Plant Growth Regul DOI 10.1007/s10725-015-0126-y
ORIGINAL PAPER
Comparative transcriptome profiling of Arabidopsis Col-0 in responses to heat stress under different light conditions Junyi Song1 • Qijun Liu1 • Biru Hu1 • Wenjian Wu1,2
Received: 8 August 2015 / Accepted: 24 September 2015 Ó Springer Science+Business Media Dordrecht 2015
Abstract Light and temperature are two of the most important environmental stimuli regulating plant development. Studies have revealed that light and temperature synergically regulate numerous developmental and metabolic processes. In this study, we adopted microarray and real-time PCR to identify common components via the analysis of Arabidopsis heat stress (HS) response under light and dark conditions. It certificated the existence of crosstalk between light and temperature signaling pathways. The analysis of microarray revealed that variable— light—influenced plant HS response in a great extent. Furthermore, various genes, e.g., two phytochrome genes (PHYA and PHYB) and one transcriptional factor gene (HY5), were recognized as light-responsive elements, and answered to HS. The expression levels of three genes rhythmically showed up-and-down oscillations after HS. These results can help to understand how plants perceive temperature signal and identify the interrelationship between signal transduction pathways. Keywords Heat stress Transcriptome responses Light signaling Crosstalk
Junyi Song and Qijun Liu have contributed equally to this work.
Electronic supplementary material The online version of this article (doi:10.1007/s10725-015-0126-y) contains supplementary material, which is available to authorized users. & Biru Hu
[email protected] 1
College of Science, National University of Defense Technology, Changsha, Hunan, China
2
State Key Lab on NBC Protection for Civilian, Beijing, China
Introduction To survive an ever-changing environment, plants have to correspondingly alter their development in response to environmental cues or stresses. This tight coordination for the surrounding environment was achieved through the complex integration of multiple stimuli (Franklin 2009; Heggie and Halliday 2005), especially for light and temperature (Su et al. 2014). Light was essential for growth and development of normal plant, as a source of energy and a stimulus regulating developmental and metabolic processes (Gilmartin et al. 1990; Hasegawa et al. 2004; Vinterhalter and Vinterhalter 2014). Light signals provided plants with spatial, temporal and seasonal information. This information was coded into light quantity (fluence), quality (wavelength), direction, and duration (Jiao et al. 2007). Plants, by using this information, could regulate multiple developmental processes throughout their lifecycle (Dodd et al. 2005). Temperature is another key physical parameter affecting organisms on Earth (Hu et al. 2012; Xin et al. 2015). Almost all living beings have evolved signaling pathways to sense mild changes in ambient temperature and adjust their metabolism and cell functions. It prevented heat-related damages (Mittler et al. 2012). Intensity, duration and rate of temperature change determined its impact. Temperature stress dramatically changed the fluidity of the cell membrane and the structure of nucleic acids and proteins. It also changed the concentration of metabolites and osmolytes inducing the production of ROS (reactive oxygen species) (Zinn et al. 2010). For quite a long time, studies of mechanisms and the influences of light and temperature were almost independent (Loveys et al. 2002). However, even the tiniest
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physiological process in plants required the regulation of multiple surrounding environmental signals (Rasmussen et al. 2013). Light and temperature, ubiquitous environmental factors and essential regulators for plants growth and development, can simultaneously work on plants in many cases. For example, they do synergically in mediating seeds germination, elongation growth and flowering in plants. To germinate at the appropriate time of year, plants have evolved complicated and precise ability to perceive light (Joseph et al. 2014) and temperature (Song et al. 2013) conditions in environment; light-grown hypocotyls could be dramatically mediated by high temperatures (Nemhauser et al. 2004) and light (Liu et al. 2013), through the regulation of auxin synthesis and transportation (Laxmi et al. 2008; Kurtyka et al. 2012); plants reproduced and flowered at favorable times of the year by measuring seasonal changes in photoperiod (Higuchi et al. 2012). Meanwhile, this light-dependent physiological phenomenon was interweaved with temperature (Song et al. 2012; Thines et al. 2014). Moreover, Larkindale (2005) reported that multiple signaling pathways, including UV-sensitive responses, were involved in thermo-tolerance aquisition in Arabidopsis mutants (Larkindale 2005); Swindell et al. (2007) revealed the extensive overlap existed between heat and non-heat stress response pathways (Swindell et al. 2007); Rasmussen et al. (2013) showed that the combination of stresses (like heat and high light intensity), compared with a single stress treatment, had unpredictable effects on Arabidopsis (Rasmussen et al. 2013). Nevertheless, none of these studies focused on cross-talk of light and temperature signaling or the identification of common signaling components in both pathways. The photoinhibition of germination (Hofmann 2014; Oh et al. 2013), photo-regulation of hypocotyl elongation (Oh et al. 2013) and flowering (Higuchi et al. 2012) were mediated by phytochrome in blue light and red/far red light conditions. The signal-transducing networks regulated by phytochrome have been established by researchers (McWatters and Devlin 2011). In contrast to light signaling, the molecular mechanisms, underlying plant temperature perception and controlling the above-mentioned processes, were poorly understood (Franklin 2009). Plants responded to small changes in temperature dramatically, yet how plants perceive temperature signal was still unknown. Early components of signal transduction pathway of temperature also remained a mystery (Penfield 2008). The work used microarray, real-time PCR and bio-informatics to identify common signaling components via the analysis of Arabidopsis heat stress response under light and dark conditions. It explored the tight connection between light and temperature signaling pathways.
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Results and discussion HS induced regulation in host gene expression Differentially expressed genes (DEGs) between control and treated were identified, using one-way ANOVA by Affymetrix company. Their results showed that there were 1852 and 792 differentially regulated genes (twofold or greater change) induced by HS under light and dark conditions, respectively. While we used open-source packages provided by bioconductor, the numbers of differentially regulated genes were 1597 and 787 [twofold or greater change, t test, p B 0.05, as described in Ref. Hanssen et al. (2011)], which was the subset of company’s results. Therefore we selected the data obtained from bioconductor to undergo subsequent analysis. Before data analysis, a qPCR test was conducted to validate the microarray results (under light condition). qPCR results revealed a high correlation (R2 = 0.9528, and p value = 0.0002) between these two experiments (see Fig. 3b). It indicated that these data were credible (see Table S2.1). Transcriptional factors were essential for regulation of gene expression. In this study, 76 and 52 DEGs encoding transcriptional factors were identified under light and dark conditions, respectively. They belonged to diverse families, such as Zinc finger, MYB, bZIP, CBF, ERF, WRKY, bHLH and HSF (see Table S2.2). Compared with the total number of DEGs between the two groups, a great part of the differentials overlapped (see Fig. 1a). Through regression analysis of these 571 genes, it
A Without Light :
With Light : 1,597 genes responded
1026
571
216
to HS significantly
to HS significantly
B
787 genes responded
With Light
Without Light
Fig. 1 DEGs under HS treatment. a The Venn diagram displaying the overlaps in all DEGs. b Induced and repressed genes in response to HS
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was found that there was a significant (R2 = 0.8960 and p value = 7.7759 9 10-282) linear correlation between two groups (see Table S2.3). This suggested that the HS response of plants in these two experiments were similar to some extent despite the difference on light conditions. Nevertheless, it was noted that under light condition, the number of HS regulated-genes doubled, compared with regulated-genes in plants grown in dark (see Fig. 1b). This difference in response intensity to HS was also reflected by the ration between up- and down-regulated genes, which accounted for 639/959 and 392/395 under light and dark conditions, respectively. Host gene repression was more pronounced than host gene induction under light condition. While under dark condition, the number of up-regulated genes equaled to the number of down-regulated genes (see Fig. 1b). It showed that light could be an essential factor influencing or even manipulating signal transduction and metabolism in plants during the process of HS response. To reflect the differences between groups, a Hierarchical clustering (HCL) was conducted on the combination of differentially regulated genes monitored with the array in two experiments (light and dark). HCL of these genes (see Fig. 2) revealed that the expression profiles were clustered by both HS treatment and light condition. This once again demonstrated that plants responded to HS significantly, substantially affected by light.
Fig. 2 Transcriptional regulation of 1597 genes represented on the Arabidopsis Genechip in the interactions of Arabidopsis with HS (with or without light). 1–3, Samples treated with HS and light. 4–5,
Therefore, the outcome of DEGs analysis in this study was consistent with previous reports that heat and non-heat response pathways overlapped to each other (Larkindale 2005; Rasmussen et al. 2013; Swindell et al. 2007). However, the information about their junctions and common components was still limited. Thus, a functional analysis on these DEGs is necessary. Gene ontology enrichment analysis of HS-regulated genes To determine whether these DEGs were functionally involved in certain biological processes and identify the impact of light on HS response, we classified them into groups according to their gene ontology (GO) annotation. A gene ontology enrichment analysis (GOEA) aimed at establishing if differentially regulated genes significantly appeared in some GO terms related with the biological processes involved in Arabidopsis. The GOEA of our microarray data strongly suggested that HS invoked numerous biological processes in plants (see Table S3.1.1). Table 1 showed plants response to HS varied under light and dark conditions. E.g., under light condition, genes regulated by HS significantly (p B 0.0001) was enriched in processes like enzyme linked receptor protein signaling pathway (GO:0007167), transmembrane
Samples treated without HS, and with light. 7–9, Samples treated with HS, and without light. 10–12, Samples treated without HS and light
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Plant Growth Regul Table 1 GOEA of genes that are significantly responded to HS treatment under light and dark conditions (p B 0.0001)
ID
Gene ontology subcategory (biological process)
pa
HS under light condition GO:0009408
Response to heat
2.65E-35
GO:0009266
Response to temperature stimulus
8.23E-25
GO:0050896
Response to stimulus
2.21E-23
GO:0009628
Response to abiotic stimulus
1.07E-19
GO:0006950
Response to stress
4.52E-18
GO:0042221
Response to chemical stimulus
8.29E-14
GO:1901700
Response to oxygen-containing compound
7.64E-13
GO:0006457
Protein folding
3.26E-12
GO:0009644
Response to high light intensity
2.45E-10
GO:0010033
Response to organic substance
3.83E-08
GO:0009719
Response to endogenous stimulus
6.32E-08
GO:0009642
Response to light intensity
6.91E-08
GO:0010200
Response to chitin
4.31E-07
GO:0009416 GO:0010243
Response to light stimulus Response to organic nitrogen
7.77E-07 9.09E-07
GO:0007154
Cell communication
1.43E-06
GO:0009314
Response to radiation
2.65E-06
GO:0044700
Single organism signaling
3.12E-06
GO:0023052
Signaling
3.20E-06
GO:0007165
Signal transduction
4.45E-06
GO:0010286
Heat acclimation
8.14E-06
GO:0007167
Enzyme linked receptor protein signaling pathway
8.33E-06
GO:0007169
Transmembrane receptor protein tyrosine kinase signaling pathway
8.33E-06
GO:1901698
Response to nitrogen compound
2.63E-05
GO:0051716
Cellular response to stimulus
3.13E-05
GO:0009725
Response to hormone stimulus
3.63E-05
HS under dark condition
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GO:0009408
Response to heat
4.74E-43
GO:0009266
Response to temperature stimulus
4.23E-30
GO:0009628 GO:0050896
Response to abiotic stimulus Response to stimulus
4.12E-23 3.70E-19
GO:0006950
Response to stress
1.34E-18
GO:0042221
Response to chemical stimulus
9.93E-18
GO:0009644
Response to high light intensity
1.65E-16
GO:1901700
Response to oxygen-containing compound
1.85E-14
GO:0006457
Protein folding
1.01E-12
GO:0009642
Response to light intensity
2.20E-12
GO:0006334
Nucleosome assembly
4.60E-12
GO:0034728
Nucleosome organization
4.60E-12
GO:0031497
Chromatin assembly
7.91E-12
GO:0065004
Protein-DNA complex assembly
9.78E-12
GO:0071824
Protein-DNA complex subunit organization
9.78E-12
GO:0006323
DNA packaging
5.49E-11
GO:0042542
Response to hydrogen peroxide
2.10E-10
GO:0006979 GO:0010035
Response to oxidative stress Response to inorganic substance
4.51E-10 4.54E-10
GO:0009416
Response to light stimulus
5.10E-10
GO:0009314
Response to radiation
1.60E-09
GO:0009719
Response to endogenous stimulus
1.68E-09
Plant Growth Regul Table 1 continued
ID
Gene ontology subcategory (biological process)
pa
GO:0006333
Chromatin assembly or disassembly
1.92E-09
GO:0000302
Response to reactive oxygen species
6.86E-09
GO:0071103
DNA conformation change
1.78E-08
GO:0010033
Response to organic substance
2.76E-08
GO:0009725
Response to hormone stimulus
1.20E-07
GO:0010286
Heat acclimation
2.81E-07
GO:0034622
Cellular macromolecular complex assembly
5.32E-06
GO:0065003 GO:0010200
Macromolecular complex assembly Response to chitin
5.68E-06 1.13E-05
GO:0010243
Response to organic nitrogen
4.45E-05
GO:0043933
Macromolecular complex subunit organization
8.43E-05
a
False discovery rate
receptor protein tyrosine kinase signaling pathway (GO:0007169) and response to nitrogen compound (GO:1901698). Meanwhile, genes involved in processes, e.g., DNA conformation change (GO:0071103) and chromatin assembly or disassembly (GO:0006333) were markedly (p B 0.0001) regulated by HS under dark condition instead of light condition. It implied under different light conditions, the signaling pathways respond to HS differed: several pathways answered to HS under light condition only, while some to HS under dark condition only. However, it was also found that there was a high similarity between these two situations. Genes responded to HS in both two experiments were enriched in biological processes such as response to heat (GO:0009408), response to temperature stimulus (GO:0009266), response to abiotic stress (GO:0009628), response to hormone stimulus (GO:0009725), response to light stimulus (GO:0009416) and response to light intensity (0009642). As a major environmental factor for plants, it was not strange that high temperature would have influence on metabolic processes of plants in large scale. What interested us was that under both light and dark conditions, many DEGs regulated by HS treatment were enriched in light-related pathways (such as GO:0009644 and GO:0009642).
Analysis on genes enriched in ‘‘response to light stimulus’’ process Table S3.2.2A showed the lists of 71 light-related genes responded to HS under light condition; Table S3.2.2C showed the lists of 51 light-related genes responded to HS under dark condition. What was worth mentioning, 38 genes were shared by these two tables (data in Table S3.2.2B). It showed that there was a high similarity between HS response under light and dark conditions.
Since GOEA ignored induction or suppression amplitudes, HCL on all light-responsive genes, monitored with the microarray, was used to cluster genes and treatments within GOEA categories. It was based on gene expression profiles to further study the behavior of these genes. HCL of these genes revealed that the expression profiles were clustered by HS treatment and light condition (see Fig. 3). Thus the same conclusion was obtained as that in Fig. 2: Plants responded to HS significantly, substantially affected by light. Moreover, the KEGG analysis of DEGs also revealed that light signaling was severely repressed when encountered with HS under light condition (see Fig. 4). The transcript abundance of genes, such as PHYB, HY5, APRR5, CCA1, TOC1, FKF1 and APRR3, were repressed, while the abundance of CDF1 was induced. The mRNA abundance reduction of PHYB and HY5 demonstrated that the photomorphogenesis in plants was undermined (McWatters and Devlin 2011); the inhibition of PRR5, CCA1 and TOC1 expression indicated that the circadian rhythm was interfered (Wang et al. 2010); while the decreased FKF1 and the increased CDF mRNA level illustrated that the flowering process in Arabidopsis was dampened by HS (Li et al. 2013). Under dark condition, the DEGs induced by HS were not enriched in this pathway. Abiotic ambient factors such as light and temperature are interconnected with each other in phytohormone signaling/biosynthesis/transduction processes (Chen and Chory 2011; Franklin and Quail 2010). Several reports have also supported the fact that the temperature- and lightresponse were somehow related. Plants’ responses to high temperature and low red to far-red (R-FR) ratio, which mimicked natural canopy signals, were highly similar. PhyA, PhyB and cytochrome photoreceptor proteins have been reported to be required for the appropriate induction of heat-induced hyponastic growth in plants (van Zanten et al. 2009, 2012). Therefore, our study revealed the
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Fig. 3 Transcriptional regulation of 84 regulated genes which are involved in light stimulus response. 1–3, Samples treated without HS and light. 4–5, Samples treated with HS, and without light. 7–9,
Samples treated with HS and light. 10–12, Samples treated without HS, and with light
Fig. 4 KEGG analysis of DEGs involved in plant circadian rhythm regulation (map04712, obtained from http://www.kegg.jp/). The map04712 located several genes which were involved in the process
of circadian rhythem regulation. In this study, the expression of genes like PHYB, PRR3/5, CCA1, TOC1, FKF1, HY5 were repressed, while the transcript abundance of CDF1 was enhanced
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expression of PHYB and HY5 were decreased and increased respectively with array analysis (the significant expression differences was confirmed by real-time PCR). PhyB was the major red light photoreceptor in Arabidopsis that regulated plant growth and development by perceiving, interpreting and transducing light signals (Quail 2002; Staiger et al. 2003). The elongated HYPOCOTYL5 (Hy5) bZIP protein, an integrator of multiple signaling pathways, also played an important role in photomorphogenic growth and light-regulated gene expression (McWatters and Devlin 2011). Here, the HS-induced response of these light signaling components certificated the tight relationship between light and temperature signaling pathways in plants. Therefore, it was reasonable that Arabidopsis response to HS was influenced so much by light condition in this study. Besides, given the mighty regulation function of these two genes (or the proteins they coded), they might be the key nodes shared by both signaling pathways, and bonds linking up two pathways. Heat shock treatments generate expression rhythms of HY5, PHYA and PHYB To explore the dynamic changes of light-response related genes, real-time PCR was applied on HY5, PHYA and PHYB mRNA levels. Seedlings were kept in culture dishes at constant 23 °C before HS treatment. After the sampling of control seedlings, dishes were moved to constant 37 °C with no change in light intensity or atmosphere humidity. Samples were collected every 5 min. Before 25-min HS treatment, the mRNA abundance of HY5, PHYA and PHYB showed a similar fluctuation trends when compared with control plants (abundance settled as ‘onefold’): it went up at 5, 15 and 25 min, while down at 10 and 20 min. After this, the expression of PHYA stayed quite stable, around onefold to twofold; meanwhile, a more dramatic fluctuation was found in the expression of HY5 and PHYB, from 2.5- to minus 5.5-fold, 1- to minus 6.5fold (see Fig. 5). Hy5 functioned downstream of multiple photoreceptors (Zhang et al. 2011). The expression level of HY5 was low at night, but high in daytime (Sellaro et al. 2011) due to the regulation from both light and photoreceptors (Karayekov et al. 2013a; Sellaro et al. 2011). The fluctuation of HY5 expression indicated that HS treatment had a regulation role on this light signaling component, and HS had successfully interrupted the light signaling pathways. The phytochrome (phy) family of receptors played a major role in perceiving red (R) (Rangani et al. 2015) and far-red (FR) light (Li et al. 2015), which carried information on the availability of photosynthetic energy and the proximity of neighboring plants (Chen and Chory 2011). Therefore, they were essential for the regulation of
Fig. 5 Heat shock treatments generate expression rhythms of HY5, PHYA and PHYB. Before 25 min, all these three genes presented a wave-like expression situation (expression rhythms). After 25 min, this fluctuation of HY5 and PHYB were intensified, while the expression of PHYA tended to be stable
developmental processes from seed germination to the timing of reproductive development (Franklin and Quail 2010). In this study, however, the expression of PHYs also responded to HS (see Fig. 3). Though only a slight oscillation could be seen on PHYA mRNA abundance, the expression of PHYB showed an essential volatility, down regulated by sixfold under HS treatment. This was consistent with previous report that heat shocks reduced the nuclear formation of phyB photobodies (Karayekov et al. 2013b). This also revealed that certain light receptors had taken part into the HS response in Arabidopsis. Real-time PCR results provided a clear picture that HS treatment induced rhythmic expression of PHYB and HY5 genes. This is not only a clue how temperature intervene in light signaling, but also a direct evidence that PhyB and Hy5 are common targets of light and temperature stimuli in plants.
Conclusion This study used the classical analysis method microarrays to monitor the Arabidopsis thaliana (Col-0) transcriptomic response to HS. Since plants HS response was an intricate physiological process, there were various signal transduction pathways interwined closely to regulate this mechanism. Therefore, it was not strange that hundreds and thousands of genes were successfully detected to answer to HS. Different from most previous researches, however, this study added another variable (light) to explore HS response and the interaction between light and temperature signaling in plants.
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There were 1597 and 787 genes regulated by HS under light and dark conditions, respectively. HCL of these genes (see Fig. 2) revealed that the expression profiles were clustered by both HS treatment and light condition. This demonstrated that plants responded to HS significantly, while this response could be substantially affected by light. With a further analysis, it was found that lots of light signaling components also answered to HS treatment. Considering the broader interaction among abiotic factors, it was not impossible for the presence of crosstalk between light and temperature signaling pathways in plants. Thus, real-time PCR experiments were conducted on three crucial light-related elements—HY5, PHYA and PHYB—to explore their dynamic expression changes during HS. The results of this experiment revealed that all these three genes presented an expression rhythm to HS, though the mRNA abundance of PHYA tended to be stable after 25-min treatment. All these findings certificated the existence of crosstalk between light and temperature signaling pathways in plants, offering a new insight to understand the extremely intricate temperature signal sensing and transduction network.
Materials and methods Plant materials and growth conditions
Microarray hybridization and analysis Three biological replicates were conducted for microarray tests (replicates were grown in exactly the same condition; each consisting of pooled RNA extracts was obtained from 25 whole seedlings; sampling interval time was \1 min.). We used Affymetrix GeneChip Arabidopsis ATH1 Genome Array designed specifically to monitor gene expression in Arabidopsisd. Microarray data analysis required hybridization quality control to detect technical anomalies affecting subsequent statistical analyses (see Fig. 6). Quality control was completed with Bioconductor packages (e.g., tcltk and scalse). Then DEGs between control and treated were identified using t test [p \ 0.05, as described in Ref. (Hanssen et al. 2011)]. To intuitively reflect the differences between different groups, a Hierarchical clustering (HCL) was conducted on DEGs. After HCL, two software, DAVID (http://david.abcc.ncifcrf.gov/) and GOEAST (http://omicslab.genetics.ac.cn/GOEAST/) enabling integration of data from transcriptomics, were used to conduct gene functional annotation and enrichment analysis. They could also be used for data analysis and exploration because large gene lists could be uploaded and queried (Zeng et al. 2014). qPCR analysis qPCR reactions were carried out with three biological replicates (same methods as mentioned in microarray
Arabidopsis (A. thaliana) plants of ecotype Col-0 were used for all experiments. Sterilized seeds were grown in MS solid medium within glass plates. After a 72 h vernalization at 4 °C in dark condition, plates were transferred to the growth camber (ambient temperature of 23 °C, relative humidity of approximate 70 %, and a photoperiod of 16-h light (approximately 100 lmol m-2 s-1) with 8-h darkness. Heat stress treatment Due to logistics and costs, we harvested plant samples after a single heat stress treatment based on previous studies (McWatters and Devlin 2011). Besides, to detect responses caused by the environmental insult instead of systemic responses occurring ‘‘downstream’’, a pilot study was performed to determine appropriate treatment duration. After 10 days of seedlings protrusion, heat-treat plants were subjected to the heat stress (37 °C), while controls were still in 23 °C. After the stress treatments, whole plant samples were collected and promptly frozen in liquid nitrogen for subsequent microarrays (HS for 30 min) and real-time PCR experiments (HS for 5–50 min).
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Fig. 6 Flow chart for microarray data analysis. Microarray data analysis required hybridization quality control to detect technical anomalies affecting subsequent statistical analyses. Subsequent statistical analyses to monitor gene transcript levels could be performed using open-source packages provided by bioconductor. After statistics, a Hierarchial clustering (HCL) was conducted to intuitively reflect the differences between different groups. Gene functional annotation and gene ontology enrichment analysis were than conducted with the apply of two on-line softwares, DAVID and GOEAST
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analysis). Control plants were sampled before HS. Then HS-treated plants were collected every 5 min (from 5 to 50 min, with a total 10 sampling points). Total RNA of Arabidopsis was extracted with an RNA extraction kit (Takara Biotechnology, Dalian, China). After treatment with Dnase I, RNA samples were used for cDNA synthesis (Thermo, Lithuania, EU). Then qPCR was performed using the LightCyclerÒ 480 SYBR Green I Master (Roche) with 10 pmol of each primer, and the reactions were run on a LightCyclerÒ 480 (Roche). For this verification, biological triplicate Col-0 samples were used. Relative log2 expression was determined by using ACTIN2 (AT3G18780) highly expressed with minimal variation across different treatments (Rasmussen et al. 2013; Yang et al. 2015). The results of qPCR were calculated and displayed using LightCyclerÒ 480 SW 1.5 software. Primer efficiencies were examined through concentration gradient experiment, and expression levels were calculated assuming 100 % efficiency. For qPCR analysis, gene-specific primers were designed using the Primer four program (see primers in Table S4.1). Acknowledgments We thank Mr. Jian Li (Hunan Normal University, China), Weisong Pan (Hunan Agricultural University, China) and Dezhi Wu, Shengguan Cai (Zhejiang University, China) for their technical guidance and assistance.
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