Open Access

Global transcriptome profiles of Camellia sinensis during cold acclimation

  • Xin-Chao Wang1,
  • Qiong-Yi Zhao2, 3,
  • Chun-Lei Ma1,
  • Zong-Hong Zhang3,
  • Hong-Li Cao1,
  • Yi-Meng Kong2,
  • Chuan Yue1,
  • Xin-Yuan Hao1,
  • Liang Chen1,
  • Jian-Qiang Ma1,
  • Ji-Qiang Jin1,
  • Xuan Li2Email author and
  • Ya-Jun Yang1Email author
Contributed equally
BMC Genomics201314:415

DOI: 10.1186/1471-2164-14-415

Received: 22 December 2012

Accepted: 18 June 2013

Published: 22 June 2013

Abstract

Background

Tea is the most popular non-alcoholic health beverage in the world. The tea plant (Camellia sinensis (L.) O. Kuntze) needs to undergo a cold acclimation process to enhance its freezing tolerance in winter. Changes that occur at the molecular level in response to low temperatures are poorly understood in tea plants. To elucidate the molecular mechanisms of cold acclimation, we employed RNA-Seq and digital gene expression (DGE) technologies to the study of genome-wide expression profiles during cold acclimation in tea plants.

Results

Using the Illumina sequencing platform, we obtained approximately 57.35 million RNA-Seq reads. These reads were assembled into 216,831 transcripts, with an average length of 356 bp and an N50 of 529 bp. In total, 1,770 differentially expressed transcripts were identified, of which 1,168 were up-regulated and 602 down-regulated. These include a group of cold sensor or signal transduction genes, cold-responsive transcription factor genes, plasma membrane stabilization related genes, osmosensing-responsive genes, and detoxification enzyme genes. DGE and quantitative RT-PCR analysis further confirmed the results from RNA-Seq analysis. Pathway analysis indicated that the “carbohydrate metabolism pathway” and the “calcium signaling pathway” might play a vital role in tea plants’ responses to cold stress.

Conclusions

Our study presents a global survey of transcriptome profiles of tea plants in response to low, non-freezing temperatures and yields insights into the molecular mechanisms of tea plants during the cold acclimation process. It could also serve as a valuable resource for relevant research on cold-tolerance and help to explore the cold-related genes in improving the understanding of low-temperature tolerance and plant-environment interactions.

Keywords

Camellia Sinensis Cold Acclimation RNA-Seq DGE Genome-wide Expression Profiles Tea Plants

Background

Low temperatures are one of the most important environmental factors that temperate plants have to cope with during their life cycles. Some plants can enhance their freezing tolerance after exposure to low but non-freezing temperatures for a period of time, a process known as cold acclimation (CA) [1]. CA is a complex process that involves cellular, physiological, metabolic and molecular modifications. When plants sense the cold temperature, a series of protective mechanisms are triggered [24]. These include resetting the cellular framework; alternating the composition, structure and function of the plasma membrane; synthesizing cryoprotectant molecules such as soluble sugars, sugar alcohols and low-molecular-weight nitrogenous compounds; decreasing the ratio of free water content; improving the scavenging activity of reactive oxygen species (ROS); and introducing antifreeze proteins. These alterations help plants maintain a metabolic balance of substance and energy in cold environments. A group of cold-related genes has been reported to regulate these aforementioned changes [27]. Moreover, changes in gene expression have been demonstrated to occur during CA in a wide range of plant species, and hundreds of cold inducible genes have been identified [8].

Tea is the most popular non-alcoholic health beverage in the world, and the tea plant (Camellia sinensis (L.) O. Kuntze) is one of the most important economic crops in China, India, Sri Lanka, Kenya, among others [9]. As an evergreen woody plant, the tea plant can be grown in tropical to subtropical climates. Due to the local climate changes, tea plants have to cope with low temperatures during the wintertime. Low temperatures are one of the most critical environmental factors that limit its growth, survival and geographical distribution [10]. Thus, finding ways to improve tea plants’ resistance to low temperatures is of great importance. Like other perennial evergreen woody crops, during the CA process, the cold tolerance of tea plants enhances with the decrease in temperature and reduces with the increase in temperature. A previous study showed that when the average air temperature decreases to around 7°C, tea plants undergo the CA process, and after the average air temperature increases to over 9°C, tea plants start the de-acclimation process [11]. There are few studies that have focused on the cellular, physiological and metabolic changes during CA in tea plants. When tea plants undergo the CA process, the thickness of palisade tissue is increased and the stability of plasma membrane is enhanced. In addition, the concentration of the cytochylema and ratio of bound water in the cytoplasm, the amount of unsaturated fatty acids and total proteins in the plasma membrane, and the content of soluble proteins in the leaf are also increased. Meanwhile, the activities of some detoxification enzymes, such as catalase (CAT), superoxide dismutase (SOD), peroxidase (POD) and esterase (EST) are increased, whereas the metabolic activity is decreased [11, 12]. Some cold-induced genes have been cloned in tea plants [13, 14]. As a complex biological phenomenon, the ability of tea plants to resist the cold is regulated by a series of genes involved in a complex regulatory network [15]. Using an ‘omics’ research strategy to understand the mechanism of CA in tea plants is the key to improving tea productivity and geographical distribution.

RNA-Seq is a recently developed approach using a massively parallel sequencing strategy to generate transcriptome profiles. It has emerged as a cost-effective approach for high-throughput sequence determination and has unprecedentedly improved the efficiency and speed of gene discovery [16, 17]. Digital gene expression (DGE) is a tag-based sequencing approach according to which short tags are generated by endonuclease. The expression level of genes in the sample is measured by counting the number of tags generated from each transcript [18]. This study demonstrates the first attempt to use a combination of RNA-Seq and DGE to study the transcriptome profiles in tea plants and thereby gain a deeper insight into the molecular mechanism of CA. The resulting transcriptome profiles from tea plants not only contributes to the in-depth knowledge of the genes involved in CA but also improves our understanding of plant-environment interactions.

Results and discussion

Cold tolerance changes in tea plant during the CA process

Cold tolerance in tea plants varies under different temperatures and can be monitored by the relative electrical conductivity using an electrolyte leakage assay. Figure 1 shows a complete course of the CA process for a natural temperature change period from December 2010 to March 2011. Before December 1, the average outdoor temperature (for five days) was above 10°C, and the relative electrical conductivity of tea-plant leaves (CK) was at ~100%, indicating that the tea plant has a low level of cold tolerance. After the tea plant underwent a period of time at relatively low temperatures (<10°C), its relative electrical conductivity decreased, and the cold tolerance of the tea plant is enhanced. When temperatures decreased to their lowest point, the relative electrical conductivity also reached its lowest level with the cold tolerance being at the highest level (CA1). Afterwards, the temperature rose and when the average temperature reached above 10°C, the relative electrical conductivity increased to over 80% and then maintained at a high level. The tea plant was subsequently de-acclimated (CA3), and its cold tolerance was weak (Figure 1).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-415/MediaObjects/12864_2012_Article_5138_Fig1_HTML.jpg
Figure 1

Changes of relative electrical conductivity and of the averaged air temperature during the CA process of tea plants. The value of relative electrical conductivity changed from 96.2% for sample CK to 42.9% for sample CA1 when tea plant underwent the cold acclimation process. Then, it returned back to 80.3% (sample CA3) after the air temperature increased. The values of averaged air temperature were also shown during this period.

To obtain the transcriptomic response to the cold environment during the CA process, we selected tea-plant leaves from three stages, non-acclimated (CK), fully acclimated (CA1) and de-acclimated (CA3) for RNA-Seq and digital gene expression (DGE) studies.

RNA-Seq and de novoassembly

We performed RNA-Seq analyses for CA1, CA3 and CK using the Illumina HiSeq2000 genome analyzer. Totally, 57.35 million paired-end reads with a read length of 90 bp were generated (hereafter referred to as dataset 1, Table 1). Since low-quality nucleotides from the ends of reads may lead to incorrect assembly outputs [19], we trimmed the low-quality or ambiguous nucleotides at both ends of the reads. De novo assembly was performed with the trimmed reads using Trinity [20]. Trinity was specially developed for de novo assembly from short-read RNA-Seq data, which has been shown to be the best single k-mer assembler [19, 20]. In total, 226,026 transcripts were reconstructed. After removing the redundant transcripts caused by small variations as described in the previous study [19], a final set of 216,831 transcripts were obtained. The average transcript size is 356 bp, and the N50 is 529 bp (Table 2).
Table 1

Summary for RNA-Seq datasets of C. sinensis

 

CK

CA1

CA3

Total

Number of reads (million)

17.54

13.65

26.16

57.35

Total bases (raw, Gb)

1.58

1.23

2.35

5.16

Total bases (trimmed, Gb)

1.50

1.16

2.30

4.96

Table 2

Summary for the outcomes of de novo transcriptome assembly using three datasets in C. sinensis

 

Dataset 1

Dataset 2

Dataset 3

Number of transcripts (≥100 bp)

226,026

177,439

295,551

Number of transcripts (remove redundancy)

216,831

170,650

282,395

Total base pairs (Mbp)

77.36

50.36

94.71

Average length (bp)

356

295

335

N50 (bp)

529

403

480

Mapping ratio (%)

76.94

64.87

76.91

The transcriptome of C. sinensis was reported in a previous study by Shi et al. [21]. They produced RNA-Seq data from the mixed tissues of C. sinensis using Illumina GA IIx (hereafter referred to as dataset 2). A combination of dataset 1 and dataset 2 was also generated, which we called dataset 3, representing all available RNA-Seq data for C. sinensis. Short reads of dataset 2 and dataset 3 were pre-processed by the procedure described above, and then used separately for de novo assembly. The assembly outcome from dataset 1 attains the longest average read length and N50, while that from dataset 3 yields the most number of transcripts and total base pairs (Table 2). In order to evaluate the efficiency of short-read usage during the de novo assembly, we mapped our RNA-Seq reads back to three sets of reconstructed transcripts, respectively. Transcripts produced from dataset 1 achieved the best performance, with the highest mapping ratio for our short reads (Table 2). More than 10% of the short reads failed to be aligned if only dataset 2 was used for the de novo assembly, indicating that previous transcriptome sequences of C. sinensis are far from saturated. Although more transcriptome sequences could be produced from de novo assembly using dataset 3 than dataset 1, the mapping ratio could not be improved (Table 2), indicating that the additional transcripts from dataset 3 are most likely transcripts that are expressed in tissues other than the leaves of tea plants. Thus these additional transcripts are unable to contribute to this study. Based on this scenario, we chose the transcripts from dataset 1 to carry out the downstream analysis.

Functional annotation of C. sinensistranscriptome

To predict and analyze the function of the assembled transcripts, non-redundant sequences were submitted to a BLASTx (E-value ≤ 10-5) search against the following databases: the NCBI’s NR database, UniRef90 [22], the Arabidopsis Information Resource (TAIR, version 10), Kyoto Encyclopedia of Genes and Genomes (KEGG, version 58) [23] and Clusters of Orthologous Groups from 7 eukaryotic complete genomes (KOG) [24]. We found that about one third of all non-redundant transcripts had significant homology with genes in either the NR or UniRef90 databases (Table 3).
Table 3

Summary for the BLASTx results of C. sinensis transcriptome against five databases

 

Sequences (n)

Transcripts

Number of transcripts

216,831

BLASTx against NR

716,831

BLASTx against UniRef90

71,709

BLASTx against TAIR10

60,392

BLASTx against KEGG

21,194

BLASTx against KOG

41,341

All annotated transcripts

72,967

Transcripts hit all five databases

16,430

Unigenes

Number of unigenes

179,753

BLASTx against NR

51,883

BLASTx against UniRef90

52,217

BLASTx against TAIR10

42,969

BLASTx against KEGG

15,086

BLASTx against KOG

29,356

All annotated unigenes

53,201

Unigenes hit all five databases

11,388

Arabidopsis thaliana is one of the most well-studied dicot plants, with a complete reference genome and comprehensively annotated gene sequences. A BLAST search against genes from Arabidopsis produced more definitive annotations and helped us to evaluate the quality and coverage of our assembled transcripts. It is notable that 16,882 Arabidopsis genes located uniformly on five chromosomes were covered by 60,392 transcripts (Tables 3 and 4).
Table 4

Distribution of BLASTx hit genes against Arabidopsis TAIR10

Chromosome

Chr1

Chr2

Chr3

Chr4

Chr5

ChrC

ChrM

Total

Total genes

9,263

5,560

6,908

5,356

8,089

88

122

35,386

No. of hits

4,374

2,445

3,293

2,600

4,080

36

54

16,882

%

47.22%

43.97%

47.67%

48.54%

50.44%

40.91%

44.26%

47.71%

A BLAST analysis of the assembled transcripts against the KEGG database showed that 21,194 transcripts were annotated with corresponding Enzyme Commission (EC) numbers and assigned to the reference canonical KEGG pathways (Table 3). A search against the KOG database reported that 41,341 transcripts had the best hits when the E-value was less than or equal to 10-5. Since some transcripts could be assigned multiple KOG functions, altogether 46,291 functional annotations were produced and all hit transcripts were grouped in 25 categories (Tables 3 and 5).
Table 5

KOG functional classification of C. sinensis transcripts

Description

Code

Transcript (n)

1 Information storage and processing

Translation, ribosomal structure and biogennesis

J

1,812

RNA processing and modification

A

1,729

Transcription

K

2,341

Replication, recombination and repair

L

1,194

Chromation structure and dynamics

B

706

2 Cellular processing and signaling

Cell cycle control, cell division, chromosome partitioning

D

1,091

Nuclear structute

Y

274

Defense mechanisms

V

385

Signal transduction mechanisms

T

4,836

Cell wall/membrane/envelope biogenesis

M

558

Cell motility

N

24

Cytoskeleton

Z

1,072

Extracellular structure

W

187

Intracellular trafficking, secretion, and vesicular transport

U

2,289

Posttranslational modification, protein turnover, chaperones

O

4,152

3 Metabolism

Energy production and conversion

C

1,800

Carbohydrate transport and metabolism

G

2,387

Amino acid transport and metabolism

E

1,514

Nucleotide transport and metabolism

F

432

Coenzyme ransport and metabolism

H

343

Lipid ransport and metabolism

I

1,440

Inorganic ion ransport and metabolism

P

1,159

Secondary metabolites biosynthesis, transport and catabolism

Q

1,428

4 Poorly characterized

General function prediction only

R

11,108

Function unknown

S

2,030

In total, 72,967 transcripts got the best hits with known proteins in at least one of the five databases and 16,430 transcripts had similarity to proteins in all of the five databases (Figure 2, Table 3).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-415/MediaObjects/12864_2012_Article_5138_Fig2_HTML.jpg
Figure 2

Venn diagram showing the BLAST results of C. sinensis transcriptome against five databases. De novo reconstructed transcript sequences were used to BLAST search against public databases including NCBI’s NR, UniRef90, TAIR10, KEGG and KOG. The number of transcripts that have significant hits (E-value ≤ 10–5) against the five databases is shown in each intersection of the Venn diagram.

To functionally categorize the assembled transcripts, gene ontology (GO) [24] terms were assigned to each transcript based on the best BLASTx hit from the NR database using Blast2GO [25]. Out of 71,289 transcripts with NR annotation, 30,115 transcripts were assigned 80,176 GO term annotations in three main GO categories including biological process, cellular component and molecular function (Table 3, Additional file 1). If a gene contained some conserved domains, the domain information would be useful for interpreting the gene’s function. To annotate the potential domains inside the reconstructed sequences, the open reading frame (ORF) was predicted for each transcript (Methods), and then all transcripts with predicted ORF were used to search against the Pfam database based on profile hidden Markov model methods. In total, 41,599 transcripts were assigned Pfam domain information and were categorized into 4,504 domains/families. Most domains/families were found to contain a small number of transcripts (Figure 3A). According to the frequency of the occurrence of C. sinensis transcripts contained in each Pfam domain, Pfam domains/families were ranked and the top 10 abundant domains/families are listed in Figure 3B, with hit results similar to the previous study [21]. Among these domains/families, “Protein kinase domain” and its subclass “Protein tyrosine kinase” are known to regulate the majority of cellular pathways. Proteins with “leucine-rich repeats” domain are known to be frequently involved in the formation of protein–protein interactions, and “PPR repeat” has been reported to be a large protein family in plants with versatile functions [26]. Moreover, the “NB-ARC” protein family, comprised of resistance proteins, was highly represented. Other protein families, such as “reverse transcriptase” and “RNA recognition motif”, which have some basic functions in plants, were also found in the top ten of the list.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-415/MediaObjects/12864_2012_Article_5138_Fig3_HTML.jpg
Figure 3

Protein families in C. sinensis transcriptome. Shown are the number of Pfam domains/families versus the occurrence of C. sinensis transcripts contained in each domain/family (A), and the 10 most abundant protein families (B) in C. sinensis.

Trinity produced all potential alternative spliced isoforms during the de novo assembly, and isoforms originated from the same gene locus were assumed to share the same “chrysalis component”, “butterfly sub-component” and some of the paths in the de Bruijn graph. We chose the longest transcript in each locus to get the unigene set, resulting in 179,753 unigenes (Table 3). Potential isoforms in each locus reported by Trinity would be useful in array/primer design for quantitative gene expression and future alternative splicing analyses.

Identification of genes involved in cold acclimation

The abundance estimation for reconstructed transcripts was determined by RSEM software package that was shown to have the ability to effectively use ambiguously-mapping reads and to accurately estimate isoform-level abundance for de novo assembled transcripts without reference [27]. The DESeq package [28] and the winflat program were then applied to identify differentially expressed genes. CA-related genes were identified based on the fold change on the abundance of each gene and the corresponding false discovery rate, which resulted in 1,770 differentially expressed genes. Of these, 1,168 were up-regulated and 602 were down-regulated (Additional file 2), indicating that more genes were activated than repressed during the CA process. Dozens of cold-regulated or cold-related genes were found in this differential expression list, including cold sensor or signal transduction genes, cold-responsive transcription factor genes, plasma membrane stabilization related genes, osmosensing-responsive genes and detoxification enzymes genes.

Cold sensor or signal transduction genes

The signal transduction pathway plays a pivotal role in the response to the stress of low temperatures [29]. It is well known that Ca2+ acts as a key messenger in regulating growth and developmental processes and plays a crucial role in stress signaling, i.e. cold stress [30]. Cold stress could activate Ca2+ channels to increase the cytosolic Ca2+ level, and then trigger phospholipase C and D, producing inositol triphosphate and phosphatidic acid, respectively. Inositol triphosphate could further amplify Ca2+ signatures, and phosphatidic acid is proposed as a membrane-based secondary messenger molecule [31, 32]. Subsequently, many signaling pathways are triggered, such as Ca2+-dependent protein kinases (CDPKs), mitogen-activated protein kinases (MAPKs), calcineurin B-like protein (CBL), calmodulin, etc. Doherty et al. [33] and Yang et al. [34] found that the calmodulin-binding transcription activator (CAMTA) and a novel calcium/calmodulin-regulated receptor kinase (CRLK1) were crucial for cold tolerance in plants. Ca2+ influx into the cell was considered to occur upstream of the expression of CBFs and COR genes in the cold signaling pathway [35, 36]. In this study, 13 genes, which were annotated as CDPKs, CBL, calmodulin, CAMTA, MAPK and phospholipase, were identified as being involved in signal transduction upon low temperature stress. Among these genes, 9 (4 calmodulin genes, 2 CDPK genes, 1 CAMTA gene and 2 phospholipase genes) were up-regulated in CA1, whereas 4 (1 phospholipase gene, 1 calmodulin gene, 1 CBL gene and 1 MAPK gene) were down-regulated.

Plant protein kinases belong to a large superfamily, some of which have been known to play a central role in cellular signaling, for example CDPKs and MAPKs. In addition, a growing body of evidence has shown that receptor-like kinases (RLKs) are involved in the perception of environmental signals [37, 38]. Histidine kinases (HKs), being localized to the cellular membranes and endoplasmic reticulum, are the major signaling molecules and are involved in the two-component signaling pathways that mediate plant-sensed environmental signals and regulate the downstream environmental stress response [39, 40]. In this study, 27 RLKs genes and 2 HKs genes were differentially expressed and all of these were up-regulated in CA1 samples, which indicates that protein kinases play an important role in the CA process in tea plants.

Cold-responsive transcription factor genes

Transcription factors (TFs) play important functions in plant development and stress tolerance [35]. Fifty-eight genes encoding putative TFs in C. sinensis were identified. These TFs could be divided into 9 groups (AP2/ERF, bHLH, WRKY, MYB, NAC, bZIP, heat shock, GARS and zinc finger protein) based on the classification of their Arabidopsis homologs, and most of them have been reported to be linked to cold stress resistance in plants [4149]. Among these TFs, 37 genes were up-regulated and 21 genes were down-regulated in our CA1 sample.

Of the 9 groups of TFs, zinc finger was the most enriched TF family, containing 31 genes of the 58 cold-responsive TFs, with 18 genes being up-regulated and 13 being down-regulated. There were 5 genes in the bHLH family (4 up- and 1 down-regulated), 5 genes in the MYB family (4 up- and 1 down-regulated), 5 genes in the WRKY family (3 up- and 2 down-regulated) and 3 genes in the NAC family (1 up- and 2 down-regulated). In addition, 2 genes in the bZIP family, 3 genes in the GARS family and 2 genes encoding heat shock proteins were all up-regulated, while 2 genes in the AP2/ERF family were down-regulated in the CA1 sample. It is interesting to find down-regulated genes in the AP2/ERF family, as these suggest that the interaction of light and temperature is of special importance for plants during the CA process. Catalá et al. [50] and Jurczyk et al. [51] have also reported that light is required for full CA in Arabidopsis and Festuca pratensis.

Genes related to the stabilization of the plasma membrane and osmosensing-responsiveness

The plasma membrane is believed to be a primary site of injury from freezing in plants. The process of CA can stabilize the membrane structure and prevent it from damage [52]. Under freezing temperatures, membranes must be kept fluid in order to sustain the functional activity of membrane proteins and membranes themselves [53]. Alterations occur in the composition of proteins and lipids (e.g. increases in the unsaturation level of the membrane lipids) in the plasma membrane in response to CA, and these are associated with an increase in freezing tolerance [53, 54]. In our study, we identified 3 lipid-transfer protein (LTP) genes and 1 fatty acid desaturase (FAD) gene. Among these, 2 LTP genes and a FAD gene were up-regulated and 1 LTP gene was down-regulated. These genes were known to regulate the level of unsaturated fatty acids, and then to further mediate the regulation of membrane fluidity [5557].

Moreover, in order to maintain the structural stabilization of the plasma membrane during the CA process, some proteins function as inhibitors to regulate the activity of ice nucleators. These proteins are so-called anti-freezing proteins (AFPs), such as β-1, 3-glucanase-like proteins (GLPs), chitinase-like proteins (CLPs), thaumatin-like proteins (TLPs), polygalacturonase inhibitor proteins (PGIPs) and late-embryogenesis-abundant proteins (LEAs). In the CA1 sample, more genes encoding these proteins were up-regulated compared with genes in non-acclimated samples [36, 58]. In our study, we found 7 AFP-related genes, including 4 CLPs, 1 TLP, 1 PGIP and 1 LEA that were up-regulated in the CA1 sample, indicating that during the CA process, tea plants became able to tolerate freezing temperatures through the enhancement of membrane stability.

The stabilization of the plasma membranes is also related to the osmotic equilibrium. In order to maintain osmotic balance, plants accumulate a range of compatible solutes, including soluble sugars (saccharose, trehalose, rafinose), sugar alcohols (ribitol, inositol, sorbitol), and low-molecular-weight compounds (such as proline, glycine betaine, glutamic acid) as cryoprotectant molecules in response to cold stress [5961]. Accordingly, the expression of these metabolism related genes also changes during CA [29, 36, 62]. We identified 13 genes related to the carbohydrate metabolic pathway from 1,770 differentially expressed genes, including 4 galactosidases (3 up- and 1 down-regulated), 5 amylases (all up-regulated), 1 galactinol synthase (up-regulated), 1 raffinose synthase (up-regulated) and 2 trehalose-6-phosphate synthases (all up-regulated). These genes are key genes of the carbohydrate metabolic pathway, and are closely involved with the CA process [63]. Three monosaccharide transporter genes (2 up- and 1 down-regulated) were identified as well. Monosaccharide transporters play an important role in sugar transport and distribution in plants. The expression of monosaccharide transporter genes is also regulated by cold stress [64]. These results suggested that the carbohydrate metabolic pathway plays a critical role in tea plants during the CA process.

Validation of RNA-Seq results by DGE and qRT-PCR

Digital gene expression (DGE) library sequencing was performed to validate the cold-regulated transcripts identified by RNA-Seq. In our study, three DGE libraries were sequenced: CA1, CA3 and CK, for which 3.69, 3.62 and 3.68 million raw tags were generated, respectively (Table 6). After removing low-quality tags, the total number of clean tags per library ranged from 3.53 to 3.60 million. Clean tags from three DGE libraries were mapped onto our assembled transcriptome sequences. Up to 24.25% of transcripts were detected by DGE tags (Table 6).
Table 6

Summary for DGE datasets

Summary

CA1

CA3

CK

Raw tags

3,689,000

3,615,500

3,675,000

Clean tags (total)

3,599,673

3,528,122

3,586,229

Aligned tags

2,027,613

2,402,599

1,810,760

Aligned tags (%)

56.33%

68.10%

50.49%

Clean Tags (unique)

882,038

456,976

932,551

Aligned unique-tags

117,591

104,604

111,505

Aligned unique-tags (%)

13.33%

22.89%

11.96%

Transcripts with aligned tags

52,590

46,331

51,333

Transcripts with aligned tags (%)

24.25%

21.37%

23.67%

Transcripts with uniquely aligned tags

33,380

28,462

32,519

Transcripts with uniquely aligned tags (%)

15.39%

13.13%

15.00%

Of the 1,770 differentially expressed transcripts identified by RNA-Seq, 1,460 were detected by DGE sequencing, but 870 were mapped by uncertain tags (tags that mapped to at least two different transcripts) and another 192 transcripts did not have enough tags (< 1 ‘tags per million’ (TPM) counts for all three samples) to differentiate expressions among CA1, CA3 and CK samples. This result illustrates that DGE sequencing was limited to identify differential expression across the full scale of transcriptome profiles, especially for genes with paralogs or multiple isoforms that shared the same tags (CATG+17bp). Of the remaining 398 transcripts, the majority of them (376/398) showed consistent expression patterns between DGE and RNA-Seq, with the corresponding Pearson’s r being 0.77 and 0.81 for CA1/CA3 and CA1/CK, respectively (Figure 4, Additional file 3), demonstrating the degree of consistency between DGE and RNA-Seq platforms.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-415/MediaObjects/12864_2012_Article_5138_Fig4_HTML.jpg
Figure 4

Heatmap representing relative expression levels of 398 differentially expressed transcripts in tea plants .Expression levels were determined by RNA-Seq and DGE, respectively. Cluster analysis of differentially regulated transcripts expressed in CA1 versus CA and CA3 samples indicating the high degree of consistency between DGE and RNA-Seq platforms.

It is worth noting that some transcripts, though not many, showed different expression patterns in the profiling results from RNA-Seq and DGE (Figure 4). Determining which method is more robust and why the two approaches yield different results would be useful for identifying the correct outcomes in this study and for other researchers to choose the appropriate approach in their future studies. To address this, 10 of these transcripts that showed inconsistent results from RNA-Seq and DGE platforms were randomly selected to assess their relative expression patterns among CK, CA1 and CA3 using quantitative RT-PCR approach (qRT-PCT). For most of these (8/10), similar expression patterns were observed compared with those from RNA-Seq results, while in the other 2 transcripts there were only partial consistencies with either RNA-Seq or DGE results (Additional file 4). In general, RNA-Seq outperforms DGE based on the results from these 10 cases. The less accurate estimation of the gene expression level by DGE approach could be due to some unknown reason(s) or to the fact that the same tags may exist in other transcripts that were partially reconstructed after de novo transcriptome assembly and lack the complete tag sequences. Since the DGE approach counts all tags to the transcript with the exactly matched tag sequences, this may result in the incorrect estimation of the expression level for some transcripts. In the remaining two genes, inconsistent expression patterns were observed among the results from the three approaches. These genes expressed at relatively variable levels may be affected by factors other than a cold environment and these kinds of false positives could be largely avoided if more biological replicates were included.

The DGE method is widely applied for studying the transcriptome. However, it has limitations in presenting a global view of transcriptome profiles. It is powerless to detect the abundance of transcripts when 1) there is no CATG site in transcripts or 2) several transcripts share the same tag, situations involving two unrelated genes, paralogs, or alternatively spliced isoforms. Both closely related paralogs and alternatively spliced isoforms might exhibit various spatial and temporal expression patterns, or even have different functions. Thus, the ability to correctly estimate isoform expression levels will be necessary for understanding complicated biological mechanisms.

To further test the reliability of outcomes produced from the next generation sequencing platform, quantitative RT-PCR (qRT-PCR) analysis was performed for 18 of the 1,770 differentially expressed transcripts. These 18 transcripts were manually selected as representatives for their potential roles in cold tolerance according to their annotations. The expression patterns of 17 genes detected by qRT-PCR fit well with those from RNA-Seq results, with one annotated as a basic helix-loop-helix DNA-binding superfamily protein being inconsistent (Figure 5).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-415/MediaObjects/12864_2012_Article_5138_Fig5_HTML.jpg
Figure 5

Quantitative RT-PCR validations. 18 genes were selected for the quantitative RT-PCR analysis, including catalase (A), thaumatin-like protein (B), ascorbate peroxidase (C), trehalose-6-phosphate synthase (D), bZIP (E), early-responsive to dehydration stress protein (F), phospholipase (G), absicisic acid 8′-hydroxylase (H), HSP70 (I), E3 ubiquitin-protein ligase (J), WRKY2 (K), sensor histidine kinase (L), MYB (M), peroxidase (N), delta-6-desaturase (O), Aquaporin 2 (P), ABA receptor (Q) and bHLH (R). The18S ribosomal gene was chosen as the reference gene.

Pathways involved during the CA process in C. sinensis

The 1,770 transcripts were used to search the KEGG pathway to determine whether the genes involved in CA were from specific pathways. In total, 200 pathways were identified, 20 of which were significantly enriched during CA (P < 0.05, Additional file 5). Of these significantly enriched, metabolism was the largest category (99 transcripts), including carbohydrate metabolism (28), glycan biosynthesis and metabolism (21), energy metabolism (15), amino acid metabolism (14), metabolism of terpenoids and polyketides (9), enzyme families (5), xenobiotics biodegradation and metabolism (4) and lipid metabolism (2). Moreover, calcium signaling pathway (9) and membrane transport pathway (7) were enriched as well. Numerous studies reported that carbohydrate metabolism plays an important role during the CA process [6569]. In this study, metabolic pathways for carbohydrates stood out from the enrichment analysis, including pathways for 49 differentially expressed transcripts (28 for carbohydrate metabolism and 21 for glycan biosynthesis and metabolism), indicating that the regulation of carbohydrates is crucial for tea plants during CA.

Previous studies have shown that calcium acts as a pivotal mediator in the signal transduction pathway during the CA process [29, 36]. Calcium is a secondary messenger in plant signaling processes, and calcium/calmodulin-mediated signaling is believed to play an important role in plants during the cold stress response [30, 34, 70]. In this study, the calcium signaling pathway was also enriched, and most of the genes in this pathway were up-regulated in the CA1 sample, proving the importance of the calcium signaling pathway for the tea plants’ response to cold stress.

Conclusions

In this study, we present a global survey for transcriptome profiles in tea plants during the CA process using RNA-Seq and DGE. A large number of genes from tea plants involved in diverse biological or molecular pathways were identified during the CA process, such as genes involved in cold signal sensors or transduction, genes related to the stabilization of plasma membranes, osmosensing-responsive genes, and stress-responsive transcription factor genes. A diagram is shown to illustrate tea plants’ responses to low temperatures during the CA process (Figure 6). The results showed that a series of complex regulatory networks were triggered in tea plants during CA. Our study provides insights into the molecular mechanisms of tea plants during the CA process. It could also serve as a valuable resource for relevant research on cold-tolerance and help to explore the cold-related genes in improving the understanding of low-temperature tolerance and plant-environment interactions.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-415/MediaObjects/12864_2012_Article_5138_Fig6_HTML.jpg
Figure 6

A diagram of tea plants’ responses to low temperatures during the CA process.

Methods

Low-temperature tolerance assays and RNA preparation

The tea plant cultivar ‘Camellia sinensis (L.) O. Kuntze cv. Longjing 43’ was planted in the China National Germplasm Hangzhou Tea Repository (CNGHTR) at the Tea Research Institute, Chinese Academy of Agricultural Sciences (TRI, CAAS). Starting in October 2010, intact mature leaves were selected in every 10–15 days until March 2011, when the average temperature became higher than 15°C. All samples were washed with distilled deionized water and divided into two parts, one for −80°C storage using liquid nitrogen for quick freezing and the other for evaluating low-temperature tolerance using an electrolyte leakage assay. RNAprep pure Plant Kit (Tiangen, Beijing, China) was used for total RNA extraction, and Agilent 2100 Bioanalyzer was used to test the RNA integrity with a minimum integrity value of 8.

The low-temperature tolerance was determined from leaf samples by electrolyte leakage assay similar with previous study [71]. Briefly, leaves were washed with deionized water. Leaf samples (5 mm in diameter) were extracted using a hole puncher and the midvein of the leaf was excluded. Leaf samples (0.5 g) were placed in closed vials containing 20 ml of deionized water and incubated at 25°C on a rotary shaker for 24 h. Then the electrical conductivity of the solution (L1) was determined. Samples were then autoclaved at 100°C for 20 min and the final electrical conductivity (L2) was determined after equilibration at 25°C. The EL (relative electrical conductivity) was defined as follows: EL (%) = (L1/L2×100%). Based on the level of electrolyte leakage, three samples including non-acclimated (CK, the date for sample collection was 1st December, 2010), fully acclimated (CA1, the date was 29th December, 2010) and de-acclimated (CA3, the date was 1st March, 2011) were selected for RNA-Seq and DGE analyses.

Library preparation and RNA-Seq

The samples for RNA-Seq were prepared using Illumina’s kit and following manufacturer’s recommendations. In short, mRNA was purified from 20 μg of total RNA using oligo (dT) magnetic beads, followed by fragmentation, in which the mRNA is fragmented into small pieces using divalent cations under elevated temperature. The cleaved RNA fragments were used for first-strand cDNA synthesis using reverse-transcriptase and random primers followed by second-strand cDNA synthesis using DNA polymerase I and RNase H. After the end repair process and ligation of adapters, the products were enriched by PCR to create the final cDNA library.

The cDNA library was sequenced from both 5′ and 3′ ends using the Illumina HiSeq™ 2000 platform according to the manufacturer’s instructions. The fluorescent image processing, base-calling and quality value calculation were performed by the Illumina data processing pipeline 1.4, in which 290 bp paired-end reads were obtained.

Short-read RNA-Seq datasets

In our study, we performed RNA-Seq for three samples from tea plants that represented three key stages during the CA process, including CA1, CA3 and CK. We called these dataset 1. The accession code of our RNA-Seq dataset is SRA061043. The previous study reported the transcriptome of C. sinensis, with 75 bp paired-end reads produced from the Illumina GAII platform, and we called this dataset 2. Its accession code is SRX020193, which includes samples from seven different tissues of C. sinensis: tender shoots, young leaves, mature leaves, stems, young roots, flower buds and immature seeds [21]. Furthermore, we combined dataset 1 and dataset 2 together as dataset 3 in order to compare the outcomes from de novo assembly using different datasets.

Preprocessing and de novoassembly

Raw data is preprocessed before de novo assembly: low-quality nucleotides (we defined nucleotides with a quality score less than 20 as low-quality nucleotides) in the last 20 cycles and ambiguous nucleotides in the first five cycles were trimmed by custom PERL script. After preprocessing, we obtained a total of ~ 4.96 G bases (Gb), ~ 1.90 Gb and ~ 6.86 Gb quality filtered short reads for dataset 1, dataset 2 and dataset 3, respectively.

De novo assemblies for these three datasets were performed separately by Trinity (release 20110713) [20]. The command-line parameters are “--seqType fq --left 1.fq --right 2.fq --paired_fragment_length 300 --min_contig_length 100 --run_butterfly --output RNASeq_Trinity --CPU 8”.

Removal of redundancy

Some isoforms reconstructed by Trinity with the same “chrysalis component” and “butterfly sub-component” had only small variations, such as SNPs, small insertions or deletions; such variations introduced redundancies for the assembly outcomes. CD-HIT-EST [72] was used to remove the shorter redundant transcripts when they were entirely covered by other transcripts with more than 99% identity. This set of transcripts was then used to count the basic assembly statistics and for downstream analysis.

Gene annotation and classification

All non-redundant transcripts (≥ 100 bp) were used to search against the NR, UniRef90 [22], TAIR10, KEGG (version 58) [23] and KOG [24] databases by BLASTALL package (release 2.2.22) with the significant threshold of E-value ≤ 10-5. Each known gene from the best BLASTx hit was parsed and assigned. Gene ontology (GO) [73] terms for each transcript were assigned based on the best BLASTx hit from the NR database using Blast2GO software (version 2.3.5) [25] with an E-value threshold of 10-5.

The ORF of assembled transcripts was determined based on the results of BLASTx search in the following order: NR, UniRef90, KEGG and KOG. Extending from both sides of the aligned region, the coding region sequences were translated into amino acid sequences with the standard codon table using custom PERL scripts. For those transcripts without any BLASTx hit against known databases, the best potential coding region was predicted using the software BestORF with parameters trained on Arabidopsis ESTs. The predicted amino sequences were submitted to search against the Pfam database (version 25.0) [74] for domain/family annotation using HMMER 3.0, with the ‘Best Match Cascade’ protocol. The “optimising allowed match overlap” method [75] was used to resolve complex overlapping protein domains.

Mapping reads to transcripts

In order to get assembly statistics for the ratio of number of reads that could be mapped back to transcripts (mapping ratio), bowtie (version 0.12.7) [76] was used to align short reads to the reconstructed transcripts, with parameters “-q --solexa1.3-quals --fr −1 fq1 -2 fq2 -k 1 -v 3 -X 300”. Custom PERL scripts were used to summarize the aligned results.

Calculation of gene expression level

RSEM (v1.1.11) [27] was used to quantify transcript abundance in each sample, with parameters “--phred64-quals --estimate-rspd --calc-ci --out-bam --fragment-length-min 100 --fragment-length-max 350” , and then the RSEM-estimated fragment counts were fed into DESeq package (1.0.6) [28] to get the ‘baseMean’ value. The false discovery rate (FDR) of each comparison (CA1 vs. CA3 and CA1 vs. CK) was calculated by the winflat program which implements a rigorous statistical analysis described by Audic and Claverie [18]. The FDR ≤ 0.01 and the absolute value of log2 ratio ≥ 1 were used as the threshold of significant differences in gene expression. Those genes that were significantly differentially expressed in both CA1 vs. CK and CA1 vs. CA3 were identified as potentially related to CA.

Digital gene expression

Tag library preparation for three samples was performed in parallel using the Illumina gene expression sample preparation kit. Briefly, 6 μg total RNA from each sample was used for mRNA capture with magnetic oligo (dT) beads. First- and second-strand cDNA were synthesized. Bead-bound cDNA was subsequently digested with NlaIII. The cDNA fragments with 3' ends were then purified with magnetic beads, and the Illumina adapter 1 was ligated to their 5' ends. The junction of the Illumina adapter 1 and CATG site is the recognition site of MmeI, which cuts the cDNA at 17 bp downstream of the CATG site, producing tags linked with adapter 1. After removing 3' fragments with magnetic beads precipitation, the Illumina adaptor 2 was ligated to the 3' ends of tags. The ligation products were enriched by PCR amplification (15 cycles) and purified by 6% TBE PAGE Gel electrophoresis. Sequencing was carried out on the Illumina HiSeq™ 2000 platform, as recommended by the manufacturer, for 35 cycles.

Raw image data was transformed by base calling into sequence data. Adaptor sequences were removed by custom PERL scripts and low-quality tags with ambiguous nucleotide(s) were discarded. All remaining tags were then aligned to the reconstructed transcripts by bowtie with parameters “-a -f -v 0”. Tags that could not be uniquely aligned were discarded. For gene expression analysis, the number of expressed tags was counted and then normalized to TPM.

Quantitative real-time RT-PCR (qRT-PCR) analysis

In order to validate the reliability of RNA-Seq and DGE experiments, 28 transcripts were selected for quantitative RT-PCR (qRT-PCR) test. The RNA (1 μg) of each sample was treated with DNase I (Tiangen, China), then real-time PCR was performed using PrimeScriptTM RT reagent qPCR Kit fromTakara (Dalian, China) under the following parameters: 95°C for 30 s, 40 cycles at 94°C for 15 s, 60°C for 34 s. Fluorescence intensity was measured using the Applied Biosystems 7300 Sequence Detection System (Carlsbad, CA, USA). Triplicates of each reaction were performed. To ensure the robustness of the reference gene used in the qRT-PCR experiment, we analyzed the gene expression stability of 4 commonly used housekeeping genes (18S RNA, β-Actin, GAPDH and α-Tubulin) across the cold acclimation process. As previously reported by others [77], our results also showed that the 18S RNA gene was the most stable one for its constant expression levels and was finally chosen as the reference gene in our study. The relative expression of the genes in the three samples was calculated using the 2−ΔΔCt method described earlier [78]. The result of the qRT-PCR was presented as fold changes in gene expression relative to that of CK sample. So, the relative value of CK is 1 and the relative values of CA1 and CA3 samples were normalized to that of CK sample. All data are shown as the mean ± SD and all primer information is provided in Additional file 6.

Notes

Declarations

Acknowledgements

We thank Dr. Enda Byrne and Miss Emily Wheeler for discussions and critical reading of the manuscript. This work was supported by the National Natural Science Foundation of China (31170650, 31100504), the Natural Science Foundation of Zhejiang Province (Z3100473), the Earmarked Fund for China Agriculture Research System (CARS-23), the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2011BAD01B01-ZCS) and the National Key Basic Research Program in China (2013CB127005).

Authors’ Affiliations

(1)
Tea Research Institute, Chinese Academy of Agricultural Sciences; National Center for Tea Improvement, Key Laboratory of Tea Plant Biology and Resources Utilization, Ministry of Agriculture
(2)
Key Laboratory of Synthetic Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences Chinese Academy of Sciences
(3)
The University of Queensland, Queensland Brain Institute

References

  1. Thomashow MF: Plant cold acclimation: freezing tolerance genes and regulatory mechanisms. Annu Rev Plant Physiol Plant Mol Biol. 1999, 50: 571-599. 10.1146/annurev.arplant.50.1.571.View ArticlePubMedGoogle Scholar
  2. Hare PD, Cress WA, Van Staden J: Dissecting the roles of osmolyte accumulation during stress. Plant Cell Environ. 1998, 21 (6): 535-553. 10.1046/j.1365-3040.1998.00309.x.View ArticleGoogle Scholar
  3. Iba K: Acclimative response to temperature stress in higher plants: approaches of gene engineering for temperature tolerance. Annu Rev Plant Biol. 2002, 53: 225-245. 10.1146/annurev.arplant.53.100201.160729.View ArticlePubMedGoogle Scholar
  4. Atici O, Nalbantoglu B: Antifreeze proteins in higher plants. Phytochemistry. 2003, 64 (7): 1187-1196. 10.1016/S0031-9422(03)00420-5.View ArticlePubMedGoogle Scholar
  5. Wang YJ, Zhang ZG, He XJ, Zhou HL, Wen YX, Dai JX, Zhang JS, Chen SY: A rice transcription factor OsbHLH1 is involved in cold stress response. Theor Appl Genet. 2003, 107 (8): 1402-1409. 10.1007/s00122-003-1378-x.View ArticlePubMedGoogle Scholar
  6. Gusta LV, Wisniewski M, Nesbitt NT, Gusta ML: The effect of water, sugars, and proteins on the pattern of ice nucleation and propagation in acclimated and nonacclimated canola leaves. Plant Physiol. 2004, 135 (3): 1642-1653. 10.1104/pp.103.028308.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Chen THH, Murata N: Glycinebetaine: an effective protectant against abiotic stress in plants. Trends Plant Sci. 2008, 13 (9): 499-505. 10.1016/j.tplants.2008.06.007.View ArticlePubMedGoogle Scholar
  8. Fowler S, Thomashow MF: Arabidopsis transcriptome profiling indicates that multiple regulatory pathways are activated during cold acclimation in addition to the CBF cold response pathway. Plant Cell. 2002, 14 (8): 1675-1690. 10.1105/tpc.003483.PubMed CentralView ArticlePubMedGoogle Scholar
  9. Chen L, Zhou Z-X, Yang Y-J: Genetic improvement and breeding of tea plant (Camellia sinensis) in China: from individual selection to hybridization and molecular breeding. Euphytica. 2007, 154: 239-248. 10.1007/s10681-006-9292-3.View ArticleGoogle Scholar
  10. Wang Y, Jiang C-J, Li Y-Y, Wei C-L, Deng W-W: CsICE1 and CsCBF1: two reanscription factors involved in cold responses in Camellia sinenesis. Plant Cell Rep. 2012, 31 (1): 27-34. 10.1007/s00299-011-1136-5.View ArticlePubMedGoogle Scholar
  11. Yang YJ, Zheng LY, Wang XC: Effect of cold acclimation and ABA on cold hardiness, contents of proline of tea plants [Camellia sinensis(L.) O. Kuntze]. J Tea Sci. 2004, 24 (3): 177-182.Google Scholar
  12. Wang XC, Yang YJ: Research progress on resistance breeding of tea plant. J Tea Sci. 2003, 23 (2): 94-98.Google Scholar
  13. Mei JF, Wang XC, Yang YJ, Li XH: Preliminary study on differential gene expression during cold acclimation in tea plant (Camellia sinensis). J Tea Sci. 2007, 27 (4): 286-292.Google Scholar
  14. Chen L, Chen J, Jiang Y, Zhang W, Jiang W, Lu Y: Transcriptomics analyses reveal global roles of the regulator AveI in Streptomyces avermitilis. FEMS Microbiol Lett. 2009, 298 (2): 199-207. 10.1111/j.1574-6968.2009.01721.x.View ArticlePubMedGoogle Scholar
  15. Sanghera GS, Wani SH, Hussain W, Singh NB: Engineering cold stress tolerance in crop plants. Curr Genomics. 2011, 12 (1): 30-43. 10.2174/138920211794520178.PubMed CentralView ArticlePubMedGoogle Scholar
  16. Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10 (1): 57-63. 10.1038/nrg2484.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Marguerat S, Bahler J: RNA-seq: from technology to biology. Cell Mol Life Sci. 2010, 67 (4): 569-579. 10.1007/s00018-009-0180-6.PubMed CentralView ArticlePubMedGoogle Scholar
  18. Audic S, Claverie JM: The significance of digital gene expression profiles. Genome Res. 1997, 7 (10): 986-995.PubMedGoogle Scholar
  19. Zhao QY, Wang Y, Kong YM, Luo D, Li X, Hao P: Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study. BMC Bioinforma. 2011, 12 (Suppl 14): S2-10.1186/1471-2105-12-S14-S2.View ArticleGoogle Scholar
  20. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al: Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011Google Scholar
  21. Shi CY, Yang H, Wei CL, Yu O, Zhang ZZ, Jiang CJ, Sun J, Li YY, Chen Q, Xia T, et al: Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds. BMC Genomics. 2011, 12: 131-10.1186/1471-2164-12-131.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, et al: The universal protein resource (UniProt): an expanding universe of protein information. Nucleic Acids Res. 2006, 34 (Database issue): D187-191.PubMed CentralView ArticlePubMedGoogle Scholar
  23. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M: The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004, 32 (Database issue): D277-280.PubMed CentralView ArticlePubMedGoogle Scholar
  24. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, et al: The COG database: an updated version includes eukaryotes. BMC Bioinforma. 2003, 4: 41-10.1186/1471-2105-4-41.View ArticleGoogle Scholar
  25. Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21 (18): 3674-3676. 10.1093/bioinformatics/bti610.View ArticlePubMedGoogle Scholar
  26. O’Toole N, Hattori M, Andres C, Iida K, Lurin C, Schmitz-Linneweber C, Sugita M, Small I: On the expansion of the pentatricopeptide repeat gene family in plants. Mol Biol Evol. 2008, 25 (6): 1120-1128. 10.1093/molbev/msn057.View ArticlePubMedGoogle Scholar
  27. Li B, Dewey CN: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 2011, 12: 323-10.1186/1471-2105-12-323.View ArticleGoogle Scholar
  28. Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol. 2010, 11 (10): R106-10.1186/gb-2010-11-10-r106.PubMed CentralView ArticlePubMedGoogle Scholar
  29. Janská A, Marík P, Zelenková S, Ovesná J: Cold stress and acclimation – what is important for metabolic adjustment?. Plant Biol. 2010, 12 (3): 395-405. 10.1111/j.1438-8677.2009.00299.x.View ArticlePubMedGoogle Scholar
  30. Reddy AS, Ali GS, Celesnik H, Day IS: Coping with stresses: roles of calcium- and calcium/calmodulin-regulated gene expression. Plant Cell. 2011, 23 (6): 2010-2032. 10.1105/tpc.111.084988.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Vergnolle C, Vaultier M-N, Taconnat L, Renou J-P, Kader J-C, Zachowski A, Ruelland E: The cold-induced early activation of phospholipase C and D pathways determines the response of Two distinct clusters of genes in Arabidopsis cell suspensions. Plant Physiol. 2005, 139: 1217-1233. 10.1104/pp.105.068171.PubMed CentralView ArticlePubMedGoogle Scholar
  32. Munnik T: Phosphatidic acid: an emerging plant lipid second messenger. Trends Plant Sci. 2001, 6 (5): 227-233. 10.1016/S1360-1385(01)01918-5.View ArticlePubMedGoogle Scholar
  33. Doherty CJ, Van Buskirk HA, Myers SJ, Thomashow MF: Roles for Arabidopsis CAMTA transcription factors in cold-regulated gene expression and freezing tolerance. Plant Cell. 2009, 21: 972-984. 10.1105/tpc.108.063958.PubMed CentralView ArticlePubMedGoogle Scholar
  34. Yang T, Shad Ali G, Yang L, Du L, Reddy AS, Poovaiah BW: Calcium/calmodulin-regulated receptor-like kinase CRLK1 interacts with MEKK1 in plants. Plant Signal Behav. 2010, 5 (8): 991-994. 10.4161/psb.5.8.12225.PubMed CentralView ArticlePubMedGoogle Scholar
  35. Chinnusamy V, Zhu JK, Sunkar R: Gene regulation during cold stress acclimation in plants. Methods Mol Biol. 2010, 639: 39-55. 10.1007/978-1-60761-702-0_3.PubMed CentralView ArticlePubMedGoogle Scholar
  36. Theocharis A, Clement C, Barka EA: Physiological and molecular changes in plants grown at low temperatures. Planta. 2012, 235 (6): 1091-1105. 10.1007/s00425-012-1641-y.View ArticlePubMedGoogle Scholar
  37. Lehti-Shiu MD, Zou C, Hanada K, Shiu SH: Evolutionary history and stress regulation of plant receptor-like kinase/pelle genes. Plant Physiol. 2009, 150 (1): 12-26. 10.1104/pp.108.134353.PubMed CentralView ArticlePubMedGoogle Scholar
  38. Osakabe Y, Yamaguchi-Shinozaki K, Shinozaki K, Tran LSP: Sensing the environment: key roles of membrane-localized kinases in plant perception and response to abiotic stress. J Exp Bot. 2013, 64 (2): 445-458. 10.1093/jxb/ers354.View ArticlePubMedGoogle Scholar
  39. Suzuki I, Los DA, Kanesaki Y, Mikami K, Murata N: The pathway for perception and transcription of low-temperature signals in Synechocystis. EMBO J. 2000, 19: 1327-1334. 10.1093/emboj/19.6.1327.PubMed CentralView ArticlePubMedGoogle Scholar
  40. Jeon J, Kim NY, Kim S, Kang NY, Novak O, Ku SJ, Cho C, Lee DJ, Lee EJ, Strnad M, et al: A subset of cytokinin two-component signaling system plays a role in cold temperature stress response in Arabidopsis. J Biol Chem. 2010, 285 (30): 23371-23386. 10.1074/jbc.M109.096644.PubMed CentralView ArticlePubMedGoogle Scholar
  41. van Buskirk HA, Thomashow MF: Arabidopsis transcription factors regulating cold acclimation. Physiol Plant. 2006, 126 (1): 72-80. 10.1111/j.1399-3054.2006.00625.x.View ArticleGoogle Scholar
  42. Toledo-Ortiz G, Huq E, Quail PH: The Arabidopsis basic/helix-loop-helix transcription factor family. Plant Cell. 2003, 15 (8): 1749-1770. 10.1105/tpc.013839.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Rushton PJ, Somssich IE, Ringler P, Shen QJ: WRKY transcription factors. Trends Plant Sci. 2010, 15 (5): 247-258. 10.1016/j.tplants.2010.02.006.View ArticlePubMedGoogle Scholar
  44. Zhang L, Zhao G, Jia J, Liu X, Kong X: Molecular characterization of 60 isolated wheat MYB genes and analysis of their expression during abiotic stress. J Exp Bot. 2012, 63 (1): 203-214. 10.1093/jxb/err264.PubMed CentralView ArticlePubMedGoogle Scholar
  45. Jakoby M, Weisshaar B, Droge-Laser W, Vicente-Carbajosa J, Tiedemann J, Kroj T, Parcy F: bZIP transcription factors in Arabidopsis. Trends Plant Sci. 2002, 7 (3): 106-111. 10.1016/S1360-1385(01)02223-3.View ArticlePubMedGoogle Scholar
  46. Hu H, You J, Fang Y, Zhu X, Qi Z, Xiong L: Characterization of transcription factor gene SNAC2 conferring cold and salt tolerance in rice. Plant Mol Biol. 2008, 67 (1–2): 169-181.View ArticlePubMedGoogle Scholar
  47. Nishizawa A, Yabuta Y, Yoshida E, Maruta T, Yoshimura K, Shigeoka S: Arabidopsis heat shock transcription factor A2 as a key regulator in response to several types of environmental stress. Plant J. 2006, 48 (4): 535-547. 10.1111/j.1365-313X.2006.02889.x.View ArticlePubMedGoogle Scholar
  48. Fode B, Siemsen T, Thurow C, Weigel R, Gatz C: The Arabidopsis GRAS protein SCL14 interacts with class II TGA transcription factors and is essential for the activation of stress-inducible promoters. Plant Cell. 2008, 20: 3122-3135. 10.1105/tpc.108.058974.PubMed CentralView ArticlePubMedGoogle Scholar
  49. Mukhopadhyay A, Vij S, Tyagi AK: Overexpression of a zinc-finger protein gene from rice confers tolerance to cold, dehydration, and salt stress in transgenic tobacco. Proc Natl Acad Sci U S A. 2004, 101 (16): 6309-6314. 10.1073/pnas.0401572101.PubMed CentralView ArticlePubMedGoogle Scholar
  50. Catalá R, Medina J, Salinas J: Integration of low temperature and light signaling during cold acclimation response in Arabidopsis. Proc Natl Acad Sci U S A. 2011, 108 (39): 16475-16480. 10.1073/pnas.1107161108.PubMed CentralView ArticlePubMedGoogle Scholar
  51. Jurczyk B, Rapacz M, Budzisz K, Barcik W, Sasal M: The effects of cold, light and time of day during low-temperature shift on the expression of CBF6, FpCor14b and LOS2 in Festuca pratensis. Plant Sci. 2012, 183: 143-148.View ArticlePubMedGoogle Scholar
  52. Conde A, Chaves MM, Geros H: Membrane transport, sensing and signaling in plant adaptation to environmental stress. Plant Cell Physiol. 2011, 52 (9): 1583-1602. 10.1093/pcp/pcr107.View ArticlePubMedGoogle Scholar
  53. Zhou Z, Wang M-J, Zhao S-T, Hu J-J, Lu M-Z: Changes in freezing tolerance in hybrid poplar caused by up- and down-regulation of PtFAD2 gene expression. Transgenic Res. 2010, 19 (4): 647-654. 10.1007/s11248-009-9349-x.View ArticlePubMedGoogle Scholar
  54. Li B, Takahashi D, Kawamura Y, Uemura M: Comparison of plasma membrane proteomic changes of Arabidopsis suspension-cultured cells (T87 Line) after cold and ABA treatment in association with freezing tolerance development. Plant Cell Physiol. 2012, 53 (3): 543-554. 10.1093/pcp/pcs010.View ArticlePubMedGoogle Scholar
  55. Upchurch RG: Fatty acid unsaturation, mobilization, and regulation in the response of plants to stress. Biotechnol Lett. 2008, 30 (6): 967-977. 10.1007/s10529-008-9639-z.View ArticlePubMedGoogle Scholar
  56. Kiełbowicz-Matuk A, Rey P, Rorat T: The organ-dependent abundance of a Solanum lipid transfer protein is up-regulated upon osmotic constraints and associated with cold acclimation ability. J Exp Bot. 2008, 59 (8): 2191-2203. 10.1093/jxb/ern088.View ArticlePubMedGoogle Scholar
  57. Tall A: Plasma lipid transfer proteins. Annu Rev Biochem. 1995, 64: 235-257. 10.1146/annurev.bi.64.070195.001315.View ArticlePubMedGoogle Scholar
  58. Griffith M, Yaish MWF: Antifreeze proteins in overwintering plants: a tale of two activities. Trends Plant Sci. 2004, 9 (8): 399-405. 10.1016/j.tplants.2004.06.007.View ArticlePubMedGoogle Scholar
  59. Griffith M, Brown GN, Huner NP: Structural changes in thylakoid proteins during cold acclimation and freezing of winter rye (secale cereale L. cv. Puma). Plant Physiol. 1982, 70 (2): 418-423. 10.1104/pp.70.2.418.PubMed CentralView ArticlePubMedGoogle Scholar
  60. Gao MJ, Schafer UA, Parkin IA, Hegedus DD, Lydiate DJ, Hannoufa A: A novel protein from Brassica napus has a putative KID domain and responds to low temperature. Plant J. 2003, 33 (6): 1073-1086. 10.1046/j.1365-313X.2003.01694.x.View ArticlePubMedGoogle Scholar
  61. Uemura M, Tominaga Y, Nakagawara C, Shigematsu S, Minami A, Kawamura Y: Responses of the plasma membrane to low temperatures. Physiol Plant. 2006, 126 (1): 81-89. 10.1111/j.1399-3054.2005.00594.x.View ArticleGoogle Scholar
  62. Usadel B, Blasing OE, Gibon Y, Poree F, Hohne M, Gunter M, Trethewey R, Kamlage B, Poorter H, Stitt M: Multilevel genomic analysis of the response of transcripts, enzyme activities and metabolites in Arabidopsis rosettes to a progressive decrease of temperature in the non-freezing range. Plant Cell Environ. 2008, 31 (4): 518-547. 10.1111/j.1365-3040.2007.01763.x.View ArticlePubMedGoogle Scholar
  63. Oono Y, Seki M, Satou M, Iida K, Akiyama K, Sakurai T, Fujita M, Yamaguchi-Shinozaki K, Shinozaki K: Monitoring expression profiles of Arabidopsis genes during cold acclimation and deacclimation using DNA microarrays. Funct Integr Genomics. 2006, 6 (3): 212-234. 10.1007/s10142-005-0014-z.View ArticlePubMedGoogle Scholar
  64. Wormit A, Trentmann O, Feifer I, Lohr C, Tjaden J, Meyer S, Schmidt U, Martinoia E, Neuhaus HE: Molecular identification and physiological characterization of a novel monosaccharide transporter from Arabidopsis involved in vacuolar sugar transport. Plant Cell. 2006, 18 (12): 3476-3490. 10.1105/tpc.106.047290.PubMed CentralView ArticlePubMedGoogle Scholar
  65. Maruyama K, Takeda M, Kidokoro S, Yamada K, Sakuma Y, Urano K, Fujita M, Yoshiwara K, Matsukura S, Morishita Y, et al: Metabolic pathways involved in cold acclimation identified by integrated analysis of metabolites and transcripts regulated by DREB1A and DREB2A. Plant Physiol. 2009, 150 (4): 1972-1980. 10.1104/pp.109.135327.PubMed CentralView ArticlePubMedGoogle Scholar
  66. Nägele T, Kandel BA, Frana S, Meißner M, Heyer AG: A systems biology approach for the analysis of carbohydrate dynamics during acclimation to low temperature in Arabidopsis thaliana. FEBS J. 2011, 278 (3): 506-518. 10.1111/j.1742-4658.2010.07971.x.View ArticlePubMedGoogle Scholar
  67. Fernandez O, Theocharis A, Bordiec S, Feil R, Jacquens L, Clement C, Fontaine F, Barka EA: Burkholderia phytofirmans PsJN acclimates grapevine to cold by modulating carbohydrate metabolism. Mol Plant Microbe Interact. 2012, 25 (4): 496-504. 10.1094/MPMI-09-11-0245.View ArticlePubMedGoogle Scholar
  68. Livingston DP, Premakumar R, Tallury SP: Carbohydrate partitioning between upper and lower regions of the crown in oat and rye during cold acclimation and freezing. Cryobiology. 2006, 52: 200-208. 10.1016/j.cryobiol.2005.11.001.View ArticlePubMedGoogle Scholar
  69. Palonen P, Buszard D, Donnelly D: Changes in carbohydrates and freezing tolerance during cold acclimation of red raspberry cultivars grown in vitro and in vivo. Physiol Plant. 2000, 110 (3): 393-401. 10.1034/j.1399-3054.2000.1100314.x.View ArticleGoogle Scholar
  70. Knight H, Trewavas AJ, Knight MR: Cold calcium signaling in Arabidopsis involves two cellular pools and a change in calcium signature after acclimation. Plant Cell. 1996, 8 (3): 489-503.PubMed CentralView ArticlePubMedGoogle Scholar
  71. Wang Y, Li J, Wang J, Li Z: Exogenous H2O2 improves the chilling tolerance of manilagrass and mascarenegrass by activating the antioxidative system. Plant Growth Regul. 2010, 61 (2): 195-204. 10.1007/s10725-010-9470-0.View ArticleGoogle Scholar
  72. Li W, Godzik A: Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006, 22 (13): 1658-1659. 10.1093/bioinformatics/btl158.View ArticlePubMedGoogle Scholar
  73. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29.PubMedGoogle Scholar
  74. Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL, Gunasekaran P, Ceric G, Forslund K, et al: The Pfam protein families database. Nucleic Acids Res. 2010, 38 (Database issue): D211-222.PubMed CentralView ArticlePubMedGoogle Scholar
  75. Yeats C, Redfern OC, Orengo C: A fast and automated solution for accurately resolving protein domain architectures. Bioinformatics. 2010, 26 (6): 745-751. 10.1093/bioinformatics/btq034.View ArticlePubMedGoogle Scholar
  76. 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 (3): R25-10.1186/gb-2009-10-3-r25.PubMed CentralView ArticlePubMedGoogle Scholar
  77. Gohain B, Bandyopadhyay T, Borchetia S, Bharalee R, Gupta S, Bhorali P, Agarwala N, Das S: Identification and validation of stable reference genes in Camellia Species. Journal of Biotechnology and Pharmaceutical Research. 2011, 2 (1): 9-18.Google Scholar
  78. Livaka KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.View ArticleGoogle Scholar

Copyright

© Wang et al.; licensee BioMed Central Ltd. 2013

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.