during RNA extraction to remove any potential genomic DNA contamination. Total RNA quantity was determined using a NanoDrop 1000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA), and RNA quality was determined using an Experion RNA StdSens Analysis kit (Bio-Rad Laboratories, Hercules, CA, USA). Only non-degraded samples with an acceptable A260 : A280 ratio (≥1.8) were used in subsequent microarray experiments. Total RNA isolated from Arabidopsis and soybean was sent to the University of Tennessee Affymetrix Core Lab for subsequent amplification, labelling and hybridization using the Affymetrix GeneChip Arabidopsis ATH1 Genome Array and GeneChip Soybean Genome Array according to the GeneChip expression analysis technical manual (#PM702232; http://www.affymetrix.com).

For poplar, total RNA was converted to ds cDNA using a SuperScript Double-Stranded cDNA Synthesis Kit (Invitrogen, Carlsbad, CA, USA) according to the protocol provided by Roche (Indianapolis, IN, USA) for NimbleGen high-density microarrays. RNase A cleanup, cDNA precipitation and sample labelling were also carried out according to the provided protocol. Pelleted samples were rehydrated in hybridization solution according to the protocol provided by Roche, and hybridized to the second-generation NimbleGen Populus 385K microarray slides (design -080401_Populus_385K_EXP) at 42 °C in a NimbleGen Hybridization System (NimbleGen Roche, Madison, WI, USA). After hybridizing for 16 h, the slides were washed and dried according to the provided protocol. Dried slides were scanned using an Agilent scanner (Agilent Technologies, Santa Clara, CA, USA) with a PMT gain of 100% at 5 µm resolution.

Microarray data analysis and network construction

For soybean and Arabidopsis, log-fold (M) over variance (A) plots (M-A plots) indicated that RMA was the most appropriate normalization method. Using the limma package, a linear model was fitted to compare all physiological states (as defined by leaf gas exchange) as contrasts, and empirical Bayes was used to compute a moderated t-statistic according to Smyth (2004, 2005). All results for differential gene analysis are presented in Supporting Information Table S1 for Arabidopsis and Supporting Information Table S2 for soybean.

For poplar, images of all scanned slides were aligned using NimbleScan version 2.5, and pair reports were created using the slide design file provided by Roche NimbleGen. An annotation package for this array was created using the pdInfoBuilder (v.2.4) Bioconductor open-access package run in the R statistical framework (Team R.D.C. 2007). Differential gene expression for this first-generation poplar expression array was determined using the RankProd (v.2.4) Bioconductor package with results presented in Supporting Information Table S3. RankProd is a non-parametric method for identifying differentially expressed genes based on the estimated percentage of false prediction and is more robust to deviations in normalization (Breitling et al. 2004).

Network analysis

Construction of the weighted gene co-expression network has been described previously (Zhang & Horvath 2005; Langfelder & Horvath 2008; Weston et al. 2008), and R-scripts (Supporting Information Table S4) along with input data (Supporting Information Tables S5–S7) are provided so that the reader can reproduce our results. Because of computational constraints, the input microarray data were restricted to 4000 genes that were most differentially expressed (most significant F-test among all three contrasts) along the distinct physiological states of the response curves. It should be noted that normalized expression values were entered into network algorithms and not ratiometric data from determination of differential gene expression. Weighted gene co-expression network (WGCNA) consists of four steps: (1) a pairwise Pearson correlation matrix created for all genes across all treatments; (2) transformation of correlations to connection strengths (connectivity) using a signed power adjacency function; (3) identification of modules or groups of highly correlated gene expression patterns by coupling linkage hierarchical clustering with topological overlap matrix; and (4) relating external gene or treatment information to network properties. In our case, the external treatment information was the physiological state of thermoinhibition. To relate network properties (modules) to external traits, we correlated the eigengene (essentially the first eigenvalue) of the module to each treatment slide (n = 16) using a Spearman rank correlation. Correlations were verified using a random seed permutation t-test with 106 iterations.

HSP17 survey and phylogenetic analysis

An overall analysis of heat response-related genes in each species was performed to determine classes of genes significantly involved in the heat shock response. This analysis, based solely on array annotation, revealed that a significant proportion of the genes that were involved in the network belonged to the sHSP class, primarily the HSP17s (Table 1). The HSP20 or sHSP family is known to exhibit strong expression response to heat in Arabidopsis (Swindell, Huebner & Weber 2007). The HSP17s represent the largest proportion of the sHSPs in Arabidopsis, accounting for 42% of the proteins in the group (Scharf, Siddique & Vierling 2001), and were prominent in the expression response pattern observed in Arabidopsis in this study.

Table 1.  Summary of heat-related gene families from microarray annotation of each genome
 ArabidopsisPoplarSoybean
Annotated heat relatedInducedIn networkAssociated moduleAnnotated heat relatedInducedIn networkAssociated moduleAnnotated heat relatedInducedIn networkAssociated module
  1. The small HSPs (sHSPs) include HSP20s, HSP18s, HSP17s, as well as other gene models annotated as containing the HSP20 alpha-crystalline domain characteristic of the sHSP.

All1658442 2178714 2196639 
dnaj983612Brown84192Brown110158Greenyellow
Black
hsf22112Brown33100 3063Greenyellow
Black
hsp100221Brown741Brown222Greenyellow
Black
hsp90211Brown620 851Black
hsp80333Brown1240 1180 
hsp7013127Brown34162Brown2253Greenyellow
hsp60111Brown640 100 
shsp241815Brown35289Brown352522Greenyellow
Black

Nineteen sHSPs have been named in Arabidopsis (Scharf et al. 2001; Swindell et al. 2007; Waters, Aevermann & Sanders-Reed 2008), including eight HSP17s. Waters et al. (2008) outlined 36 sHSPs in P. trichocarpa, including 12 HSP17s. Through a combination of BLAST and domain scanning of the soybean genome (downloaded from http://www.phytozome.net/), we identified 72 genes in soybean containing the Pfam HSP20 domain (PF00011) corresponding to Arabidopsis sHSPs (data not shown).

We used the ExPASy ProtParam tool to determine protein molecular weight for the 72 soybean genes (http://ca.expasy.org/tools/protparam.html) in order to perform phylogenetic analysis, and identified 25 genes exhibiting both high identity to Arabidopsis HSP17s and molecular weights ranging from 17.1 to 17.9 kD (Supporting Information Table S8). Note that the theoretical molecular weight of two of these genes was actually >18 kD; however, based on chromosomal position and sequence similarity, they represent tandem duplicates of HSP17 genes and were included to provide a complete analysis of putative HSP17 orthologs (Supporting Information Table S8).

Tandemly duplicated genes in the soybean and poplar genomes were identified based on a Smith–Waterman alignment E-value ≤ 10−25 within a 100 kb window. Whole-genome duplication events in poplar and soybean were confirmed by alignment of whole-genome assemblies using the VISTA 2.0 Genome browser (http://pipeline.lbl.gov/cgi-bin/gateway2).

Eight Arabidopsis, 13 poplar (including a 16.1 kD protein expressing in the network) and 25 soybean putative HSP17 protein sequences were aligned with ClustalW. Only the conserved C-terminal domain residues of the 46 sequences were aligned (reference At2g29500.1, residues 49–152). The evolutionary history was inferred using the UPGMA method (Sneath & Sokal 1973). Support for branches on the tree was generated from a bootstrap test (1000 replicates) (Felsenstein 1985). The evolutionary distances were computed using the Poisson correction method (Zuckerkandl & Pauling 1965) and are based on the number of amino acid substitutions per site. All positions containing gaps and missing data were eliminated from the data set (complete deletion option). There were a total of 84 positions in the final data set. Phylogenetic analyses were conducted in MEGA4.0.2 (available http://www.megasoftware.net/) (Tamura et al. 2007).

Quantitative real-time PCR (qPCR) analysis

The relative expression levels of several candidate HSP17 genes were compared across treatments and within the three plant species using relative qPCR. The candidate genes included eight HSPs from Arabidopsis, 14 HSPs from soybean and six HSPs from poplar (Supporting Information Table S9). Total RNA was extracted from leaf tissue using a Spectrum Plant Total RNA Extraction Kit (Sigma-Aldrich), and an on-column DNase I treatment (Sigma-Aldrich) was performed according to the provided protocol to remove any potential genomic DNA contamination. One microgram of DNase-free RNA was used for cDNA synthesis. The cDNA synthesis was carried out using a SuperScript III First-Strand Synthesis SuperMix for qPCR (Invitrogen) according to the protocol provided.

Amplification reactions (25.0 µL) were carried out using iQ SYBR Green Supermix with ROX according to instructions provided by Bio-Rad Laboratories. Each reaction contained a cDNA template (2.0 µL), SYBR Green supermix (12.5 µL), sterile water (9.5 µL) and the appropriate forward and reverse primer mix (10 µm each) (1.0 µL). PCR amplification reactions were performed in triplicate. The amplification was carried out using a StepOne Plus Real Time PCR detection system (Applied Biosystems, Foster City, CA, USA) with the following amplification conditions: 3 min at 95 °C, 40 cycles of 95 °C for 15 s, 55 °C for 20 s and 72 °C for 20 s, 1 min at 95 °C, 80 cycles at 55 °C for 10 s with the temperature increase of 0.5 °C after each cycle, and then a hold at 4 °C until plates were removed from the machine. The genes used as endogenous controls to normalize the data for differences in input RNA and efficiency of reverse transcription between the samples were a soybean Fbox gene (Libault et al. 2008), an Arabidopsis actin gene set (An et al. 1996) and a poplar ubiquitin gene (UBQ10b: estExt_fgenesh4_pg.C_LG_I1883; Yang, personal communication). The forward and reverse primers used in the qPCR were designed using Primer Express (Applied Biosystems).

Relative quantitation (RQ) values were determined from the cycle threshold (Ct) values using StepOne software (Applied Biosystems). RQ was calculated for each gene within biological treatment groups, with the initial 25 °C biological treatment group as the reference sample for each plant species. Values >1 represent induction relative to the reference group, and values ≤1 represent repression relative to the reference group.

Experimental design

Data were analysed using the R statistical framework (Team R.D.C. 2007). For gas exchange and biochemical measurements, four to six random individual plants per species were subjected to temperature response curves. Temperature response curves were generated by increasing the growth chamber and gas exchange cuvette temperature in parallel in stepwise (2 °C per 10 min) increments from 22 to 42 °C. Because of growth chamber limitations, temperatures above 42 °C (44 °C was our maximum observation) were created by the gas exchange cuvette. Plants were acclimated to the observation temperature for 20 min, and then monitored for stabilization of photosynthesis using the CV parameter (% coefficient of variation) for conductance and photosynthesis. Stabilization occurred within 10 min for all observations, and tissue was collected immediately after gas exchange observation. The entire experiment was repeated and data are presented as the means of two separate growth chamber experiments (n = 2) ± SE.

For the microarray analysis, fully expanded leaf samples from four randomly collected plants per species were harvested at four physiological states as determined from prior gas exchange measurements (growth temperature – baseline, photosynthetic optimum, 20% inhibition of optimum and 30% inhibition of optimum). Refer to Supporting Information Table S13 for species-specific temperature collections. Induction of temperature response curves and tissue collection were performed as above, except tissue was used for RNA extraction and not biochemical analyses. This design resulted in 16 separate samples per species hybridized to independent microarrays totalling 48 microarrays (4 replicates × 4 physiological states × 3 species). An independent growth chamber study was conducted to validate microarray results using qPCR. To account for differences in day length, we set the photoperiod for all species to the same solar noon, which was 1200 h EST. All samples for microarrays and biochemistry were collected within a 3 h time block centred on solar noon. A single network was constructed for each species from the normalized expression array values (n = 16 per species).

Availability to methods and data

As mentioned earlier, the microarray data used for input into the network algorithms and the R-scripts used to create the networks are available to the reader (Supporting Information Tables S4–S7). In addition, the entire microarray analysis is available as Supporting Information Tables S1–S3. Our raw and processed microarray data are available at the NCBI GEO repository as series GSE26199.

RESULTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Response of steady-state photosynthesis to elevated temperature

Maximum Asat was 27 and 14% greater for soybean relative to Arabidopsis and poplar, respectively (Fig. 1a). The thermal optimum of Asat was determined by fitting a modified four-parameter Gaussian function to the response curves. The temperature optimum for Asat was greatest for soybean (34.3 °C, SE = 0.32), followed by poplar (32.6 °C, SE = 0.57) and Arabidopsis (30.9 °C, SE = 0.24). A linear regression was fitted to the data from optimum to 42 °C to determine the rate of Amax decline. Poplar had the greatest decrease in slope (−1.34, SE = 0.042, r2 = 0.95) followed by Arabidopsis (−1.22, SE = 0.172, r2 = 0.95) and soybean (−0.95, SE = 0.62, r2 = 0.91).

image

Figure 1. Temperature-dependent species-specifc optima of: (a) light-saturated rate of photosynthesis (Asat). Arrows point to calculated optimum for each speices; (b) photosystem II (PSII) electron transport rate (ETR); (c) the ratio of internal CO2 (Ci) to ambient CO2 (Ca); and (d) the dark respiration of Arabidopsis thaliana, Populus and Glycine max. Values are means of two separate growth chamber experiments (±SE), n = 2.

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The estimated proportion of electrons passing through PSII (ETR) as measured by chlorophyll a fluorescence showed a similar, yet slightly more robust response to temperature than that observed for Asat. The optimum for ETR was again greatest for soybean (37.2 °C, SE = 0.43), followed by poplar (34.8 °C, SE = 0.67) and Arabidopsis (33.2 °C, SE = 0.89; Fig. 1b). The ratio of intercellular to ambient CO2 (Ci/Ca) was plotted against temperature and showed little evidence for stomatal limitation (Fig. 1c). Dark respiration showed the typical rise in respiration as leaf temperature increased (Fig. 1d). Increasing and decreasing leaf temperature from 22 to 42 °C indicated no hysteresis in the response of gas exchange or chlorophyll fluorescence parameters (data not shown).

Differential gene expression at distinct states of photosynthetic thermoinhibition

To investigate the core genomic reprogramming underlying the observed photosynthetic decline in response to heat, gene transcripts from leaf samples for all three species were profiled at distinct physiological states of inhibition. Physiological states were defined by percentage of inhibition from optimum (Fig. 2a), and were divided into four physiological states of interest: (1) baseline (the growth temperature); (2) optimum (temperature producing the maximum net CO2 assimilation rate); (3) 20% inhibition from optimum; and (4) 30% inhibition from optimum. Differential gene expression was determined progressively along the temperature response curve using three statistical contrasts: from baseline to temperature optimum, optimum to inhibition 20% and inhibition 20% to inhibition 30% (Fig. 2a,b, data presented in Supporting Information Tables S1–S3).

image

Figure 2. Comparison of differentially expressed transcripts at distinct physiological states of photosynthetic thermoinhibition. (a) Photosynthetic temperature response curves were taken and samples were collected at physiological states of growth temperature (baseline), photosynthetic optimum, 20% inhibition of optimum and 30% inhibition of optimum. Physiological states were analysed as three contrasts (optimum versus baseline, 20% inhibition versus optimum, 30% inhibition versus 20% inhibition) for the determination of differential gene expression. (b) Venn diagrams showing the intersections of total numbers of transcripts induced for the three contrasts of poplar, Arabidopsis and soybean. Note that physiological states were calculated separately for each species and subsequent sampling temperatures are provided in Supporting Information Table S13.

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Comparison of inferred metabolic pathway alterations underlying heat shock

The PageMan (Usadel et al. 2006) and MapMan (Thimm et al. 2004; Usadel et al. 2009b) bioinformatics software packages were used to infer metabolic pathways, cellular processes and hormonal regulation underlying distinct phases of photosynthetic temperature response from transcriptome profiles. Under- and over-represented functional groups are determined based on Fisher's exact test and Wilcoxon rank summary test statistics, and displayed using false colours (Abarca et al. 2001; Usadel et al. 2005, 2006, 2009b). As temperature increased from baseline to photosynthetic optimum, Arabidopsis drastically suppressed transcription of genes participating in photosynthesis, particularly those contributing to PSII (Fig. 3). This is in contrast to soybean and poplar, both of which displayed a clear induction of PSII to optimum. Furthermore, this trend continued through inhibition 20% for poplar. Induction of the abiotic stress pathway was apparent for Arabidopsis and soybean from baseline to inhibition 20%. Poplar exhibited a cyclical trend in the abiotic stress pathway with induction from baseline to optimum followed by pathway suppression at inhibition 20% and induction again at inhibition 30%. Similar trends for protein synthesis pathways were observed for all species with induction from baseline to optimum, followed by repression to inhibition 20% and induction once again to inhibition 30%. Major carbohydrate synthesis pathways were relatively unaltered for Arabidopsis according to this analysis (although one synthesis category was suppressed; Fig. 3), while soybean carbohydrate synthesis pathways were suppressed from optimum to inhibition 30%. Poplar exhibited an unexpected induction in carbohydrate synthesis from optimum to inhibition 20%, followed by repression at inhibition 30%. In soybean, the minor carbohydrate raffinose pathway was induced from baseline to optimum (Fig. 3), and this was mirrored with clear induction of galactinol synthase 1 (GolS1) and raffinose synthase homologs (Supporting Information Fig. S1). Poplar exhibited a similar trend with induction of GolS1 and 2 homologs and raffinose synthase homologs from baseline to optimum. In Arabidopsis, the raffinose/galactinol pathway was induced from optimum to inhibition 20% with elevated expression of GolS1 and 2.

image

Figure 3. PageMan display of selected gene categories for stress-related, and secondary and primary metabolism pathways. An unpaired Wilcoxon rank sum test was used to determine if the median fold change within a particular ontological group is the same as the median fold change of all genes not in that group. Multiple testing was corrected with Bengermani Hochberg. Resultant P values were transformed to z values with P = 0.05 set to 0. False colours are used to distinguish among over-(yellow) and under-(blue) represented categories.

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Co-expression network modules at distinct states of photosynthetic decline

The above analysis is based on organizing genes into functional bins representative of cellular pathways determined from model organisms such as Arabidopsis. Extending these analytical approaches to other species requires genes to be categorized into bins based primarily on sequence homology. Although useful, such an approach may not accurately characterize novel species-specific pathways and could falsely categorize genes that are not yet well assembled and propagate annotation errors. The recent genome duplication of poplar and soybean relative to Arabidopsis, and the potential effects of redundancy on understanding cellular pathways from inferred sequence homology alone illustrate the need to complement this approach with additional analyses.

To address this, we constructed a weighted gene co-expression network for the most differentially expressed (most significant F-test among all three contrasts) 4000 genes along the distinct physiological states of the response curves (see Fig. 2a for sampling schematic). These networks are composed of modules that contain genes with high topological overlap, which is representative of the relationship similarity between the expression of two genes relative to all other genes within the network. Thus, modules contain genes sharing highly correlated expression patterns and are often involved in the same biological function (Barabasi & Oltvai 2004; Subramanian et al. 2005). This has recently been termed the ‘guilt by association’ paradigm, allowing one to infer biological function of genes based on network neighbourhood (Usadel et al. 2009a). R-scripts and input network data for this analysis are available as Supporting Information Tables S4–S7.

The weighted gene co-expression network algorithms generated five modules for Arabidopsis and eight modules for soybean that grouped into two large meta-modules (correlated gene expression patterns) for each species (Fig. 4a,c). The poplar transcriptome generated 10 smaller yet distinct modules (Fig. 4e). Entire network results are reported in Supporting Information Tables S10–S12. The eigenvalue for each module was correlated to treatment to identify patterns of module expression, and thus potential underlying pathway expression to physiological states.

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Figure 4. Weighted gene co-expression network construction and module correlation to physiological state. (a) (Arabidopsis), (c) (soybean) and (e) (poplar) are multi-dimensional scaling plots of the gene co-expression network. Each circle represents a single gene and the colour of the circle corresponds to module designation. The distance between circles is a function of topological overlap and provides a visual representation of gene and module relationships within network. (b) (Arabidopsis), (d) (soybean) and (f) (poplar) report the positive (+) and negative (−) Spearman correlation of the module eigenvalue (y-axis) with the physiological state (x-axis). Symbols indicate ratio was significantly different than zero at P < 0.001 (***), P < 0.01 (**), P < 0.05 (*).

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We expected that modules negatively correlated to baseline and optimum, and positively correlated to photosynthetic inhibition states would contain genes underlying the heat shock response (Fig. 4b,d,f). Significant trends were observed for the Arabidopsis black (permutation t-test, P = 0.027) and blue (permutation t-test, P = 0.0005) modules during 30% inhibition of photosynthesis. Gene characterization analysis using the PageMan over-representation software (Usadel et al. 2006) identified the Arabidopsis black module to be most significantly enriched with genes involved in carbohydrate metabolism (P = 5.8 × 10−3) and lipid metabolism (P = 8.8 × 10−3). The Arabidopsis blue module was most significantly enriched with genes involved in amino acid metabolism (P = 1.5 × 10−20) and protein degradation (P = 2.9 × 10−11).

Contrary to our expectations, the Arabidopsis module eigenvalue most significantly correlated with optimum photosynthesis (brown module; Fig. 4b, permutation t-test, P = 0.0007) was significantly enriched with genes responsive to abiotic stress and heat (P = 1.5 × 10−19). This module contained 15 sHSPs including seven HSP17, two HSP18, four HSP20s, one HSP60s, seven HSP70s, three HSP80s, one HSP90, one HSP101, two HSFs and 12 DNAJs (see Table 1; Supporting Information Table S10). In addition, the eigenvalue of the Arabidopsis brown module significantly correlated with the expression of the known heat shock responsive marker gene, for example, AtHSP18.1-Cl (At5g59720; Pearson cor = 0.932, P = 0.0234) as previously identified (Takahashi, Naito & Komeda 1992; Prandl et al. 1998; Swindell 2006).

The soybean network greenyellow (permutation t-test, P = 0.002) and black (permutation t-test, P = 0.005) modules have significant eigenvalue correlations to 30% inhibition of photosynthesis (Fig. 4d). According to the over-representation analysis, both modules were enriched with genes participating in abiotic stress and heat in particular (greenyellow, P = 5.6 × 10−48; black, P = 4.3 × 10−11). The greenyellow module contains numerous heat shock members including 11 HSP17 family members, three HSP18s, seven HSP20s, three HSP70s and two HSFs (Table 1; Supporting Information Table S11), thereby identifying a clear function for this module in the heat shock response.

The poplar network has a significant correlation between the brown module eigenvalue and 30% inhibition of photosynthesis (permutation t-test, P = 0.024) and was significantly enriched with genes responsive to abiotic stress and heat (P = 1.2 × 10−8). As seen in Table 1 and Supporting Information Table S12, the poplar brown module contains numerous genes with strong homology to Arabidopsis HSPs, including two DNAJ domain containing proteins, three HSP17s, two HSP18s, two HSP70 and one HSP101. In addition, the turquoise module had a significant correlation to 30% inhibition and was enriched with genes participating in abiotic stress (P = 5.0 × 10−5).

Putative expansion of soybean HSP17s

The large number of soybean HSP17s within the heat shock module relative to Arabidopsis and poplar motivated us to perform a more inclusive comparative phylogenetic analysis of this gene family (Fig. 5). It appears that the HSP17s in soybean are greatly expanded relative to Arabidopsis and poplar. We identified 25 genes exhibiting both high identity to Arabidopsis HSP17s and molecular weights in the specified range to include in our analysis (Supporting Information Table S8). This expansion appears to be related to extensive segmental as well as tandem duplication occurring in the soybean genome (http://www.phytozome.net/soybean).

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Figure 5. Evolutionary relationships of 46 HSP17 genes in Arabidopsis, Populus and soybean. Genes for Arabidopsis and poplar are based on Waters et al. 2008. Locus identifiers for soybean are from Phytozome (http://www.phytozome.net/soybean). Phylogenetic analyses were conducted in MEGA4 (Tamura et al. 2007). •, genes that are part of the heat shock network analysis modules in the respective species.

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All of the Arabidopsis HSP17s were components of the heat shock network; only four of the putative poplar HSP17s (following Waters et al. 2008) were in the network, although all were represented on the NimbleGen arrays used for expression analysis. Despite our identification of 72 JGI soybean gene models containing the HSP20 protein domain, only 34 of these had corresponding probes on the Affymetrix array, and only 22 sHSP genes were represented in the network. However, the soybean chip is based on EST data available prior to the release of reference genome sequence, and the assembly and annotation of the reference soybean genome are ongoing. All of the soybean HSP17 genes that had corresponding probes on the Affymetrix microarray were evident in the network modules for heat stress.

Real-time PCR confirmation of HSP17 gene expression

To validate the network findings in expression of the heat shock module to temperature and to confirm HSP17 family member expression, qPCR was conducted on leaf samples collected from an independent growth chamber experiment. Clear differences in expression patterns among species were observed, resulting in two discreet groupings within our hierarchical cluster dendrogram (Supporting Information Fig. S2a). The first cluster group consists entirely of soybean HSP17s and was expressed later in the heat shock response curve at 20 and 30% thermoinhibition, while the second group consisting of Arabidopsis HSP17s began expression at temperature optimum. Because only a few poplar HSP17 genes were observed to be induced during the conditions analysed by the microarray experiment, the results for poplar are inconclusive (Supporting Information Fig. S2b).

Specialization of ROS production and scavenging

Within the identified heat shock modules for each of the respective species, there were several genes annotated for ROS scavenging. In the Arabidopsis brown module, for example, ascorbate peroxidase 2 (APX2; At3g09640), APX1 (At1g07890), copper/zinc SOD 3 (At5g18100) and glutathione binding/transferases (At1g78380, At2g29420, At2g47730) were present (Supporting Information Table S10). Genes encoding antioxidant enzymes were also featured in the poplar brown and soybean black heat shock modules (Supporting Information Tables S11 & S12), suggesting potential co-regulation of heat shock with the antioxidant system.

To investigate this notion further, total H2O2 was measured for all species along the temperature response curve. In Arabidopsis and poplar, H2O2 levels increased with increasing temperature after a slight decline from baseline to optimum (Supporting Information Fig. S3). Maximum H2O2 values were observed at 30% photosynthetic inhibition for both species. In soybean, maximum H2O2 accumulation spiked at optimum photosynthesis (40% over the baseline), and then declined to 10 and ∼20% over baseline at 20 and 30% photosynthetic inhibition, respectively.

The total antioxidant capacity, including enzymatic and non-enzymatic processes, was determined by estimating the rate of oxidation of a fluorescent probe (fluorescein) using the ORAC assay as described by Gillespie et al. (2007). In all species, ORAC increased with an increase in temperature from baseline to optimum (Fig. 6a). Arabidopsis showed the greatest increase in ORAC over baseline (103%) relative to soybean and poplar at 30% inhibition. Unlike poplar and Arabidopsis, there was a slight decrease in ORAC from 20 to 30% inhibition for soybean, suggesting that the antioxidant system was not entirely compromised.

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Figure 6. Effect of heat on reactive oxygen species (ROS) scavenging in leaves of Arabidopsis, soybean and poplar. (a) Oxygen radical absorbance capacity (ORAC), an indicator of antioxidant status of the samples, was measured as Trolox equivalents and expressed as the % of the baseline sample per gram of fresh weight. (b) Total soluble peroxidase activity per milligram of protein measured from the leaf extracts. (c) Total glutathione reductase (GR) activity per milligram of protein measured from the leaf extracts. (d) Total Cu–Zn superoxide dismutase (SOD) activity per milligram of protein measured from the leaf extract. All the values are expressed as percent of the baseline activity derived from means of two separate growth chamber experiments (±SE), n = 2.

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The enzymatic contribution to the plant antioxidant system was determined by measuring the activities of three key ROS-sequestering enzymes: PEROX, GR and SOD. The GR activities for all three species were greatest at 30% inhibition, although trends throughout the curves did vary (Fig. 6b). Poplar and Arabidopsis gradually increased the GR activity, while soybean GR activity displayed a steep increase at optimum followed by a sharp decline at 20% inhibition. The soybean PEROX and GR activities were slightly different, as the highest GR activity was observed at 30% photosynthetic inhibition rather than optimum (Fig. 6b,c). In Arabidopsis and poplar, total PEROX activity decreased from baseline to optimum by ∼12.2 and 8.9%, respectively, while soybean displayed its greatest measured peroxidase activity at optimum (60% relative to baseline; Fig. 6c). Peroxidase activity in soybean declined to near baseline levels and remained relatively unaltered throughout the curve. Arabidopsis and poplar peroxidase activity continued to rise throughout the temperature response curve. Like GR, the SOD activity increased with the increase in temperature in both Arabidopsis and poplar. Alternatively, soybean SOD activity remained relatively unaltered throughout the temperature response curves (Fig. 6d).

DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Considerable variation in temperature-dependent net CO2 assimilation has been reported both among and within species (reviewed by Berry & Björkman 1980). The mechanisms underlying this inhibition cascade through the traditional primary photosynthetic biochemical limitations (e.g. Rubisco, RuBP regeneration – electron transport, Pi) to complex cellular protective networks. Although recent studies have attributed specific roles for HSPs in enhanced electron transport under heat (e.g. Allakhverdiev et al. 2008; Salvucci 2008), we have yet to link these single gene-level findings to underlying cellular protective networks and physiological scales. In this study, we attempt to bridge these scales by using recent advances in network theory and computational biology along with traditional plant physiology methods. This approach allowed us to determine the conservation and specialization of the core genomic networks underlying the photosynthetic temperature response curves among poplar, Arabidopsis and soybean. Our results indicate that soybean has a higher temperature optimum relative to Arabidopsis and poplar when grown under similar conditions. Underlying these photosynthetic results is species-specific variation in abiotic stress and raffinose pathways, HSP17 protein family members and regulation of the heat shock and antioxidant system modules.

What aspects of the heat shock response have diversified among species?

Diversification in the heat shock network

Variation in HSP expression and family member constituents among species has been known for quite some time (Kee & Nobel 1986; Mansfield & Key 1987; Necchi, Pogna & Mapelli 1987). How this variation partitions among species at defined rates of photosynthetic thermoinhibition and interactions with cellular networks is not fully understood. Gene enrichment analysis using the PageMan and MapMan software packages indicated that genes participating in the abiotic heat shock pathway were significantly induced from baseline to photosynthetic optimum for all species relative to other cellular pathways. Direct pathway comparisons among species were complicated because of the difficulties in assigning gene function, which is typically based on gene and translated protein sequence homology. Although an informative initial step enabling gross overviews of cellular processes, homology-based comparison alone may not accurately characterize pathways because of high sequence identity to ancestral homologs that may be undergoing functional diversification. Weighted gene co-expression network analysis groups genes based on the correlation of expression patterns irrespective of sequence homology. This provided putative evidence for the expansion of the HSP17 protein family in soybean relative to Arabidopsis and poplar. Although poplar has nearly twice the number of HSPs17s as Arabidopsis, only three (four if you include Pt16.2) were expressed during heat stress. There is clear evidence that in poplar at least, subfunctionalization may be occurring in paralogous PtHSP17 gene pairs: Pt17.6AI is in the network, Pt17.8AI is not; likewise, Pt17.8BI is in the network and its paralog Pt17.7I is not. In this case, the two functional genes in the heat stress network are on scaffold 1 (corresponding to Chr 1), whereas the non-functioning duplicates are on scaffold 9 (corresponding to Chr 9; data not shown).

There is evidence that putative soybean homologs of At17.6B I (At2g29500.1) are greatly expanded within the soybean genome as a result of both chromosomal and tandem duplication (Supporting Information Table S12 and Fig. S5). There is a large tandemly duplicated region of five HSP17 transcripts on soybean Gm07 (corresponding to Chr 7) and their corresponding chromosomal duplicate transcripts on Gm13, as well as a tandemly duplicated region of three HSP17 transcripts on Gm08, all exhibiting high homology to At2g29500.1, and to a lesser extent, evidence of expansion of putative At17.6 II (At5g12020.1) homologs on several other chromosomes. Poplar also exhibited an expansion of At17.6B I homologs that were significantly induced during heat stress. These putative homologs accounted for 61% of the 18 HSP17s induced under heat stress (data not shown), although only one of these homologs was expressed in the network module. This suggests that poplar may retain the ability to utilize this complex interaction of sHSP to avoid heat shock, but other mechanisms not captured by this study may be just as effective in maintaining thermotolerance. Soybean is estimated to have had two major genome duplication events at 15 million years ago (MYA) and 44 MYA (Shoemaker et al. 2008) since the last estimated duplication in poplar (∼60 MYA; Tuskan et al. 2006), which may account for twice the number of putative HSP17 orthologs. The difficulty in interpreting expression or functionality of these genes is that not all of these duplication events are represented by corresponding probes on the Affymetrix array.

Acquired thermotolerance caused by increased production and accumulation of HSPs in soybean is a well-recognized phenomenon (Lin, Roberts & Key 1984), and the HSP17 family expansion may have a pervasive influence on the soybean heat shock response mechanism. In contrast to HSP60s, HSP70s and HSP90s, the HSP17s are relatively well characterized and have multiples roles in the heat stress response. The chaperone capability of HSP17s has been demonstrated with GFP protection studies in vitro (e.g. At-Hsp17.6A; Sun et al. 2001) and in vivo using protoplasts (e.g. Hsp17.7 and 17.3; Low et al. 2000). In addition to protein refolding and stabilization, HSP17s stabilize heat-stressed membranes in the cyanobacterium Synechocystis (Torok et al. 2001; Tsvetkova et al. 2002). In higher plants, the introduction of carrot HSP17.7 stimulates potato membrane stability (Ahn & Zimmerman 2006) and thermotolerance (Malik et al. 1999). A role for transcriptional regulation was demonstrated (Allakhverdiev et al. 2008) for HSP17.4, which acts as a co-repressor of HSFA2 in tomato (Port et al. 2004). The expansion and maintenance of genes which are apparently involved in multiple roles in the heat shock response may be the result of the slow rate of decay of certain functional categories of genes following a whole-genome duplication (Maere et al. 2005), and may provide a generalist approach to heat shock avoidance in soybean compared to Arabidopsis, with its short life cycle and abundant seed production; and to poplar, which as a perennial may be more resilient to short-term changes in abiotic conditions.

Diversification in the antioxidant system

In Arabidopsis, a network of approximately 152 genes controls the production and scavenging of ROS (Mittler et al. 2004). Our analysis revealed the enrichment of genes encoding ROS-sequestering enzymes within co-expression networks for each of the three species investigated. Although ROS are produced continuously in chloroplasts, mitochondria and peroxisomes as a consequence of normal plant metabolism, heat shock often results in the production of ROS to exceed ROS scavenging and is considered a principal cause of induced damage to photosynthetic machinery (Allakhverdiev et al. 2008). Therefore, efficient mechanisms for ROS scavenging are necessary for photosynthesis and overall plant performance under elevated temperatures. In the current study, network algorithms clustered ROS scavenging and HSP network transcripts into the same module for Arabidopsis and poplar, while soybean ROS-scavenging transcripts were clustered into a separate module from heat shock, suggesting divergence in ROS regulation among these species.

The network-derived findings suggesting species-specific variation in the antioxidant regulation to heat were supported by enzyme activity measures for SOD, GR, PEROX and total antioxidant capacity as estimated by ORAC. In Arabidopsis and poplar, enzyme activities tended to increase gradually as temperature increased. Total antioxidant capacity differed between the two species with poplar displaying a gradual increase, while Arabidopsis had a more disjunct increase, suggesting possible differences in non-enzymatic antioxidant regulation. In soybean, the ORAC estimated total antioxidant capacity gradually increased with temperature, similar to that seen in poplar, but had abrupt stepwise increases and decreases in PEROX and GR activities. Although antioxidant comparisons among poplar, soybean and/or Arabidopsis under heat have not been conducted to our knowledge, they have been investigated individually for Arabidopsis (Panchuk, Volkov & Schoffl 2002; Wachter et al. 2005), poplar (Noctor et al. 1996) and soybean (Malenčić, Popović & Miladinović 2003). These studies and others have reported a complex mode of regulation between the ROS gene networks and its modulation of key biological processes in plants (Miller et al. 2007) with considerable variation within species. For example, in soybean, the SOD activity of 16 varieties varies greatly in response to heat (Malenčićet al. 2003), and Arabidopsis ecotypes have allelic variation in SOD genes conferring differences in abiotic stress tolerance (Abarca et al. 2001). In cotton, genotypic differences in thermotolerance have been linked to antioxidant protection of the photosynthetic machinery (Snider et al. 2010) and support the notion that variation in antioxidant regulation (within or among species) may play an adaptive role for coping with elevated leaf temperature.

Raffinose and galactose pathway

Heat shock has been shown to induce the production of sugars including raffinose and galactinol (Busch et al. 2005). The raffinose family of oligosaccharides has been implicated in the scavenging of hydroxyl radicals (Nishizawa, Yabuta & Shigeoka 2008), protecting liposomes from desiccation through direct sugar–membrane interactions in soybean (Hincha, Zuther & Heyer 2003), and intracellular accumulation under environmental stress in general (see Nishizawa et al. 2008, and citations within). GolS1, which catalyses the first committed step in the synthesis of raffinose polysaccharide (Smith, Kuo & Crawford 1991), has shown to be regulated by HSF (Panikulangara et al. 2004) in Arabidopsis, indicating direct interaction between heat shock and raffinose metabolism. In the current study, induced expression of PtGolS1 and 2, and GmGolS1 was apparent at temperature optimum, while induction of AtGolS1 was observed at photosynthetic thermoinhibition. Induction of hydrolase transcripts (raffinose synthases), which execute a galactosyl transfer to sucrose to form raffinose, mirrored the expression of galactinol synthase, suggesting that the relationship between pathway induction and net CO2 assimilation was species specific.

What is the relationship between the ROS and heat shock cellular protective networks with photosynthetic decline under heat?

The magnitude of photosynthetic thermoinhibition varies considerably both within and among species (Berry & Björkman 1980; Medlyn et al. 2002), and is generally assessed using the three primary biochemical limitations of photosynthesis (Rubisco, RuBP regeneration and/or Pi). Complicating our ability to assign molecular mechanisms to thermoinhibition are the many steps underlying each of these limitations and their potential interactions with cellular protective mechanisms (e.g. heat shock response and ROS scavenging). RuBP regeneration, for example, is affected by electron transport that can be compromised at multiple sites, including the water-splitting complex (Havaux 1993; Havaux & Tardy 1996), leaky thylakoid membranes (Schrader et al. 2004) and activation of cyclic electron flow (Sharkey 2005). Although the exact mechanism(s) of thermal-induced RuBP regeneration limitation is unknown (June, Evans & Farquhar 2004; Sage & Kubien 2007), it has been demonstrated that purified chloroplast-localized HSPs conferred photosynthetic stability to the oxygen evolution complex and thus the electron transport chain of isolated chloroplasts (Heckathorn et al. 1998). These results have been corroborated in vivo by the demonstration of PSII protection from sHSPs for tomato (Neta-Sharir et al. 2005), Agrostis stolonifera (Heckathorn et al. 2002) and Chenopodium album (Barua, Downs & Heckathorn 2003). Furthermore, the chloroplast-localized HSP member cpn60β has been shown to stabilize Rubisco activase at high temperatures (Salvucci 2008). These results suggest a potentially complex interaction between the heat shock chaperone network and primary biochemical limitations of photosynthesis under heat.

Our co-expression approach allowed us to scale genes into functionally relevant network modules, and then correlate module expression to defined physiological states of photosynthetic thermoinhibition. This approach identified clear differences in the regulation and architecture of the heat shock networks among species. In Arabidopsis, the heat shock module was induced at photosynthetic optimum, while poplar and soybean expression occurred later in the heat response curve. These network findings were validated with an independent qPCR experiment and imply that Arabidopsis is more responsive to heat than soybean or poplar. Our data suggest that Arabidopsis uses a feedforward regulation strategy for the heat shock response, where HSPs are produced immediately upon heat exposure. This rapid response results in less certainty in the HSP abundance needed to mitigate heat damage relative to feedback regulation, where HSPs are produced in response to denatured proteins. Feedback regulation has a time lag cost, but is more precise in matching HSP production with heat-induced damage (Angilletta 2009a,b). Shudo, Haccou & Iwasa (2003) have modelled both strategies and found that feedforward should be the dominant form of heat shock regulation except when there is uncertainty in the amount of damage caused by heat. It remains an open question as to how growth strategy (e.g. perennials versus annuals) and adaptation to thermally contrasting environments would influence such model predictions. One could hypothesize, for example, that the vast seasonal heterogeneity in temperature experienced by perennials would increase uncertainty in heat damage, thereby favouring selection for feedback regulation. However, caution must be taken with this interpretation in timing of the heat shock response, because there are relative differences among species between baseline and optimum temperatures.

CONCLUSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Photosynthetic metabolism is known to be highly robust to environmental perturbation through cooperative regulation of cellular processes (Luo et al. 2009). The abiotic stress pathways, including the heat shock response, mediate cellular protective mechanisms contributing to the maintenance of metabolic homeostasis. The mechanisms contributing to plant thermotolerance have been summarized by Wahid et al. (2007) and include membrane stability, ROS scavenging and antioxidant production, induction of mitogen-activated protein kinase (MAPK), calcium-dependent protein kinase (CDPK), and most importantly, chaperone signalling and transcriptional activation. In the current study, we provide a unique glimpse into the transcriptional networks and inferred pathways underlying two of these mechanisms (HSP chaperone, and ROS production and scavenging) among poplar, soybean and Arabidopsis experiencing thermoinhibition of CO2 assimilation. In regard to chaperone mechanisms, our results indicate that there is species-specific plasticity in HSF-mediated signalling for differential expression of HSF, expansion of HSP17 gene family members, interaction with other cellular protective mechanisms and timing in the expression of the heat shock module. In regard to the ROS-driven mechanisms, there is species-specific variation in the network architecture of the heat shock and antioxidant modules between soybean and that of Arabidopsis and poplar. This finding was verified with biochemical assays for ROS production, activity of antioxidant enzymes and total antioxidant capacity. In addition, our transcriptome analysis identified species-specific variation in the expression of the raffinose family of oligosaccharides that has previously been implicated in the scavenging of hydroxyl radicals.

The current study is a pioneering attempt to link transcriptional and biochemical measures to networks underlying physiological traits in a species comparative manner. Enabling this endeavor was the molecular physiology approach and the use of a weighted gene co-expression network analysis. This network approach bypasses multiple testing problems commonly associated with differential gene expression and allows one to infer biological function of genes based on network neighbourhood. Once partitioned into functional modules, correlations can be made from function to whole module expression and physiology, thereby linking single gene findings to networks underlying physiological traits. One challenge with cross-species comparisons involving molecular and physiological observations is the inherent differences in speed of response, sensitivity to temperature and optimal growth temperature. Therefore, caution must be taken when defining physiological state, and interpreting the timing of transcriptional and biochemical events between species exposed to a common treatment.

ACKNOWLEDGMENTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

We thank Dr Elizabeth Ainsworth for assistance in soybean gene annotation, Julia Gouffon from the University of Tennessee Affymetrix Core Lab for aid in Arabidopsis and soybean microarray hybridization and scanning, Dr Stephen DiFazio for the poplar array design, Axel Nagel and the MapMan team for assistance with PageMan and inferred pathway analysis, and Dr David Hyten for soybean seed. Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-000R22725.

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSION
  8. ACKNOWLEDGMENTS
  9. REFERENCES
  10. Supporting Information

Figure S1. MapMan results for raffinose/galactinol pathway.

Figure S2. qPCR results for within network HSP17s.

Figure S3. Estimated H2O2 production in response to heat.

Table S1. Differential gene expression analysis for Arabidopsis ATH1 Affymetrix array.

Table S2. Differential gene expression analysis for soybean Affymetrix array.

Table S3. Differential gene expression analysis for poplar NimbleGen array.

Table S4. R-code tutorial.

Table S5. Network input data for Arabidopsis.

Table S6. Network input data for soybean.

Table S7. Network input data for poplar.

Table S8. Soybean HSP17 used in phylogenetic analysis (Fig. 5).

Table S9. qPCR primer design.

Table S10. Co-expression network results for Arabidopsis.

Table S11. Co-expression network results for soybean.

Table S12. Co-expression network results for poplar.

Table S13. Tissue collection temperatures.

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