The temporal foliar transcriptome of the perennial C3 desert plant Rhazya stricta in its natural environment

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The temporal foliar transcriptome of the perennial C3 desert plant Rhazya stricta in its natural environment BMC Plant Biology 2014, 14:2


Steven A Yates ([email protected]) Igor Chernukhin ([email protected]) Ruben Alvarez-Fernandez ([email protected]) Ulrike Bechtold ([email protected]) Mohammed Baeshen ([email protected]) Nabih Baeshen ([email protected]) Mohammad Z Mutwakil ([email protected]) Jamal Sabir ([email protected]) Tracy Lawson ([email protected]) Philip M Mullineaux ([email protected])

ISSN Article type

1471-2229 Research article

Submission date

17 September 2013

Acceptance date

23 December 2013

Publication date

4 January 2014

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The temporal foliar transcriptome of the perennial C3 desert plant Rhazya stricta in its natural environment Steven A Yates1 Email: [email protected] Igor Chernukhin1 Email: [email protected] Ruben Alvarez-Fernandez1 Email: [email protected] Ulrike Bechtold1 Email: [email protected] Mohammed Baeshen2 Email: [email protected] Nabih Baeshen2 Email: [email protected] Mohammad Z Mutwakil2 Email: [email protected] Jamal Sabir2 Email: [email protected] Tracy Lawson1 Email: [email protected] Philip M Mullineaux1* * Corresponding author Email: [email protected] 1

School of Biological Sciences, University of Essex, Colchester CO4 3SQ, UK


Department of Biological Sciences, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Kingdom of Saudi Arabia

Abstract Background 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.

Results 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.

Conclusions 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.

Keywords Next generation sequencing, Transcriptomics, Circadian clock, Rhazya stricta, Perennial desert plants, Heat stress, Salinity stress, C3 photosynthesis

Background 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 [5–8]. 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 [11]), have a range of climatic and meteorological features which impact on the vegetation [12]. 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 [12–14]. 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 [12]. Studies of such plants in their natural extreme environment are a contrast to the many laboratory-based experiments that focus on a single stress [3]. 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 [15]. 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 [16]; transcriptome analysis of the resurrection plant, Craterostigma plantagineum, for dessication tolerance [17]; a comparative survey of the transcriptome of two mangrove species, Rhizophora mangle and Heritiera littoralis [4] for multiple abiotic stresses and Populus euphratica a desert tree adapted to drought and salinity [18]. 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 [17–19]. 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 [20] (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 [14] and has been found to have improved growth in wadis [21], suggesting a deep root system, like other arid zone perennial species [12]. It is able to tolerate a wide range of soil mineral salt compositions by accumulating Na+, K+, Ca2+ and Cl- in its leaves [21]. 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.

Results Physiology Figure 1a-d illustrates the diurnal changes in photosynthesis assimilation rate (A), stomatal conductance (Gs), internal CO2 concentration (Ci) and transpiration rate (E) at three measurement periods: morning, midday and dusk. Also shown (Figure 1e-f) is the leaf

temperature and vapour pressure deficit (VPD). Gs was greatest in the morning, and showed a midday depression which was maintained throughout the rest of the day (Figure 1a). A followed the diurnal pattern of irradiance (Figure 1b), with the highest values obtained at midday (despite reduced Gs). Ci was greatly reduced during this period of high assimilation and low Gs suggesting that stomatal control of CO2 uptake limited A. It is noteworthy that E did not mirror GS (Figure 1e and d). This was most likely due to the 18 oC increase in leaf temperature and VPD increase to 6 kPa observed between morning and midday (Figure 1e). Figure 1 Gas exchange measurements. Mean instantaneous measurement of (a) stomatal conductance, (b) assimilation rate, (c) transpiration, (d) internal CO2 concentration (e) leaf temperature and (f) vapour pressure deficit captured on two leaves from 4 different plants. Measurements were taken on mature leaves (dark grey bars) and leaves from the apex (light grey bars) at three different times throughout the day.

De novo transcriptome reconstruction and annotation The de novo transcriptome assembly produced 28018 unique transcript sequences with a mean length of 1643 bp and N50 2199 bp (Figure 2b). 27617 sequences were uploaded to transcriptome shotgun assembly (TSA) Accession GAMW01000000, according to TSA criteria; the full list is available upon request. We were able to assign a functional description and gene ontology to 71.2% (19950) and 68.0% (19046) of the contigs, respectively (Figure 2c). The annotations are provided in Additional file 2 and the mean count per million (CPM) per leaf at each time in Additional file 3, and FDR significance results for 23037 quantifiable genes. Figure 2 Transcriptome reconstruction pipeline and results for assembly process at each stage and annotation metrics. a) Flow diagram of de novo reconstruction using 454 pyrosequencing and Illumina data. Illumina data is shown in blue and 454 in purple, the combined data (hybrid assembly) is shown in orange. b) Summary statistics of each stage of de novo transcriptome reconstruction in R.stricta, uing 454 and Illumina sequencing data. Bars show the number of contigs created with the scale on the primary (left) axis and lines show mean contig length on the secondary (right) axis. The x-axis shows the assembly stages. Names pre-fixed with ‘454’ and ‘Illumina’ designate the source of the data for assembly (purple and blue respectively), ‘Hybrid’ means a combination of 454 and Illumina (orange). The remaining x-axis names show the software/stage used, except ‘Super’ which shows merging of the data. C, annotation metrics of R.stricta de novo transcriptome reconstruction, the layered pie chart shows the source of the annotion; TAIR, green; Uniprot, blue; and unannotated (unknowns) pink. The inner layer shows the source of transcripts annotated, the middle layer shows the proportion annotated with a functional description in solid colour and without in faded colour. The outer layer shows the proportion assigned at least one GO term in solid colour and without in faded colour. For unknowns the inner layer is solid colour to show unannotated and the middle and outer are by default faded as they contain no GO or description.

RNA-seq To calculate expression, all reads were mapped against the de novo reconstructed transcriptome. To estimate the variation both between and within plants the biological coefficient of variation (BCV) was calculated (Table 1). The results show the BCV within plants was low (between 14-22%), but the BCV between plants was relatively greater. The

latter is likely due to the inclusion of four plants, sampled over a four hour time frame. The next step was to use multivariate analysis to determine how well the gene expression profiles distinguished between the sampling time points and factors. This was done using a multi dimension scaling plot (MDS; Figure 3). The plot could clearly be divided into six groups which fitted the time and leaf type sampling factors. The number of quantifiable transcripts (see Methods) was 23037. Of these, 12761 genes were significantly differentially expressed in at least one pairwise comparison. Table 1 Variation in RNA-seq data between and within plants, quantified as biological coefficient of variation (BCV) Factor A1-8 EQ1-8 F1-8 G1-8 H1-8 L1-8 All Midday Nested

BCV 0.17 0.2 0.22 0.14 0.16 0.21 0.19 0.34 0.29

Factors; A1-8, EQ1-8, F1-8, G1-8, H1-8, L1-8 shows BCV within plants when leaf type is taken as a factor. “All” is the same, but all plants and leaf types are considered as factors. For “Midday” this includes only midday samples (as defined in Materials and Methods) and uses leaf type as a factor. For “Nested”, all factors except plant, but leaf type is nested within time. Figure 3 Multi-dimensional scaling (MDS) plot of RNA-seq expression profiles in two dimensions. For visual aid semi-transparent shapes have been overlaid to highlight the groupings of the data. On the right hand side all of the mature leaf samples are plotted (5-8) and on the left all of the apical leaves (1-4), coloured orange and green respectively. Then from top to bottom, the morning samples are at the top (A) (pink), the midday samples in the middle (E,Q,F,G,H) (Blue) and the dusk samples at the bottom (L) (pink).

Quantitative PCR validation Seven genes were validated using QPCR, the results were quantitatively similar to the RNAseq data (Figure 4). Also the results were validated using mature leaf material collected from the following year (2012; Figure 4). The data from the 2012 samples were consistent between RNA-seq and previous QPCR estimates of gene expression from the 2011 samples. Figure 4 Q-PCR validation, results show gene expression estimated by Q-PCR and RNA-seq. Graphs show the log2 fold change in expression relative to morning, for midday and dusk samples. The Results are shown for QPCR estimates from the same samples in red and RNA-seq derived fold change shown in green. Also QPCR results shown from samples collect in the following year are shown in blue, where samples were only collected from mature leaves. The data is split between apical and mature leaves (left and right) and the seven genes tested.

Gene ontology analysis From the GO analysis (Figure 5) two trends were observed over the time course of the sampling. First, GO biological processes that increased throughout the day and then decreased from dusk to morning (Figure 5), included cell wall biogenesis (GO:0042546), pigment accumulation (GO:0043476), UV response (GO:0009411) and cell development (GO:0048468). In contrast response to stress (GO:0006950) and secondary metabolic process (GO:0019748) decreased morning to dusk. In the morning time point, GO terms relating to plastid localization (GO:0051644) and cellular homeostasis (GO:0019725) were over expressed (Figure 5c). As well as the trends described above, there were multiple signaling GO terms recruited throughout the day including protein phosphorylation, RNA modification (GO:0009451), DNA packaging (GO:0006323), hormone transport (GO:0009914) and ion transport (GO:0006810). Figure 5 Map of enriched GO terms at different times of the day, by pairwise comparison. The map shows parent/child connections between GO terms. GO terms are coloured pink, blue and green depending on the enriched expression profile. The GO map is annotated with summary descriptions highlighted by larger circling of GO terms. The GO cluster descriptions colours correspond to the GO terms and which are described in the key. Pairwise comparisons are displayed in a, Morning – Midday; b, Midday – Dusk and c. Dusk – Morning

Comparison with Arabidopsis microarray data On a daily basis, R. stricta is exposed to a number of extreme abiotic challenges (see Introduction). The samples collected in this study represent a limited diel sampling which could include genes associated with responses to abiotic stress in other species. To test this proposition, a comparison was carried out of the R. stricta DE gene sets with microarray data from the response of the transcriptome of Arabidopsis subjected to several single and combined abiotic stresses [25]. For the comparisons of DE genes at the different sampling times and between apical and mature leaves, homologous genes between R. stricta and Arabidopsis were identified and the significance of the intersection of these datasets was calculated. The majority of abiotic stress comparisons were highly significant (Table 2). However, we reasoned instead that since R. stricta is adapted to these extreme conditions (see Introduction), then the apparent significance of this comparison might be misleading. Therefore, a comparison was made between R. stricta DE gene sets and those identified from a circadian regulated gene meta-analysis in Arabidopsis [26]. Assuming that R. stricta is adapted to its harsh habitat then many stress-associated genes could be differentially expressed because they were circadian regulated and not because they were responding to stressful environmental conditions. This is because circadian rhythm would not be disrupted in R. stricta leaves if it is acclimated to these extreme conditions. The results shown in Table 2, show approximately 29-34% of R. stricta DE genes are homologs to circadian regulated genes in Arabidopsis (based on homologous comparisons) and at all time points the comparisons were highly significant (Table 2; P < 0.01). When all circadian regulated genes were removed from the comparisons, the number of significant overlaps reduced, especially in apical leaves (Table 2). In mature leaves, overlaps only with heat, high light + salt and high light + heat Arabidopsis datasets were highly significant (P < 0.01). The genes identified with significant overlap are shown in Additional file 4. In the heat stress comparisons, midday to dusk DE genes encoding several small heat shock proteins and WRKY transcription factors (TFs) were evident. The morning to midday DE gene set overlapping with heat stress

responsive genes included developmental homologs to GIGANTEA and NO POLLEN GERMINATION RELATED1. For the salt + high light from morning to midday a number of homologs to Arabidopsis genes encoding transporter proteins were found (AT5G12080, AT5G11800, AT5G57490, AT4G05120, AT5G15640). In the high light + heat, midday to dusk, four homologs to xyloglucan endotransglucosylase/hydrolases were found, which are involved in cell wall modification. Table 2 Comparison of R. stricta differentially expressed (DE) genes with Arabidopsis microarray datasets Leaf type Treatment DE genes in R. stricta DE homologs in R. stricta Circadian Heat Salt Cold Light Salt + Light Cold + Light Heat + Light Heat – circadian Salt – circadian Cold – circadian Light – circadian Salt + Light – circadian Cold + Light – circadian Heat + Light – circadian

DE homologs (Arabidopsis)

Apical Morning to Midday to Dusk to midday dusk Morning





4611 715 293 464 1049 1153 833 782 504 209

1103 183 83 136 274 269 217 196 126 56









743 130 52 138 221 208 175 148 82 31








480 543


1210 161 77 146 283 263 236 165 99 47











* *** *** ns *** * * ns


6163 ***


Mature Morning to Midday to Dusk to Midday dusk Morning


1803 310 130 201 428 523 333 348 217 89












*** ns *** *** *** *** *** *** ns

8116 6185

1133 182 61 139 296 282 241 202 112 33












ns ns *** *** ns *** ns ns ns

1906 284 119 210 451 476 373 291 185 79


























*** ** *** *** *** ** *** *** *

*** ns *** *** *** *** *** * ns

* ns *** *** *** *** ns ns ns

Circadian genes refer to meta-analysis of circadian regulated genes (Covington et al., 2008). The table shows the Arabidopsis treatment (with or without circadian genes) against two leaf types apical and mature leaves and the pairwise comparisons between time points (morning, midday and dusk). The results show number of homologs and results of hypergeometric test, which is Bonferroni corrected for multiple testing p-values; significance is shown by * < 0.05, ** < 0.01, *** < 0.001 and ns > 0.05. The top row shows number of R. stricta genes DE and the remainder of the table shows numbers of homologous genes between R. stricta and Arabidopsis.

Hormone synthesis To investigate enzymes of hormone metabolism, the program Mapman was used and the results for mature leaves are shown in Figure 6, from which, three trends in the data were observed. First, the expression of genes encoding abscisic acid (ABA) biosynthesis enzymes increased from morning to midday, and then fell. The abundance of transcripts coding for NCED and ZEP peaked at midday. NCED and ZEP catalyse important reactions in the ABA

biosynthetic pathway [27,28]. In contrast, most other DE genes encoding hormone metabolism decreased their abundance from morning to midday and then increased over the day. A closer inspection of the expression of DE genes encoding enzymes of hormone biosynthesis was conducted (Table 3). Transcripts encoding two isoforms of ACC synthase and one ACC oxidase respectively, which are determinants of ethylene synthesis [29] were the inverse of the expression pattern of ABA synthesis genes (Table 3). Transcripts coding for AOS, AOC and OPR3, key enzymes of jasmonic acid (JA) biosynthesis [30] were found. Both AOS and AOC transcripts showed a decrease in abundance from morning through the day, and OPR3 transcript abundance rose over the same period. The isochorismate pathway synthesises salicylic acid in chloroplasts [31]. Annotation revealed two genes encoding ICS genes whose transcript abundance increased from morning to dusk and thus differed in their pattern of expression compared with other hormone biosynthesis genes (Table 3). Figure 6 Mapman diagrams for pairwise comparisons of hormone synthesis gene expression data. (Log(2)FC) at different time points (from – to). Diagram shows comparisons in mature leaves, where blue is increased expression and red is decreased. The data is divided between Mapman Bin codes corresponding hormone synthesis pathway genes, which were identified as being enriched from Mapman Wilcoxon rank sum test. The plots show the hormone synthesis group and the relevant Bin code (17.x) and * to indicated significant enrichment or not. Each square represent s a single gene.

Table 3 Differentially expressed genes in R. stricta discussed in the Results section ID

Group Abv

17395 HS 4650 HS 11855 HS 7628 HS 22227 HS 2272 HS 11383 HS 13155 HS 11166 HS 12704 HS 16795 HS 10633 HS 14357 HS 8521 TF 15403 TF 15603 TF 15532 TF 10837 TF 9003 TF 11103 TF 7261 TF 10590 TF 16151 TF 13680 TF 6139 TF 7222 TF

ACC oxidase 1 ACC oxidase 2


1-aminocyclopropane1-carboxylate oxidase 1-aminocyclopropane1-carboxylate oxidase 1-aminocyclopropaneACC1 1-carboxylate synthase 1-aminocyclopropaneACC2 1-carboxylate synthase AOC Allene oxide cyclase AOS Allene oxide synthase ICS1 Isochorismate synthase ICS2 Isochorismate synthase IPT2 Isopentenyltransferase 9-cis-epoxycarotenoid NCED dioxygenase 12-oxophytodienoate OPR3 reductase ZEP Zeaxanthin epoxidase IPT Isopentenyltransferase circadian clock CCA1 associated1 cytokinin response CRF2a factor 2 cytokinin response CRF2b factor 2 dehydration response DREB2 element binding factor 2 pseudo response PRR5a regulators 5 pseudo response PRR5b regulators 5 pseudo response PRR7a regulators 7 pseudo response PRR7b regulators 7 RAP2.12a RAP2.12 RAP2.12b RAP2.12 RAP2.4a RAP2.4 RAP2.4b RAP2.4 timing of CAB TOC1b expression1

LogFC Apical Morning – Midday

LogFC LogFC LogFC LogFC LogFC Apical Apical Mature Mature Mature Midday Dusk – Morning Midday Dusk - Dusk Morning - Midday - Dusk Morning

-1.92 ***

1.02 ***

0.89 *

-0.97 *** -0.51 ns

1.48 ***

-2.72 ***

2.14 ***

0.58 ns

-3.82 ***

1.05 **

2.77 ***

-0.58 **

-0.29 ns

0.87 **

-1.27 *** -0.22 ns

1.48 ***

-0.33 ns

-0.35 ns

0.69 **

-0.39 *

-0.61 ***

1.01 ***

-1.21 *** 1.31 *** -2.21 *** -0.38 ns 0.93 * 0.41 ns * 0.87 0.39 ns * 0.48 -0.31 ns

-0.09 ns 2.59 *** -1.35 ** -1.26 ** -0.16 ns

-0.81 *** 0.94 *** -0.69 ns -1.18 ns 0.76 ns 0.07 ns ns 0.71 0.03 ns *** 0.90 -0.21 ns 1.26 *

-2.56 ***

-0.13 ns 1.87 * -0.83 ns -0.74 ns -0.70 ** 1.30 ns

0.56 ns

-3.39 ***

1.38 *** -0.54 ns

-0.83 ns

-0.03 ns

0.07 ns

2.62 *** -2.13 *** 3.55 *** -2.14 ***

-0.50 ns -1.41 *

2.26 *** -2.75 *** 0.76 ns -0.52 ns -3.94 *** -5.43 ***

2.83 *** -0.04 ns 0.50 ns -0.25 ns

-4.78 *** -3.80 ***

8.58 ***

9.37 ***

-0.14 ns

-0.45 ns

0.59 ns

1.94 ***

0.11 ns

-2.05 ***

-0.87 **

0.84 **

0.02 ns

-2.48 ***

0.11 ns

2.37 ***

3.66 *** -0.80 ns

-2.86 ***

3.71 *** -1.44 ***

-2.27 ***

1.90 *** -1.41 ***

-0.49 ns

1.73 *** -0.88 ***

-0.85 **

1.87 *** -1.39 ***

-0.49 ns

1.70 *** -0.87 **

-0.83 **

3.48 *** -1.05 ***

-2.43 ***

2.89 *** -0.72 *

-2.18 ***

1.87 *** -1.32 ***

-0.54 ns

1.85 *** -1.83 ***

-0.01 ns

-2.18 *** 0.99 *** -2.18 *** 1.00 *** -0.56 * -0.67 * *** -1.25 0.61 * 0.33 ns

0.08 ns

1.19 *** 1.18 *** 1.23 *** 0.64 ns -0.41 ns

-3.20 *** 1.45 *** -3.24 *** 1.50 *** -1.38 *** -0.42 ns -1.76 *** -0.40 ns 0.07 ns

0.40 *

1.75 *** 1.74 *** 1.79 *** 2.17 *** -0.46 ns

Showing R. stricta contig identifier in first column, a code for section of results discussed (HS, hormone synthesis; TF, transcription factor) in second column, abbreviation (Abv) and description in third and fourth columns respectively. The remaining columns show the log(2) fold change between time points (from – to) within leaf type, along with FDR corrected significance test (
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