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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] [键入文字] 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 –2– [键入文字] 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 –3– [键入文字] 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 –4– [键入文字] 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 –5– [键入文字] 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 –6– [键入文字] 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 –7– [键入文字] 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. –8– [键入文字] 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. –9– [键入文字] 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 – 10 – [键入文字] 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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Screening and Identification of Waterlogging Tolerant Rapeseed (Brassica napus L.) During Germination Stage. 2013 Third International Conference on Intelligent System Design and Engineering Applications (Isdea), 1248-1253. Zou X L, Hu C W, Zeng L, Cheng Y, Xu M Y, Zhang X K. 2014b. A Comparison of Screening Methods to Identify Waterlogging Tolerance in the Field in Brassica napus L. during Plant Ontogeny. PLOS ONE, 9, 1-9. Zou X L, Jiang Y Y, Liu L, Zhang Z X, Zheng Y L. 2010b. Identification of transcriptome induced in roots of maize seedlings at the late stage of waterlogging. Bmc Plant Biology, 10, 1-16. Zou X L, Tan X Y, Hu C W, Zeng L, Lu G Y, Fu G P, Cheng Y, Zhang X K. 2013c. The transcriptome of Brassica napus L. roots under waterlogging at the seedling stage. International Journal of Molecular Sciences, 14, 2637-2651. – 15 – [键入文字] 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. – 16 – [键入文字] 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. – 17 – [键入文字] 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 – 18 – [键入文字] 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 – 19 – [键入文字] 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 – 20 – [键入文字] 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 – 21 – [键入文字] 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 – 22 – [键入文字] 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 – 23 – [键入文字] 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 – 24 – Q-PCR in ZS9 1) Q-PCR in GH01 [键入文字] 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. – 25 – [键入文字] – 26 – [键入文字] – 27 – [键入文字] – 28 – [键入文字] – 29 –