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Research Article

Genetic and DNA Methylation Changes in Cotton (Gossypium) Genotypes and Tissues

  • Kenji Osabe,

    Affiliation: CSIRO, Plant Industry, ACT, Australia

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  • Jenny D. Clement,

    Affiliation: CSIRO, Plant Industry, ACT, Australia

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  • Frank Bedon,

    Affiliation: CSIRO, Plant Industry, ACT, Australia

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  • Filomena A. Pettolino,

    Affiliation: CSIRO, Plant Industry, ACT, Australia

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  • Lisa Ziolkowski,

    Affiliation: CSIRO, Plant Industry, ACT, Australia

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  • Danny J. Llewellyn,

    Affiliation: CSIRO, Plant Industry, ACT, Australia

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  • E. Jean Finnegan,

    Affiliation: CSIRO, Plant Industry, ACT, Australia

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  • Iain W. Wilson mail

    Iain.Wilson@csiro.au

    Affiliation: CSIRO, Plant Industry, ACT, Australia

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  • Published: January 20, 2014
  • DOI: 10.1371/journal.pone.0086049

Abstract

In plants, epigenetic regulation is important in normal development and in modulating some agronomic traits. The potential contribution of DNA methylation mediated gene regulation to phenotypic diversity and development in cotton was investigated between cotton genotypes and various tissues. DNA methylation diversity, genetic diversity, and changes in methylation context were investigated using methylation-sensitive amplified polymorphism (MSAP) assays including a methylation insensitive enzyme (BsiSI), and the total DNA methylation level was measured by high-performance liquid chromatography (HPLC). DNA methylation diversity was greater than the genetic diversity in the selected cotton genotypes and significantly different levels of DNA methylation were identified between tissues, including fibre. The higher DNA methylation diversity (CHG methylation being more diverse than CG methylation) in cotton genotypes suggest epigenetic regulation may be important for cotton, and the change in DNA methylation between fibre and other tissues hints that some genes may be epigenetically regulated for fibre development. The novel approach using BsiSI allowed direct comparison between genetic and epigenetic diversity, and also measured CC methylation level that cannot be detected by conventional MSAP.

Introduction

Cytosine methylation is a flexible epigenetic regulatory mechanism that controls gene expression by inhibiting proteins binding to DNA and by changing the structure of the associated chromatin. In plants, DNA methylation can occur on cytosines in any context (CG, CHG and asymmetric CHH, where H is A, C or T) with CG being the most commonly methylated dinucleotide [1], [2]. CG and non-CG methylation can silence transposons and pseudogenes, and regulate plant development and tissue specific gene expression [3], [4]. CG, CHG and CHH methylation are established through de novo methylation dependent on small RNAs, but maintained through different processes [5]. In Arabidopsis, CG methylation is maintained by METHYLTRANSFERASE1(MET1), CHG methylation is maintained by DOMAINS REARRANGED METHYLTRANSFERASE1/2 (DRM1/2) and CHROMOMETHYLASE3 (CMT3), while DECREASE in DNA METHYLATION1 (DDM1) is required for both CG and non-CG methylation [6][10].

Changes in DNA methylation levels between developmental stages or tissues can indicate the involvement of epigenetic regulation. The total DNA methylation level measured by Methylation-sensitive amplified polymorphism (MSAP) in different tissues or developmental stages in maize [11], rice [12], sorghum [13], or Arabidopsis [14] is around 16–40%. Generally, the methylation level increases as the tissue matures [14], [15], and endosperm tissue is often hypomethylated [16][19]. In cotton, changes in the levels of DNA methylation between cotyledon, seedling leaf, mature plant leaf, and roots were observed [20][22], but the relative level of DNA methylation in cotton fibre compared to other tissues is not known. Changes in DNA methylation level between developmental stages or tissues suggest that epigenetic regulation may be important in creating phenotypic diversity.

The requirement for DNA methylation in plant development has been demonstrated by the pleiotropic phenotypes observed when the epigenome was disrupted by down-regulating genes such as DDM1 and MET1 that are required for DNA methylation [10], [23], [24], or by chemical treatment [25], [26]. Loss of DNA methylation can influence plant traits such as yield, fruit ripening, seed size, flowering time, plant size, plant stature, sex determination, and pathogen resistance [10], [27][32]. In cotton, there have been reports of DNA methylation changes related to response to light quality, heterosis, salt-tolerance, alkali stress, and annual habit [20][22], [33][35].

Epigenetic changes can occur more frequently than spontaneous genetic mutations [36], [37], allowing phenotypic plasticity and divergence. Higher DNA methylation diversity compared to the genetic diversity has been reported previously in Viola cazorlensis [38] and Brassica oleracea [39], which suggests the potential involvement of epigenetic regulation of phenotypic traits. Cotton has limited genetic diversity [40][43] due to a relatively recent polyploidization event [44] and subsequent domestication, but the extent of DNA methylation polymorphism is greater compared to the genetic polymorphisms in G. hirsutum accessions collected from different geographical regions around the world [45]. DNA methylation may be contributing to increased phenotypic diversity in cotton, including in fibre traits that have been selected during domestication and breeding.

Cotton fibres, which are widely used for textile production, are elongated single cell seed trichomes growing from the epidermis of the outer integument of ovules. Cotton ovules have two layers of integuments, outer and inner integuments, which develop into the seed coat after fertilisation. About 30% of the epidermal cells in the outer integument form fibre initials [46], which expand, elongate, and thicken over about 50 days post anthesis (dpa) to form mature fibres. G. hirsutum L. and G. barbadense are the most common cultivated cotton species. G. hirsutum dominates global cotton production being grown for its high fibre yield, whereas G. barbadense is grown for its high fibre quality. Both species are allotetraploids (AADD-genome) derived from diploid progenitors similar to present day G. arboreum (A-genome) and G. raimondii (D-genome) species, and have superior fibre yield and quality relative to their ancestors [44], [47]. Complex genomic and epigenomic change affecting gene expression is thought to accompany polyploid formation [48][51], and this is likely to have contributed to the improved fibre traits of the polyploids. Many fibre-related genes change expression during fibre development [52], but it is not known whether these genes are epigenetically regulated.

To understand the potential contribution of DNA methylation regulation to the phenotypic diversity and plant development in cotton, the change in DNA methylation between cotton genotypes and various tissues was investigated. The DNA methylation diversity and genetic diversity was compared using methylation-sensitive amplified polymorphism (MSAP) assays and the total methylation level was measured by high-performance liquid chromatography (HPLC). MSAP analyses demonstrated higher DNA methylation diversity than genetic diversity in the selected cotton genotypes, and significantly different levels of DNA methylation were observed between tissues of the G. hirsutum cultivar Coker 315-11.

Materials and Methods

Plant Materials

Gossypium hirsutum L. genotypes Namcala, Delta Pine 16 (DP16), Sicot 75, Sicot 71, Coker 315-11 and three advanced breeding lines CSX6280, CSX5150, and CSX4184, and G. barbadense genotypes Sipima 280 and CPX12 were used for genotype comparisons. The genotypes were selected to represent a range of short/long fibre length and weak/strong fibre strength (Fig. S1). G. barbadense genotypes were used as a genetic outlier for our analysis. Pedigree analysis (data not shown) shows Namcala as the most distant genotype to other G. hirsutum genotypes. DP16 and Coker 315 are closely related, and Sicot 71, Sicot 75, CSX6280, CSX5150, and CSX4184 form a separate group.

Field experiments were grown at the Australian Cotton Research Institute (ACRI) near Narrabri, NSW, Australia (30°S; 150°E). The soil type was heavy grey clay, Vertosol classified as Ug5.2 [53]. Genotypes were planted in three rows by 12 meter plots with three replications. Field experiments were sown in early October 2010 in rows 100 cm apart. Crops were managed with full irrigation, spraying for insect pests as required and weeds controlled by pre-planting application of herbicides such as trifluralin and Fluorometuron followed by inter-row cultivation prior to flowering.

Cotton leaf samples were taken in February 2011 with the crop being near the cutout stage of development. Five fully expanded leaves from twenty individual plants were sampled from the inner row of each plot. The samples were placed immediately in liquid nitrogen, and transferred to −80°C for storage. Care was taken to select leaves at a similar developmental stage to minimize potential epigenetic variability. After harvesting, cotton was ginned on a 20 saw laboratory gin, and fibre quality was analysed with a High Volume Instrument (HVI; Uster technologies Inc., Charlotte, NC) for fibre length and strength.

A separate glasshouse experiment was performed from July to September 2011 for tissue analyses in Coker 315 grown at Canberra, ACT, Australia. Three-week-old plants were used to collect cotyledon, stems and total root tissues, while 6-month-old mature plants were used to collect fully expanded (mature) leaves, primary roots, 0 dpa and 3 dpa ovules, and 35 dpa fibre (manually separated from seeds). The outer integument (OI) and inner integument (II) were dissected from 0 dpa ovules harvested between 13:00–15:00 [54]. Two flowers were combined as one replicate for OI and II, and for each tissue, three or four biological replicates were collected from individual plants.

DNA Extraction

All DNA extraction was performed using modified a DNeasy mini plant DNA extraction kit (Qiagen, Melbourne, Australia). DNA extraction for genotype comparisons was performed by adding polyvinylpyrrolidone (20 mgml−1) to Buffer AP1, and followed the manufacturer’s instructions. DNA extraction from tissues was performed by adding polyvinylpyrrolidone (20 mgml−1) to buffer AP1, and including a 10 minute incubation with 10 mM dithiothreitol, and 0.5 mgml−1 Proteinase K (final concentration) at 65°C, after the RNase A incubation step, and followed the manufacturer’s instructions. DNA quality was determined by the 260/280 ratio from the Nanodrop spectrophotometer (Thermo Scientific, Melbourne, Australia) and visual integrity of the DNA bands by gel electrophoresis in 1.2% TAE agarose.

HPLC

DNA digest for quantifying methylcytosine was performed as previously described [55], and the digested DNA was separated using a reverse-phase HPLC [56] with modifications. Modifications were made on the HPLC run as follows: Hold at methanol/50 mM KH2PO4 [2.5/97.5](v/v) for 5 min, linear gradient to methanol/KH2PO4 [25/75] over 8 min, and linear gradient back to methanol/KH2PO4 [2.5/97.5] over 2 min. HPLC System Gold (Beckman Coulter, Sydney, Australia) fitted with ZORBAX Eclipse XBD-C18 column, 4.6×150 mm, 5-micron (Agilent, Sydney, Australia) was used for DNA separation, and absorbance at 280 nm (and reference absorbance at 320 nm) was measured by a diode array detector.

Standards containing cytidine, uridine, guanosine, adenosine, 2′-deoxy cytidine, 5-methyl-2′-deoxycytidine, thymidine, deoxy-guanosine, deoxy-adenosine at concentration of 1 µg/ml each, were used to determine retention times of each nucleoside. The average area under the peak were standardised to six 2-fold dilution series of the standards, and the area of deoxy cytidine (dC) and methyl dC was used to calculate the percentage of methyl dC (%mdC) to the amount of total cytosine (methyl dC+dC). Oligonucleotides (5′-TCGAATTCGGCCATGGCCGAATTCGA-3′) containing 0%, 28.5%, and 57.1% methylcytosine (substituted at two or four C’s with methyldeoxycytosine) were synthesized (Sigma, Sydney, Australia) and used as controls for DNA digest and methylcytosine quantification. Peaks were analysed using System Gold (Beckman Coulter, Sydney, Australia) and Microsoft Excel, and error rates were calculated for DNA extraction, digest, and HPLC runs (Table S1).

MSAP

MSAP was performed for each genotype in three biological replicates [57], with modifications. Modification made were: 500 ng of template DNA, double-digest using combinations of EcoRI and BsiSI (Jenabioscience, Jena, Germany), HpaII (New England Biolabs, Arundel, Australia) or MspI (New England Biolabs, Gold Coast, Australia), PCR using FastStart Taq polymerase (Roche diagnostics, Sydney, Australia), fluorescently labelled reactions (FAM, VIC, NED, PET) were mixed, and peaks were separated using an ABI3130×l capillary sequencer (Applied Biosystems, Melbourne, Australia). Traces were analysed using GeneMarker software (SoftGenetics, Pennsylvania, USA). Adaptors and oligonucleotides used are listed in Table S2. The pre-amplification and selective amplification cycling conditions were performed as manufacturer’s instruction with 40 cycles for the selective amplification.

Scoring of each CCGG site was automated to assess the presence (“1”) or absence (“0”) of peaks using GeneMarker. A panel for each oligonucleotide pair was constructed based on all methylation insensitive (EcoRI/BsiSI) data of G. hirsutum and G. barbadense genotypes. The panel was manually refined by selecting the peaks that were strong and consistent in at least two of the three replicates. The panel constructed using methylation insensitive data was applied to all genotype/tissue samples to produce binary data for genetic analysis. To minimize biological and technical scoring errors that occur between each replicate, a consensus score was constructed for each site, producing a single binary data point for each genotype/tissue. All three enzyme combinations recognize the same CCGG site, hence the panels constructed from the methylation insensitive data was applied to the methylation sensitive EcoRI/HpaII and EcoRI/MspI data to automate binary data for DNA methylation analysis. The inclusion of BsiSI is a novel modification of the conventional MSAP method that allowed analysis of additional sites that could not otherwise be assessed, and to directly compare the genetic diversity to the DNA methylation diversity at the same CCGG site.

MSAP data analysis.

A total of 28 oligonucleotide pairs were used to generate 389 bands that could be scored reliably across the tissues, and 44 primer pairs were used to generate 1120 bands that could be scored reliably across the ten genotypes, with 1084 bands (subtracting the G. barbadense specific bands) from just the G. hirsutum genotypes. The polymorphism ratio within each EcoRI/BsiSI data set was determined by calculating the total number of polymorphic sites within the genotypes divided by the total number of sites analysed. The percentage polymorphism of the methylation sensitive enzyme was calculated by total number of DNA methylation polymorphic sites identified, divided by the total number of sites analysed. The calculated DNA methylation polymorphic sites do not exclude sites that are polymorphic both genetically and by DNA methylation.

DNA methylation level was quantified for each genotype and tissue using the MSAP binary data. Presence of peaks in the EcoRI/MspI and absence in EcoRI/HpaII was considered CG methylated site, presence of peaks in EcoRI/HpaII and absence in EcoRI/MspI was considered CHG methylation site, and absence of peaks in both were considered CC methylation (Table S3). However, when CHG is methylated on both strands HpaII and MspI cannot cleave the site, and is represented in the CC methylation (i.e. CC is inclusive of double-strand CHG methylation). The CG, CHG, and CC methylation level was calculated by the number of absent peaks in EcoRI/HpaII or EcoRI/MspI divided by the total number of peaks analysed in the EcoRI/BsiSI data. Genetically polymorphic sites were excluded in the analysis.

The similarity coefficient (Simple matching), cluster analysis, Mantel’s test, and the principal component analysis (PCA) were performed using NTSYS v2.2 [58]. Simple matching method considers the double-absence of peaks as additional information in a pair-wise comparison for closely related species [59]. This is also appropriate for assessing the G. hirsutum genotypes as these genotypes are expected to have low heterozygosity, and the presence/absence of the bands are likely due to homology rather than homoplasy (different DNA fragments from different ancestral origin comigrating). Use of Jaccard and Dice coefficients [60], [61] for closely related species is commonly used when it is not known whether the double-absence of peaks in pair-wise comparison are due to DNA sequence polymorphism or homoplasy [62]. However, in this study, the methylation insensitive (EcoRI/BsiSI) data had bands present across most genotypes and showed that the absence of peaks were not due to sequence polymorphism, and the potential contribution of homoplasy is expected to be very small. Absence of bands in pair-wise comparison of genotypes in both EcoRI/HpaII and EcoRI/MspI data indicates that this region is likely to share the same methylation state. Unweighted pair group method with arithmetic mean (UPGMA) dendrogram was constructed using the simple matching similarity coefficient and DendroUPGMA [63]. Principal component analysis (PCA) was performed using a correlation matrix of the polymorphic fragments of the genotypes as previously described [45], and used to visualize the relatedness of the genotypes using the “PCA batch module” of NTSYS v2.21 to represent the relatedness between each sample by its spacial distance.

Statistical Analysis

Significant differences between each sample’s methylation level determined by HPLC or MSAP were analysed by One-way ANOVA and Tukey’s test using BrightStat [64]. The dendrograms generated from MSAP were supported by Mantel’s test with 1000 permutations (Table S4) and Bootstrap test with 2000 permutations (95% accuracy) [65] using NTSYS v2.21 and Winboot [66], respectively. The average error rate per locus was calculated for each tissue/genotype and each MSAP enzyme combination [67] and are included in Table S4.

Comparison of Fibre Phenotypic Diversity to MSAP Diversity

The Euclidean distance of fibre length and strengths was calculated using NTSYS v2.21 as a measure of distance between each genotype. The computed Euclidean distance matrix and the dissimilarity matrix computed from the simple matching correlation coefficient were assessed for correlation using Mantel’s test with 1000 permutations (Table S5).

Results

DNA Methylation Analysis of Tissues

HPLC and MSAP assays were used to monitor DNA methylation level and context in different tissues of cotton that were grown in the glasshouse. DNA methylation level of tissues harvested from 3-week-old plantlet (cotyledon, stems, roots) and 6-month-old mature plant (mature leaves, stem internode, mature roots, 0 dpa ovules, 3 dpa ovules, 35 dpa fibres, outer integument and inner integument of 0 dpa ovules) were assessed by HPLC (Figure 1). The 0 dpa ovules, 3 dpa ovules, and 35 dpa fibres represent fibre initiation, early elongation, and late elongation stage [47], [68], respectively. Both inner integument and outer integument are derived from the ovule primordium, and the inner integument develops more slowly and independently of the outer integument [69], [70]. The outer integument gives rise to fibre initials (visible at 0 dpa) that develop into fibres and the remaining epidermal cells form the epidermis of the seed coat. The separation of fibre initials from surrounding epidermal cells is difficult, and recently, a method has been developed to isolate the outer integument of ovules that enriches for cells (~30% of epidermal cells) that produce fibre [54]. The comparison of the fibre forming cells on the outer integument to the non-fibre forming inner integument may be useful to understand the changes during fibre development.

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Figure 1. Percentage of total methylation (%mdC) level of Coker 315-11 tissues determined by HPLC.

Different letters above the bars denotes samples that have significantly different levels of methylation (p-value of <0.05). The error bars represent the standard error of the mean. Different tissues from 3-week-old plantlet and 6-months-old mature plant were used.

doi:10.1371/journal.pone.0086049.g001

Plantlet roots had the lowest percentage of total methylation (17%mdC) and cotyledon, stem internodes, mature leaves, 35 dpa fibre, and inner and outer integument had the highest methylation (~23–25%mdC). DNA methylation levels in plantlet cotyledon, stems and roots were significantly different from each other, and the level of methylation in cotyledons was comparable to that of mature leaves and stem internodes of mature plants. Plantlet roots had lower methylation (17%mdC) than mature roots (20%mdC). Both 0 dpa ovules and 3 dpa ovules possessed significantly lower methylation (20%mdC) than the outer and inner integument harvested from ovules at 0 dpa (23%mdC). The 35 dpa fibre (~23%mdC) was comparable to 0 dpa outer and inner integument.

A subset of tissues used in HPLC analysis was selected for MSAP analysis to investigate the methylation context in fibre-developing tissues relative to other tissues (Figure 2). MSAP assays examine the context of DNA methylation using the methylation-sensitive isoschizomers HpaII and MspI, allowing discrimination between CG and CHG methylation. The addition of BsiSI (a methylation insensitive isoschizomer of HpaII and MspI) permitted the identification CC methylation of the CCGG sites that were not cleaved by either HpaII or MspI. Methylation was divided into three categories, scored as CG, CHG and CC methylation according to MSAP data (Table S3). Significant differences were found only in the CC methylation context (Figure 2). Lowest CC methylation was found in 3 dpa ovules and the highest were 35 dpa fibre and 0 dpa inner integument.

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Figure 2. Methylation level of Coker 315-11 tissues determined by MSAP.

Methylation was categorized into CG, CHG and CC methylation and represented as a bar graph. Significant differences are denoted by different letters for CC methylation. The error bars represent the standard error of mean from at least three biological replicates. No significant differences for CG and CHG methylation were found.

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To visualise the relationship of CG and CHG methylation between different tissues, the MSAP data were used to construct a dendrogram (Fig. S2). The overall relationship between the tissues for CG and CHG was similar, indicating a potential relationship in the CG and CHG methylation pattern in different tissues.

The number of methylation polymorphic sites was determined for outer integument, inner integument, and 35 dpa fibre to identify any DNA methylation changes that occur between these tissues during fibre development. Using BsiSI, 389 fragments were detected; of these 28 CG (7.2%) and 23 CHG (5.9%) methylation polymorphisms were found between outer and inner integuments. There were 55 CG (14.1%) and 56 CHG (14.4%) methylation polymorphisms between outer integument and 35 dpa fibre fragments, and fibre had the most number of unique polymorphisms amongst the three tissues (Fig S3).

DNA Methylation Comparison between Genotypes

HPLC.

The methylation level of DNA for two biological replicates was measured by HPLC for each cotton genotype (Figure 3). There were no significant differences of %mdC between genotypes within G. hirsutum, and the average methylation level between species was very similar (24.8% and 24.2%) for G. hirsutum and G. barbadense, respectively.

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Figure 3. Methylation level of leaves for each genotype quantified by HPLC.

The average global methylation level measured by HPLC for G. hirsutum and G. barbadense were 24.8% and 24.2%, respectively. The error bars represent the standard error of mean calculated from two biological replicates. No significant differences were identified between genotypes.

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Level of DNA methylation by MSAP (CG, CHG, CC).

The ten genotypes, including both G. hirsutum and G. barbadense, were selected to represent diversity of fibre length and strength (Fig. S1). The changes in DNA methylation context between the ten genotypes were measured by MSAP to compare the genetic diversity and fibre quality diversity. The use of BsiSI allowed a direct comparison between genetic and DNA methylation diversity of the selected cotton genotypes.

The extent of methylation at CCGG sites was determined using MSAP data (Figure 4). The average CG, CHG, and CC methylation level of the eight G. hirsutum genotypes were 37.8%, 5.2%, and 6.7% (49.7% total methylation), respectively. The average CG, CHG, and CC methylation level for the two G. barbadense genotypes was comparable to that of G. hirsutum. Within G. hirsutum, the total methylation of CCGG sites assessed was 7.5–8 percentage points different between Sicot 75 and CSX5150/CSX6280 (p<0.05). The amount of CHG methylation differed between Sicot 75 and CSX4184/CSX5150/CSX6280/Namcala (p<0.05), but no significant differences were observed across G. hirsutum and G. barbadense.

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Figure 4. Methylation level of cotton genotypes determined by MSAP.

The average genome-wide methylation level measured by MSAP for G. hirsutum and G. barbadense was 49.7% and 51.9%, respectively. No significant differences in CG or CC methylation were found between genotypes. Significant differences found in CHG methylation is denoted by different letters.

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Diversity analysis by Simple Matching (SM) coefficient.

The proportion of polymorphic sites (number of polymorphic sites within the total number of assessed bands) for all ten genotypes and within G. hirsutum genotypes were calculated for each of the enzyme combinations (Table S6) Within G. hirsutum genotypes, the number of CHG polymorphisms was about 1.5-fold and CG polymorphism was 3-fold more than the genetic polymorphism. The similarity coefficient determined using the simple matching method for the ten genotypes averaged 0.878 (range = 0.729–0.976) for EcoRI/BsiSI, 0.877 (range = 0.797–0.937) for EcoRI/HpaII, and 0.837 (range = 0.709–0.945) for EcoRI/MspI. Considering the G. hirsutum genotypes only, the similarity coefficient averaged 0.932 (range = 0.85–0.975) for EcoRI/BsiSI, 0.9 (range = 0.834–0.935) for EcoRI/HpaII, and 0.88 (range = from 0.768–0.945) for EcoRI/MspI. As expected, G. barbadense genotypes were genetically more distant than any of the G. hirsutum genotypes. Irrespective of whether they were compared across all genotypes or within G. hirsutum, the genetic diversity was very low and the DNA methylation diversity was greater than the genetic diversity. Comparing CG and CHG methylation, the CHG methylation was more diverse for both species.

Simple matching similarity coefficients were used to construct dendrograms to represent the relationship between genotypes and their DNA methylation state (Figure 5). Genotypes methylation analysis formed four clades, three clades differentiated by the genetic or DNA methylation state and one differentiating G. barbadense and Coker 315-11. Coker 315-11 is the most distant genotype both genetically and in DNA methylation to the other G. hirsutum genotypes. The dendrogram relationship pattern of the G. hirsutum genotypes (except Coker 315-11) differs within each genetic or DNA methylation clade, indicating that the genetic relation between each genotype is distinct from the DNA methylation relation.

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Figure 5. Dendrogram of the ten genotypes constructed using the similarity coefficient.

G. hirsutum species are indicated by “Gh” and G. barbadense species are indicated by “Gb”. The separation of species and Coker 315-11 from other G. hirsutum genotypes is consistent in all dendrograms, but the relationships between G. hirsutum genotypes are different. Separation of genetic, CHG and CG methylation clusters in G. hirsutum genotypes (except Coker 315-11) show clear genetic/DNA methylation divergence in cultivated cotton.

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The genetic and DNA methylation dendrograms were used to compare with the fibre length or strength-based dendrograms to assess whether DNA methylation was contributing more than the genetic component to fibre quality. There were no statistically significant relations between fibre lengths to the genetic or DNA methylation diversity. Weak but statistically significant positive correlation at p<0.05 level between fibre strength to the genetic and DNA methylation diversity was found, but the DNA methylation was no more correlated to fibre strength than the genetic component.

Visualizing diversity by Principal Component Analysis (PCA).

Principal component analysis (PCA) was performed to visualise the relative distance of each genotype, the genetic and DNA methylation state, using the similarity coefficient (Figure 6). The first and second dimension contributes 27.3% and 14.3% (cumulates to 41.6%), respectively, of the relationship in the multivariate space. CHG methylation was closer to the genetic similarity of G. hirsutum genotypes, and CG methylation was distant to both genetic and CHG methylation. Coker 315-11 and the two G. barbadense genotypes were distant from the other genotypes in both their genetic and methylation relationships.

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Figure 6. PCA of the ten genotypes for each enzyme combinations plotted in two dimensions.

The spatial distance on the graph represents the genetic/DNA methylation relationship between each genotype. Genetic and DNA methylation relationship between the genotypes is more distant between the genetic and DNA methylation state forming genetic, CG methylation, CHG methylation clusters. The CHG methylation and genetic component are closely related, whereas the CG methylation is more distant. The G. barbadense genotypes (CPX12 and Sipima 280) and Coker 315-11 are outliers that are distant from the three clusters, genetically and epigenetically.

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Bootstrap analysis indicated that the divergence of Coker 315-11 and the two G. barbadense genotypes (CPX12, and Sipima 280) from the others are statistically significant (p<0.05) in their genetic relationship. Within the G. hirsutum only, the divergence of Coker 315-11 and other G. hirsutum genotypes was statistically significant for genetic, CG, and CHG methylation relationships (p-value of <0.05).

Discussion

Epigenetic regulation is known to be involved in some traits in cotton [20][22], [33], [34], and has the potential to create phenotypic diversity that can improve agronomical performance. Spontaneous DNA methylation changes resulted in epigenomic divergence over as little as 30 generations in Arabidopsis thaliana [36], [37], suggesting that the 1–2 million years since allopolyploidization of cotton [44] would be sufficient to allow significant genetic, epigenetic, and phenotypic divergence between cotton species. We also found changes in absolute DNA methylation levels between various tissues, including fibre, but no significant difference was found between DNA methylation in leaf tissues across the ten genotypes representing a range of fibre length and strength. By contrast, MSAP analyses showed that DNA methylation diversity of the ten genotypes was higher than the genetic diversity. The higher level of DNA methylation diversity may change gene expression, adding to the higher phenotypic diversity that cannot be explained by the limited genetic diversity in cotton [41][43], [71], [72]. Our results demonstrate that epigenetic regulation is likely to be involved in cotton development and phenotypic diversity, and therefore has potential value for improving agronomic performance.

A unique feature of the MSAP method presented for the first time in this study is the inclusion of the enzyme, BsiSI, which is an isoschizomer of HpaII and MspI that recognises CCGG sequence, but is not methylation sensitive. With the aid of BsiSI, the fully methylated sites containing both CG and CHG methylation have been classified as CC (also includes double-strand CHG methylation, because HpaII cannot cleave CCGG methylated on the outer C on both strands), adding further sites that could not be assessed by the conventional MSAP method. More importantly, the use of BsiSI allowed us to directly compare the genetic diversity to the DNA methylation diversity at the same CCGG site. The genetic diversity of G. hirsutum genotypes was very low, as expected in this species that has gone through a number of genetic bottlenecks during polyploidization, domestication and modern breeding [41][43], but the CG and CHG methylation diversity was always higher than the genetic diversity. Notably, the CHG methylation polymorphism was less frequent (thus less differentiated from the genetic polymorphisms) but occurred more randomly across the genotypes for each site, leading to high diversity and provides some evidence for possible small RNA mediated regulation of phenotypes. Consistent with this, CHG methylation can be guided and maintained by small RNAs, CMT3 and DRMs [73], and small RNAs have been shown to be involved in fibre development and altered by viral infection during fibre development in cotton [74][78].

Although no differences were observed for total DNA methylation level measured by HPLC, the pattern of DNA methylation, as determined by MSAP was different between some genotypes. HPLC was technically more accurate than MSAP in determining the total methylation level, probably due to technical errors of the MSAP method [67], [79]. However, while HPLC quantifies the methylated cytosine content of DNA, it cannot distinguish between the different methylation contexts. MSAP can measure CG and CHG methylation context changes, but generally under-estimates the methylation level as it: does not detect hypermethylated sites, and the dominant nature of the AFLP detects mixed methylated/non-methylated loci as non-methylated site. Despite this, the total methylation level estimated by MSAP (50%) was approximately double the HPLC measurement. Similar results were seen in Brassica oleracea where more than 3-times global methylation was detected by MSAP compared to HPLC [39]. The difference between HPLC and MSAP may partly be caused by the bias of assessing more methylated region of the genome (CCGG sites) in the MSAP method, where CG methylation is the most common context of DNA methylation [1], [2], whereas the HPLC method assesses the total methylated cytosines of the genome (including the non-methylated organellar genomes).

Higher DNA methylation diversity than the genetic diversity has also been demonstrated in another study where cotton plants from different geographic regions were sampled to monitor CG methylation patterns using conventional MSAP [45]. In this study of 20 G. hirsutum accessions grown in different geographical regions, the level of CG methylation polymorphism was 67%. This was somewhat higher than we observed in the eight G. hirsutum genotypes (59.2% CG polymorphism), although the reasons for this are unclear. Our study demonstrated that the DNA methylation diversity remains high even in cotton genotypes that were grown in the same environment over many generations. Similar results have been reported in other cultivated plants that suggest the involvement of epigenetic variation compensating for the lack of genetic variation [80], [81].

G. hirsutum (CSX4184, CSX5150, CSX6280, DP16, Namcala, Sicot 71, Sicot 75, and Coker 315) and G. barbadense (CPX12, and Sipima 280) total DNA methylation level determined by MSAP were 43% and 44.8% (CG and CHG, without CC methylation), respectively, and falls within the DNA methylation level range (16–60%) for other plant species [39], [48], [82], [83]. A large range in total methylation level in leaf DNA has been reported from other studies of G. hirsutum (19–37%), and the total methylation level measured in our study was about 6 percentage points higher than the upper level of these studies [20], [21], [33], [45]. The higher methylation level may result from the difference of technical errors, genotypes and/or environmental conditions used.

The level and context of DNA methylation of selected cotton genotypes and various tissues of Coker 315–11 were assessed using HPLC and MSAP. Higher methylation in mature tissues compared to developing tissues has been reported in other plant species [84][87], and this was also observed in cotton stems and roots, perhaps reflecting the accumulation of methylation over time. Care is needed in the interpretation of methylation changes between tissues as the number of plastids can vary depending on the tissue [88], and plastid DNA is generally not (or very lowly) methylated [89], [90], leading to underestimation of the total methylation level for plastid rich tissues (e.g. leaf). Isolation of nuclei is more accurate for quantifying DNA methylation level, but is technically difficult especially in fibre where limited amount of tissue was available (e.g. only small amount of outer and inner integument material can be obtained from each flower).

The significant increase of total DNA methylation in 35 dpa fibre compared to 0 dpa, 3 dpa ovules, stems and roots indicates a possible involvement of epigenetic regulation during fibre development. The relatively low DNA methylation level in 0 dpa ovules compared to the dissected outer and inner integuments suggests that nucellar tissues are hypomethylated, causing an overall decrease in ovule DNA methylation level. By 3 dpa, the nucellus appears smaller and the endosperm is undergoing rapid development [91]. The endosperm of Arabidopsis and rice is known to be hypomethylated [16], [19], which may also decrease the total DNA methylation level of 3 dpa ovules relative to other tissues.

There were no statistical differences between the total DNA methylation level between the outer integument and 35 dpa fibres, but MSAP analyses showed that there was considerable methylation polymorphism (97 loci). This contrasts strongly with the similarity of methylation level and fewer polymorphism seen between inner and outer integument (31). The polymorphism between 35 dpa fibre and outer integument may be associated with genes involved in fibre development, and the polymorphism between outer integument to inner integument may represent candidate loci involved in fibre initiation that are epigenetically regulated. However, as the outer integument consists of both fibre initials (about 30%) and epidermal cells, changes in methylation may not necessarily (only) be associated with fibre development. Nevertheless, the change in DNA methylation between fibre and other tissues hint that some genes may be epigenetically regulated for fibre development, supported by other studies that show potential involvement of small RNA directed DNA methylation [35], [76], [78]. Sequencing of differentially amplified fragments may provide further insight into the role of DNA methylation and gene expression during these fibre development stages.

DNA methylation changes during fibre development have shown the potential involvement of epigenetic regulation that may influence fibre quality. However, the DNA methylation pattern (of leaves) did not show any more correlation to fibre length and fibre strength, compared to the genetic pattern. The number of cotton genotypes that was assessed was too small to identify an association between DNA methylation and fibre quality. It is important to note that DNA methylation is only one level of multi-layered epigenetic regulation (such as histone modifications), and the DNA methylation diversity measured in this study may be underestimating the epigenetic diversity. Further work investigating DNA methylation and other epigenetic changes during different fibre developmental stages or fibre cells across multiple cultivars may provide an understanding of the epigenetic regulation of fibre traits. The high DNA methylation polymorphism may provide sufficient diversity for epigenome based breeding, even in crops with limited genetic diversity, with further investigation to link the polymorphisms to traits of interest.

Supporting Information

Figure S1.

Statistically significant differences are denoted by different letters. Fibre length and strength of the ten genotypes measured using HVI. Both graphs are arranged in ascending order. The G. hirsutum genotypes represent a range of fibre lengths and strengths, and G. barbadense represents longer and stronger fibre compared to G. hirsutum.

doi:10.1371/journal.pone.0086049.s001

(TIF)

Figure S2.

Dendrogram representing the relationship between tissues and CG/CHG methylation. “EH” represents EcoRI/HpaII, and “EM” represents EcoRI/MspI. The tissues are each represented by; 0 dpa = 0 dpa ovules, 3 dpa = 3 dpa ovules, Cot = Cotyledons, St = plantlet stems, RT = plantlet roots, ML = mature (fully expanded) leaf from mature plant, OI = 0 dpa ovule outer integument, II = 0 dpa ovule inner integument, and 35F = 35 dpa fibres. Mantel’s test supports the reliability of the dendrogram (r = 0.985). The error rate for each tissue comparison was determined by the number of absent peaks in either of the tissue in the EcoRI/BsiSI data (i.e. all tissues should be genetically identical). Comparison of Outer integument-35 dpa fibre had 1.8% error rate, and Outer integument-Inner integument had 2.57% error rate.

doi:10.1371/journal.pone.0086049.s002

(TIF)

Figure S3.

Venn diagram representing the number of CG and CHG polymorphic fragments that are unique to each tissue and the number of polymorphic fragments that are shared between tissues. The 35 dpa fibre was unique with the highest numbers of specific polymorphism between the three tissues, for both CG and CHG methylation context.

doi:10.1371/journal.pone.0086049.s003

(TIF)

Table S1.

Error rate of HPLC method. The standard error of mean of different steps of the HPLC method was evaluated from six DNA extraction replicates, two sets of four DNA digest replicates, five HPLC run replicates (using the same DNA sample), and four day-to-day independent runs of standards. The %mdC and standard error of mean was calculated for each trial. The highest variation was observed in the day-to-day run with a standard error of mean at +/−0.54%.

doi:10.1371/journal.pone.0086049.s004

(DOCX)

Table S2.

List of oligonucleotides used for MSAP. Selective oligonucleotides with fluorescent labels indicated beside primer name (FAM, VIC, NED or PET).

doi:10.1371/journal.pone.0086049.s005

(DOCX)

Table S3.

Classification of methylation type for MSAP. Example of four types of methylation classification and the possible polymorphisms is represented by comparing two genotypes. “1” represents presence of bands and “0” represents absence of bands. Example of determining the methylation state is shown from “Cultivar A” and the polymorphism determined from comparing “Cultivar A” and “Cultivar B”.

doi:10.1371/journal.pone.0086049.s006

(DOCX)

Table S4.

Statistical validation of dendrogram and calculated error rate for MSAP. The r-value was determined for each constructed dendrogram (r-value >0.9 indicates good reliability of data). The average error rate per locus was calculated from the three biological replicates for EcoRI/BsiSI, EcoRI/HpaII, and EcoRI/MspI from all genotypes.

doi:10.1371/journal.pone.0086049.s007

(DOCX)

Table S5.

Matrix correlation (Mantel’s test) between fibre quality and genetic/methylation relationship of cotton genotypes. When comparing the correlation coefficient between matrices with n = 10, coefficient above 0.282 is statistically significant at the 5% level and 0.445 at the 1% level (Lapointe & Legendre, 1992).

doi:10.1371/journal.pone.0086049.s008

(DOCX)

Table S6.

Percentage of polymorphisms identified in each enzyme combination. The percentage represents the number of polymorphic sites within the total number of sites analysed. The percentage polymorphism in the EcoRI/HpaII and EcoRI/MspI does not include the polymorphic sites identified in EcoRI/BsiSI.

doi:10.1371/journal.pone.0086049.s009

(DOCX)

Acknowledgments

We would like to acknowledge Walter Tate for his guidance during the optimization of the MSAP protocol. We would like to thank Dr. Shiming Liu, Dr. Greg Constable, and Dr. Elizabeth Dennis for their advices and valuable discussions throughout this study. Kenji Osabe was supported by CSIRO Office of Chief Executive (OCE) postdoctoral fellowship scheme.

Author Contributions

Conceived and designed the experiments: KO EJF DJL IWW. Performed the experiments: KO JDC LZ. Analyzed the data: KO. Contributed reagents/materials/analysis tools: KO JDC LZ FB FAP. Wrote the paper: KO EJF DJL IWW.

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