Waterlogging is one of the most widespread abiotic determinants for crop growth, leading to the depletion of oxygen, which is vital to plants [1
]. The depletion of oxygen is a major feature of waterlogging because the diffusion of oxygen in water is 10−4
times slower than that in air [2
]. The imbalance between the slow diffusion of gases and the rate that oxygen is consumed by micro-organisms and plant roots drastically reduces the supply of oxygen [3
], which is vital to the roots of plant.
During recent years, gene expression studies in Arabidopsis [4
], maize [8
], rice [10
], and other species [12
] exposed to low oxygen have demonstrated that low oxygen stress causes drastic changes in gene expression. Although the expression of a majority of global genes was depressed, the accumulation of mRNAs was revealed for many genes under hypoxia. These genes included anaerobic proteins (ANPs) involved in sugar phosphate metabolism [15
]. Studies have subsequently identified signal transduction components that are involved in the activation of RopGAP4
(Rop GTPase activating protein4
], and transient induction of mitochondrial alternative oxidase
(AOX), induction of calmodulin and CAP (calmodulin-associated peptide) [15
]. Moreover, the induction of plant growth regulators under waterlogging stress are involved in signaling cascades that influence cellular responses, including increases in ethylene [19
], abscisic acid (ABA) [21
], gibberellic acid (GA) [26
], and auxin (IAA) [27
] and a reduction in cytokinin (CK) [29
]. Transcriptional factors (TFs) play an extremely important role in waterlogging tolerance. In rice, two TFs, Snorkel [31
] and Submergence-1A [32
], have been cloned by mapped based cloning, and both of them encode ethylene-responsive factor-type transcription factors that have evolved opposite functions to adapt to different types of flood. In Arabidopsis, studies have revealed that oxygen sensing is mediated by group VII ERF (ethylene response factor) TFs through the N-End rule pathway [33
Although many transcriptomic studies on waterlogging have addressed similar topics with regard to gene expression in response to waterlogging, this response has proven to have a very complex mechanism. Indeed, understanding the mechanisms that coordinate the regulation of waterlogging tolerance remains a fundamental challenge. Furthermore, there is still no report of a large-scale of gene expression analysis of the response to waterlogging in rapeseed (Brassica napus L.).
Rapeseed is particularly sensitive to waterlogging. The plants experience waterlogging when directly sown in paddy field planted as a rotation crop following rice in China, the largest rapeseed-planting country in the world [29
]. Because there is a need to understand the response to waterlogging in rapeseed, it is necessary and helpful to study expression profiles under waterlogging in a tolerant variety of rapeseed.
To gain comprehensive insight into how rapeseed responds to waterlogging and to identify the genes important in mounting a response of waterlogging tolerance, here we report a detailed analysis of gene expression profiling in ZS9, a waterlogging-tolerant variety [29
], at the vegetative growth stage under waterlogging using digital gene expression (DGE) method, a powerful tool for studying high-throughput gene expression profiling [34
]. We identified sets of positively and negatively significantly expressed genes in response to waterlogging. Our analysis suggests that waterlogging affects a broad spectrum of functional categories and that the regulation of waterlogging tolerance is complex, involving with multiple levels of regulation. The mechanism of the response to waterlogging is discussed.
3. Experimental Section
3.1. Plant Materials and Waterlogging Treatment
] with high waterlogging tolerance was used in this study. Seeds were germinated on moist filter paper. After three days, germinated seeds were individually transplanted to sand chambers. All the plants were grown with 16/8 h day/night cycles at 30 °C/22 °C and a light intensity of 500 μ·mol·m−2
. Seedlings with two leaves were used. Uniform seedlings were selected and divided into two groups: one group was cultured with normal water supply as the control and the other was submerged in water with all leaves in air as the treatment (Figure S2
). Roots treated for 12 h and roots of the controls were harvested at the same time, and were stored at −80 °C.
3.2. RNA Isolation
Total RNA was isolated using TRIzol (Invitrogen, California, CA, USA) according to the manufacture’s instructions followed by RNase-free DNase treatment (Takara, Dalian, China). RNA quantity and quality were assessed by a Nanodrop spectrophotometer and by agarose gel electrophoresis.
3.3. DGE-Tag Profiling
Two DGE libraries were constructed using total RNA of roots of seedlings waterlogged for 12 h and that of the control with Illumina’s Digital Gene Expression Tag Profiling Kit according to the manufacturer’s protocol (Version 2.1B). The two tag libraries underwent Illumina proprietary sequencing chip for 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.
For the raw data, low quality tags, adaptor sequences, tags with unknown nucleotides N, empty reads, tags that were too short or too long, and tags with only one copy, were filtered to get clean reads. The types of clean tags were represented as the distinct clean tags. Subsequently, we classified the clean tags and distinct clean tags according to their copy number in the library and showed their percentage in the total clean and distinct tags, and analyzed saturation of the two libraries.
For annotation, all the tags were mapped to the reference sequences, including NCBI EST database of Brassica napus L., and unigenes of the Brassica oleracea Genomics Database because there is no genome sequence of Brassica napus L. and its genome (AC genome) is a polyploidy of Brassica rapa genome (A genome) and Brassica oleracea genome (C genome). Only no more than 1-bp nucleotide mismatch was allowed.
3.4. Identification of Differentially Expressed Genes
The expression level of each gene was normalized to RPKM based on the number of clean tags. Genes were deemed significantly differentially expressed with a p-value < 0.005, FDR < 0.01 and a relative change threshold of two-fold in the sequence counts across libraries. Functional classification of differentially expressed genes was carried out according to the functional categories of GO.
3.5. Quantitative Real-Time PCR Analysis
Three biological replications with two technique replications of total RNA were used for quantitative real-time PCR analysis. Total RNA was treated with RNase-free DNase. Reverse transcription of total RNA (5 μg) was performed with M-MLV RTase cDNA Synthesis Kit (Takara, Dalian, China).
Real time PCR was carried out using a CFX96 Real-Time System C1000 Thermal Cycler (Bio-RAD, Hercules, CA, USA) using SYBRGreen PCR Master Mix (Takara, Dalian, China). Primers were designed using PRIMER3 software [50
] and were listed in Table S1
. The expression of actin
was used as a control. PCR amplification conditions and the data analysis were referred to Zou’s report [8
In summary, based on several pieces of evidence derived from this study, we speculate that the response to waterlogging is mediated by the regulation of the levels of transcription, post-transcription, translation and post-translation, including phosphorylation and protein degradation. Certainly, it is better to reveal the changes in the real activity of genes under waterlogging through metabolite profiling [13
], which can consider all the levels of regulation. However, because the study of genes transcription is more convenient and less consuming and expensive, this approach is still a good choice to study the abundance of transcripts from the beginning of regulation, which also affects other levels of regulation.
A major objective of our study was to reveal the mechanism of waterlogging tolerance. Our study has demonstrated that gene regulation in response to oxygen deprivation is mediated by transcription, post-transcription, translation, and post-translation, including phosphorylation and protein degradation; in particular, protein degradation might be involved in the negative regulation of the stress response. A large number of differentially expressed genes always leads to difficulty in the characterization of the genes that are actually related to waterlogging tolerance. Based on our analysis, the genes related to multiple levels of regulation might be good choice for further study. This analysis provides a good starting point for future functional studies.