Download The Comparison of Transcriptomes Undergoing Waterlogging at the

Journal of Integrative Agriculture
Advanced Online Publication: 2015
Doi: 10.1016/S2095-3119(15)61138-8
The comparison of transcriptomes undergoing waterlogging at the
seedling stage between tolerant and sensitive varieties of Brassica
napus L.1
ZOU Xi-ling, ZENG Liu, LU Guang-yuan, CHENG Yong, XU Jin-song, ZHANG Xue-kun
Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil
Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan, 430062,
P.R.China
ABSTRACT
RNA sequencing of the sensitive GH01 variety of Brassica napus L. seedling roots under 12
h of waterlogging was compared with previously published data of the ZS9 tolerant variety to
unravel genetic mechanisms of waterlogging tolerance beyond natural variation. A total of 2977
genes with similar expression patterns and 17 genes with opposite expression patterns were
identified in the transcription profiles of ZS9 and GH01. An additional 1438 genes in ZS9 and
1861 genes in GH01 showed strain specific regulation. Analysis of the overlapped genes between
ZS9 and GH01 revealed that waterlogging tolerance is determined by ability to regulate genes
with similar expression patterns. Moreover, differences in both gene expression profiles and ABA
contents between the two varieties suggest that ABA may play some role in waterlogging
tolerance. This study identifies a subset of candidate genes for further functional analysis.
Keywords, rapeseed (Brassica napus L.), waterlogging, DGE (digital gene expression), roots,
transcriptome, comparative analysis
1
Correspondence ZHANG Xue-kun, E-mail: [email protected]
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INTRODUCTION
Waterlogging is one of the most important abiotic stresses affecting global crop production
(Voesenek and Bailey-Serres 2013). Under waterlogging, oxygen diffuses at a lower rate (10-4) in
water compared with in air. Imbalance between oxygen supply and demand causes root hypoxia or
even anoxia, which is detrimental to plants (Jackson and Colmer 2005).
Molecular regulation and physiological processes underlying the response to waterlogging in
plants has been well studied. Under waterlogging conditions, plants adjust mitochondrial
respiration to glycolysis by fermentation of pyruvate to ethanol via pyruvate decarboxylase (PDC)
and alcohol dehydrogenase (ADH) due to a deficiency of oxygen (Sachs et al. 1996). Anaerobic
peptides (ANP) involved in carbohydrate metabolism include PDC, ADH, aldolase, enolase,
glucose phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, and sucrose synthase
(Sachs et al. 1980). Some waterlogging-tolerant plants can enhance oxygen transport from shoot
to root tip by forming aerenchyma and barriers to prevent radial oxygen loss in roots (Kennedy et
al. 1998). Under hypoxic conditions, rice, tomato, maize, and Arabidopsis accumulate ethylene
following induction of ethylene synthesis by 1-aminocyclopropane-1-carboxylic acid (ACC)
synthase (ACS) and ACC oxidase (ACO) gene expression ( Gunawardena et al. 2001; Geisler-Lee
et al. 2010; Horchani and Aschi-Smiti 2011; Barnawal et al. 2012; Yang 2014). Ethylene
promotes the formation of adventitious roots, an adaptive trait under waterlogging (Negi et al.
2010; Vidoz et al. 2010). Transcription factors, such as Sub1A (Xu et al. 2006; Fukao and
Bailey-Serres 2008), SNORKEL1 and 2 in rice (Hattori et al. 2009) and WRKY22 (Hsu et al.
2013) and AP2/ERF transcription factors (RAP2.12, HRE1) in Arabidopsis (Gibbs et al. 2011),
have been found to regulate gene expression under hypoxia. A low oxygen-sensing N-end rule
proteolytic pathway confers tolerance to hypoxia in Arabidopsis (Sasidharan and Mustroph
2011a).
Rapeseed is the second largest oil crop worldwide. In China, the largest planting country in the
world, 80% of rapeseed is planted along the Yangtze River. In this area, rapeseed is planted in paddy
fields as a rotation crop after rice during a season subject to large amounts of rainfall, leading to
substantial waterlogging (Zou et al. 2013b). Waterlogging always results in a yield loss for
rapeseed. Plant soluble sugars ， root MDA (malondialdehyde) content ， SOD (superoxide
dismutase) and CAT (catalase) activity of seedling leaves initially increase then decrease under
waterlogging (Zhou and Lin 1995; Zhang et al. 2008). Photosynthetic content of rapeseed leaves
is decreased; however, tolerant germplasms have an attenuated decrease compared with sensitive
ones (Zhou et al. 1997). Most research has focused on physiological and morphological traits of
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the response to waterlogging in rapeseed. However, few studies have investigated the underlying
molecular mechanisms. Zou et al. (2013c) analysed the transcriptome of the tolerant rapeseed
variety ZS9 under waterlogging at the seedling stage and revealed that gene expression under
waterlogging is mediated by multiple levels of transcriptional, post-transcriptional, translational,
and post-translational regulation, including phosphorylation and protein degradation.
There is natural variation in rapeseed waterlogging tolerance (Zou et al. 2013b, 2014b). One
way to exploiting waterlogging tolerance is to unravel genetic mechanisms beyond natural
variation. Comparative transcriptome analysis of tolerant and sensitive varieties under
waterlogging can be performed to determine these differences.
Here, we provided an analysis of the gene expression profile of the waterlogging-sensitive
rapeseed variety GH01 (Zou et al. 2014b) at the seedling stage under waterlogging using digital
gene expression (DGE) method (Hong et al. 2011; Linsen and Cuppen 2012). We further
performed a comparative transcriptome analysis between the GH01 and the ZS9 (published data)
rapeseed varieties (Zou et al. 2013c) to enhance our understanding of molecular mechanisms that
facilitate survival of waterlogging.
METHODS
Plant materials and waterlogging treatment
GH01 seeds were germinated on moist filter paper for 3 days and individually transplanted to
sand chambers. Seedlings were grown under 16/8 h day/night cycles at 30°C/22 °C with a light
intensity of 500 μmol m–2 s–1. Uniform seedlings with two leaves were selected and divided into
two groups. The control group was cultured with a normal water supply and the treatment group
was waterlogged for 12 h. Plant roots from the treatment and the control groups were harvested at
the same time for RNA extraction.
RNA isolation
Total RNA was isolated under liquid N2 conditions using TRIzol (Invitrogen, USA) based on
the manufacturer’s instructions followed by RNase-free DNase treatment (Takara, Japan). RNA
quantity and quality were evaluated by agarose gel electrophoresis and by Nanodrop
spectrophotometer.
DGE-tag profiling
Two DGE libraries were constructed using total RNA from the treatment and the control
groups using an Illumina Digital Gene Expression Tag Profiling Kit according to the manufacturer's
protocol (Version 2.1B). These two libraries underwent Illumina proprietary sequencing chip for
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cluster generation through in situ amplification and were deep-sequenced using Illumina Genome
Analyzer. The image files generated were processed to produce digital-quality sequence data.
Data was processed by the same methods used by Zou et al. (2013c). Tags that were either
too short or too long, adaptor sequences, low quality tags, empty reads, tags with N, and tags with
only one copy, were filtered from raw data to provided clean reads. Subsequently, clean and distinct
clean tags were classified according their copy number in the library, and their percentage of reads
from the total number of clean and distinct tags was calculated to determine the saturation of the two
libraries.
Tags were mapped to reference sequences, including NCBI EST database of Brassica napus L.
and unigenes of the Brassica oleracea Genomics Database for annotation. No mismatch greater than
1-bp was allowed.
Identification of differentially expressed genes
The expression level of each gene was normalized to RPKM (reads per kilo bases per million
reads) based on the number of clean tags. The threshold for differential expression was set at
P-value<0.005, FDR (False Discovery Rate)<0.01 and a relative change threshold of 2-fold in the
sequence counts across libraries. Functional classification was performed based on GO (gene
ontology) term categories, which was obtained from Phytozome v9.1 BioMart annotation with the
selected organism as Brassica oleracea (http,//www.phytozome.net/) and a p value < 0.05.
Quantitative Real-Time PCR Analysis
Three biological replications with two technique replications of total RNA were used for
quantitative real-time PCR analysis. Reverse transcription of total RNA (5 µg) was performed with
M-MLV RTase cDNA Synthesis Kit (Takara, Japan).
Real time PCR was performed using a CFX96 Real-Time System C1000 Thermal Cycler
(Bio-RAD, USA) with SYBRGreen PCR Master Mix (Takara, Japan). Primers used were
previously published by Zou et al. (2013c). Actin expression was used as a control. PCR was
performed and data was analysed by the same methods as reported (Zou et al. 2010b).
The measurement of ABA (abscisic acid) content
Samples for ABA measurement were prepared the same way as those for RNA sequencing
with the only difference that they were collected at 5 different treatment periods (6 h, 12 h, 1 d, 2
d, and 3 d). The experiment contained three impendent biological replications, and each sample
had at least 10 plants per treatment. The measurement was performed with the plant ABA ELISA
kit (AGDIA, USA) based on the manufacturer’s instructions. The data analysis was performed by
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Excel, and the expression pattern was described by the ratio of ABA content under waterlogging
compared to that under the control.
RESULTS
Analysis of DGE libraries
Approximately 32 million original sequencing tags were produced using GH01 seedling roots,
representing 17501595 raw reads from the treatment library and 14944864 raw reads from the
control library (Table 1). The junk tags were filtered before mapping these tag sequences to the
reference sequences, producing approximately 17.5 and 14.9 million clean sequence tags, for the
treatment and control, respectively. For the two libraries, 65.39 and 61.46% of clean tags were
unambiguously mapped, with 6157758 (53.80% of the clean tags) and 4761524 (51.84% of the
clean tags) clean tags mapped with a stringent criterion of 0 mismatches within the 16-nucleotide
tag alignments. 7.25 and 6.38% of the clean tags mapped to duplicated genes, alternate transcripts,
or repeated sequences. A total of 31335 and 29533 unique genes representing 10614675 and
8598570 clean tags from the control and the treatment libraries were obtained, and the counts for
each unique gene were normalized to RPKM.
The sequencing saturation of the two libraries was analysed to estimate if the depth of
sequencing was sufficient for the transcriptome coverage. The genes mapped by all clean tags and
unambiguous clean tags increased with the total number of tags.
Changes in global gene transcription under waterlogging in GH01
Using DGE technique, we analysed the transcriptome of the root of GH01 under 12 h of
waterlogging. In total, we identified 4857 unigenes that were significantly changed following
waterlogging. Of these genes, nearly half (46.8%, 2274/4857) were annotated as “unknown
function”, “unnamed protein product”, “hypothetical protein” or “unknown protein”. An additional
27 genes were defined as “no homology”.
To examine the functions of the differentially expressed genes, these 2556 annotated genes
were classified into several functional categories based on GO terms (Fig. 1). Among these
differentially expressed genes, more than half could be grouped into at least one functional
category, whereas 462 genes (18.1%) remained unclassified. The functional groups with the
highest number of genes (8.8%) included those involved in transporter facilitation. The
differentially expressed genes were classified to the category of basal metabolism (12.5%),
including “nitrogen metabolism” (3.1%),”lipid metabolism” (2.3%), and “carbohydrate
metabolism” (7.1%). Genes related to “nitrogen metabolism” included glutathione synthetase,
glutamate dehydrogenase, delta 1-pyrroline-5-carboxylate synthetase A. Genes related to
transcriptional regulation (11.6%) were prominently expressed. Approximately, 51 (2.0%)
translational regulation related genes, including elongation factor 1 alpha, Ribosomal
RNA-processing protein 8 and aminoacyl-tRNA synthetase family were differentially expressed
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under waterlogging stress. It is interesting to note that 3.1% of differentially expressed transcripts
encode products that are involved in pathways related to cell-wall loosening enzymes. An
additional 6.7% of the differentially expressed transcripts were predicted to encode enzymes
related to protein degradation. Interestingly, 97 genes (3.8%) encoding enzymes involved in
oxidation reduction were differentially expressed under waterlogging stress (Appendix A). 199
various genes (7.8%) encoding kinases also showed differential expression in response to
waterlogging stress. The universal stress related group consisted of 141 genes. Genes encoding
signal transducers were concentrated (i.e., approximately 4.1%) in a gene cluster containing 105
genes. A large number of genes (124, 4.9%) in the “DNA or RNA binding” category were also
identified. We also found highly abundant, differentially expressed transcripts (87, 3.4%) encoded
proteins involved in secondary metabolism. A complete list of genes in different clusters along
with their expression levels is provided in Appendix A.
The data of GH01 was analysed with the recently published transcriptome dataset of the
tolerant variety ZS9 under the same treatment (Zou et al. 2013c; Zou et al. 2014a).
A total of 2994 overlapping genes between GH01 (4857) and ZS9 (4432) were identified
(Appendix B). A comprehensive analysis of these 2994 genes based on GO terms (Table 2)
revealed that 3 major categories, “transcription regulation”, “carbohydrate metabolism” and
“transporter facilitation” were revealed in these clusters. A total of 197 overlapping genes
involved in “transcription regulation” were identified. In these sets, both ZS9 and GH01 had 100
up-regulated and 97 down-regulated genes. There were 135 “carbohydrate metabolism” related
genes, accounting for 4.5% of the overlapping genes. Among these 135 “carbohydrate
metabolism” related genes, most were down regulated. A total of 95 and 96 “carbohydrate
metabolism” related genes were down regulated in ZS9 and GH01, respectively. For example, the
gene encoding glycoside hydrolase family 28 protein was increased in ZS9 but was decreased in
GH01. 134 “transporter facilitation” related genes were regulated in opposing direction in ZS9 and
GH01, with expression of 76 genes decreased and expression of 58 genes increased in both
varieties. Interestingly, both varieties showed up-regulation of genes related to carbohydrate
metabolism, lipid metabolism, and nitrogen metabolism. Notably, with the exception of the genes
mentioned above, 17 additional genes showed opposing expression patterns between ZS9 and
GH01 (Appendix C). Of these 17 genes, 7 genes had unknown function and the other 10 genes
were related to pathways and functions including “cell wall”, “protein degradation”, “signal
transduction”, “signal transduction”, “small molecular” and “universal stress related.”
Further, the change fold of the 2977 genes in ZS9 and GH01 was compared (Appendix D). In
the 1891 overlapped genes showing decreased expression, 1405 genes (74.3%) were
down-regulated more strongly in ZS9 than that in GH01 (Fig. 2). As presented in Appendix D,
74.1% of genes of GH01 in the “cytoskeleton” category showed greater decreases compared with
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ZS9. However, in other categories, expression of most of these down-regulated genes was more
greatly decreased in ZS9 compared with GH01. For example, the gene encoding
“phosphoenolpyruvate carboxylase” (Bra024730) was decreased by 4.65-fold change in ZS9
compared with a 4.04-fold change in GH01. The change fold of “glucose-6-phosphate
dehydrogenase” (Bra030031) was -2.57 and -1.86 in ZS9 and GH01, respectively. In total, 63.2%
of decreased genes related to “carbohydrate metabolism” showed greater down-regulation in ZS9
compared with GH01. In the 1086 genes showing up-regulation in ZS9 and GH01, 595 genes
(54.7%) showed greater up-regulation in ZS9 compared with GH01. In 11 categories (11/20, 55%),
gene expression in ZS9 showed greater increases compared with GH01 (Fig. 3), including
cytoskeleton (100.0%), DNA or RNA binding (75.0%), No homology (61.5%), oxidation
reduction (61.5%), protein degradation (60.6%), secondary metabolism (52..0%), signal
transduction (58.8%), transcription regulation (64.0%), translation regulation (78.6%), transporter
facilitation (51.7%), universal stress related (73.2%), and unknown function (55.2%).
A total of 1438 of the regulated genes were specifically regulated in ZS9, with 612 being
up-regulated and 826 genes down-regulated in this tolerant variety (Appendix E). The GO term
analysis (Table 3) showed that the 3 most significant identified GO terms were “transcription
regulation”, “transporter facilitation” and “kinase”. Especially striking were the large number of
genes (59 genes, accounting 9.6% of the unique up-regulated genes in ZS9) up-regulated in the
“transcription regulation” related pathways. More specifically, within these GO categories, 39, 42,
and 49 genes were decreased and 59, 21, 10 genes were increased in the categories of
“transcription regulation” (6.8%), “transporter facilitation” (4.4%) and “kinase”, respectively. A
total of 83 regulated genes specific to ZS9 were categorized to “signal transduction” pathways.
The comparison of differentially expressed genes between ZS9 and GH01 revealed 31 “DNA or
RNA binding” related transcripts, of which 20 were decreased and 10 were increased.
In this analysis, GH01 was shown to have a unique gene expression profile with 1863 of
genes increased or decreased compared with ZS9 (Appendix F). Similar to ZS9, the categories of
“transcription regulation”, “transporter facilitation” and “kinase” were 3 most significant
identified functional GO terms (Table 4). However, the percent of down-regulated and
up-regulated genes were different from that of ZS9. A total of 68, 64, and 64 genes were
decreased and 32, 27, 13 genes were increased in the categories of “transcription regulation”
(5.4%), “transporter facilitation” (4.9%) and “kinase” (4.1%), respectively.
Verification of Differentially Expressed Genes
Real-time PCR was performed to verify the differential expression using 12 genes previously
chosen for ZS9 (Zou et al. 2013c) were chosen. Expression of these genes was tested using three
biological replications including the same RNA sample that were used for RNA sequencing plus
an additional two biological replications (Zou et al. 2013c). The real-time PCR data showed a
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similar expression pattern compared with the RNA sequencing data, although the change fold did
not exactly match the DGE analysis (Table 5). The 3 selected genes not differentially expressed by
DGE in GH01 also did not exhibit >2-fold expression changes measured by Q-PCR in response to
waterlogging.
The analysis of ABA content
Differences in ABA content were observed between GH01 and ZS9 (Fig. 4). GH01 exhibited
a lower level of ABA at the early stage of 6h and 12h, and the level of ABA was higher at 1 d and
2 d compared to that of the control. The early increase in ABA content at 12h was detected in ZS9,
and the level of ABA was back to the normal level compared to that of the control after 1d. At the
treatment of 3d, both GH01 and ZS9 exhibited the same level of ABA as the control.
DISCUSSION
The identification of specific genes affecting complex traits is one of the most difficult tasks
in genetics. Transcriptional profiling of GH01 and ZS9 allowed us to determine similarities and
dissimilarities between genotypes in transcriptome responses to waterlogging.
Genotype-specific responding genes of ZS9 and GH01
The ultimate aim of this research is cloning of genes responsible for tolerance to
waterlogging. In genome-wide analysis of genes differentially expressed under stress, the biggest
challenge is to identify specific genes for further study from thousands of differentially expressed
genes. In order to find relevant candidate genes, it is necessary to minimize the effects of other
factors on gene expression during the course of the experiment. For example, the existence of a
common waterlogging response regulatory pathway in the two varieties, and mechanical damage
to the roots during sample preparation triggers the expression of "general" stress-responsive genes
and may lead to false conclusions. A comparative analysis between waterlogging tolerant and
sensitive varieties will help to minimize the effects of irrelevant factors.
Based on this hypothesis, a comparative analysis of ZS9 and GH01 were performed in our
study, highlighting differences between ZS9 and GH01 and leading to identification of many
genes with a genotype-specific response. As ZS9 is a waterlogging tolerant variety, genes
specifically responding to waterlogging should be more relevant to our goals than those of the
sensitive GH01 variety. Accordingly, the ZS9 specific responding genes were discussed below.
As shown in Appendix E, a comparative analysis of the 1438 genes regulated specifically in
ZS9 narrowed the list of candidate genes related to waterlogging tolerance. For example, a total of
151 genes related to “universal stress” were differentially expressed under waterlogging in ZS9;
47 universal stress related genes were identified in ZS9 but not GH01; of these genes, 25 were
up-regulated by waterlogging. In another example, 295 genes related to “transcription regulation”
were differentially expressed under waterlogging in ZS9; 98 of these universal stress related genes
were identified in ZS9 but not GH0; of these genes 59 genes were up-regulated by waterlogging.
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Great progress has been made in showing waterlogging tolerance involves ethylene response
factors (ERFs) such as Sub1A, Snorkel1, Snorkel2, HRE1, HRE2, and RAP2.2 ( Hinz et al. 2010;
Gibbs et al. 2011; Licausi et al. 2011; Sasidharan and Mustroph 2011b). In the transcription
profile of ZS9, 5 ERF coding genes were induced, ERF2, ERF4, ERF7, ERF11, and ERF54. The
comparison of differentially expressed genes in ZS9 and GH01 indicated 4 ERF encoding genes
were induced specifically in ZS9. In total, 1438 of 4332 annotated genes were uniquely
differentially expressed in ZS9. The comparison of the transcriptome of ZS9 and GH01 seedlings
under waterlogging greatly reduced the number of candidate genes. Although further verification
of these genes is required, this study helps to limit the number of the genes to be investigated by
more specific and detailed functional analysis.
Different response ability revealed by the overlapped genes between ZS9 and GH01
Under waterlogging, plants respond to low oxygen through specific transcriptional alterations.
In our study, the analysis between waterlogging-tolerant and sensitive varieties showed a total of
2977 differentially expressed overlapping transcripts showed similar expression patterns in GH01
and ZS9 as a result of waterlogging stress.
Some of the genes encoding TFs common to the two varieties, such as C3HC4-type RING
finger and bHLH TFs, in maize (Zou et al. 2010b), and WRKY in Arabidopsis (Hsu et al. 2011)
were also up-regulated. This suggests that some of the gene expression responses to waterlogging
stress may be modulated by similar TFs and involve similar signal transduction pathways. Our
study identified many common differentially expressed genes in ZS9 and GH01. Among these
genes showing overlap “carbohydrate metabolism” was a primary category. Most of these
“carbohydrate metabolism” related genes were down-regulated in response to waterlogging, in
accordance with prior research. Under conditions of waterlogging, lack of oxygen effectively
blocks mitochondrial aerobic respiration and ATP synthesis (Fukao and Bailey-Serres 2004). The
expression of genes including glucose-6-phosphate dehydrogenase 3, sucrose-phosphatase 3, and
phosphoenolpyruvate carboxylase were decreased in both ZS9 and GH01 under waterlogging
conditions. To adapt to the stress, the ATP source for the cell is accessed through stimulation of
glycolysis (Ismond et al. 2003). Thus, genes related to glycolysis pathways were up-regulated by
waterlogging, such as pyruvate kinase, phosphoenolpyruvate carboxylase kinase 2, and alcohol
dehydrogenase. However, glycolysis is relatively inefficient at energy production compared with
mitochondrial respiration, leading to energy crisis (Geigenberger 2003). Under stress, metabolism
was inhibited, and most of the overlapping genes (1891/2977) were down-regulated in the
response to waterlogging in both ZS9 and GH01, involving genes related to all pathways.
Although enhancing glycolysis was believe to be the critical factor for improving tolerance to
waterlogging (Dennis et al. 2000), our study suggest that genes related to glycolysis were
increased under waterlogging in both waterlogging tolerant and sensitive varieties of rapeseed.
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The up-regulation of glycolysis is a common response in plants, regardless of tolerance to
waterlogging. Thus, up-regulation or down-regulation of these overlapping genes is not the key
factor underling waterlogging tolerance between different genotypes.
Notably, most of the 1891 genes expressed in both ZS9 and GH01 showed greater
down-regulation ZS9 compared with GH01, suggesting a greater decrease of gene expression in
this variety in response to waterlogging (Fig. 2). Under conditions of waterlogging, one of the
most important limitations is energy crisis (Dennis et al. 2000). A tolerant variety should have a
greater ability to maintain basic metabolism with lower energy consumption. Thus, plants must
decrease unnecessary metabolic functions to the lowest possible level under waterlogging stress;
one way to achieve this is through decreasing the expression level of those genes (Voesenek et al.
2006). Hence, in response to waterlogging, ZS9 showed a greater ability to down-regulate
non-critical gene expression compared with GH01. To some extent, the ability to tolerate
waterlogging is not only determined by genes with different expression patterns in different
varieties, but also by the ability to regulate genes with the same expression patterns. If those genes
can be quickly and maximally down-regulated, tolerance should increase, thus this study provides
a new approach to improving tolerance to waterlogging in plants.
The opposing gene expression patterns between GH01 and ZS9 revealed an ABA signal that
may contribute to waterlogging tolerance
In addition to the genes with similar expression patterns between GH01 and ZS9, 17 genes
showed opposing expression patterns under waterlogging. Unfortunately, 7 of these genes were
functional unknown, and the other genes were involved in several pathways, including “universal
stress related” and “signal transduction”.
Of note, the function of one gene, “CYP707A1” encoding a (+)-abscisic acid 8'-hydroxylase,
is clear. ABA is an important hormone that plays a key role in a number of physiological
processes (Ye et al. 2012; Hong et al. 2013). In Arabidopsis, 4 members of the CYP707A gene
family (CYP707A1 to CYP707A4) encode ABA 8'-hydroxylases, which are responsible for
inactivation of ABA (Umezawa et al. 2006). In our study, CYP707A1 (Bra012551) was repressed
in ZS9, while it was increased in GH01 (Table 5). Additionally, two other genes encoding
CYP707A3 were also detected in our study. One was repressed in both ZS9 and GH01, and the
other was repressed in ZS9 and not differentially expressed in GH01 under waterlogging.
Endogenous ABA levels are regulated by both biosynthesis and catabolism of the hormone.
Additionally, a gene (Bra021558) encoding nine-cis-epoxycarotenoid dioxygenase3, that is a key
enzyme in ABA biosynthesis, was up-regulated in ZS9, but did not show differential expression in
GH01 under waterlogging. Taking catabolism rates of the hormone into consideration, this result
indicated that the level of ABA increased in ZS9 and decreased in GH01 under waterlogging at
the early stage. It was proved by the following experiment (Fig. 4). In the early stage, the level of
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ABA increased in ZS9 due to the enhancement of ABA synthesis and repression of ABA
inactivation, while the level of ABA decreased in GH01 due to enhance of ABA inactivation. This
result is in contrast to previous studies, which reported that the level of ABA was decreased under
submergence in rice (Saika et al. 2007) and Rumex (Benschop et al. 2005) and that internodal
elongation was related to reduction of ABA levels in deepwater rice (Kende et al. 1998) and shoot
elongation in Rumex palustris (Voesenek et al. 2003). In these studies, the rice underwent hypoxia
for rice and the Rumex was submerged, which is clearly different from the waterlogging of
rapeseed in our study. Notably, as rapeseed is a waterlogging-sensitive species, and both rice and
Rumex are plants that grow in deep water, a difference in the response to hypoxia is expected.
Interestingly, the sensitive GH01 rapeseed variety showed similar gene expression patterns with
that of rice and Rumex, and the tolerant ZS9 rapeseed variety showed had the opposing patterns.
Undoubtedly, ABA is involved in the hypoxic response, although no detailed study has yet
revealed how it works.
ABA is a crucial phytohormone that functions during the adaptive response to environmental
stresses (Ye et al. 2012; Hong et al. 2013). Physiological roles of the regulation of ABA
biosynthesis and catabolism in waterlogging response should be evaluated by loss-of-function and
gain-of-function experiments in further studies, and the genes identified as differentially expressed
in ZS9 and GH01 in this study can be important candidate genes.
CONCLUSIONS
In summary, by comparing RNA-seq data under waterlogging at the seedling stage between
waterlogging tolerant and sensitive varieties, we were able to separate the genotypically general
and specific gene. The transcriptome comparison between ZS9 and GH01 reduced the number of
candidate genes and helped to identify a limited number of genes for investigation by a more
specific and detailed functional analysis. Analysis of overlapped genes between ZS9 and GH01
revealed that waterlogging tolerance is not only determined by genes with different expression
patterns in different varieties but also by the ability to regulate genes with the same expression
patterns in response to stress. Additionally, opposing gene expression patterns between GH01 and
ZS9 and the analysis of ABA level revealed an ABA signal may contribute to waterlogging
tolerance. Thus, not only will this study be valuable in understanding waterlogging tolerance, but
it can also support further study.
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FIGURE LEGENDS
Fig. 1 Functional categorization of all annotated differentially expressed genes in GH01 under
waterlogging.
This analysis was based on 2387 annotated differentially expressed genes in GH01 under
waterlogging, not including genes with “unknown function” or “no homology”.
Fig. 2 Genes down-regulated in both ZS9 and GH01 in response to waterlogging
For the down regulated genes, ZS9>GH01 means that genes in GH01 were decreased more
strongly than that in ZS9, and ZS9<GH01 means that genes in ZS9 were decreased more strongly
than that in GH01.
Fig. 3 Genes up-regulated in both ZS9 and GH01 in response to waterlogging
For the up regulated genes, ZS9<GH01 means that genes in ZS9 were increased more
strongly than that in GH01, and ZS9<GH01 means that genes in GH01 were increased more
strongly than that in ZS9
Fig. 4 Hormone level of abscisic acid (ABA)
The Y axis was described by the ratio of ABA concentration under waterlogging compared to
that under the control, and error bars indicated S.E.
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Table 1 Summary of the two digital gene expression libraries
0h
12 h
0 h 1)
12 h 1)
0 h 2)
12 h 2)
Clean Reads 17501595 14944864
Mapped
11444809 9185242 65.39% 61.46%
Reads
Perfect
6157758 4761524 35.18% 53.80% 31.86% 51.84%2)
match
<=2 bp
5287051 4423718 30.21% 46.20% 29.60% 48.16%2)
mismatch
Unique
10614675 8598570 60.65% 92.75% 57.54% 93.61%2)
match
Multi-position
830134
586272
4.74% 7.25% 3.92% 6.38%2)
match
Unmapped
6056786 5759622 34.61% 38.54%
Reads
1)
Ratio was from the comparison between the number of sequences and the number
of clean reads.
2)
Ratio was from the comparison between the number of sequences and the number
of mapped reads.
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Table 2. The analysis of 2994 overlapped genes between the regulated
genes of GH01 and that of ZS9 under waterlogging
Overlapped
genes
ZS9
GH01
Gene
Nu
ontology
mbe Percenta
cytoskeleton
Down-re
Up-reg
Down-re
Up-reg
r
ge (%)
gulated
ulated
gulated
ulated
29
1.0
27
2
27
2
4.5
95
40
96
39
1.5
31
15
33
13
1.7
40
12
40
12
carbohydrate
135
metabolism
cell wall
46
DNA or RNA
52
binding
Energy
63
2.1
53
10
53
10
kinase
122
4.1
84
38
84
38
1.3
22
18
22
18
lipid
40
metabolism
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nitrogen
45
metabolism
1.5
22
23
22
23
no homology
16
0.5
3
13
3
13
others
306
10.2
244
62
242
64
1.8
24
29
24
29
3.4
67
34
68
33
1.5
19
25
19
25
1.5
28
17
26
19
2.1
46
16
45
17
6.6
97
100
97
100
1.1
20
14
20
14
4.5
76
58
76
58
oxidation
53
reduction
protein
101
degradation
secondary
44
metabolism
signal
45
transduction
small
62
molecular
transcription
197
regulation
translation
34
regulation
transporter
134
facilitation
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universal
105
stress related
unknown
136
function
5
3.5
63
42
63
42
45.6
836
529
841
524
Table 3 The analysis of 1438 genes regulated specifically in ZS9 under
waterlogging
All regulated
Gene ontology
Down regulated
Percentage
Up regulated
Percentage
Percentage
Number
(%)
Number
(%)
Number
(%)
6
0.4
4
0.5
2
0.3
43
3.0
35
4.2
8
1.3
cell wall
12
0.8
8
1.0
4
0.7
DNA or RNA binding
31
2.2
20
2.4
11
1.8
Energy
29
2.0
19
2.3
10
1.6
kinase
59
4.1
49
5.9
10
1.6
lipid metabolism
21
1.5
15
1.8
6
1.0
cytoskeleton
carbohydrate
metabolism
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nitrogen metabolism
15
1.0
9
1.1
6
1.0
no homology
10
0.7
3
0.4
7
1.1
others
134
9.3
91
11.0
43
7.0
oxidation reduction
40
2.8
16
1.9
24
3.9
protein degradation
51
3.5
29
3.5
22
3.6
secondary metabolism
21
1.5
13
1.6
8
1.3
signal transduction
27
1.9
16
1.9
11
1.8
small molecular
30
2.1
27
3.3
3
0.5
transcription regulation
98
6.8
39
4.7
59
9.6
translation regulation
48
3.3
18
2.2
30
4.9
transporter facilitation
63
4.4
42
5.1
21
3.4
universal stress related
47
3.3
22
2.7
25
4.1
unknown function
653
45.4
351
42.5
302
49.3
Total
1438
100.0
826
100.0
612
100.0
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Table 4 The analysis of 1863 genes regulated specifically in GH01 under
waterlogging
All regulated
Gene ontology
cytoskeleton
Down regulated
Percentage
Up regulated
Percentage
Percentage
Number
(%)
Number
(%)
Number
(%)
9
0.5
8
0.6
1
0.2
46
2.5
34
2.7
12
2.0
34
1.8
27
2.1
7
1.2
carbohydrate
metabolism
cell wall
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DNA or RNA binding
72
3.9
61
4.8
11
1.9
Energy
17
0.9
15
1.2
2
0.3
kinase
77
4.1
64
5.0
13
2.2
lipid metabolism
20
1.1
14
1.1
6
1.0
nitrogen metabolism
35
1.9
19
1.5
16
2.7
no homology
11
0.6
10
0.8
1
0.2
others
156
8.4
117
9.2
39
6.6
oxidation reduction
44
2.4
20
1.6
24
4.1
protein degradation
69
3.7
45
3.5
24
4.1
secondary metabolism
43
2.3
17
1.3
26
4.4
signal transduction
60
3.2
48
3.8
12
2.0
small molecular
17
0.9
13
1.0
4
0.7
transcription regulation
100
5.4
68
5.3
32
5.4
translation regulation
17
0.9
12
0.9
5
0.8
transporter facilitation
91
4.9
64
5.0
27
4.6
universal stress related
36
1.9
24
1.9
12
2.0
unknown function
909
48.8
592
46.5
317
53.6
Total
1863
100.0
1272
100.0
591
100.0
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Table 5 Verification of DEG (digital gene expression) results by real time
PCR.
Gene ID
Bra038
700
Bra021
558
Bra003
Annotation
polygalacturonase
inhibitory protein
nine-cis-epoxycarotenoid
dioxygenase3
AP2 domain containing
DGE
DGE
in
in
ZS91)
GH01
1.70
-1.23
3.28±0.21
-1.51±0.17
1.63
-
2.26±0.32
0.58±0.25
1.32
-
2.85±0.30
-0.32±0.12
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Q-PCR in
ZS9
1)
Q-PCR in
GH01
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701
Bra014
080
Bra007
609
Bra016
729
Bra022
115
Bra004
778
Bra012
551
Bra019
528
Bra015
693
Bra030
945
1)
2)
protein RAP2.5
hydrolase
glycoside hydrolase family
28 protein
glyceraldehyde-3-phosphat
e dehydrogenase 1
transcription factor
1.29
-1.20
4.63±0.06
-1.72±0.33
1.19
-1.12
3.60±0.15
-1.31±0.28
2.05
1.89
2.65±0.30
1.77±0.41
1.08
-
3.14±0.20
0.37±0.22
Stearoyl-acyl carrier protein
desaturase
abscisic acid
8'-hydroxylase/ oxygen
binding
betaine aldehyde
dehydrogenase
4.09
3.60
6.19±0.03
3.54±0.28
-1.58
1.07
-2.06±0.0
6
0.98±0.15
-1.73
-1.01
-1.74±0.0
5
-1.23±0.32
alcohol dehydrogenase
4.02
3.85
3.58±0.14
3.19±0.36
phosphoenolpyruvate
carboxylase
-1.76
-2.34
-2.32±0.2
2
-2.46±0.14
The data has been published (Zou et al. 2013c);
-, the gene was not identified to be differentially expressed in GH01.
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