Single-base resolution methylomes of upland cotton (Gossypium hirsutum L.) reveal epigenome modifications in response to drought stress
© The Author(s). 2017
Received: 6 December 2016
Accepted: 5 April 2017
Published: 13 April 2017
DNA methylation, with a cryptic role in genome stability, gene transcription and expression, is involved in the drought response process in plants, but the complex regulatory mechanism is still largely unknown.
Here, we performed whole-genome bisulfite sequencing (WGBS) and identified long non-coding RNAs on cotton leaves under drought stress and re-watering treatments. We obtained 31,223 and 30,997 differentially methylated regions (representing 2.48% of the genome) after drought stress and re-watering treatments, respectively. Our data also showed that three sequence contexts, including mCpG, mCHG, mCHH, all presented a hyper-methylation pattern under drought stress and were nearly restored to normal levels after the re-watering treatment. Among all the methylation variations, asymmetric CHH methylation was the most consistent with external environments, suggesting that methylation/demethylation in a CHH context may constitute a novel epigenetic modification in response to drought stress. Combined with the targets of long non-coding RNAs, we found that long non-coding RNAs may mediate variations in methylation patterns by splicing into microRNAs. Furthermore, the many hormone-related genes with methylation variations suggested that plant hormones might be a potential mechanism in the drought response.
Future crop-improvement strategies may benefit by taking into account not only the DNA genetic variations in cotton varieties but also the epigenetic modifications of the genome.
KeywordsMethylomes Upland cotton Epigenome modifications Long non-coding RNAs Drought stress
An increasing number of documents have evidenced the importance of DNA methylation in the process of plant growth and development, plant defense, and the response to adversity stress, including salt, drought and heavy metal stress. Naturally occurring modifications in a single gene locus in plants may yield heritable morphological changes without alteration of the DNA sequence [1, 2], and some of these modifications may even be passed down through several generations. Arabidopsis, a model plant, is the first plant for which a whole-genome methylome map was deciphered, for its compact genome and rapid cycle [3–5]. Cotton has always been known as a primary model plant for studying genome polyploidization , cell fate determination, cell elongation and cell wall formation . Currently, the genomes of diploid cottons G. arboreum (AA) and G. raimondii (DD) and complex allotetraploid G. hirsutum L. (AADD) have all been completed [8–10], and high-quality reference genomes and improving sequencing technologies may better ensure the whole-genome methylome analyses of cotton and some other moderate-sized crop genomes, such as the recent discovery of the tomato fruit methylome and of hypomethylation in the rice endosperm [11, 12]. Here, we investigate whether whole-genome epigenome reprogramming occurs during the biologically important processes of cotton in response to drought stress.
The response to drought stress in plants is a complicated process, involving several genes and metabolic networks, and DNA methylation is one of them. Publications have shown that drought can induce alterations of the DNA methylation locus and patterns in plants with variety specificity, tissue specificity and stress specificity [13–15]. The establishment and maintenance of DNA methylation patterns that result in gene expression modulation is one of the key steps in epigenetic regulation during normal growth and developmental programs. Documents about transcriptome analysis and drought-response miRNA identification and functional analysis have been publicated [16–18]. Recent discoveries have identified long non-coding RNAs (lncRNAs, defined as transcripts of >200 bp without protein-coding capacity) as new important players in DNA methylation regulation . A recent study published in Nature showed a novel lncRNA arising from the CEBPA gene locus (termed ecCEBPA) that is critical for regulating DNA methylation at this site through the binding of ecCEBPA with DNA methyltransferase1, DNMT1 . Interestingly, a robust increase was observed in the levels of DNA methylation of the CEBPA promoter region following the depletion of this non-coding transcript . However, the alterations of the DNA methylome and the possible roles of lncRNAs in regulating the occurrence of methylation are still unclear under drought conditions in cotton.
Here, we provide a high-resolution and high-coverage map comprising the methylation status of individual cytosines throughout the cotton genome in response to drought stress. On the basis of the possible regulatory functions of lncRNAs, and to investigate the roles of lncRNAs in adjusting the epigenome modifications, we integrated the DNA methylome and lncRNAs. Based on this resource, future studies can use low-coverage methylome sequencing to determine the impact of differentially methylated regions on gene expression, chromosome biology, and transgenerational inheritance. For example, this will allow breeders to determine the contribution of epigenetic modifications to phenotypic variations , and the breeders can utilize a form of “epigenetic selection” to analogously select the individuals with desired epigenomic patterns in breeding programs.
Genome-wide patterns of DNA methylation
Bisulfite sequencing summary
Mapping rate (%)
Statistical results of methylated cytosines in different contexts
Duplication rate (%)
mC percent (%)
mCpG percent (%)
mCHG percent (%)
mCHH percent (%)
Cytosine DNA methylation may have sequence preference
Differentially methylated regions (DMRs) responding to drought stress
Based on the annotation of gene elements (including promoters, exons and introns) , we compared the methylation levels in each context in each gene element (Fig. 2b), which can help to identify the functions of methylation alterations in each element in response to drought stress. We found that promoters displayed significantly higher levels of methylation than did exons and introns, suggesting that the methylation levels of C may be correlated with gene elements. In each gene element, the frequencies of each context, including CG methylation, CHG methylation and CHH methylation, were disparate, and CG methylation accounted for approximately half of the total mC, which suggested that CG methylation was the most important methylation pattern in the three gene elements. Previous publications have showed that methylation most frequently occurs in the so-called CpG islands in the 5’ regulatory gene regions (promoters) . We then compared the methylation levels of different contexts in promoter, exon and intron pre- and post-drought stress, and minuscule changes were found, except for CHH methylation (Fig. 2b). Interestingly, we found that the CHH methylation in the promoter region exhibited an up-regulated pattern under drought stress compared with the control and a slightly down-regulated pattern after the re-watering treatment compared with drought stress. This also suggested that the CHH methylation level is dynamic with environments and that CHH methylation may be very closely correlated with drought stress. Studies have shown that methylation in the CHH context increases compared with the CG and CHG context, and this CHH methylation can be explained by genes promoting de novo methylation on flanking intergenic chromatin (particularly within a few kb of gene starts, that is, always in promoter regions) . Consequently, we inferred that the mechanism through which methylation is altered in promoter regions can be correlated with genes promoting de novo methylation to regulate gene expression in response to drought stress.
To assess the role of methylation in response to drought stress, we examined differentially methylated regions (DMRs) under drought stress compared with controls, which were always considered to participate in the process of gene regulation. We discovered 31,223 DMRs under drought stress compared with the control, and the number of DMRs decreased to 30,997 after the re-watering treatment (Fig. 3b), which indicated that some DMRs were dynamically changed along with the external environments and that, when the adversity was removed, the methylation sites would return to the normal pattern. We also performed a DMR length analysis in each chromosome (Additional file 4: Figure S4, Additional file 5: Figure S5 and Additional file 6: Figure S6) and found that the length of DMRs varied among 26 chromosomes, which may be correlated with the gene numbers, transposon numbers and certain other elements in each chromosome. To determine the role of DMRs during the response to drought stress, we performed Gene Ontology term enrichment analysis of differentially methylated genes (Fig. 3c). Genes involved in protein binding, ADP binding, defense response and transporter activity were highly enriched. Moreover, a GO analysis of hyper- and hypo- methylated genes showed that these genes with great changes in methylation levels or methylation patterns may be closely correlated with drought (Additional file 7: Figure S7 and Additional file 8: Figure S8), in turn suggesting that methylation alterations may be a mechanism to regulate gene expression in response to drought stress. The KEGG pathway analysis results showed that pathways such as ribosomes, RNA degradation, protein processing in the endoplasmic reticulum, and plant hormone signal transduction may be closely correlated with the response to drought (Additional file 9: Figure S9). Above all, we inferred that plants can change the methylation levels or patterns of certain genes in some pathways related to stress to regulate gene expression, which is a mechanism to respond to stress.
Long non-coding RNAs (lncRNAs) may mediate the occurrence of DNA methylation
In plants, DNA methylation typically occurs by RNA-directed DNA methylation (RdDM), which directs transcriptional gene silencing of transposons and endogenous transgenes, and RdDM is driven by non-coding RNAs (ncRNAs) . Publications have reported that de novo DNA methylation can be directed by many long non-coding RNAs (80-nucleotide) and small interfering RNAs (24-nucleotide) [34, 35]. We also performed repetitive sequence and transposon analyses of lncRNAs (Fig. 4c), as many 20–24 nucleotide siRNAs were derived from repetitive sequences and transposons. Consequently, we speculated that it may be a regulatory mechanism through which long non-coding RNAs mediate the occurrence by splicing into microRNAs during the drought response process. Based on lncRNA-seq data, we screened the DMRs with a corrected p-value ≥ 0.05, and 514 target genes were identified with high confidence. The GO analysis of these genes is presented in Fig. 4d.
DNA methylation may participate in the regulation of plant hormones
Methyltransferase inhibition could enhance drought resistance
Drought stress, together with other adversities, such as salt, cold and biotic factors, generates serious challenges to plant growth and development. Hence, plants have developed remarkable capabilities to modulate the physiological and molecular machinery through genome-wide gene-expression changes in response to these environmental perturbations . This study provides insights into the potential of a dynamic epigenome in the drought response in Gossypium hirsutum L. Our data showed that drought stress could induce an up-regulated epigenome, in which three sequence contexts, including mCpG, mCHG, mCHH contexts, all showed a hyper-methylation pattern after drought stress, which was consistent with previous research . Furthermore, the methylation level decreased to some extent but was still slightly higher in re-watered cotton seedlings than in the control, which suggested that some methylation variations could be retained by memory and even inherited by the next few generations and that many methylation variations changed with the treatments; when the stress was removed, these methylation sites could be restored to their original state. Thus, drought-induced epigenetic changes in the cotton genome can be considered a very important regulatory mechanism for cotton plants to adapt to drought and possibly other environmental stresses. Moreover, more significant changes were found in asymmetric CHH contexts than in CG and CHG contexts, changing dynamically with the external environments, which suggested that CHH methylation may be mostly correlated with environments. Publications have shown that CHH DNA methylation/demethylation may constitute a potential novel epigenetic modification mechanism that regulates growth performance in higher plants over the stress period . Thus, we deduced that methylation/demethylation in CHH contexts may be correlated with external environments and different growth stages, comprising a complex epigenetic regulatory pathway with other epigenetic modifications, such as histone acetylation and histone methylation.
Based on the fact that DNA methylation is always associated with nucleotide sequences , we found that the preference for DNA methylation is possibly associated with the symmetric sequence context of CG and CHG and sequence regions with a high or low density of DNA methylation. Many lines of evidence have proven that epigenetic modifications, such as DNA methylation and histone modification, play a crucial role in regulating gene expression in response to various environmental stresses in plants [41, 42]. DNA methylation state alterations in different regions (promoters, exons and introns) cause different gene expression changes. Unlike the CG context and the CHG context, CHH context methylation in promoter regions presents hyper-methylation under drought stress and then recovers to normal levels, which shows that CHH context methylation dynamically changes and is most closely related to the environments. Publications have proven that methylation in the CHH context promotes de novo methylation on flanking intergenic chromatin (particularly within 1 kb of gene starts and ends) . Thus, the disproportionally high levels of CHH relative to CG and CHG that we found in this study suggested that a skewed ratio of de novo methylation near genes would be beneficial for the drought response. Furthermore, methylation alterations in promoter regions would lead to a greater change in expression than those in other gene elements, such as exons and introns, which may be because methylation in promoter regions would influence the transcription of genes with microRNAs and RNA polymerases. Studies in humans have also shown that DNA methylation is an important epigenetic mechanism for gene silencing and cancer progression and that aberrant methylation is mainly found in CpG dinucleotides within promoter regions, which is an important pathway for the repression of gene transcription [44, 45].
This study provides insights into the regulatory network of the dynamic epigenome in response to drought. In addition, previous searches for the “cryptic regulatory role” that promotes responding to stresses and is of significant agricultural value were focused on the expression of stress-tolerance genes, stress-related molecular markers and hormone signaling [48–51]. Indeed, plant breeding projects depend on DNA-base molecular markers (such as QTLs and SNPs) and may overlook much epigenetic variation. Hence, our studies highlight the significance of epigenetic modifications, which are beneficial for future crop-improvement strategies and technologies that consider not only genetic variations but also epigenetic modifications. During the process of responding to drought stress, the DMRs associated with drought tolerance, as well as the hormone-related genes and transcription factors described here, provide an initial set of targets for analyzing epigenomic variations across the identified drought-tolerant cotton varieties and for assessing variations that may form the basis of future expanded selection strategies. Moreover, other pathways, for example, metabolic pathways, signal transduction and ripening, may involve methylation variations, which will require additional experimental data for confirmation.
Sample collection and preparation
Drought-tolerant upland cotton (Gossypium hirsutum L.) ZhongH177 seeds were preserved in the Cotton adversity research laboratory (our laboratory) in Institute of Cotton Research of Chinese Academy of Agricultural Sciences for many years. The seeds were sterilized with 0.1% HgCl2 and placed in a sterile culture dish to accelerate germination before planting into the sand. Cotton seedlings with uniform size were planted into a sand container (10 seedlings per container) in the greenhouse (14 h/day, 30 °C and 10 h/night, 24 °C) of the Institute of Cotton Research of Chinese Academy of Agricultural Sciences. At trefoil stage (three true leaves), drought stress was conducted by withholding watering till the relative water content (RWC) in pots reaches to about 7.0%, while the control pots were watered as before. For methyltransferase inhibitor treatment, 500 μl (a small part of which would be wasted in the injection process) of 1 mM 5-azacytidine aqueous solution was injected into the cotyledons. Then the second and third true leaves from ten plants were harvested together, snap frozen with liquid nitrogen, and stored at −80 °C until use. The method used for sampling was the same for both the control and treatment samples. And the long non-coding RNA sequencing was repeated with three times and whole-genome bisulfite sequencing was conducted with the sequencing depth was 30×.
DNA, RNA isolation, quantification and qualification
Genomic DNA and RNA were extracted with CTAB method . Genomic DNA and RNA degradation and contamination were monitored on 1% agarose gels. DNA and RNA purity were checked using the NanoPhotometer spectrophotometer (IMPLEN, CA, USA). DNA concentration was measured using Qubit DNA Assay Kit in Qubit 2.0 Flurometer (Life Technologies, CA, USA).
Library preparation and quantification
A total amount of 5.2 microgram genomic DNA spiked with 26 ng lambda DNA were fragmented by sonication to 200–300 bp with Covaris S220, followed by end repair and adenylation. Cytosine-methylated barcodes were ligated to sonicated DNA as per manufacturer’s instructions. Then these DNA fragments were treated twice with bisulfite using EZ DNA Methylation-Gold Kit (Zymo Research). And the resulting single-strand DNA fragments were PCR amplificated using KAPA HiFi HotStart Uracil + Ready Mix (2X). Library concentration was quantified by Qubit 2.0 Flurometer (Life Technologies, CA, USA) and quantitative PCR, and the insert size was checked on Agilent Bio-analyzer 2100 system. The method of lncRNAs library construction, sequencing, data analysis were shown as Lu et al. .
Clustering and sequencing
The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq 2000/2500 platform and 100 bp/50 bp single-end reads were generated. Image analysis and base calling were performed with the standard Illumina pipeline, and finally 100 bp paired-end reads were generated.
Read sequences produced by the Illumina pipeline in FastQ format were first pre-processed through in-house perl scripts. Firstly, as a subset of reads contained all of part of the 3’adapter oligonucleotide sequence, every read was scanned for the adapter sequence, and if detected the read was filtered out. Secondly, since some reads had N (unknown base) in their sequences, the percentage of Ns in each read was calculated, and if the percentage of Ns was larger than 10% the read was removed. Thirdly, reads with low quality (PHRED score ≤ 5, and percentage of the low quality bases ≥ 50%) were trimmed. At the same time, Q20, Q30andGC content of the data were calculated. The remaining reads that passed the filters were called as clean reads and all of the subsequent analyses were based on them.
Reads mapping to the reference genome
Bismark software (version 0.12.5)  was used to perform alignments of bisulfite-treated reads to a reference genome with the default parameters. The reference genome was firstly transformed into bisulfite-converted version (C-to-T and G-to-A converted) and then indexed using bowtie2 . Sequence reads were also transformed into fully bisulfite-converted versions (C-to-T and G-to-A converted) before they are aligned to similarly converted versions of the genome in a directional manner. Sequence reads that produce a unique best alignment from the two alignment processes (original top and bottom strand) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. The same reads that aligned to the same regions of genome were regarded as duplicated ones. The sequencing depth and coverage were summarized using deduplicated reads. The results of methylation extractor were transformed into bigWig format for visualization using IGV browser. The sodium bisulfite non-coversion rate was calculated as the percentage of cytosines sequenced at cytosine reference positions in the lambda genome.
Estimating methylation level
Differentially methylated regions analysis
Differentially methylated regions (DMRs) were identified using the swDMR software (http://126.96.36.199/swDMR/), which uses a sliding-window approach. The window is setto 1000 bp and step length is 100 bp. Fisher test is implemented to detect the DMRs.
GO and KEGG enrichment analysis of DMR-related genes
Gene Ontology (GO) enrichment analysis of genes related to DMRs was implemented by the GO-seq R package , in which gene length bias was corrected. GO terms with corrected P-value less than 0.05 were considered significantly enriched by DMR-related genes. KEGG  is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-through put experimental technologies (http://www.genome.jp/kegg/). We used KOBAS software  to test the statistical enrichment of DMR-related genes in KEGG pathways.
MicroRNA prediction analysis
Based on the 10,820 long non-coding RNAs, we conducted microRNA prediction analysis with rfam-scan program. Besides, cmsearch program was also used to search homologous microRNAs in database rfam11, and the screening threshold value was E value < =1E-05. And the method used here was the same as the description by Lu et al .
5 − Azacytidine treatment
At trefoil stage, 5-azac was diluted with ddH2O to 5 mg · ml−1. The diluted 5-azaC was injected into cotton true leaves with an injection syringe, as the method described by Zhong. et al , while the blank control and negative control were performed with nothing and equal amount ddH2O, respectively. The injection work was ceased when the liquid spread to 90% area of the leaves. The samples, both the controls and treatment, were harvested when the leaves turned yellow (nearly 24 h).
Methylation specific PCR (MSP)
Before carrying out MSP test, DNA bisulfite conversion should be done with DNA bisulfite conversion kit (TIANGEN). In which, Bisulfite mix should be prepared firstly and a 120 μl volume system, containing DNA 3.3 μl (~300 ng•μl-1), Bisulfite mix 90 μl and ddH2O 26.7 μl, was used in the conversion process. After the preparation, the program used in the reaction was 95 °C, 10 min, then 64 °C, 60 min, then 4 °C, forever. If DNA content was < 500 ng, time could be shorten to 30 min at 64 °C, treatment would be all right. If DNA content was > 500 ng, 60 min should be used. After the bisulfite conversion, bisulfite-treated DNA should be purified as the direction.
Purified bisulfite-treated DNA was used as templates for methylation specific PCR (MSP), and MSP primers were designed with online program (http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi) . 20 μl volume system, containing template DNA 3.5 μl (~100 ng•μl-1), forward primer 1 μl (10 μM), reverse primer 1 μl (10 μM), dNTPs 1.6 μl (2.5 mM), MSP DNA polymerase 0.5 μl (2.5 U · μl-1), 10 × PCR buffer 2 μl and ddH2O 10.3 μl was used. Program in the MSP reaction was 95 °C, 5 min, 94 °C, 20 s, 60 °C, 30 s, 72 °C, 20 s, 72 °C, 5 min, 35 cycles. After the MSP reaction, 10 μl PCR production was used to detection by 1% agarose gel electrophoresis.
Hormone content determination
Plant hormones content determination method was shown as Lu et al .
Differentially methylated cytosines
Differentially methylated regions
Endogenous target mimicry
Long non-coding RNAs
RNA-directed DNA methylation
Relative water content
Whole-genome bisulfite sequencing
We thank Ruifeng Cui and Binglei Zhang for the help with the preparation of the materials, and Mingge Han and Qi Li and other members in our group for the help with the experiments. We also thank for the members involved in the project in Novogene (Beijing) for the help with whole-genome bisulfite sequencing and data analysis. We also thank reviewers for checking our manuscript and the editors for editing the paper. All authors have declared that no competing interests exist.
This project was supported by grants from the National 13th Five-Year major science and technology projects (Functional genomes and Networks of fibre quality and adversity-resistance in cotton: 2016YFD0101006).
Availability of data and materials
All analysis results data generated during this study are included in this article and its supplementary information. The raw data from this study was uploaded to BioSample database accession ID SRX2012512 (released until 2019-01-01).
XKL and WWY designed the experiments and wrote manuscript. XGW, XGC, NS, JJW and DLW helped the experiments. SW, WLF and LXG managed the materials in the field. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
All the cotton materials were collected from the Institute of Cotton Research, Chinese Academy of Agricultural Sciences, which are public and available for non- commercial purpose.
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