The temporal foliar transcriptome of the perennial C3 desert plant Rhazya stricta in its natural environment
© Yates et al.; licensee BioMed Central Ltd. 2014
Received: 17 September 2013
Accepted: 23 December 2013
Published: 4 January 2014
The perennial species Rhazya stricta (R. stricta) grows in arid zones and carries out typical C3 photosynthesis under daily extremes of heat, light intensity and low humidity. In order to identify processes attributable to its adaptation to this harsh environment, we profiled the foliar transcriptome of apical and mature leaves harvested from the field at three time periods of the same day.
Next generation sequencing was used to reconstruct the transcriptome and quantify gene expression. 28018 full length transcript sequences were recovered and 45.4% were differentially expressed (DE) throughout the day. We compared our dataset with microarray experiments in Arabidopsis thaliana (Arabidopsis) and other desert species to identify trends in circadian and stress response profiles between species. 34% of the DE genes were homologous to Arabidopsis circadian-regulated genes. Independent of circadian control, significant overlaps with Arabidopsis genes were observed only with heat and salinity/high light stress-responsive genes. Also, groups of DE genes common to other desert plants species were identified. We identified protein families specific to R. stricta which were found to have diverged from their homologs in other species and which were over -expressed at midday.
This study shows that temporal profiling is essential to assess the significance of genes apparently responsive to abiotic stress. This revealed that in R. stricta, the circadian clock is a major regulator of DE genes, even of those annotated as stress-responsive in other species. This may be an important feature of the adaptation of R. stricta to its extreme but predictable environment. However, the majority of DE genes were not circadian-regulated. Of these, some were common to other desert species and others were distinct to R. stricta, suggesting that they are important for the adaptation of such plants to arid environments.
The greater than 250,000 extant angiosperm species are a consequence of diversification that has driven adaptation to virtually all terrestrial ecosystems [1, 2]. These include many with a wide range of challenging climatic zones compared with those in which we grow our major crop species. Challenging environments include those that have extremes of temperature, low and erratic precipitation and daily flooding with seawater [3, 4]. Such zones have a low diversity of plant species as a consequence of the extensive adaptations required for successful growth and reproduction in such environments –. In these conditions, speciation is driven more by climate and other abiotic factors than by competition with other plant species [6, 9, 10].
Arid zones (60-250 mm of annual rainfall ), have a range of climatic and meteorological features which impact on the vegetation . This ranges from rocky plateaux that permit the growth of only shallow rooted annuals and mesophyte ephemerals to dry river bed wadis that can support deep rooted perennials –. The study of perennial plants adapted to function for extended periods in arid environments could provide rewarding insights to help develop novel traits and genes for crop improvement in the face of abiotic factors such as heat, high light intensities, salinity and low nutrient and water availability . Studies of such plants in their natural extreme environment are a contrast to the many laboratory-based experiments that focus on a single stress .
The advent of massively parallel DNA sequencing technologies and allied developments in bioinformatics has brought genome-scale studies of orphan plant species within the range of many laboratories . Several plant species adapted to extreme environments have been subjected to such investigations. For example, genome sequencing of Thellungiella parvula, a species adapted to a saline and resource poor environment ; transcriptome analysis of the resurrection plant, Craterostigma plantagineum, for dessication tolerance ; a comparative survey of the transcriptome of two mangrove species, Rhizophora mangle and Heritiera littoralis for multiple abiotic stresses and Populus euphratica a desert tree adapted to drought and salinity . A feature of many of these species is that their adaptations are extensive, such as recovery from desiccation in resurrection plants and the morphological and biochemical developments associated with crassulacean acid metabolism (CAM) photosynthesis –. While such features are intrinsically worthy of study, such adaptations do not lend themselves readily to transfer to crop plants because the control of the underlying genes that determine these processes remain unknown.
In this study, we report an initial survey of the transcriptome of the perennial desert plant species Rhazya stricta Decne growing in its natural environment. R. stricta is a glabrous erect perennial evergreen shrub approximately 90 cm high with dense semi-erect branches  (see also Additional file 1). It is native to South Asia and the Middle East and belongs to the family Apocynacae (Asterid clade), which includes species of medicinal value [21, 22]. R. stricta is common in arid zones at elevations of 100-700 m above sea level  and has been found to have improved growth in wadis , suggesting a deep root system, like other arid zone perennial species . It is able to tolerate a wide range of soil mineral salt compositions by accumulating Na+, K+, Ca2+ and Cl- in its leaves .
Our reason for studying the transcriptome of R. stricta is that it has a typical C3 photosynthetic physiology, which is able to function well under typical desert conditions of very high light intensities, temperatures and vapor pressure deficits [23, 24]. The analysis of gene expression patterns may reveal mechanisms associated with stress tolerance and protection of the photosynthetic apparatus. This would identify adaptations that contribute to the success of R. stricta in its arid environment and may be worthy of future study and transfer to C3 crop species. This paper reports our initial survey and interpretation of changes in the patterns of global gene expression at three periods of the day and a comparison of these responses with published studies on transcriptome responses to stress in a range of plant species.
De novo transcriptome reconstruction and annotation
Variation in RNA-seq data between and within plants, quantified as biological coefficient of variation (BCV)
Quantitative PCR validation
Gene ontology analysis
Comparison with Arabidopsis microarray data
Comparison of R. stricta differentially expressed (DE) genes with Arabidopsis microarray datasets
DE homologs (Arabidopsis)
Morning to midday
Midday to dusk
Dusk to Morning
Morning to midday
Midday to dusk
Dusk to morning
DE genes in R. stricta
DE homologs in R. stricta
Salt + Light
Cold + Light
Heat + Light
Heat – circadian
Salt – circadian
Cold – circadian
Light – circadian
Salt + Light – circadian
Cold + Light – circadian
Heat + Light – circadian
Differentially expressed genes in R. stricta discussed in the Results section
LogFC Apical Morning – Midday
LogFC Apical Midday - Dusk
LogFC Apical Dusk – Morning
LogFC Mature Morning - Midday
LogFC Mature Midday - Dusk
LogFC Mature Dusk - Morning
ACC oxidase 1
ACC oxidase 2
Allene oxide cyclase
Allene oxide synthase
circadian clock associated1
cytokinin response factor 2
cytokinin response factor 2
dehydration response element binding factor 2
pseudo response regulators 5
pseudo response regulators 5
pseudo response regulators 7
pseudo response regulators 7
timing of CAB expression1
The Apelta2 (AP2) TF family is large with many sub-groups (Riechmann & Meyerowitz, 1998) which were represented among the DE genes (Table 3). Two genes encoding homologs of RAP2.12 were found (RAP2.12a & RAP2.12b; Table 3) which is an oxygen sensing TF . The expression of both genes decreased during the day (Table 3). Similarly genes encoding two homologs of RAP2.4 were found (RAP2.4a & RAP2.4b) and their transcript abundance decreased from morning to dusk (Table 3). Both genes in Arabidopsis are down-regulated by high light . Genes for members of the cytokinin response factor (CRF) TF families were found to be differentially expressed. Two CRF2 homologs (CRF2a and CRF2b) decreased at midday. Like the gene coding for CRF2, a DREB2 gene homolog was found to increase expression over midday. This class of DREB is associated with osmotic stress .
The group annotated as genes encoding “pseudo” ARRs by Mapman was expanded to look at other circadian clock TF genes. Two homologs coding for PRR5 and PRR7 (PRR5a, PRR5b and PRR7b) were found (Table 3) and all showed a similar trend in differential expression, with peak expression at midday. This was shown for PRR5 in Arabidopsis although PRR7 showed peak expression in the morning . The expression of two other well-characterised circadian clock genes were also investigated TOC1 (two homologs in R. stricta TOC1a and TOC1b) and CCA1. The expression profile of these two genes is the same as that found in Arabidopsis (Table 3) .
Overlap with transcriptomic datasets from P. euphratica and C. plantagineum
Comparison with P. euphratica dataset and pairwise comparisons in R. stricta
Morning – midday
Midday – dusk
Dusk - morning
Morning – midday
Midday – dusk
Dusk - morning
Metallothionein type 3b
ATP-dependent Clp protease ATP-binding subunit clpA
Plastid terminal oxidase
Aluminium induced protein
Putative phospholipase C
Granule-bound starch synthase
Galactinol synthase, isoform GolS
Galactinol synthase, isoform GolS
Ribulose bisphosphate carboxylase/oxygenase activase
1,4-Benzoquinone reductase-like, Trp repressor binding protein-like
Drought responsive ATP-binding motif containing protein
High mobility group B3 protein
Lipid transfer protein
Proline-rich protein/Lipid transfer protein
Putative aquaporin (tonoplast intrinsic protein gamma)
Chlorophyll a/b-binding protein
Orthologous and novel proteins
Table of R. stricta unique genes over expressed at midday in both leaf types
Putative uncharacterized protein
Homolog of mammalian P58IPK
Protein phosphatase 2C family protein
Tetratricopeptide repeat (TPR)-like superfamily protein
Rad23 UV excision repair protein family
Syntaxin/t-SNARE family protein
Ubiquitin family protein
Type I (26 kD) CP29 polypeptide
Histone h1/h5, putative
MAP kinase 20
Cysteine-rich RLK (RECEPTOR-like protein kinase) 8
NADH:ubiquinone oxidoreductase subunit-17.2
Here we present the transcriptome profile of R. stricta quantified by RNA-seq. The transcriptome reconstruction produced 28018 transcripts (Figure 2b) which is similar to the number of genes found in other diploid plant species. For example, Cucumis sativus has ~28,000 genes  Cannabis sativa ~30,000  and Arabidopsis ~31,000 . The average contig length (1643 bp, Figure 2b) suggests the vast majority are full length transcripts since the corresponding values of 1107, 1046 and 1551 bases for C.sativus, C.sativa and Arabidopsis respectively are of a similar size –. In substantiation of the integrity of this assembly, annotation assigned a function to 80% of the contigs (Figure 2c). Also a strong overlap with the sequenced genomes of other Eudicot species was found (Figure 8).
The BCV was found to be 19% within samples and 29% between sampling factors (Table 1). Typically, laboratory based RNA-seq experiments on Arabidopsis report BCVs of ~26% . In general, we interpret this degree of variation in the data, comparable to plants in controlled environments, as reflecting the stability of the environment that R. stricta inhabits for long periods of time. The largest source of variation was the ‘midday’ samples (Table 1). At the outset of our field campaign in September 2011 it was known that VPD, temperature and humidity changed rapidly and reached a maximum 3 hours after dawn and stayed at this plateau for at least 5 hours (Figure 1e-f). This view on the predictability of R. stricta’s environment was reinforced by the same gene expression profiles being observed 12 months later in 2012 (Figure 4). This explains why the multivariate statistics was able to clearly assign RNA-seq data to specific sampling times and development state (Figure 3).
We found significant overlap between Arabidopsis circadian clock- regulated genes and their DE homologs in R. stricta in every pairwise time comparison (Table 2). In Arabidopsis, 30% of expressed genes are circadian regulated [26, 58]. In R. stricta, 29-34% of all DE genes appear circadian regulated based on homologies to Arabidopsis genes regulated by the clock (Table 2). We are not in a position with R. stricta to test if the many homologs to Arabidopsis circadian-regulated genes are subject to this and/or a diel-regulated cue. However, in support of the argument here, we identified homologs of classic clock regulatory genes coding for TOC1, CCA1 and PRRs which showed an expression profile (Table 3) consistent with their Arabidopsis counterparts [42, 43]. Moreover, many of the MYB and BHLH TF genes that were significantly differentially expressed in R. stricta (Figure 7) over a diel are implicated in circadian regulation in Arabidopsis .
As with other species [26, 60, 61], there is a clear adaptive advantage to R. stricta in placing many genes under circadian control. In the predictable environment R. stricta grows in, circadian- or diel-regulated processes can govern the timing of the appearance of expression of large groups of genes, while still remaining potentially responsive to unexpected environmental cues that could occur. This can allow anticipation of regular if extreme changes in the environment by the clock, modulating gene expression and restricting inappropriate responses, thus conferring a fitness benefit –. This reconfiguration of stress-responsive gene expression in advance of changes in the environment may be an adaptive feature of all plant species, even when growing in controlled environments [58, 63, 64]. For example, transcriptome profiling of drought-stressed Arabidopsis plants revealed only 10% of genes were differentially expressed as a consequence of stress imposition, the rest were circadian regulated .
We found increasing expression of NCED and ZEP genes (Table 3) which may drive changes in ABA content during the day consistent with the observed patterns of stomatal closure (Figure 1a; [24, 27, 65]). In addition in Arabidopsis, foliar ABA levels and signaling influence responses to high light as well as relative humidity [66, 67]. Also we have seen no evidence for the significant differential expression of drought-responsive genes. Therefore it is unlikely that changes in the expression of NCED and ZEP genes (Table 3) reflect a response to severe water deficit. ABA levels have been shown to be regulated by the circadian clock, as is the expression of ABA-responsive genes  suggesting that the expression of genes coding for ABA metabolism and action in R. stricta is part of a daily cycle and not a stress response. Interestingly, the genes coding for enzymes involved in cytokinin and ethylene metabolism in Arabidopsis are not circadian regulated [26, 62, 68] and yet show very clear patterns of differential expression over the diel (Table 3). However again, from studies in Arabidopsis, the expression of these genes in R. stricta could be linked to changes in ABA metabolism. For example, cytokinin action has been shown to be mutually regulated with ABA in response to stress . Conversely, the expression of genes of ethylene synthesis enzymes is the inverse of those involved in ABA biosynthesis . The activity of salicylic acid signaling can negatively correlate with ABA signaling in Arabidopsis when subject to pathogen infection [64, 70, 71]. However in R. stricta, the expression of ICS genes increased throughout the day and peaked at dusk (Table 3). While classically associated with signaling to control resistance to biotrophic pathogens, salicylic acid is also important in eliciting thermotolerance  and decreasing susceptibility to photoinhibition induced by high light –, a role for salicylic acid of perhaps greater relevance in R. stricta.
The above considerations comparing R. stricta with Arabidopsis prompt the question ‘do the daily changes in the transcriptome of R. stricta, reflect any stress response?’. Stress is defined as environmental conditions that reduce growth and yield below optimum levels . It can be assumed that R. stricta is well adapted to its environment and the conditions it is normally exposed to, while extreme (Figure 1e-f), are a daily occurrence that fall within its tolerance range. Therefore, the large changes in the expression of many genes that were observed here are most likely not an exceptional response to stress, but part of the daily pattern for this plant. In summary, the large repertoire of DE genes over the day, annotated as stress-responsive or stress-defensive and which in temperate crops would indicate detrimental conditions, are for this species part of its routine diurnal cycle.
Despite the apparent predominance of circadian control of the R. stricta transcriptome, 66-71% of DE genes involved in responses to heat stress and salinity stress do not have circadian regulated homologs in Arabidopsis (Table 2). This could mean again that R. stricta differs from Arabidopsis and has brought these genes under circadian control. However, it may equally not be advantageous to have these genes regulated in this way. Heat stress responses may not be dictated entirely by the external environment but also by the internal leaf environment. The closure and opening of stomata in R. stricta may occur more frequently throughout the day . Consequently, leaf internal temperature would fluctuate caused by changing evapo-transpiration. Furthermore, accompanying restrictions in photosynthesis leading to temporary increases in reactive oxygen species  may trigger changes in the expression of genes usually associated with heat stress. Heat shock protein and heat shock transcription factor gene expression is induced by ROS such as hydrogen peroxide (H2O2) in Arabidopsis and other species – and this may be the case in R. stricta. The potentially variable interactions between leaf internal temperature and ROS content may make it particularly important that such genes do not come under circadian control in this species.
Leaves of R. stricta have genes coding for components of non-photochemical quenching (NPQ) such as the enzymes of the xanthophyll cycle  (Additional file 5) and a homolog of PsbS (NPQ4; ). However, NPQ alone does not provide complete capacity to dissipate excess excitation energy under the types of high light conditions encountered by R. stricta. R. stricta photosynthesis is very resistant to photoinhibition, even at peak PPFDs . To dissipate excess excitation energy under these conditions requires photochemical quenching processes . The most prominent of these processes in R. stricta is photorespiration  and in support of this, the expression of many of the genes encoding enzymes of the photorespiratory cycle can be recognised (Additional file 5). Allied to this are a set of genes coding for enzymes of antioxidant and ROS metabolism (Additional file 5) and other enzymes and pathways that may play roles in photochemical quenching, such as the malate:oxaloacetate cycle (the malate valve; ) and PTOX. Most of the genes discussed here were not differentially expressed (the exception being PTOX and ZEP) and there is no means of assessing whether expression of the protective mechanisms in R. stricta are more highly expressed than in other plant species. Nevertheless, the clear presence of all these protective processes underscores their importance to R. stricta.
The identification of salinity-induced gene homologs in the DE gene set (Table 2) may indicate that at the Bahrah site, the plants were tapping groundwater with a high mineral content like P. euphratica. However, groundwater in wadis is susceptible to changes in flow rate as a consequence of localized rainfall elsewhere in their catchment . Therefore, changes in the mineral content of the groundwater supply to R. stricta may be variable, requiring a more flexible response independent of circadian regulation. This argument suggests that R. stricta experiences conditions in its natural environment reminiscent of those faced by irrigated crop plants. Irrigation does expose plants to water of varying mineral content and responses to salinity are important for crops in this type of agriculture . The group of salinity-responsive genes identified in R. stricta may be worthy of more study in this context.
A considerable number of the DE genes in R. stricta may reflect more specialised adaptations to its desert environment. Highly significant overlaps were detected of R. stricta DE genes with smaller sized datasets from P. euphratica and C. plantigineum (Table 4; [17, 18]). Overlapping of all datasets revealed a set of genes common to these three distantly related species (Table 4), which may reflect common challenges posed by their arid habitats. In particular DE genes coding for senescence-associated multiple cysteine proteinase isoforms  were detected in all three species. However, their expression is also responsive to a range of environmental stresses in other species . Cysteine proteases are most likely to be involved in degradation of misfolded proteins, to maintain protein turnover under stress conditions . Also in common were altered expression of RuBisCo activase (see also ) and raffinose synthesis genes (Table 4; see Results), which have both been found to have protective properties for high temperatures –.
We identified a group of R. stricta-specific protein families (Figure 8) and then used the expression profiles of their genes to identify those that were over-expressed in the middle of the day (Figure 9). The rationale was to identify genes responsive to the increased temperature, light intensities and VPD at midday. Although we focused on this group, the other R. stricta specific-protein families without annotation detail and whose genes were not differentially expressed, may also include among them genes which are important for the adaptation of R.stricta to its environment. This is because these genes must have diversified and duplicated to give rise to a protein family, suggesting an evolutionary advantage. But given the large number of genes identified in this way, it is difficult to speculate on their function and such work is beyond the scope of this research. However of those genes that were differentially expressed, we did identify genes associated with photosynthesis and respiration. Work here and by Lawson et al., has highlighted the resilience of this species’ photosynthesis in this harsh environment. Complementary to this, our analysis may also indicate that modifications to respiration may be part of R.stricta’s adaptation of its primary metabolism. While there are several other genes worthy of comment (see Results), one very interesting gene is that encoding RAD23.The involvement of Arabidopsis RAD23 in the cell cycle  perhaps suggests that this more diversified protein family in R. stricta could be linked to a broader adaptive trait. In particular we see a significant enrichment of developmental GO terms (Figure 5) from the midday onwards. Therefore the apparent modulation of cell cycle and developmental genes in mature leaves could be part of a novel adaptive mechanism in R. stricta. However the work reported here is a transcriptomics study of a species in its natural environment and which, to our knowledge, has not been grown in the laboratory. Therefore, this and other potential novel adaptations in R. stricta await further technical developments to confirm the bioinformatic analyses reported here.
The development of crop genotypes able to withstand greater climate change will require new sources of genetic variation. One source of such variation could be novel alleles in plant species adapted to more extreme environments. R. stricta is a perennial species dominant in its hot arid environment and yet displays a photosynthetic physiology typical of many major crop species . As with all orphan species, identifying genes from R. stricta important for adaptation to its environment poses challenges in annotating novel genes and alleles. Moreover, recent studies on Arabidopsis has shown that determining the temporal pattern of stress responses is critical for distinguishing between genes subject to circadian or diurnal regulation and those responsive to the imposed stress . Therefore, we determined the expression, over a diel, of the R. stricta foliar transcriptome in situ from field samples. We used these comprehensive datasets to evaluate the significance of stress-responsive mechanisms for the adaptation of this species. From early morning to dusk, changes in the transcriptome of R. stricta were extensive, 55% of 23000 quantifiable genes being differentially expressed. The results were highly reproducible with selected genes showing the same expression patterns 12 months later. These data highlight the potential of RNA-seq to investigate orphan plant species in situ with high precision. We found significant (P < 0.01) overlap between all pairwise comparisons of DE genes in R. stricta and Arabidopsis circadian regulated genes . The many genes annotated as stress-responsive from laboratory studies are, in R. stricta, most likely to be subject to circadian regulation and represent a major adaptation to its predictable, if harsh, environment. Of the genes classified as not subject to circadian regulation, significant overlap were found of their expression profiles with genes from Arabidopsis responsive to salinity or combined heat and high light stress. This may reflect more unpredictable changes in the mineral content of groundwater and internal leaf temperature fluctuations. A comparison with more limited datasets from other arid zone species has identified several groups of genes commonly prominent to them all. Finally, we have identified genes which may represent adaptations not hitherto associated with improved tolerance to abiotic stress. Thus our comparative analyses and extensive temporal RNA-seq datasets have identified groups of genes for future study by the plant science community. Moreover this study reinforces that temporal profiling of global gene expression patterns is essential to identify potential stress-adaptive processes in plants.
Leaf material and sampling
All material was collected from a site at N21°26.456’, E39°31.847’ at Bahrah near Jeddah in the Kingdom of Saudi Arabia. Leaves were picked and snap frozen in liquid nitrogen and stored at -80°C until required for extraction. Apical leaves were those originating from the top crown of each branch, while mature leaves were picked from the fourth node down from the crown (Additional file 1). From each bush four apical and mature leaves were collected, one from each branch, i.e. for analysis four replicates were used. The two types of leaves were harvested from seven individual plants at the following times; A, 07:10; E, 12:40; F 13:25; G, 14:05; H, 14:30; L, 18:27 and Q, 10:40. Samples A and L were hereafter termed “morning” and “dusk” respectively. Sample A was collected immediately after sunrise and samples L collected immediately before sunset. For brevity, samples E-H and Q were designated collectively as “midday” samples. Samples A-L were collected on 18/10/2011 and Q 20/11/2011. For plants E and Q only apical leaves and mature leaves respectively were available for analysis.
Leaf gas exchange and photosynthesis
All methods were performed exactly as described by Lawson et al.
Leaf tissue was ground to a fine powder in liquid nitrogen using a pestle and mortar, then ca. 100 mg of tissue was RNA extracted using Trizol® (Life technologies, Carlsbad, USA) as described by . The RNA was then purified using a Spin Column Reaction Cleanup Kit (NBS Biologicals, Huntingdon, UK) as described by the manufacturer. The amount of all samples were determined and their quality checked by electrophoresis through Tris-borate agarose gels (1% v/v; ) and by microfluidics electrophoresis using an Agilent (Agilent technologies, Santa Clara, USA) Bioanalyzer with RNA 6000 Nano Kit according to the manufacturer’s instructions. All RNA samples were stored at -80˚C.
RNA samples were sequenced by Genome Enterprise Limited (GEL) at The Genome Analysis Centre (Norwich, UK) across 8 lanes of an Illumina HiSeq 2000 (Illumina, San Diego USA); using 51 bp paired end reads, insert length ~200 bp. The data was de-multiplexed and quality checked using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) by GEL. In total 1.72 billion paired end reads were returned for analysis which were uploaded to short read archive (SRA) as study SRP028238.
A multi-isolate sample was created using the total RNA samples above and other plant material, collected at the Barah site and a second site. This pool also comprised both apical and mature leaves. From the pooled sample 16 μg RNA was sent to Evrogen Ltd (Moscow, Russia) for library normalisation using service CS010-1A (http://www.evrogen.com/services/cDNA-normalization/service-cdna-normalization_Terms2.shtml) Briefly, total RNA sample was used for double-stranded cDNA synthesis using the SMART approach . SMART-prepared amplified cDNA was then normalized using the duplex-specific nuclease (DSN) normalization method . Normalisation included cDNA denaturation/re-association treatment (DSN, ) and amplification of the normalized fraction by PCR.
454 sequencing and quality checking
The normalised library prepared by Evrogen was sent to the Centre for Genomic Research (University of Liverpool, UK) for sequencing using 454 GS FLX platform (Roche, Basel, Switzerland). Data were quality checked using FastQC, and quality trimmed using clean_reads (http://bioinf.comav.upv.es/clean_reads) and all default settings. All bacterial reads were removed by aligning against all bacterial genomes downloaded from NCBI (ftp://ftp.ncbi.nlm.nih.gov/genomes/Bacteria/). Finally the SMART vector was removed using fastx_clipper (http://hannonlab.cshl.edu/fastx_toolkit/index.html) all sequences uploaded to SRA as study SRP028239.
De novotranscriptome reconstruction
A flow diagram of the assembly process is shown in Figure 2. Briefly, the following steps were carried out:
Three assemblers were used for transcriptome SRP028239 reconstruction; CAP3 (version Date 10/15/07 ), MIRA (version 18.104.22.168, ) and GS De Novo Assembler (version 2.6, Roche Basel, Switzerland) (all default). To merge all three assemblers, CAP3 was used with increased stringency -o 40 and -p 90, to create the 454 super assembly.
The SRP028238 data were divided into twelve pools (per plant and leaf type), then assembled first using Velvet (version 22.214.171.124 ) at two K-mers 37 and 43. Then Oases (version 0.2.0.8 ), was applied to the output from Velvet (both K-mers). Using the output from Oases, a Perl script was designed to select the ‘longest’ transcript when multiple isoforms are produced per locus (see Oases; ). The ‘longest’ contigs from the 37 and 43 k-mer assemblies were then combined using CAP3 (default settings). To merge the twelve individual assemblies a clustering strategy was used where all twelve assemblies were concatenated into a single file. Once concatenated CD-HIT-EST (version 4.5.4 ) was used with the following parameters: aS 0.4 -s 0.7 -aL 0.5. Clusters which had at least three contigs were selected using an in-house Perl script, to create the Illumina super assembly.
The 454 super assembly and Illumina super assembly were concatenated then clustered using CD-HIT-EST default settings (90% similarity). The representative cluster contigs were then split back into the assemblies which they were derived from (454 or Illumina), using an in-house Perl script. The Illumina assembly was then used to make a BLAST  database, to which the 454 contigs were searched using BLAST to find alignments with a minimum E-value 1e-20. 454 clusters without significant BLAST hits were then extracted and concatenated with the Illumina clusters to produce the final transcriptome.
For annotation of the transcriptome the sequences were matched by BLASTX against the UNIPROT (http://www.uniprot.org) database. These were then annotated for functional descriptions and GO terms. Contigs without functional description (or un-annotated) were then screened by BLASTX against canonical Arabidopsis thaliana (Arabidopsis) proteins from TAIR 10  and annotated using TAIR 10 functional descriptions and GO terms. In all cases, BLASTX minimum e-value 1e-15 was used.
Read mapping and transcript quantification
Bowtie (1, version 4.1.2 ) was used for read aligning; the reads were mapped using --best and --strata reporting, maximum 100 mappings per read and maximum insert length 1000 bp. The output Sam file was then directly used to estimate contig expression. This was done using an in-house Perl script and combination of UNIX commands to parse the data, and is freely available upon request. This uses the multi-fraction counting method as described by .
To detect differentially expressed genes, EdgeR was used , the multi-fraction count data were rounded up to whole integers. For the analysis the following GLM model matrix was used (~Leaf + Time:Leaf), the nested model accounts for the unbalanced design of the experiment, where four plants were sampled at midday compared to just one at morning and dusk; by taking into account the effect of each leaf within the sampling time. As part of this method, transcripts with a count per million (CPM) expression profile of >2 CPM in at least four samples were retained. The biological coefficient of variation (BCV) was calculated from the square root of the common dispersion as described by .
Gene ontology analysis
For gene ontology (GO) analysis TopGO was used from Bioconductor in R . This was done for all DE genes, split between over and under-expressed genes identified by EdgeR, for each pairwise comparison. Fishers test was implemented in TopGO to identify enriched GO terms per comparison. From this, all GO terms with a P < 0.01 were selected. Then Cytoscape  was used to visualise the selected GO terms, using Arabidopsis GO network.
To use Mapman , a mapping file was created using the BLAST results described above. This was used to find homologus TAIR identifiers and then parse the corresponding Bin codes from Mapmans Arabidopsis mapping file and convert to R. stricta. For experimental data the log(2)FC values calculated from EdgeR were used. All files are available upon request.
Arabidopsis microarray data comparisons
For comparisons with microarray data in Arabidopsis, the NCBI series GSE41935 was used and the robust multi-array average (RMA; ) values were downloaded. The dataset includes accession Col-0 subjected to a series of single and double abiotic stress experiments . For calculation of DE genes one-way ANOVA was applied for treated vs. control. Homologs between Arabidopsis and R. stricta were identified by BLASTX of R. stricta (nucleotide) against Arabidopsis canonical proteins (filter e < 1e-15). For enrichment test, hypergeometric tests (phyper) were used in R, the results were Bonferroni-corrected (90 tests in total). For circadian genes, a meta-analysis  (the C + E intersection group) was used.
For comparison with P. euphratica data , all ESTs which were differentially expressed were downloaded from Genbank, then screened using BLASTX against TAIR proteins to find homologs. These were then compared in the same way as the Arabidopsis microarray datasets to find overlap. Comparison with C. plantagineum was not possible using homology based comparison as the sequences used by  were not available. Instead these comparisons were made using the functional and GO terms described by .
cDNA was made using First Strand cDNA synthesis (Thermo-scientific, Waltham, USA) as described by the manufacturer, using random hexamers for priming. Quantitative (Q)-PCR reactions were made using Bioline (London, UK) sensiFAST SYBR master mix and performed on a Bio-Rad CFX-96 (Bio-Rad Laboratories, Hercules, USA) as described by the manufacturer. The relative expression software tool (REST-384) was used to calculate the relative fold change in gene expression (http://www.gene-quantification.info/)  using the mathematical model described by Pfaffl . Primer efficiencies were measured using a serial dilution of stock cDNA at 1:1, 1:10 and 1:100. All primers are shown in Additional file 6. R.stricta.10855, ‘Chromatin remodeling complex subunit’ was chosen as the reference gene, this was chosen as it had the lowest variation and stable expression across all samples (mean CPM 5.45 and standard deviation 0.12). Each treatment was replicated with three biological samples, and each biological sample included three technical replicates. In addition, cDNA was also made and QPCR tested from mature leaves collected in September 2012 at the same time periods and location. For all Q-PCR estimates the fold change in gene expression was quantified using the ‘morning’ sample was used as the ‘control’. All calculations were made for each leaf type and year.
Orthologous protein families
To identify protein coding sequences in R. stricta the transcriptome was searched against the A.thaliana (TAIR10) protein database, above, with minimum e-value 1e-5. To identify open reading frames OrfPredictor was used  using the offline Linux version 3.0. All protein sequences are available upon request.
For comparison of orthologous genes, the derived protein sequences for Solanum lycopersicum and Solanum tuberosum were downloaded from Ensembl  using the Perl API tool . To ensure that full length proteins were downloaded once for each gene, canonical proteins were downloaded . Arabidopsis (TAIR10) protein sequences were also used for comparison. To identify orthologous protein sequences OrthoMCL  was used.
Novel differentially expressed genes
To identify protein families unique to R. stricta, the unique protein family proteins identified from orthologous analysis (293 families ~ 623 proteins (Figure 8)) were extracted. The expression patterns were then subject to cluster analysis. The CPM data was first scaled (by subtracting mean across treatments) then Euclidean distance calculated and hierarchical clusters determined for six groups using the ‘Ward’ method using R, ‘hclust’ function.
Statistical analysis and Graphs
All statistical analysis was done using R .
SAY carried out bioinformatics analysis, QPCR and drafted manuscript and IC assisted in script writing for bioinformatics. RAF designed and carried out RNA extraction protocol, UB participated in experimental design and draft of manuscript. MB and , MZM participated in the coordination and sampling of the experimental design. Both NB and JS conceived the study and assisted drafting of manuscript. TL performed and analysed all of the photosynthetic measurements. PMM conceived and coordinated the study and drafted the manuscript. All authors read and approved the final manuscript.
This project was funded by the Deanship of Scientific Research (DSR), King Abdul Aziz University (KAU), Jeddah, Saudi Arabia, under grant number (D 008/431). The authors, therefore, acknowledge with thanks DSR and KAU’s Vice President for Educational affairs (Prof. Dr. Abdulrahman O.Alyoubi) for technical and financial support. Also thanks to; Ahmed Bahieldin, Fotouh El-Domyati, Sharif Idris, Ahmed Shokry and Refaai Hassan. We also thank the two anonymous reviewers for their critical advice which improved the manuscript and analysis.
- Stebbins GL: Why are there so many species of flowering plants?. Bioscience. 1981, 31: 573-577.Google Scholar
- Magallon S, Castillo A: Angiosperm diversification through time. Am J Bot. 2009, 96: 349-365.PubMedGoogle Scholar
- Oh D-H, Dassanayake M, Bohnert HJ, Cheeseman JM: Life at the extreme: lessons from the genome. Genome Biol. 2012, 13: 241-PubMed CentralPubMedGoogle Scholar
- Dassanayake M, Haas JS, Bohnert HJ, Cheeseman JM: Shedding light on an extremophile lifestyle through transcriptomics. New Phytol. 2009, 183: 764-775.PubMedGoogle Scholar
- Colinvaux P: Ecology. New York, USA: Wiley; 1986.Google Scholar
- Ehleringer JR: Annuals and Perennials of Warm Deserts, Physiological Ecology of North American Plant Communities. New York USA: Chapman and Hall;1985:162–179.Google Scholar
- McNeely JA: Biodiversity in arid regions: values and perceptions. J Arid Environ. 2003, 54: 61-70.Google Scholar
- de Bello F, Leps J, Sebastia M-T: Variations in species and functional plant diversity along climatic and grazing gradients. Ecography. 2006, 29: 801-810.Google Scholar
- Crain CM, Bertness MD: Ecosystem engineering across environmental gradients: Implications for conservation and management. Bioscience. 2006, 56: 211-218.Google Scholar
- Menge BA, Olson AM, Dahlhoff EP: Environmental stress, bottom-up effects, and community dynamics: Integrating molecular-physiological and ecological approaches. Integr Comp Biol. 2002, 42: 892-908.PubMedGoogle Scholar
- Noy-Meir I: Desert ecosystems: environment and producers. Annu Rev Ecol Syst. 1973, 4: 25-51.Google Scholar
- Perry RA, Goodall DW: Arid land ecosystems: structure, functioning andmanagement. Cambridge UK: Cambridge University Press; 2009.Google Scholar
- Mulroy TW, Rundel PW: Annual plants - adaptations to desert environments. Bioscience. 1977, 27: 109-114.Google Scholar
- Batanouny KH, Baeshin NA: Plant communities along the medina-badr road across the Hejaz mountains, Saudi Arabia. Vegetatio. 1983, 53: 33-42.Google Scholar
- Hornett EA, Wheat CW: Quantitative RNA-Seq analysis in non-model species: assessing transcriptome assemblies as a scaffold and the utility of evolutionary divergent genomic reference species. BMC Genomics. 2012, 13: 361.PubMed CentralPubMedGoogle Scholar
- Dassanayake M, Oh DH, Haas JS, Hernandez A, Hong H, Ali S, Yun DJ, Bressan RA, Zhu JK, Bohnert HJ, Cheeseman JM: The genome of the extremophile crucifer Thellungiella parvula. Nat Genet. 2011, 43: 913-918.PubMed CentralPubMedGoogle Scholar
- Rodriguez MCS, Edsgärd D, Hussain SS, Alquezar D, Rasmussen M, Gilbert T, Nielsen BH, Bartels D, Mundy J: Transcriptomes of the desiccation-tolerant resurrection plant Craterostigma plantagineum. Plant J. 2010, 63: 212-228.PubMedGoogle Scholar
- Brosché M, Vinocur B, Alatalo ER, Lamminmäki A, Teichmann T, Ottow EA, Djilianov D, Afif D, Bogeat-Triboulot MB, Altman A, Polle A, Dreyer E, Rudd S, Paulin L, Auvinen P, Kangasjärvi J: Gene expression and metabolite profiling of Populus euphratica growing in the Negev desert. Genome Biol. 2005, 6: R101.PubMed CentralPubMedGoogle Scholar
- Cushman JC, Michalowski CB, Bohnert HJ: Developmental control of crassulacean acid metabolism inducibility by salt stress in the common ice plant. Plant Physiol. 1990, 94: 1137-1142.PubMed CentralPubMedGoogle Scholar
- Jafri SKH: Flora of Karachi (coastal west Pakistan). The Book CorporationKarachi. Karachi: Pakistan Book Company; 1966.Google Scholar
- Emad El-Deen HM: Population ecology of Rhazya stricta Decne in western Saudi Arabia. International journal of agriculture & biology. 2005, 7: 932-938.Google Scholar
- Marwat SK, Fazal Ur R, Usman K, Shah SS, Anwar N, Ullah I: A review of phytochemistry, bioactivities and ethno medicinal uses of Rhazya stricta Decsne (Apocynaceae). Afr J Microbiol Res. 2012, 6: 1629-1641.Google Scholar
- Rehman S, Al-Hadhrami LM: Extreme temperature trends on the west coast of Saudi Arabia. Atmospheric and Climate Sciences. 2012, 2: 351-361.Google Scholar
- Lawson T, Davey PA, Yates SA, Bechtold U, Baeshen M, Baeshen N, Sabir J, Mullineaux PM: C3 photosynthesis in the desert plant Rhazya stricta is fully functional at high temperatures and light intensities. New Phytol. 2013, in press: doi:10.1111/nph.12559.Google Scholar
- Rasmussen S, Barah P, Suarez-Rodriguez MC, Bressendorff S, Friis P, Costantino P, Bones AM, Nielsen HB, Mundy JM: Transcriptome responses to combinations of stresses in Arabidopsis. Plant Physiol. 2013, 161: 1783-1794.PubMed CentralPubMedGoogle Scholar
- Covington MF, Maloof JN, Straume M, Kay SA, Harmer SL: Global transcriptome analysis reveals circadian regulation of key pathways in plant growth and development. Genome Biol. 2008, 9: R130.PubMed CentralPubMedGoogle Scholar
- Seo M, Koshiba T: Complex regulation of ABA biosynthesis in plants. Trends Plant Sci. 2002, 7: 41-48.PubMedGoogle Scholar
- Nambara E, Marion-Poll A: Abscisic acid biosynthesis and catabolism. Annu Rev Plant Biol. 2005, 56: 165-185.PubMedGoogle Scholar
- Argueso CT, Hansen M, Kieber JJ: Regulation of ethylene biosynthesis. J Plant Growth Regul. 2007, 26: 92-105.Google Scholar
- Avanci NC, Luche DD, Goldman GH, Goldman MHS: Jasmonates are phytohormones with multiple functions, including plant defense and reproduction. Genet Mol Res. 2010, 9: 484-505.PubMedGoogle Scholar
- Vicente MR-S, Plasencia J: Salicylic acid beyond defence: its role in plant growth and development. J Exp Bot. 2011, 62: 3321-3338.Google Scholar
- Toledo-Ortiz G, Huq E, Quail PH: The Arabidopsis basic/helix-loop-helix transcription factor family. Plant Cell. 2003, 15: 1749-1770.PubMed CentralPubMedGoogle Scholar
- Feller A, Machemer K, Braun EL, Grotewold E: Evolutionary and comparative analysis of MYB and bHLH plant transcription factors. Plant J. 2011, 66: 94-116.PubMedGoogle Scholar
- Kizis D, Lumbreras V, Pages M: Role of AP2/EREBP transcription factors in gene regulation during abiotic stress. Febs Lett. 2001, 498: 187-189.PubMedGoogle Scholar
- Pandey SP, Somssich IE: The role of WRKY transcription factors in plant immunity. Plant Physiol. 2009, 150: 1648-1655.PubMed CentralPubMedGoogle Scholar
- Rushton PJ, Somssich IE, Ringler P, Shen QJ: WRKY transcription factors. Trends Plant Sci. 2010, 15: 247-258.PubMedGoogle Scholar
- Eulgem T, Rushton PJ, Robatzek S, Somssich IE: The WRKY superfamily of plant transcription factors. Trends Plant Sci. 2000, 5: 199-206.PubMedGoogle Scholar
- Kakimoto T: Perception and signal transduction of cytokinins. Annu Rev Plant Biol. 2003, 54: 605-627.PubMedGoogle Scholar
- Licausi F, Kosmacz M, Weits DA, Giuntoli B, Giorgi FM, Voesenek LA, Perata P, van Dongen JT: Oxygen sensing in plants is mediated by an N-end rule pathway for protein destabilization. Nature. 2011, 479: 419-422.PubMedGoogle Scholar
- Lin RC, Park HJ, Wang HY: Role of Arabidopsis RAP2.4 in regulating light- and ethylene-mediated developmental processes and drought stress tolerance. Mol Plant. 2008, 1: 42-57.PubMedGoogle Scholar
- Kim JS, Mizoi J, Yoshida T, Fujita Y, Nakajima J, Ohori T, Todaka D, Nakashima K, Hirayama T, Shinozaki K, Yamaguchi-Shinozaki K: An ABRE promoter sequence is involved in osmotic stress-responsive expression of the DREB2A gene, which encodes a transcription factor regulating drought-inducible genes in Arabidopsis. Plant Cell Physiol. 2011, 52: 2136-2146.PubMedGoogle Scholar
- Nakamichi N, Kiba T, Henriques R, Mizuno T, Chua N-H, Sakakibara H: PSEUDO-RESPONSE REGULATORS 9, 7, and 5 Are transcriptional repressors in the Arabidopsis circadian clock. Plant Cell. 2010, 22: 594-605.PubMed CentralPubMedGoogle Scholar
- Gendron JM, Pruneda-Paz JL, Doherty CJ, Gross AM, Kang SE, Kay SA: Arabidopsis circadian clock protein, TOC1, is a DNA-binding transcription factor. Proc Natl Acad Sci U S A. 2012, 109: 3167-3172.PubMed CentralPubMedGoogle Scholar
- Grudkowska M, Zagdanska B: Multifunctional role of plant cysteine proteinases. Acta Biochim Pol. 2004, 51: 609-624.PubMedGoogle Scholar
- Rokka A, Zhang LX, Aro EM: Rubisco activase: an enzyme with a temperature-dependent dual function?. Plant J. 2001, 25: 463-471.PubMedGoogle Scholar
- Nishizawa A, Yabuta Y, Shigeoka S: Galactinol and raffinose constitute a novel function to protect plants from oxidative damage. Plant Physiol. 2008, 147: 1251-1263.PubMed CentralPubMedGoogle Scholar
- Taji T, Ohsumi C, Iuchi S, Seki M, Kasuga M, Kobayashi M, Yamaguchi-Shinozaki K, Shinozaki K: Important roles of drought- and cold-inducible genes for galactinol synthase in stress tolerance in Arabidopsis thaliana. Plant J. 2002, 4: 417-426.Google Scholar
- Streb P, Josse EM, Gallouet E, Baptist F, Kuntz M, Cornic G: Evidence for alternative electron sinks to photosynthetic carbon assimilation in the high mountain plant species Ranunculus glacialis. Plant Cell Environ. 2005, 28: 1123-1135.Google Scholar
- Laureau C, DE Paepe R, Latouche G, Moreno-Chacón M, Finazzi G, Kuntz M, Cornic G, Streb P: Plastid terminal oxidase (PTOX) has the potential to act as a safety valve for excess excitation energy in the alpine plant species Ranunculus glacialis L. Plant Cell Environ. 2013, 36: 1296-1310.PubMedGoogle Scholar
- Min XJ, Butler G, Storms R, Tsang A: OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 2005, 33 (2): W677-W680.PubMed CentralPubMedGoogle Scholar
- Li L, Stoeckert CJ, Roos DS: OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003, 13: 2178-2189.PubMed CentralPubMedGoogle Scholar
- Farmer LM, Book AJ, Lee KH, Lin YL, Fu H, Vierstra RD: The RAD23 family provides an essential connection between the 26S proteasome and ubiquitylated proteins in Arabidopsis. Plant Cell. 2010, 22: 124-142.PubMed CentralPubMedGoogle Scholar
- Meyer EH, Taylor NL, Millar AH: Resolving and identifying protein components of plant mitochondrial respiratory complexes using three dimensions of gel electrophoresis. J Proteome Res. 2008, 7: 786-794.PubMedGoogle Scholar
- Huang S, Li R, Zhang Z, Li L, Gu X, Fan W, Lucas WJ, Wang X, Xie B, Ni P, Ren Y, Zhu H, Li J, Lin K, Jin W, Fei Z, Li G, Staub J, Kilian A, van der Vossen EA, Wu Y, Guo J, He J, Jia Z, Ren Y, Tian G, Lu Y, Ruan J, Qian W, Wang M, et al: The genome of the cucumber, cucumis sativus L. Nat Genet. 2009, 41: 1275-1281.PubMedGoogle Scholar
- van Bakel H, Stout JM, Cote AG, Tallon CM, Sharpe AG, Hughes TR, Page JE: The draft genome and transcriptome of Cannabis sativa. Genome Biol. 2011, 12: R102.PubMed CentralPubMedGoogle Scholar
- Swarbreck D, Wilks C, Lamesch P, Berardini TZ, Garcia-Hernandez M, Foerster H, Li D, Meyer T, Muller R, Ploetz L, Radenbaugh A, Singh S, Swing V, Tissier C, Zhang P, Huala E: The Arabidopsis Information Resource (TAIR): gene structure and function annotation. Nucleic Acids Res. 2008, 36: D1009-D1014.PubMed CentralPubMedGoogle Scholar
- Robinson MD, McCarthy DJ, Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010, 26: 139-140.PubMed CentralPubMedGoogle Scholar
- Wilkins O, Braeutigam K, Campbell MM: Time of day shapes Arabidopsis drought transcriptomes. Plant J. 2010, 63: 715-727.PubMedGoogle Scholar
- Hanano S, Stracke R, Jakoby M, Merkle T, Domagalska MA, Weisshaar B, Davis SJ: A systematic survey in Arabidopsis thaliana of transcription factors that modulate circadian parameters. BMC Genomics. 2008, 9: 182-PubMed CentralPubMedGoogle Scholar
- Gardner MJ, Hubbard KE, Hotta CT, Dodd AN, Webb AAR: How plants tell the time. Biochem J. 2006, 397: 15-24.PubMed CentralPubMedGoogle Scholar
- Dodd AN, Salathia N, Hall A, Kevei E, Toth R, Nagy F, Hibberd JM, Millar AJ, Webb AAR: Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science. 2005, 309: 630-633.PubMedGoogle Scholar
- Robertson FC, Skeffington AW, Gardner MJ, Webb AAR: Interactions between circadian and hormonal signalling in plants. Plant Mol Biol. 2009, 69: 419-427.PubMedGoogle Scholar
- Sanchez A, Shin J, Davis SJ: Abiotic stress and the plant circadian clock. Plant Signal Behav. 2011, 6: 223-231.PubMed CentralPubMedGoogle Scholar
- Goodspeed D, Chehab EW, Min-Venditti A, Braam J, Covington MF: Arabidopsis synchronizes jasmonate-mediated defense with insect circadian behavior. Proc Natl Acad Sci U S A. 2012, 109: 4674-4677.PubMed CentralPubMedGoogle Scholar
- Schroeder JI, Allen GJ, Hugouvieux V, Kwak JM, Waner D: Guard cell signal transduction. Annu Rev Plant Physiol Plant Mol Biol. 2001, 52: 627-658.PubMedGoogle Scholar
- Galvez-Valdivieso G, Fryer MJ, Lawson T, Slattery K, Truman W, Smirnoff N, Asami T, Davies WJ, Jones AM, Baker NR, Mullineaux PM: The high light response in Arabidopsis involves ABA signaling between vascular and bundle sheath cells. Plant Cell. 2009, 21: 2143-2162.PubMed CentralPubMedGoogle Scholar
- Xie X, Wang Y, Williamson L, Holroyd GH, Tagliavia C, Murchie E, Theobald J, Knight MR, Davies WJ, Leyser HM, Hetherington AM: The identification of genes involved in the stomatal response to reduced atmospheric relative humidity. Curr Biol. 2006, 16: 882-887.PubMedGoogle Scholar
- Mizuno T, Yamashino T: Comparative transcriptome of diurnally oscillating genes and hormone-responsive genes in Arabidopsis thaliana: insight into circadian clock-controlled daily responses to common ambient stresses in plants. Plant Cell Physiol. 2008, 49: 481-487.PubMedGoogle Scholar
- Nishiyama R, Watanabe Y, Fujita Y, Le DT, Kojima M, Werner T, Vankova R, Yamaguchi-Shinozaki K, Shinozaki K, Kakimoto T, Sakakibara H, Schmülling T, Tran LS: Analysis of cytokinin mutants and regulation of cytokinin metabolic genes reveals important regulatory roles of cytokinins in drought, salt and abscisic acid responses, and abscisic acid biosynthesis. Plant Cell. 2011, 23: 2169-2183.PubMed CentralPubMedGoogle Scholar
- Tanaka Y, Sano T, Tamaoki M, Nakajima N, Kondo N, Hasezawa S: Cytokinin and auxin inhibit abscisic acid-induced stornatal closure by enhancing ethylene production in Arabidopsis. J Exp Bot. 2006, 57: 2259-2266.PubMedGoogle Scholar
- De Torres-Zabala M, Truman W, Bennett MH, Lafforgue G, Mansfield JW, Rodriguez Egea P, Bögre L, Grant M: Pseudomonas syringae pv. tomato hijacks the Arabidopsis abscisic acid signalling pathway to cause disease. Ecography. 2007, 26: 1434-1443.Google Scholar
- Dat JF, Foyer CH, Scott IM: Changes in salicylic acid and antioxidants during induction of thermotolerance in mustard seedlings. 1998, 118: 1455-1461.Google Scholar
- Karpinski S, Gabrys H, Mateo A, Karpinska B, Mullineaux PM: Light perception in plant disease defence signalling. Curr Opin Plant Biol. 2003, 6: 390-396.PubMedGoogle Scholar
- Bechtold U, Karpinski S, Mullineaux PM: The influence of the light environment and photosynthesis on oxidative signalling responses in plant-biotrophic pathogen interactions. Plant Cell Environ. 2005, 28: 1046-1055.Google Scholar
- Mateo A, Funck D, Mühlenbock P, Kular B, Mullineaux PM, Karpinski S: Controlled levels of salicylic acid are required for optimal photosynthesis and redox homeostasis. J Exp Bot. 2006, 57: 1795-1807.PubMedGoogle Scholar
- Cramer GR, Urano K, Delrot S, Pezzotti M, Shinozaki K: Effects of abiotic stress on plants: a systems biology perspective. BMC Plant Biol. 2011, 11: 163-PubMed CentralPubMedGoogle Scholar
- Asada K: Production and scavenging of reactive oxygen species in chloroplasts and their functions. Plant Physiol. 2006, 141: 391-396.PubMed CentralPubMedGoogle Scholar
- Swindell WR, Huebner M, Weber AP: Transcriptional profiling of Arabidopsis heat shock proteins and transcription factors reveals extensive overlap between heat and non-heat stress response pathways. BMC Genomics. 2007, 8: 125-PubMed CentralPubMedGoogle Scholar
- Miller G, Mittler R: Could heat shock transcription factors function as hydrogen peroxide sensors in plants?. AnnBot-London. 2006, 98: 279-288.Google Scholar
- Piterková J, Luhova L, Mieslerova B, Lebeda A, Petrivalsky M: Nitric oxide and reactive oxygen species regulate the accumulation of heat shock proteins in tomato leaves in response to heat shock and pathogen infection. Plant Sci. 2013, 207: 57-65.PubMedGoogle Scholar
- Eskling M, Arvidsson PO, Akerlund HE: The xanthophyll cycle, its regulation and components. Physiol Plantarum. 1997, 100: 806-816.Google Scholar
- Li XP, Bjorkman O, Shih C, Grossman AR, Rosenquist M, Jansson S, Niyogi KK: A pigment-binding protein essential for regulation of photosynthetic light harvesting. Nature. 2000, 403: 391-395.PubMedGoogle Scholar
- Eberhard S, Finazzi G, Wollman FA: The dynamics of photosynthesis. Annu Rev Genet. 2008, 42: 463-515.PubMedGoogle Scholar
- Scheibe R: Malate valves to balance cellular energy supply. Physiol Plantarum. 2004, 120: 21-26.Google Scholar
- Hillel D: Salinity management for sustainable irrigation integrating science, environment, and economics. Washington USA: World Bank Publications;2000.Google Scholar
- Roberts IN, Caputo C, Criado MV, Funk C: Senescence?associated proteases in plants. Physiol Plantarum. 2012, 145: 130-139.Google Scholar
- Wagemaker MJ, Welboren W, van der Drift C, Jetten MS, Van Griensven LJ, Op den Camp HJ: The ornithine cycle enzyme arginase from Agaricus bisporus and its role in urea accumulation in fruit bodies. Biochim Biophys Acta. 2005, 168: 107-115.Google Scholar
- Maniatis T, Fritsch EF, Sambrook JT: Molecular Cloning a Laboratory Manual.New York, USA: Cold Spring Harbor Laboratory; 1982.Google Scholar
- Zhu YY, Machleder EM, Chenchik A, Li R, Siebert PD: Reverse transcriptase template switching: a SMART (TM) approach for full-length cDNA library construction. Biotechniques. 2001, 30: 892-897.PubMedGoogle Scholar
- Zhulidov PA, Bogdanova EA, Shcheglov AS, Vagner LL, Khaspekov GL, Kozhemyako VB, Matz MV, Meleshkevitch E, Moroz LL, Lukyanov SA, Shagin DA: Simple cDNA normalization using kamchatka crab duplex-specific nuclease. Nucleic Acids Res. 2004, 32: e37.PubMed CentralPubMedGoogle Scholar
- Shagin DA, Rebrikov DV, Kozhemyako VB, Altshuler IM, Shcheglov AS, Zhulidov PA, Bogdanova EA, Staroverov DB, Rasskazov VA, Lukyanov S: A novel method for SNP detection using a new duplex-specific nuclease from crab hepatopancreas. Genome Res. 2002, 12: 1935-1942.PubMed CentralPubMedGoogle Scholar
- Huang XQ, Madan A: CAP3: A DNA sequence assembly program. Genome Res. 1999, 9: 868-877.PubMed CentralPubMedGoogle Scholar
- Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Müller WE, Wetter T, Suhai S: Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 2004, 14: 147-1159.Google Scholar
- Zerbino DR, Birney E: Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008, 18: 821-829.PubMed CentralPubMedGoogle Scholar
- Schulz MH, Zerbino DR, Vingron M, Birney E: Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012, 28: 1086-1092.PubMed CentralPubMedGoogle Scholar
- Li W, Godzik A: Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006, 22: 1658-1659.PubMedGoogle Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215: 403-410.PubMedGoogle Scholar
- Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10: R25.PubMed CentralPubMedGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat methods. 2008, 5: 621-628.PubMedGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5: R80.PubMed CentralPubMedGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13: 2498-2504.PubMed CentralPubMedGoogle Scholar
- Thimm O, Bläsing O, Gibon Y, Nagel A, Meyer S, Krüger P, Selbig J, Müller LA, Rhee SY, Stitt M: MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004, 37: 914-939.PubMedGoogle Scholar
- Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of affymetrix GeneChip probe level data. Nucleic Acids Res. 2003, 31: e15.PubMed CentralPubMedGoogle Scholar
- Pfaffl MW, Horgan GW, Dempfle L: Relative expression software tool (REST©) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002, 30 (9): e36.PubMed CentralPubMedGoogle Scholar
- Pfaffl MW: A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Res. 2001, 29 (9): e45.PubMed CentralPubMedGoogle Scholar
- Flicek P, Amode MR, Barrell D, Beal K, Brent S, Carvalho-Silva D, Clapham P, Coates G, Fairley S, Fitzgerald S, et al: Ensembl. Nucleic Acids Res. 2012, 40: D84-D90.PubMed CentralPubMedGoogle Scholar
- McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F: Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010, 26 (16).Google Scholar
- Hubbard TJP, Aken BL, Ayling S, Ballester B, Beal K, Bragin E, Brent S, Chen Y, Clarke CP: Ensembl 2009. Nucleic Acids Res. 2009, 37: D690-D697.PubMed CentralPubMedGoogle Scholar
- R Development Core Team: R: A Language and Environment for StatisticalComputing. Vienna, Austria: Foundation for Statistical Computing; 2011.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.