- Research article
- Open Access
Transcriptional regulation of the CRK/DUF26 group of Receptor-like protein kinases by ozone and plant hormones in Arabidopsis
- Michael Wrzaczek†1,
- Mikael Brosché†1,
- Jarkko Salojärvi1,
- Saijaliisa Kangasjärvi2,
- Niina Idänheimo1,
- Sophia Mersmann3,
- Silke Robatzek3, 4,
- Stanisław Karpiński5,
- Barbara Karpińska6 and
- Jaakko Kangasjärvi1Email author
© Wrzaczek et al; licensee BioMed Central Ltd. 2010
- Received: 14 October 2009
- Accepted: 25 May 2010
- Published: 25 May 2010
Plant Receptor-like/Pelle kinases (RLK) are a group of conserved signalling components that regulate developmental programs and responses to biotic and abiotic stresses. One of the largest RLK groups is formed by the Domain of Unknown Function 26 (DUF26) RLKs, also called Cysteine-rich Receptor-like Kinases (CRKs), which have been suggested to play important roles in the regulation of pathogen defence and programmed cell death. Despite the vast number of RLKs present in plants, however, only a few of them have been functionally characterized.
We examined the transcriptional regulation of all Arabidopsis CRKs by ozone (O3), high light and pathogen/elicitor treatment - conditions known to induce the production of reactive oxygen species (ROS) in various subcellular compartments. Several CRKs were transcriptionally induced by exposure to O3 but not by light stress. O3 induces an extracellular oxidative burst, whilst light stress leads to ROS production in chloroplasts. Analysis of publicly available microarray data revealed that the transcriptional responses of the CRKs to O3 were very similar to responses to microbes or pathogen-associated molecular patterns (PAMPs). Several mutants altered in hormone biosynthesis or signalling showed changes in basal and O3-induced transcriptional responses.
Combining expression analysis from multiple treatments with mutants altered in hormone biosynthesis or signalling suggest a model in which O3 and salicylic acid (SA) activate separate signaling pathways that exhibit negative crosstalk. Although O3 is classified as an abiotic stress to plants, transcriptional profiling of CRKs showed strong similarities between the O3 and biotic stress responses.
- Reactive Oxygen Species
- Salicylic Acid
- Reactive Oxygen Species Production
- Jasmonic Acid
Receptor-like/Pelle kinases (RLKs) are important components in the regulation of plant development, hormone signalling, abiotic, and biotic stress responses in plants. RLKs are serine-threonine protein kinases that typically contain a signal peptide, a variable extracellular domain, a transmembrane region, and a conserved intracellular protein kinase domain. The extracellular ligand-binding domain perceives signals and is commonly used to classify RLKs into distinct subgroups . The RLKs are one of the largest gene families in Arabidopsis with more than 600 members, [1–4], but only relatively few of them, mostly leucine-rich repeat RLKs (LRR-RLK), have been functionally characterized. CLAVATA1, a LRR-RLK, binds the small extracellular protein CLAVATA3 to regulate meristem proliferation . FERONIA (a member of a previously uncharacterized group of RLKs) is central to the regulation of male-female interactions during pollen tube reception in Arabidopsis  and in Brassica the S-locus Receptor Kinase and its ligand are critical determinants of self-incompatibility [7, 8]. In Arabidopsis, ERECTA (a LRR-RLK) is a multifaceted regulator of development and physiological processes as well as environmental responses . BRASSINOSTEROID INSENSITIVE 1 (BRI1, a LRR-RLK) binds the plant hormone brassinosteroid and dimerizes with BRI1-ASSOCIATED RECEPTOR KINASE 1/SOMATIC EMBRYOGENESIS RECEPTOR KINASE 3 (BAK1/SERK3) [10, 11]. BAK1 also inducibly dimerizes with the RLK FLAGELLIN SENSITIVE 2 (FLS2, a LRR-RLK), which recognizes bacterial flagellin and is important in plant immunity [12, 13]. Other RLKs contributing to pathogen recognition include EFR (the Arabidopsis receptor for EF-Tu) and rice Xa21 (a LRR-RLK), which recognizes a sulfonated peptide produced by the pathogen Xanthomonas oryzae pv. oryzae [14–18].
The DUF26 (Domain of Unknown Function 26; PFAM domain PF01657) RLKs, also known as Cysteine-rich RLKs (CRKs), form a large subgroup of the RLK family with more than 40 members [1, 19]. The extracellular region of the protein contains two copies of the DUF26 domain which has four conserved cysteines (three of them form the motif C-8X-C-2X-C) that may form disulphide bridges as potential targets for thiol redox regulation. The CRKs are transcriptionally induced by oxidative stress, pathogen attack and application of salicylic acid (SA) [19–22]. Accordingly several members of the CRK subgroup of RLKs are involved in the regulation defence reactions and cell death in Arabidopsis leaves. Constitutive over-expression of CRK5 led to increased resistance to the virulent bacterial pathogen Pseudomonas syringae pv. tomato DC3000 but also to enhanced growth of the plant leaves . Over-expression of CRK4, CRK5, CRK19 and CRK20 by a chemically inducible promoter, on the other hand, caused cell death [19, 22]. Genetic analysis suggested that CRK5 regulated cell death independently of SA . Conversely the enhanced resistance to Pseudomonas upon overexpression of CRK13 required increased SA levels .
Reactive oxygen species (ROS) have been established as important signalling molecules for inter- and intracellular communication in plants, animals and yeast [24–26]. ROS are produced in strictly defined locations in reponse to specific stimuli . Pathogen infection rapidly induces an extracellular oxidative burst while light stress and specific chemicals, including paraquat and norflurazon, induce ROS production in the chloroplast [27–29]. Plant cells can differentiate between the type and localization of ROS resulting in very specific responses. Furthermore, ROS production in specific cellular compartments can have impact on ROS generation and signalling in other locations [30, 31]. This crosstalk is likely accomplished through interplay between separate signalling pathways rather than direct interaction of the ROS molecules themselves [30, 31]. However, the molecular components and mechanisms involved are still poorly defined [31, 32]. In addition, it is unknown how ROS are sensed and how specificity in ROS signalling is achieved. The gaseous molecule ozone (O3) induces a burst of ROS in the apoplast similar to the oxidative burst in plant-pathogen interactions . Other similarities between O3 and pathogen infection include the production of SA and ethylene (ET) . O3 is a convenient system to experimentally address the effects of apoplastic ROS since the plant is not exposed to other effector proteins or toxins which might induce defence responses. O3 permits the study of the apoplastic oxidative burst undisturbed by manual manipulation of the plant material.
Plant hormones are a group of unrelated small compounds which are central to signalling during environmental adaptation and developmental regulation [33, 34]. SA, jasmonic acid (JA) and ET are viewed as the main hormonal determinants of plant pathogen defence [35, 36]. Abscisic acid (ABA) modulates plant defence and is a negative regulator of SA responses . In addition, ABA is a key regulator of the high light response . The interaction of hormone and ROS signalling is well documented. ROS can induce cell death in a SA-dependent and independent manner . Cell death and ROS induce ET synthesis, which feeds into a positive forward amplification loop enhancing ROS production . ROS-induced JA is critical in limiting cell death . Thus, the successful outcome of a given response is not determined by one hormone, but is achieved through balance, interaction and constant recalibration of different plant hormones.
Despite extensive research on ROS signalling, the exact components mediating ROS signalling, ROS sensing, and perception in particular are still unknown. Here we have analysed transcriptional regulation and the involvement of hormonal signalling in regulating the expression of the whole Arabidopsis CRK gene subfamily by ROS. The effects of ROS production in different subcellular compartments was analysed by using O3- and light stress treated plant material and publicly available microarray data. We show that O3-induced transcriptional responses are blocked in the defense, no death 1 (dnd1) mutant, and they are altered in hormone biosynthesis or signalling mutants. Collectively this reveals alternate pathways in the regulation of ROS responses.
CRK transcriptional response to O3
Several groups of RLKs are transcriptionally regulated in response to biotic stresses . We identified several CRKs which were differentially regulated by O3 (MB and JK unpublished microarray data). These results suggest a strong transcriptional regulation of the CRKs during stress responses. Therefore we chose to investigate further the transcriptional regulation of the whole CRK subfamily by ROS.
Nomenclature of the CRKs/DUF26 RLKs.
CRK transcriptional response to light stress
CRK transcriptional response to PAMPs is similar to the O3response
Our qPCR analysis confirmed the changes caused by flg22 in the expression profile of the CRKs obtained from publicly available microarray data (Additional file 2 and Figure 4). Treatments with benzothiadiazole S-methylester (BTH; an active SA analog) resulted in two-fold or higher up-regulation of 12 CRKs, some of which also exhibited elevated expression in response to O3. Interestingly, in the non-expressor of pathogenesis-related genes 1 (npr1) mutant these genes were not regulated by BTH treatment (Figure 4), indicating that SA regulation of these genes was dependent on NPR1-mediated signalling. Application of methyl-jasmonate (MeJA) did not cause any major changes in CRK expression (Figure 4), whilst ABA treatment resulted in decreased expression of CRK25, CRK30, CRK28, CRK29, CRK19, CRK21 and CRK22 at late time points. Overall, the CRK expression profile in response to BTH clustered together with that triggered by O3, pathogen and PAMP treatments; whereas CRK transcriptional regulation upon ABA application clustered together with paraquat, norflurazon, rotenone and MeJA treatments (Figure 4).
Taken together, these results demonstrate that the CRK expression profile in response to O3 is not related to treatments which mediate ROS production in the chloroplast or the mitochondria. However, there is a substantial overlap between the transcriptional responses to O3 and pathogen infection/PAMP perception, which may be a result of apoplastic ROS commonly generated by all these stimuli.
CRKs display different expression in hormone mutants
To test the robustness of gene expression in this set of hormone mutants, we compared two different growth conditions. These differed in photoperiod, light composition and intensity, soil composition and humidity (see Materials and Methods for a detailed description of the differences in the growth conditions), subsequently referred to as Weiss chamber (Figure 5A) and Phytotron (Figure 5B). Notably, the dnd1 mutant did not grow under Phytotron conditions. The higher transcript abundance of CRKs in ein2 and fad3/7/8 observed in plants grown under Weiss chamber growth conditions was largely absent in plants grown under Phytotron growth conditions (Figure 5B). Moreover, the CRKs which showed higher gene expression in npr1 under Weiss chamber growth conditions, were unaltered (or had even reduced transcript levels) in the Phytotron. Taken together, these results indicate that hormones play a major role in the transcriptional regulation of many CRKs. However, environmental growth conditions also have a large impact on the extent of this regulation especially in soil grown plants [49, 50].
O3-response of the CRKs in hormone mutants
Light stress response of the CRKs in hormone mutants
CRK promoter analysis
Motifs overrepresented in the promoters of the CRK family.
CRKs O3 up
CRKs O3 up up
ACACNNG(+/-) × TTGAC(+)
DPBF1&2 × W-box
CRKs O3 up
ACACNNG(-) × ACTTTG(+)
DPBF1&2 × T-box
CRKs O3 up
ACACNNG(-) × TTGAC(+)
DPBF1&2 × W-box
CRKs O3 up
A [AC]C [AT]A [AC]C(-) × TTGAC(+)
MYB4 × W-box
CRKs O3 up
CAACA(-) × TTGAC(+)
RAV1-A × W-box
CRKs O3 up
CAACA(-) × TTGAC(-)
RAV1-A × W-box
CRKs O3 up
ACACNNG(-) × A [AC]C [AT]A [AC]C(-)
DPBF1&2 × MYB4
CRKs O3 up
ACACNNG(-) × TTGAC(-)
DPBF1&2 × W-box
CRKs O3 up
A [AC]C [AT]A [AC]C(-) × TTGAC(-)
MYB4 × W-box
ACACNNG(+/-) × TTGAC(+)
DPBF1&2 × W-box
ACTTTG(+/-) × TTGAC(-)
T-box × W-box
ACACNNG(-) × TTGAC(+)
DPBF1&2 × W-box
GATAAG(-) × AAATTAGT(+)
Ibox × BS2
CAACA(-) × TTGAC(+)
RAV1-A × W-box
CAACA(-) × TTGAC(-)
RAV1-A × W-box
ACACNNG(-) × TTGAC(-)
DPBF1&2 × W-box
GATAAG(-) × ACTAATTT(-)
Ibox × BS3
A [AC]C [AT]A [AC]C(-) × TTGAC(-)
MAB4 × W-box
The RLK family is one of the largest gene families in the Arabidopsis thaliana genome. Several RLKs have previously been described to be involved in plant-microbe interactions [14, 15, 57–59] and abiotic stress [60, 61]. Based on statistical analysis of gene expression data, RLKs in general, as well as the CRK subfamily, are more likely to have altered expression in response to abiotic and biotic stress than other Arabidopsis genes [40, 62]. We analyzed the expression profile of the CRKs in detail using qPCR and array analysis under various stresses, growth conditions, and in different genetic backgrounds to obtain a better understanding of the signalling pathways leading to transcriptional regulation of the CRKs and to elucidate the role of apoplastic ROS in stress signalling.
The use of ROS as signalling molecules is a common feature of many stress responses . Pathogen attack and perception of PAMPs are often associated with an oxidative burst in the apoplast . Similarly, a hallmark of the early O3 response is the generation of an oxidative burst in the apoplast . ROS are also produced in other subcellular compartments, including the chloroplast, where light stress or treatments with the herbicides paraquat or norflurazon elicit elevated ROS production. In addition, crosstalk between pathways elicited by apoplastic ROS and chloroplast-derived ROS is important for the regulation of cell death . The transcriptional response to apoplastic ROS, e.g. induced by O3, is strikingly different from chloroplast-derived ROS, e.g., induced by paraquat . To further dissect the role of apoplastic ROS, we clustered several treatments triggering ROS production in distinct subcellular compartments together with various biotic stress experiments. Our results showed that the CRK expression profile upon O3 exposure was most similar to those stimulated by PAMP perception (flg22 and HrpZ) and pathogen infection (Bgh) (Figure 4). By contrast, treatments, which increased ROS levels in the chloroplast (norflurazon and paraquat) or mitochondria (rotenone; which might also lead to ROS production in the chloroplast ) either had no effect on CRK gene expression or resulted in down-regulation. These results show that transcriptional induction of the CRKs can be triggered by apoplastic ROS, whereas chloroplastic ROS mainly lead to decreased expression. Furthermore, cluster analysis separated the effects of plant hormones: BTH (SA analog) caused a similar expression profile as O3 and PAMP treatments, whereas CRK expression in response to ABA and MeJA was related to norflurazon and paraquat treatments.
To extend the microarray meta-analysis, transcript accumulation of the CRK subfamily was monitored in response to O3 and light stress by qPCR. Out of 44 CRKs, 32 showed increased expression after exposure to O3 at both time points while five members exhibited decreased expression. Light stress treatment led to a decrease in expression of the majority of the CRKs. Thus, in agreement with the results from array analysis, ROS production in different cellular compartments produces strikingly different transcriptional profiles on the CRK gene subfamily.
To further dissect the O3 response, mutants deficient in biosynthesis, perception and signalling of SA (sid2, npr1), JA (fad3/7/8) and ET (ein2) were exposed to O3 and the expression of a subset of CRKs was analyzed by qPCR. The O3-induced increase in transcript levels of the CRKs was higher in sid2 and npr1 implying that SA acts as a negative regulator of the ROS signalling pathway. The O3-mediated transcriptional induction of CRKs was almost abolished in fad3/7/8 and attenuated in ein2, suggesting that JA, and to a lesser extent ET are required for the proper transcriptional induction of CRKs in response to O3. This role for SA, JA and ET in O3 signalling has been previously proposed based on the results from cDNA macroarray analysis . The effect of light stress on the CRK expression in various mutant backgrounds was very different compared to the effect of the O3 response. Whereas ET acts as positive regulator of CRK expression in the O3 response, it appears to be a negative regulator in light stress since several CRKs displayed light stress-induced expression only in the ein2 mutant (Figure 8). Under light stress conditions, the decreased expression of CRKs seen in wild type was even more pronounced in the SA mutants sid2 and npr1 and the JA mutant fad3/7/8.
DND1 encodes CYCLIC NUCLEOTIDE GATED CHANNEL2 (CNGC2) which transports Ca2+ into the cell and regulates nitric oxide production . The complete lack of an effect of O3 on CRK and marker gene expression in dnd1 suggests an important role for CNGC2 in the O3 response pathway, possibly by regulating Ca2+ levels (Figure 6 and 7B). Previous studies have shown that O3 rapidly invokes Ca2+ transients [68, 69] and blocking of Ca2+ transport can prevent ROS-induced cell death . The dnd1 mutant also has several pleiotropic phenotypes which include elevated SA levels and constitutive defence responses . Consequently, the lack of O3 response in dnd1 could be due to "dominance" of SA signaling over the ROS signalling pathway, and O3 would have no effect when the SA pathway is fully stimulated. Previous reports have shown that several members of the CRK subfamily were transcriptionally induced through an external application of SA  or BTH (Figure 4). The response of CRKs to BTH was completely blocked in npr1, indicating that the SA pathway for regulating CRKs requires NPR1.
Intriguingly, different growth conditions had a strong impact on the expression of CRKs in various mutants. Several CRKs were expressed to higher levels in ein2 and fad3/7/8 in Weiss chamber-grown plants compared to Phytotron-grown plants. In contrast, the decreased expression of several CRKs in sid2 and npr1 was similar between two different growth conditions (Weiss chamber and Phytotron, Figure 5). A strong effect of environmental conditions on mutant phenotypes, transcript profiles and other parameters are well known and a common problem when comparing results from different laboratories . There could be several reasons for the differences in the expression levels of the CRKs between the Weiss chambers and the Phytotron growth conditions. Plants were tested at slightly different ages and grown in different soil (see materials and methods section). Illumination in the Weiss chambers was provided using fluorescent lamps while in lighting in the Phytotron was using metal halide lamps with different light spectra. Notably, the CRKs are responsive to UV-B . This suggests that light conditions could have an effect on the expression profile of this RLK family. Another reason for this variation of gene expression could be that under control conditions most CRKs were expressed at very low levels; consequently, a minor perturbation either by genetic mutation or growth condition could lead to altered expression. Thus, expression of CRKs is very sensitive to the surrounding environment. Similar observations have been reported for the expression of the classical PDF1.2 marker gene [49, 50]. This gene has long been used to exemplify co-regulation by JA/ET. However, PDF1.2 is only regulated by both hormones when plants are grown in vitro . When plants are grown in soil, either hormone alone (JA or ET) is sufficient to induce expression. Thus, growth in soil is able to induce or prime defence signalling pathways.
Is it possible to separate the roles of chloroplastic and apoplastic ROS in the regulation of CRK expression? Chloroplast-derived ROS production is known to be involved in the regulation of cell death during pathogen infection and in response to abiotic stress [74, 75]. Specific removal of chloroplastic ROS prevents pathogen-induced cell death but has no impact on defence gene expression . Furthermore, chloroplastic 1O2 regulates cell death dependent on EXECUTER1 . In comparison, apoplastic ROS might be involved with intra- and intercellular signalling . Thus, apoplastic ROS would have a role in regulating defence gene expression and chloroplastic ROS in regulation of cell death. In addition, there is crosstalk between apoplastic ROS and chloroplast ROS; rapid ROS production in the chloroplast can be detected in response to O3 and blocking of ROS production in the chloroplast reduces O3-induced cell death [32, 77]. Clearly, ROS regulation of defence signalling and/or cell death is very complex and several other regulatory components have been identified, including LESION SIMULATING DISEASE 1 (LSD1), ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1) and PHYTOALEXIN DEFICIENT 4 (PAD4), which are also involved in acclimation to light stress [42, 78]. The only known phenotypes for CRKs have been obtained by ectopic overexpression, which induces HR-like cell death independent or dependent on SA signalling (depending on the specific CRK) [22, 23]. How this induction of cell death might be achieved is still unclear since transcriptional regulation of CRKs occurs in response to apoplastic rather than chloroplastic ROS. Some members of the RLK family might participate in a positive feed-forward loop to regulate ROS production, defence gene expression, cell death and hormone signalling. This regulatory loop might be deregulated after overexpression of the CRKs leading to the observed cell death phenotypes. However, this will require experimental verification in the future.
What is the role of CRKs in plants and why are they regulated by PAMPs and O3 treatment? The external domain of these RLKs could be the receptor for as yet uncharacterized PAMPs and they could be part of plant immune responses. An intriguing feature of the DUF26 domain is the presence of a conserved cysteine motif C-8X-C-2X-C. The configuration of cysteines is similar to the cysteine motif in the GRIM REAPER protein, which has been shown to be involved in the regulation of ROS induced cell death . Despite the ubiquitous role of ROS as signalling molecules in plants, no direct receptor for ROS has been described. Since cysteines are sensitive to redox modifications, could the DUF26 domain act as sensor of ROS in the apoplast and be the putative ROS sensor as depicted in Figure 9?
Plant growth conditions and treatments
Weiss chamber growth conditions
For exposure to O3, Arabidopsis thaliana Col-0 or mutant plants were grown in a peat/vermiculite (1:1) mixture for 21 days in Weiss 1300 growth cabinets (photon flux density 250 μmol m-2 sec-1; tubular fluorescent lamps) under 12 hours day length (day: 23°C 70% relative humidity; night 18°C 90% relative humidity). Lights were switched on at 7 AM and off at 7 PM. O3 treatments were started at 9 AM. 21-day old plants were used and exposed to 250 parts per billion (ppb) O3 for 6 hours. Samples were harvested at the times indicated in the respective experiments after the onset of the O3 treatment. Samples were taken in parallel from O3 treated and clean air control plants and immediately shock-frozen in liquid nitrogen.
Phytotron growth conditions
For light stress treatments, plants were grown on a pre-fertilized garden soil/vermiculite (1:1) mixture for 28 days under 8 h/16 h light/dark at 22 or 20°C, respectively, and 50% humidity at a light intensity of 130 μmol m-2 sec-1 photon flux density (Metal halide lamps). For light stress treatment, plants were shifted to 1300 μmol m-2 sec-1 photon flux density for up to 4 hours. Subsequently, plants were returned to a light intensity of 130 μmol m-2 sec-1 photons. Controls were kept at 130 μmol photon flux density throughout the duration of the treatment and samples were taken in parallel with the light stress-treated plants. Samples were harvested at the times indicated in the respective experiments after the onset of the light stress treatment and immediately shock-frozen in liquid nitrogen.
For flg22 treatments, plants were grown on MS plates with Nitsch vitamins (MSN). After 7 days, seedlings were transferred to liquid MSN media and cultivated for 7 days. Before the flg22 treatment, fresh medium was added. After a 1 hour recovery period, the seedlings were treated with 100 nM flg22. Controls were treated with H2O. Samples were harvested at the times indicated in the respective experiments after the onset of the treatment and in parallel from corresponding controls and immediately shock-frozen in liquid nitrogen.
RNA extraction and qPCR analysis
RNA was isolated as described . 5 μg total RNA was DNaseI treated (Fermentas) and used for cDNA synthesis with RevertAid Premium Reverse Transcriptase (Fermentas) and Ribolock RNase Inhibitor (Fermentas) according to manufacturers' instructions. The reaction was diluted to a final volume of 50 μl and 1 μl cDNA was used as template for PCR using LightCycler 480 SYBR Green I master mix (Roche Diagnostics) on a LightCycler 480 (Roche Diagnostics) in triplicate. Primer sequences and the primer amplification efficiency (E x ; determined according to manufacturers instructions) are available in additional file 4.
For the normalization of the data several genes were evaluated to select a suitable gene for normalization based on the method of Vandesompele et al. . Actin-2 (At3g18780) was found to be stably expressed in control and ozone treated plants and was subsequently used for normalization. The raw Ct values were normalized to Actin-2 and used to compare the results from untreated control samples with treated samples using the 2-ΔΔCtmethod. The resulting normalized cycle differences were used to calculate the average (μ) and standard deviation (σ) of the biological repeats and the p-value (using SPSS) based on . The p-value was calculated using the one-sample t-test in SPSS and calibrated using the Benjamini-Hochberg false discovery rate (FDR) correction . The 95% confidence intervals (CI±; lower and upper bound) were calculated according to , where E x is the efficiency of the reaction x. The μ, σ, CI and p-value for all qPCR experiments are shown in additional file 1. The mean μ of the normalized cycle difference was used to calculate the fold-change of expression using E x (Additional file 4).
RLK kinase domains were identified using PrositeScan http://au.expasy.org/tools/scanprosite/. Sequence alignments were performed using the ClustalW2 program . Neighbour-joining trees were constructed with 1000 bootstrap sets using the Mega4 software package .
Affymetrix raw data was downloaded from NASCArrays http://affymetrix.arabidopsis.info/narrays/experimentbrowse.pl (accession number NASCARRAYS-143, paraquat; NASCARRAYS-353, ZAT12; NASCARRAYS-176, ABA time course experiment 1; NASCARRAYS-192, Ibuprofen), ArrayExpress http://www.ebi.ac.uk/microarray-as/ae/(accession numbers E-GEOD-12856, Blumeria graminis sp. hordei; E-GEOD-5684, Botrytis cinerea; E-ATMX-13, Methyl Jasmonate; E-MEXP-739, Syringolin A; E-MEXP-1797, Rotenone), Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/(accession numbers GSE5615, Elicitors LPS, HrpZ, Flg22 and NPP1; GSE5685, Virulent and avirulent Pseudomonas syringae:; GSE9955, BTH experiment 1, GDS417 E. cichoracearum; GSE5530, H2O2; GSE5722, O3; GSE12887, Norflurazon; GSE10732, OPDA and Phytoprostane; GSE7112, ABA experiment 2) and The Integrated Microarray Database System http://ausubellab.mgh.harvard.edu/imds (Experiment name: BTH time course, BTH experiment 2).
The raw Affymetrix data was preprocessed with RMA using probe set annotations (custom cdf files) from http://brainarray.mbni.med.umich.edu/, version 11.0.1. Biological repeats of each experiment were combined by computing a mean of the measured gene expression. Gene expression was summarized by computing a log2 ratio of the treatment and control expressions (differential expression, DE). A visualization of the DE values is shown in Figure 4. Variation of differential expression in an experiment e, , was estimated by summing the variances of (logarithm of) treatment and control gene expressions.
Parametric bootstrapping was implemented by generating 1000 samples for each experiment and each gene from a Gaussian distribution with the estimated DE as the mean and as the variance.
Bootstrap samples were discretized to down regulated (log2 DE < -1), no regulation (-1 < log2 DE < 1), and up regulated (log2 DE > 1) genes. Bayesian agglomerative hierarchical clustering algorithm was then applied to the discretized bootstrap data. The Bayesian hierarchical clustering algorithm computes the best number of clusters by Bayesian hypothesis testing. For each pair of genes (and experiments, depending on the clustering direction), the number of times they were assigned to the same cluster was computed. These gene (or experiment) similarities were then used as distances for computing the hierarchical clustering (ward method) shown in Figure 4.
TAIR 9 version of promoter sequences of 500 bases and 1000 bases upstream of the Arabidopsis genes was downloaded from http://www.arabidopsis.org/. A list of verified Arabidopsis promoter elements was taken from http://arabidopsis.med.ohio-state.edu/AtcisDB/bindingsites.html. The set of CRKs was divided into three groups (all, ozone up-regulated and ozone down-regulated) and the plus and minus strands of the promoters were searched for significant enrichment of single promoter elements or a combination of two promoter elements in either of the strands. Fisher exact test with false discovery rate correction (q-values; ) was used for measuring the significance of the enrichment; q-value of 0.05 was used as the threshold.
We would like to thank Dr. Sarah Coleman, Dr. Pinja Jaspers, Dr. Adrien Gauthier, Dr. Kirk Overmyer and Dr. Jorma Vahala for feedback, discussion and critical reading of the manuscript. Tuomas Puukko is thanked for excellent technical assistance. The work was supported by the Finnish Academy Centre of Excellence Programme (2006-2011). MW is supported by a Helsinki University Postdoctoral Researcher Grant. MB was supported by an Academy of Finland Post-doctoral grant (# 108760). SLK is supported by the Academy of Finland project # 130595. SK is financed from Welcome 2008/1 project operated within the Foundation for Polish Science Welcome Program co-financed by the European Regional Development Fund. SM is supported by the International Max-Planck-Research School (IMPRS). The project was carried out within the framework of the ERA-PG research program in the PROSIG consortium with funding from the Academy of Finland and the Deutsche Forschungsgemeinschaft.
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