Experimental Methods in Methylation Researchs

[77] Vitale G, Caraglia M, Ciccarelli A, Lupoli G, Abbruzzese A, Tagliaferri P, Oncogene. 2012;**31**(35):3961-3972. DOI:


[85] Bellet D, Lavaissiere L, Mock P, Laurent A, Sabourin JC, Bedossa P, et al. Identification of pro-EPIL and EPIL peptides translated from insulin-like 4 (INSL4) mRNA in human placenta. The Journal of Clinical Endocrinology and Metabolism. 1997;**82**(9):3169-3172. DOI:

[86] Brandt B, Kemming D, Packeisen J, Simon R, Helms M, Feldmann U, et al. Expression of early placenta insulin-like growth factor in breast cancer cells provides an autocrine loop that predominantly enhances invasiveness and motility. Endocrine-Related Cancer. 2005;**12**(4):823-837. DOI: 10.1677/

[87] Maldonado-Saldivia J, van den Bergen J, Krouskos M, Gilchrist M, Lee C, Li R, et al. Dppa2 and Dppa4 are

[88] John T, Caballero OL, Svobodova SJ, Kong A, Chua R, Browning J, et al. ECSA/DPPA2 is an embryo-cancer antigen that is coexpressed with cancertestis antigens in non-small cell lung cancer. Clinical Cancer Research. 2008;

closely linked SAP motif genes restricted to pluripotent cells and the germ line. Stem Cells. 2007;**25**(1):19-28. DOI: 10.1634/stemcells.2006-0269

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[84] Vitale G, Dicitore A, Pepe D, Gentilini D, Grassi ES, Borghi MO, et al. Synergistic activity of everolimus and 5-

thyroid carcinoma cell lines. Molecular Oncology. 2017;**11**(8): 1007-1022. DOI: 10.1002/

10.1038/onc.2011.556

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[78] Manfredi GI, Dicitore A, Gaudenzi G, Caraglia M, Persani L, Vitale G. PI3K/Akt/mTOR signaling in medullary thyroid cancer: A promising molecular target for cancer therapy. Endocrine. 2015;**48**(2):363-370. DOI:

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[80] Guy GR, Jackson RA, Yusoff P, Chow SY. Sprouty proteins: Modified modulators, matchmakers or missing links? The Journal of Endocrinology. 2009;**203**(2):191-202. DOI: 10.1677/

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DOI: 10.1677/JME-09-0024

Fabregas M, Santacana M,

**116**

**119**

**Chapter 7**

**Abstract**

entiation and development.

developmental epigenomics

**1. Introduction**

Atlas of Age- and Tissue-Specific

*Moumouni Konate, Mike J. Wilkinson, Benjamin T. Mayne,* 

*Eileen S. Scott, Bettina Berger and Carlos M. Rodríguez López*

The barley (*Hordeum vulgare*) genome comprises over 32,000 genes, with differentiated cells expressing only a subset of genes; the remainder being silent. Mechanisms by which tissue-specific genes are regulated are not entirely understood, although DNA methylation is likely to be involved. To shed light on the dynamic of DNA methylation during development and its variation between organs, methylation-sensitive genotyping by sequencing (ms-GBS) was used to generate methylation profiles for roots, leaf-blades and leaf-sheaths from five barley varieties, using seedlings at the three-leaf stage. Robust differentially methylated markers (DMMs) were characterised by pairwise comparisons of roots, leaf-blades and leaf-sheaths of three different ages. While very many DMMs were found between roots and leaf parts, only a few existed between leaf-blades and leaf-sheaths, with differences decreasing with leaf rank. Organspecific DMMs appeared to target mainly repeat regions, implying that organ differentiation partially relies on the spreading of DNA methylation from repeats to promoters of adjacent genes. Identified DMMs indicate that different organs do possess diagnostic methylation profiles and suggest that DNA methylation is important for both tissue differentiation and organ function and will provide the basis to the understanding of the role of DNA methylation in plant organ differ-

**Keywords:** epigenomics, *Hordeum vulgare*, leaf, root, tissue-specific methylation,

DNA methylation is an important characteristic of plant genomes [1, 2], and can occur in all cytosine contexts (CG, CHG and CHH, where H = A, C or T) [3]. The effect of DNA methylation variants on plant development has been demonstrated through methylation alteration tests, which led to plant abnormalities [4, 5]. Furthermore, DNA methylation has been reported to vary from tissue to tissue in

DNA Methylation during Early

Development of Barley

(*Hordeum vulgare*)

#### **Chapter 7**

## Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley (*Hordeum vulgare*)

*Moumouni Konate, Mike J. Wilkinson, Benjamin T. Mayne, Eileen S. Scott, Bettina Berger and Carlos M. Rodríguez López*

#### **Abstract**

The barley (*Hordeum vulgare*) genome comprises over 32,000 genes, with differentiated cells expressing only a subset of genes; the remainder being silent. Mechanisms by which tissue-specific genes are regulated are not entirely understood, although DNA methylation is likely to be involved. To shed light on the dynamic of DNA methylation during development and its variation between organs, methylation-sensitive genotyping by sequencing (ms-GBS) was used to generate methylation profiles for roots, leaf-blades and leaf-sheaths from five barley varieties, using seedlings at the three-leaf stage. Robust differentially methylated markers (DMMs) were characterised by pairwise comparisons of roots, leaf-blades and leaf-sheaths of three different ages. While very many DMMs were found between roots and leaf parts, only a few existed between leaf-blades and leaf-sheaths, with differences decreasing with leaf rank. Organspecific DMMs appeared to target mainly repeat regions, implying that organ differentiation partially relies on the spreading of DNA methylation from repeats to promoters of adjacent genes. Identified DMMs indicate that different organs do possess diagnostic methylation profiles and suggest that DNA methylation is important for both tissue differentiation and organ function and will provide the basis to the understanding of the role of DNA methylation in plant organ differentiation and development.

**Keywords:** epigenomics, *Hordeum vulgare*, leaf, root, tissue-specific methylation, developmental epigenomics

#### **1. Introduction**

DNA methylation is an important characteristic of plant genomes [1, 2], and can occur in all cytosine contexts (CG, CHG and CHH, where H = A, C or T) [3]. The effect of DNA methylation variants on plant development has been demonstrated through methylation alteration tests, which led to plant abnormalities [4, 5]. Furthermore, DNA methylation has been reported to vary from tissue to tissue in

many species [6–10], and these methylation changes seemed to be essential for normal plant development [11, 12].

Additionally, tissue-specific methylation was proposed to have a strong correlation with the differential expression of some tissue-specific genes. Examples include tissue-specific pigmentation in maize, which is reported to be under epigenetic control [13], and differential gene expression between organs attributed to differentially methylated regions in soybean [14] and sorghum [10]. These studies extended our understanding of the functional importance of tissue-specific DNA methylation, including its role in setting developmental trajectories [9, 13, 15].

A substantial proportion of developmentally expressed genes have alternative promoters (multiple promoters that regulate the same gene) which are under different regulatory programmes [16]. Maunakea et al. [17] proposed that alternative promoters are, at least sometimes, controlled by intragenic DNA methylation. This form of developmental gene regulation is reasoned to be dependent on transposon activity [16] and by implication would mean that silencing of transposons due to DNA methylation may be central to tissue-specific gene expression. Also, tissuespecific gene expression has been associated with methylation changes in promoter regions [2, 18, 19], especially CG islands within promoters [20]. These studies indicate that tissue-specific gene expression does not rely on a single methylation pattern in the genome but, probably, on a combination of variable DNA methylation features.

The magnitude of differential methylation between tissues has been the subject of controversy. It was believed that significant distinctive DNA methylation existed only between specialised tissues such as endosperm, pollen, leaves and roots [9, 10, 21, 22]. Nevertheless, many of these studies also showed that differential DNA methylation between organs, such as roots and leaves, was minor in rice [23], maize [24], sorghum [10] and Arabidopsis [9]. DNA methylation differences between roots and leaves were small in both mCG and mCHG contexts [9, 10], with about 1% and 5% divergence, respectively, reported in Arabidopsis [9]. While these studies of differential DNA methylation between tissues generally compared the overall methylation levels [9, 10, 24], these results differ from comparisons made with differentially methylated markers (DMMs) between the same tissues [10], probably due to differences in methylation profiling methods, making it difficult to compare results from different studies. Therefore, it is difficult to know whether differences in the results concerning tissue-specific DNA methylation are due to the plant species or to the approach taken. The study of DNA methylation patterns in plant tissues is important for a better understanding of how these epigenetic markers determine tissue differentiation. Thus, further investigation is warranted to clarify organ specificity of cytosine methylation and the distribution patterns of tissue-specific DNA methylation markers in the plant genome.

To undertake such an investigation, we used barley, a globally important cereal crop, the genome of which has been sequenced recently [25]. The availability of a reference genome made barley a model for the study of cereal crops such as wheat, oats or rye. In this study, we assessed differential DNA methylation between two barley (*Hordeum vulgare*) organs (roots and leaves), using methylation-sensitive genotyping by sequencing (ms-GBS) on five genetically distinct varieties (Barque 73, Flagship, Hindmarsh, Schooner and Yarra). For the sake of simplicity and consistency with the literature, roots and leaves or leaf parts (sheath, blade) may be referred to here as tissues and not organs.

**121**

for 10 min.

**2.2 DNA isolation**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley…*

Five spring barley varieties (Barque 73, Flagship, Hindmarsh, Schooner and Yarra), were selected based on their similarity in phenology in order to minimize epigenetic variability between varieties associated with developmental differences. Seeds from all varieties were provided by the Salt Focus Group at the Australian Centre for Plant Functional Genomics (ACPFG, Adelaide, South Australia), and planted at the same time in potting mix comprising 50% UC (University of California at Davis), 35% coco-peat and 15% clay/loam (v v<sup>−</sup><sup>1</sup>

in 3.3 L pots, 17.5 cm deep, free-draining and placed on saucers. The experiment was conducted from 30th January to 20th February 2015 in a greenhouse at the Waite Campus, University of Adelaide, South Australia (34°58′11″S, 138°38′19″E). The seedlings were grown under natural photoperiod, while temperatures were set at 22°C/15°C (day/night). The experiment consisted of five randomized blocks of five varieties (25 seedlings per block). Pots were watered

to weight every 2 days to a gravimetric water content of 16.8% (w w<sup>−</sup><sup>1</sup>

from each of the 5 plants per variety used in the study).

**2.3 Methylation-sensitive genotyping by sequencing (ms-GBS)**

field capacity) [26] until sampling 21 days after sowing, when seedlings were at three-leaf stage (Zadok stage 13 [27]). Blades and sheaths of leaves 1–3 were sampled separately. Leaves 1 and 2 were fully expanded prior to sampling, whilst leaf 3 had just completed growth. About 50 mg of plant material was cut from the middle section of each leaf blade and each leaf sheath and collected in 2 ml micro tubes. Roots were cut from the seedlings and washed using tap water to remove soil particles, then blotted dry with paper towels before sampling 50 mg of root tissue. All samples were snap frozen in liquid nitrogen, and then stored at −80°C until DNA extraction. In total, 175 tissue samples were collected, including 25 root samples (i.e. 5 plants per each of the five varieties used in the study), 75 leaf blade samples (i.e. from leaves 1, 2 and 3 from each of the 5 plants per variety used in the study) and 75 leaf sheath samples (i.e. from leaves 1, 2 and 3

Prior to DNA extraction, frozen plant material was homogenized in a bead beater (2010-Geno/Grinder, SPEX SamplePrep®, USA). DNA isolation was performed from pulverised plant samples using a Qiagen DNeasy kit and following the manufacturer's instructions. DNA samples were quantified using a NanoDrop® 1000 Spectrophotometer (V 3.8.1, ThermoFisher Scientific Inc., Australia) and concentrations were standardized to 10 ng/μl for subsequent library preparation.

The ms-GBS was performed using a modified version [28] of the original GBS technique [56]. Genomic DNA was digested using the combination of a methylation-insensitive rare cutter, *Eco*RI (GAATTC), and a frequent and methylation-sensitive cutter, *Msp*I (CCGG). Each sample of DNA was digested in a reaction volume of 20 μl containing 2 μl of NEB Smartcut buffer, 8 U of HF-*Eco*RI (High-Fidelity) and 8 U of *Msp*I (New England BioLabs, Australia). The reaction was performed in a BioRad 100 thermocycler at 37°C for 2 h, followed by enzyme inactivation at 65°C

)

) (0.8 ×

*DOI: http://dx.doi.org/10.5772/intechopen.90886*

**2.1 Plant material and growth conditions**

**2. Materials and methods**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley… DOI: http://dx.doi.org/10.5772/intechopen.90886*

#### **2. Materials and methods**

*DNA Methylation Mechanism*

plant development [11, 12].

trajectories [9, 13, 15].

tion features.

many species [6–10], and these methylation changes seemed to be essential for normal

A substantial proportion of developmentally expressed genes have alternative promoters (multiple promoters that regulate the same gene) which are under different regulatory programmes [16]. Maunakea et al. [17] proposed that alternative promoters are, at least sometimes, controlled by intragenic DNA methylation. This form of developmental gene regulation is reasoned to be dependent on transposon activity [16] and by implication would mean that silencing of transposons due to DNA methylation may be central to tissue-specific gene expression. Also, tissuespecific gene expression has been associated with methylation changes in promoter regions [2, 18, 19], especially CG islands within promoters [20]. These studies indicate that tissue-specific gene expression does not rely on a single methylation pattern in the genome but, probably, on a combination of variable DNA methyla-

The magnitude of differential methylation between tissues has been the subject of controversy. It was believed that significant distinctive DNA methylation existed only between specialised tissues such as endosperm, pollen, leaves and roots [9, 10, 21, 22]. Nevertheless, many of these studies also showed that differential DNA methylation between organs, such as roots and leaves, was minor in rice [23], maize [24], sorghum [10] and Arabidopsis [9]. DNA methylation differences between roots and leaves were small in both mCG and mCHG contexts [9, 10], with about 1% and 5% divergence, respectively, reported in Arabidopsis [9]. While these studies of differential DNA methylation between tissues generally compared the overall methylation levels [9, 10, 24], these results differ from comparisons made with differentially methylated markers (DMMs) between the same tissues [10], probably due to differences in methylation profiling methods, making it difficult to compare results from different studies. Therefore, it is difficult to know whether differences in the results concerning tissue-specific DNA methylation are due to the plant species or to the approach taken. The study of DNA methylation patterns in plant tissues is important for a better understanding of how these epigenetic markers determine tissue differentiation. Thus, further investigation is warranted to clarify organ specificity of cytosine methylation and the distribution patterns of tissue-specific DNA methylation markers in the plant

To undertake such an investigation, we used barley, a globally important cereal crop, the genome of which has been sequenced recently [25]. The availability of a reference genome made barley a model for the study of cereal crops such as wheat, oats or rye. In this study, we assessed differential DNA methylation between two barley (*Hordeum vulgare*) organs (roots and leaves), using methylation-sensitive genotyping by sequencing (ms-GBS) on five genetically distinct varieties (Barque 73, Flagship, Hindmarsh, Schooner and Yarra). For the sake of simplicity and consistency with the literature, roots and leaves or leaf parts (sheath, blade) may be

Additionally, tissue-specific methylation was proposed to have a strong correlation with the differential expression of some tissue-specific genes. Examples include tissue-specific pigmentation in maize, which is reported to be under epigenetic control [13], and differential gene expression between organs attributed to differentially methylated regions in soybean [14] and sorghum [10]. These studies extended our understanding of the functional importance of tissue-specific DNA methylation, including its role in setting developmental

**120**

genome.

referred to here as tissues and not organs.

#### **2.1 Plant material and growth conditions**

Five spring barley varieties (Barque 73, Flagship, Hindmarsh, Schooner and Yarra), were selected based on their similarity in phenology in order to minimize epigenetic variability between varieties associated with developmental differences. Seeds from all varieties were provided by the Salt Focus Group at the Australian Centre for Plant Functional Genomics (ACPFG, Adelaide, South Australia), and planted at the same time in potting mix comprising 50% UC (University of California at Davis), 35% coco-peat and 15% clay/loam (v v<sup>−</sup><sup>1</sup> ) in 3.3 L pots, 17.5 cm deep, free-draining and placed on saucers. The experiment was conducted from 30th January to 20th February 2015 in a greenhouse at the Waite Campus, University of Adelaide, South Australia (34°58′11″S, 138°38′19″E). The seedlings were grown under natural photoperiod, while temperatures were set at 22°C/15°C (day/night). The experiment consisted of five randomized blocks of five varieties (25 seedlings per block). Pots were watered to weight every 2 days to a gravimetric water content of 16.8% (w w<sup>−</sup><sup>1</sup> ) (0.8 × field capacity) [26] until sampling 21 days after sowing, when seedlings were at three-leaf stage (Zadok stage 13 [27]). Blades and sheaths of leaves 1–3 were sampled separately. Leaves 1 and 2 were fully expanded prior to sampling, whilst leaf 3 had just completed growth. About 50 mg of plant material was cut from the middle section of each leaf blade and each leaf sheath and collected in 2 ml micro tubes. Roots were cut from the seedlings and washed using tap water to remove soil particles, then blotted dry with paper towels before sampling 50 mg of root tissue. All samples were snap frozen in liquid nitrogen, and then stored at −80°C until DNA extraction. In total, 175 tissue samples were collected, including 25 root samples (i.e. 5 plants per each of the five varieties used in the study), 75 leaf blade samples (i.e. from leaves 1, 2 and 3 from each of the 5 plants per variety used in the study) and 75 leaf sheath samples (i.e. from leaves 1, 2 and 3 from each of the 5 plants per variety used in the study).

#### **2.2 DNA isolation**

Prior to DNA extraction, frozen plant material was homogenized in a bead beater (2010-Geno/Grinder, SPEX SamplePrep®, USA). DNA isolation was performed from pulverised plant samples using a Qiagen DNeasy kit and following the manufacturer's instructions. DNA samples were quantified using a NanoDrop® 1000 Spectrophotometer (V 3.8.1, ThermoFisher Scientific Inc., Australia) and concentrations were standardized to 10 ng/μl for subsequent library preparation.

#### **2.3 Methylation-sensitive genotyping by sequencing (ms-GBS)**

The ms-GBS was performed using a modified version [28] of the original GBS technique [56]. Genomic DNA was digested using the combination of a methylation-insensitive rare cutter, *Eco*RI (GAATTC), and a frequent and methylation-sensitive cutter, *Msp*I (CCGG). Each sample of DNA was digested in a reaction volume of 20 μl containing 2 μl of NEB Smartcut buffer, 8 U of HF-*Eco*RI (High-Fidelity) and 8 U of *Msp*I (New England BioLabs, Australia). The reaction was performed in a BioRad 100 thermocycler at 37°C for 2 h, followed by enzyme inactivation at 65°C for 10 min.

Then the ligation of adapters to individual samples was achieved in the same plates by adding 0.1 pmol of the respective barcoded adapters with an *Msp*I cut site overhang, 15 pmol of the common Y adapter with an *Eco*RI cut site overhang, 200 U of T4 Ligase and T4 Ligase buffer (New England BioLabs, Australia) in a total volume of 40 μl. Ligation was carried out at 24°C for 2 h followed by an enzyme inactivation step at 65°C for 10 min.

DNA samples were allocated to plates, 81 samples each, including the negative control, water. Prior to pooling plate samples into a single 81-plex library, the ligation products were individually cleaned up to remove excess adapters using an Agencourt AMPure XP purification system (Beckman Coulter, Australia) at a ratio of 0.85 (AMPure magnetic beads/ligation product), following the manufacturer's instructions. Individual GBS libraries were produced by pooling 25 ng of DNA from each sample. Each constructed library was then amplified in eight separate PCR (25 μl each) containing 10 μl of library DNA, 5 μl of 5× Q5 high fidelity buffer, 0.25 μl polymerase Q5 high fidelity, 1 μl each of Forward and Reverse common primers at 10 μM, 0.5 μl of 10 μM dNTP and 7.25 μl of sterile pure water. PCR amplification was performed in a BioRad T100 thermocycler, consisting of DNA denaturation at 98°C (30 s) and 10 cycles of 98°C (30 s), 62°C (20 s) and 72°C (30 s), followed by 72°C for 5 min. PCR products were next pooled to reconstitute libraries. DNA fragments between 200 and 350 bp in size were captured using AMPure XP magnetic beads following the manufacturer's instructions. Bead-captured fragments were eluted in 35 μl of water, of which 30 μl were collected in a new labelled microtube. Libraries were next paired-end sequenced in an Illumina HiSeq 2500 (Illumina Inc., USA) at the Australian Genome Research Facility (AGRF, Melbourne Node, Australia). Sequencing results were deposited in the European Nucleotide Archive (ENA) (Study Accession Number: PRJEB27251).

#### **2.4 Analysis of global differences in DNA methylation between samples**

Differences in ms-GBS profiles between samples were explored by performing principal component-linear discriminant analysis (PC-LDA) (a supervised clustering approach for high dimensional data), using different levels of hierarchy between samples as the putative drivers in DNA methylation differences (i.e. grouping samples by organ (root vs. leaf), tissue (root vs. blade vs. sheath, and tissue) and age (root vs. leaf 1 vs. leaf 2 vs. leaf 3 vs. sheath 1 vs. sheath 2 vs. sheath 3)). PC-LDA was implemented using the R package FIEmspro 1.1-0 [29] on the standardized coverage, the count per million reads (CPM) of the 913,697 ms-GBS markers generated. PC-LDA results were visualized by a scatter plot of the first two discriminant factors (DFs), and a 3D plot using the first three DFs. Finally we used an unsupervised hierarchical cluster analysis to generate a dissimilarity tree based on Mahalanobis distance [30] generated also based on the standardized coverage (CPM) of the 913,697 ms-GBS markers.

#### **2.5 Detection of DMMs in barley**

Differentially methylated DNA was assessed in mCCGG motifs (recognised by *Msp*I), between barley leaf parts (blade and sheath) and roots. To do so, samples were grouped according to organ type (root, blade and sheath) regardless of the genotype of origin, making 25 samples per organ. This approach aimed to minimise genotype-dependent methylation markers. DMMs were identified using the package, *msgbsR*, developed by Mayne et al. [31]. DMMs were selected based on FDR adjusted P-values with a threshold of 0.05 [32, 33]. The significance of the marker also fulfilled the condition that the read counts reached at least 1 CPM and was

**123**

**Table 1.**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley…*

present in at least 20 samples per organ type (maximum sample per group = 25). The *log*FC (logarithm 2 of fold-change) was computed to estimate the intensity and directionality of differential DNA methylation between tissues. Determining the directionality of DNA methylation uses the fold change as an inverse proxy for change in the methylation level. That is, higher methylation levels on a specific locus will reduce the number of *Msp*I restriction products and therefore reduce the

To test whether there was a relationship between tissue-specific DMMs and particular genomic features (e.g., genes and repeat regions as defined in Ensembl database (http://plants.ensembl.org/biomart/martview/)), DMM distribution was assessed in the barley genome. Therefore, DMMs stable between tissues were mapped to the barley reference genome. Then, the number of DMMs within genomic features (repeats, genes, exons, UTRs and tRNA genes) and per 1 kb bins within 5 kb flanking regions [24, 28] was tallied using the shell module,

The sequencing of the 170 samples of barley tissue which met DNA quality requirements yielded over 900 million raw reads, with more than 91% bases above Q30 (99.9% accuracy of base call [36]) across all samples (**Table 1**). Of these reads, 99.27% contained the barcode and *Eco*RI/*Msp*I adapters ligated during library construction. Further filtering was performed to retain reads that strictly aligned with the barley reference genome. In this way, we obtained nearly 450 million reads (50.10%), with a mean of 2,637,916 high quality reads per sample. These high-quality reads accounted for 913,697 sequence tags, representing 32.30% of the 2,828,642 CCGG sites in the barley genome. Of these sequence tags, 748,594 (80.62%) showed

**3.2 Estimation of tissue- and tissue rank-dependent epigenetic differentiation**

The PC-LDA plots revealed clear evidence of structuring of methylation between samples (**Figure 1a**). A 3D plot using the first three discriminant factors

**Sequencing parameters Yield** Raw reads 901,617,058 Reads that matched barcodes 895,013,295 Reads aligned to barley reference genome 448,445,748 Samples 170 Average reads per sample 2,637,916 Total unique tags 913,697 Polymorphic tags 748,594

*DOI: http://dx.doi.org/10.5772/intechopen.90886*

*bedtools*/*2.22.0* [35].

**3. Results**

number of sequences generated for that locus [34].

**2.6 Distribution of DMMs around genomic features**

**3.1 Methylation-sensitive genotyping by sequencing**

some form of polymorphism for methylation between samples.

*Data yields from ms-GBS, generated using the Illumina HiSeq 2500 platform.*

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley… DOI: http://dx.doi.org/10.5772/intechopen.90886*

present in at least 20 samples per organ type (maximum sample per group = 25). The *log*FC (logarithm 2 of fold-change) was computed to estimate the intensity and directionality of differential DNA methylation between tissues. Determining the directionality of DNA methylation uses the fold change as an inverse proxy for change in the methylation level. That is, higher methylation levels on a specific locus will reduce the number of *Msp*I restriction products and therefore reduce the number of sequences generated for that locus [34].

#### **2.6 Distribution of DMMs around genomic features**

To test whether there was a relationship between tissue-specific DMMs and particular genomic features (e.g., genes and repeat regions as defined in Ensembl database (http://plants.ensembl.org/biomart/martview/)), DMM distribution was assessed in the barley genome. Therefore, DMMs stable between tissues were mapped to the barley reference genome. Then, the number of DMMs within genomic features (repeats, genes, exons, UTRs and tRNA genes) and per 1 kb bins within 5 kb flanking regions [24, 28] was tallied using the shell module, *bedtools*/*2.22.0* [35].

#### **3. Results**

*DNA Methylation Mechanism*

inactivation step at 65°C for 10 min.

(ENA) (Study Accession Number: PRJEB27251).

(CPM) of the 913,697 ms-GBS markers.

**2.5 Detection of DMMs in barley**

**2.4 Analysis of global differences in DNA methylation between samples**

Differences in ms-GBS profiles between samples were explored by performing principal component-linear discriminant analysis (PC-LDA) (a supervised clustering approach for high dimensional data), using different levels of hierarchy between samples as the putative drivers in DNA methylation differences (i.e. grouping samples by organ (root vs. leaf), tissue (root vs. blade vs. sheath, and tissue) and age (root vs. leaf 1 vs. leaf 2 vs. leaf 3 vs. sheath 1 vs. sheath 2 vs. sheath 3)). PC-LDA was implemented using the R package FIEmspro 1.1-0 [29] on the standardized coverage, the count per million reads (CPM) of the 913,697 ms-GBS markers generated. PC-LDA results were visualized by a scatter plot of the first two discriminant factors (DFs), and a 3D plot using the first three DFs. Finally we used an unsupervised hierarchical cluster analysis to generate a dissimilarity tree based on Mahalanobis distance [30] generated also based on the standardized coverage

Differentially methylated DNA was assessed in mCCGG motifs (recognised by *Msp*I), between barley leaf parts (blade and sheath) and roots. To do so, samples were grouped according to organ type (root, blade and sheath) regardless of the genotype of origin, making 25 samples per organ. This approach aimed to minimise genotype-dependent methylation markers. DMMs were identified using the package, *msgbsR*, developed by Mayne et al. [31]. DMMs were selected based on FDR adjusted P-values with a threshold of 0.05 [32, 33]. The significance of the marker also fulfilled the condition that the read counts reached at least 1 CPM and was

Then the ligation of adapters to individual samples was achieved in the same plates by adding 0.1 pmol of the respective barcoded adapters with an *Msp*I cut site overhang, 15 pmol of the common Y adapter with an *Eco*RI cut site overhang, 200 U of T4 Ligase and T4 Ligase buffer (New England BioLabs, Australia) in a total volume of 40 μl. Ligation was carried out at 24°C for 2 h followed by an enzyme

DNA samples were allocated to plates, 81 samples each, including the negative control, water. Prior to pooling plate samples into a single 81-plex library, the ligation products were individually cleaned up to remove excess adapters using an Agencourt AMPure XP purification system (Beckman Coulter, Australia) at a ratio of 0.85 (AMPure magnetic beads/ligation product), following the manufacturer's instructions. Individual GBS libraries were produced by pooling 25 ng of DNA from each sample. Each constructed library was then amplified in eight separate PCR (25 μl each) containing 10 μl of library DNA, 5 μl of 5× Q5 high fidelity buffer, 0.25 μl polymerase Q5 high fidelity, 1 μl each of Forward and Reverse common primers at 10 μM, 0.5 μl of 10 μM dNTP and 7.25 μl of sterile pure water. PCR amplification was performed in a BioRad T100 thermocycler, consisting of DNA denaturation at 98°C (30 s) and 10 cycles of 98°C (30 s), 62°C (20 s) and 72°C (30 s), followed by 72°C for 5 min. PCR products were next pooled to reconstitute libraries. DNA fragments between 200 and 350 bp in size were captured using AMPure XP magnetic beads following the manufacturer's instructions. Bead-captured fragments were eluted in 35 μl of water, of which 30 μl were collected in a new labelled microtube. Libraries were next paired-end sequenced in an Illumina HiSeq 2500 (Illumina Inc., USA) at the Australian Genome Research Facility (AGRF, Melbourne Node, Australia). Sequencing results were deposited in the European Nucleotide Archive

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#### **3.1 Methylation-sensitive genotyping by sequencing**

The sequencing of the 170 samples of barley tissue which met DNA quality requirements yielded over 900 million raw reads, with more than 91% bases above Q30 (99.9% accuracy of base call [36]) across all samples (**Table 1**). Of these reads, 99.27% contained the barcode and *Eco*RI/*Msp*I adapters ligated during library construction. Further filtering was performed to retain reads that strictly aligned with the barley reference genome. In this way, we obtained nearly 450 million reads (50.10%), with a mean of 2,637,916 high quality reads per sample. These high-quality reads accounted for 913,697 sequence tags, representing 32.30% of the 2,828,642 CCGG sites in the barley genome. Of these sequence tags, 748,594 (80.62%) showed some form of polymorphism for methylation between samples.

#### **3.2 Estimation of tissue- and tissue rank-dependent epigenetic differentiation**

The PC-LDA plots revealed clear evidence of structuring of methylation between samples (**Figure 1a**). A 3D plot using the first three discriminant factors


#### **Table 1.**

*Data yields from ms-GBS, generated using the Illumina HiSeq 2500 platform.*

#### **Figure 1.**

*Analysis of the differentiation of DNA methylation profiles of barley roots, leaf sheaths and leaf blades. (a) Scatter plot of the first two discriminant factors of the principal component-linear discriminant analysis (PC-LDA) (DF1 and DF2) using 913,697 ms-GBS markers generated from genomic DNA of roots, leaf sheaths and leaf blades, collected from 25 barley plants at the three-leaf stage (21 days after sowing), comprising five varieties (Barque 73, Flagship, Hindmarsh, Schooner and Yarra). (b) Three-dimensional plot of the first three discriminant factors of the PC-LDA of the same ms-GBS data. (c) Hierarchical cluster of the distances between sample group centres, based on Mahalanobis distance. Blade 1-3 and sheath 1-3 indicate the rank of the organ type, first, second and third leaf of seedlings, respectively.*

(DF1, DF2 and DF3) revealed that blades and sheaths were further grouped according to the rank of the leaf from which they were harvested. The distance between blades and sheaths seems to shrink with leaf rank (**Figure 1b**). This leaf rank-dependent grouping was also supported by hierarchical cluster analysis (HCA) of the distances between sample group centres (**Figure 1c**), based on the Mahalanobis distance [29, 30], and sample clusters matched the leaf developmental age (**Figure 1c**). Leaf rank-dependent DNA methylation differences were further assessed between tissues by comparing the methylation profiles of blades and sheaths for each rank of leaf appearance. No DMMs were observed between the three leaf blades, whereas sheaths 1 and 3 presented 18 DMMs (**Table 2**).

#### **3.3 Differentially methylated DNA markers between roots and leaves**

DMMs between barley roots and leaves were obtained through comparison of the read count per million of tissue types, independently of genotypes.

**125**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley…*

**Blade 1 Blade 2 Blade 3 Sheath 1 Sheath 2 Sheath 3**

This comparison revealed substantial DMMs between both roots vs. blades and roots vs. sheaths (**Figure 2a**), and there were more DMMs between roots and blades (6510 DMMs **Figure 2b**) than between roots and sheaths (4116 DMMs **Figure 2c**). Of these markers, 3266 DMMs were present in both blades and sheaths when compared to roots, and their methylation changed consistently in the same direction in each comparison (**Figure 3a**). The number of DMMs between roots and leaf blades increased with leaf-rank, whereas DMMs between roots and leaf sheaths did not show any relationship with rank (**Figure 2a**). Tissue-specific DMMs were predominantly hypomethylated (95–98%) in leaf parts (sheath or blade) compared to roots (**Figure 2a**). This result was in line with the median of the fold-changes of DMMs, which indicated an overall DNA hypomethylation in leaves (**Figure 4a** and **b**). From here on, DMMs consistently present in roots vs. sheaths and roots vs.

Sheath 3 0 1 1 18 0 — *Differentially methylated markers (FDR <0.05) were obtained from 913,697 ms-GBS tags generated from genomic DNA of barley roots, leaf sheaths and leaf blades, collected from 25 plants at three-leaf stage (21 days after sowing) of five barley varieties (Barque 73, Flagship, Hindmarsh, Schooner and Yarra). Blade 1–3 and sheath 1–3 indicate* 

blades will be designated as stable markers between roots and leaves.

sheath 2 and 1 DMM between blade 3 and sheath 3.

**3.5 Distribution of tissue-specific DMMs around genes**

**3.4 Differentially methylated DNA markers between the leaf blade and sheath**

Relatively few of the tissue-specific DMMs were located around gene exons. Indeed, of the 3266 stable DMMs between root and leaf samples, only 60 (1.8%) were located within 5 kb of a gene, including 21 overlaps with genes and 39 DMMs that were spread within 5 kb upstream and downstream of genes (**Figure 5a**). Apart from the absence of DMMs within 1 kb upstream of transcription start sites, there was no obvious tissue-specific DMM distribution pattern around the genes

There was only a small number of DMMs between leaf blades and sheaths (0–73 DMMs, **Table 2** and **Figure 2d**). These DMMs were basically between leaf blades and sheaths 1 and 2; and there was none between blade 1 and sheath 3. There was only 1 DMM between sheath 3 and blades 2 and 3 (**Table 2** and **Figure 2d**). Pairwise comparisons between blades 1–2 and sheaths 1–2 revealed 20 common DMMs, which were all hypermethylated in sheaths compared to blades (**Figures 2e** and **4b**). Half of the 20 common DMMs between blades and sheaths were located on chromosome 5H. Furthermore, there were no DMMs in pairwise comparisons among blades 1–3 and among sheaths 1–3, except between sheath 1 and sheath 3 which had 18 DMMs (**Table 2**). However, comparing blades and sheaths of the same leaf rank showed 32 DMMs between blade 1 and sheath 1, 36 DMMs between blade 2 and

*DOI: http://dx.doi.org/10.5772/intechopen.90886*

Blade 2 0 —

Blade 3 0 0 —

*the rank of the leaf; first, second and third, respectively, on seedlings.*

Sheath 1 32 37 73 —

*Number of differentially methylated markers in barley tissues of different ages.*

Sheath 2 29 36 40 0 —

Blade 1 —

**Table 2.**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley… DOI: http://dx.doi.org/10.5772/intechopen.90886*


*Differentially methylated markers (FDR <0.05) were obtained from 913,697 ms-GBS tags generated from genomic DNA of barley roots, leaf sheaths and leaf blades, collected from 25 plants at three-leaf stage (21 days after sowing) of five barley varieties (Barque 73, Flagship, Hindmarsh, Schooner and Yarra). Blade 1–3 and sheath 1–3 indicate the rank of the leaf; first, second and third, respectively, on seedlings.*

#### **Table 2.**

*DNA Methylation Mechanism*

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**Figure 1.**

(DF1, DF2 and DF3) revealed that blades and sheaths were further grouped according to the rank of the leaf from which they were harvested. The distance between blades and sheaths seems to shrink with leaf rank (**Figure 1b**). This leaf rank-dependent grouping was also supported by hierarchical cluster analysis (HCA) of the distances between sample group centres (**Figure 1c**), based on the Mahalanobis distance [29, 30], and sample clusters matched the leaf developmental age (**Figure 1c**). Leaf rank-dependent DNA methylation differences were further assessed between tissues by comparing the methylation profiles of blades and sheaths for each rank of leaf appearance. No DMMs were observed between the three leaf blades, whereas sheaths 1 and 3 presented 18 DMMs (**Table 2**).

*type, first, second and third leaf of seedlings, respectively.*

*Analysis of the differentiation of DNA methylation profiles of barley roots, leaf sheaths and leaf blades. (a) Scatter plot of the first two discriminant factors of the principal component-linear discriminant analysis (PC-LDA) (DF1 and DF2) using 913,697 ms-GBS markers generated from genomic DNA of roots, leaf sheaths and leaf blades, collected from 25 barley plants at the three-leaf stage (21 days after sowing), comprising five varieties (Barque 73, Flagship, Hindmarsh, Schooner and Yarra). (b) Three-dimensional plot of the first three discriminant factors of the PC-LDA of the same ms-GBS data. (c) Hierarchical cluster of the distances between sample group centres, based on Mahalanobis distance. Blade 1-3 and sheath 1-3 indicate the rank of the organ* 

**3.3 Differentially methylated DNA markers between roots and leaves**

DMMs between barley roots and leaves were obtained through comparison of the read count per million of tissue types, independently of genotypes. *Number of differentially methylated markers in barley tissues of different ages.*

This comparison revealed substantial DMMs between both roots vs. blades and roots vs. sheaths (**Figure 2a**), and there were more DMMs between roots and blades (6510 DMMs **Figure 2b**) than between roots and sheaths (4116 DMMs **Figure 2c**). Of these markers, 3266 DMMs were present in both blades and sheaths when compared to roots, and their methylation changed consistently in the same direction in each comparison (**Figure 3a**). The number of DMMs between roots and leaf blades increased with leaf-rank, whereas DMMs between roots and leaf sheaths did not show any relationship with rank (**Figure 2a**). Tissue-specific DMMs were predominantly hypomethylated (95–98%) in leaf parts (sheath or blade) compared to roots (**Figure 2a**). This result was in line with the median of the fold-changes of DMMs, which indicated an overall DNA hypomethylation in leaves (**Figure 4a** and **b**). From here on, DMMs consistently present in roots vs. sheaths and roots vs. blades will be designated as stable markers between roots and leaves.

#### **3.4 Differentially methylated DNA markers between the leaf blade and sheath**

There was only a small number of DMMs between leaf blades and sheaths (0–73 DMMs, **Table 2** and **Figure 2d**). These DMMs were basically between leaf blades and sheaths 1 and 2; and there was none between blade 1 and sheath 3. There was only 1 DMM between sheath 3 and blades 2 and 3 (**Table 2** and **Figure 2d**). Pairwise comparisons between blades 1–2 and sheaths 1–2 revealed 20 common DMMs, which were all hypermethylated in sheaths compared to blades (**Figures 2e** and **4b**). Half of the 20 common DMMs between blades and sheaths were located on chromosome 5H. Furthermore, there were no DMMs in pairwise comparisons among blades 1–3 and among sheaths 1–3, except between sheath 1 and sheath 3 which had 18 DMMs (**Table 2**). However, comparing blades and sheaths of the same leaf rank showed 32 DMMs between blade 1 and sheath 1, 36 DMMs between blade 2 and sheath 2 and 1 DMM between blade 3 and sheath 3.

#### **3.5 Distribution of tissue-specific DMMs around genes**

Relatively few of the tissue-specific DMMs were located around gene exons. Indeed, of the 3266 stable DMMs between root and leaf samples, only 60 (1.8%) were located within 5 kb of a gene, including 21 overlaps with genes and 39 DMMs that were spread within 5 kb upstream and downstream of genes (**Figure 5a**). Apart from the absence of DMMs within 1 kb upstream of transcription start sites, there was no obvious tissue-specific DMM distribution pattern around the genes

#### **Figure 2.**

*Analysis of the number of DMMs among three barley tissues. (a) Number of DMMs between roots and leaf blades (root vs. blade) and roots and sheaths (roots vs. sheaths). Histogram colour indicates whether the DMMs are hypomethylated (blue) or hypermethylated (red) in leaf parts compared to roots. (b and c) Venn diagram showing the number of DMMs stable between root and blade tissues (b) and between root and sheath tissues (c). (d) Number of DMMs from pairwise comparison between leaf blades 1–3 and sheaths 1–3. Histogram colour indicates whether the DMMs are hypomethylated (blue) or hypermethylated (red) in sheaths compared with blades. (e) Venn diagram showing the number of DMMs common in pairwise comparisons between leaf blades 1–3 and sheaths 1–2. Tissue samples were collected from seedlings at the three-leaf stage of five barley varieties grown in five replicates for 21 days after sowing. Blade 1–3 and sheath 1–3 indicate the rank of the organ type; first, second and third, respectively, on seedlings. DMMs were selected based on the significance of the false discovery rate, FDR, <0.05. DMMs present in both sheaths and blades when compared with roots, have been designated as markers between roots and leaves.*

(**Figure 5a**). The same assessment process showed that, as with common DMMs, only a small proportion of blade-specific DMMs (44 of 3246, 1.3%) was positioned close to a gene (**Figure 5b**). Of these, 15 DMMs overlapped with a gene transcript, whereas the remaining 29 DMMs were distributed within 5 kb of the gene without any clear pattern (**Figure 5b**), except that the number of DMMs located between 2 and 3 kb bins was higher both upstream and downstream, than any other 1 kb bin within the 5 kb flanking regions (**Figure 5b**). There were fewer sheath-specific methylation markers within 5 kb from genes than blade-specific markers (13 of

**127**

**Figure 4.**

**Figure 3.**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley…*

*Hierarchical clustering analysis of the DMMs. (a) The 3266 common DMMs between roots and all leaf parts (sheath 1–3, blade 1–3). The colours in the heat map indicate whether the DMM is hypomethylated (blue) or hypermethylated (red) in leaf parts compared to roots. (b) Hierarchical clustering of the 20 stable DMMs between blades and sheaths. In this heat map the red colour shows hypermethylation of DMMs in sheaths compared with blades. Blade and sheath samples were collected from seedlings at three-leaf stage of five barley varieties grown in five replicates for 21 days after sowing. Blade 1–3 and sheath 1–3 indicate the rank of the leaf on seedlings, first, second and third, respectively. The first number of the marker label on the y axis indicates* 

*Directionality of the methylation in tissue-specific DNA methylation markers. (a) Boxplots showing the spread of the fold-change of locus read counts between blades and sheaths, roots and blades, and roots and sheaths. (b) Detail of boxplots, highlighting the median of methylation fold-change of all loci in each comparison. The* 

*in read counts for each sequenced locus between pairwise comparisons of tissues collected from three-leaf stage barley seedlings. Leaf blades were the reference state for blade-sheath comparison, whereas roots were the reference for root-blade and root-sheath comparisons. Negative and positive values on the y axis indicate respectively, hypermethylation and hypomethylation of the tissue that is compared to the reference. Locus coverage was estimated for each tissue by using 25 replicates for roots and 75 for blades and sheaths (5 plants from each of the 5 varieties included in the study (DNA was extracted from 1 single root and from 3 independent leaves per plant)).*

*log2FC), with* log*2FC = logarithm 2 of fold-change* 

*DOI: http://dx.doi.org/10.5772/intechopen.90886*

*the chromosome number on which the marker is located.*

*fold-change of DNA methylation was estimated by computing 2(*

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley… DOI: http://dx.doi.org/10.5772/intechopen.90886*

#### **Figure 3.**

*DNA Methylation Mechanism*

**126**

**Figure 2.**

*been designated as markers between roots and leaves.*

(**Figure 5a**). The same assessment process showed that, as with common DMMs, only a small proportion of blade-specific DMMs (44 of 3246, 1.3%) was positioned close to a gene (**Figure 5b**). Of these, 15 DMMs overlapped with a gene transcript, whereas the remaining 29 DMMs were distributed within 5 kb of the gene without any clear pattern (**Figure 5b**), except that the number of DMMs located between 2 and 3 kb bins was higher both upstream and downstream, than any other 1 kb bin within the 5 kb flanking regions (**Figure 5b**). There were fewer sheath-specific methylation markers within 5 kb from genes than blade-specific markers (13 of

*Analysis of the number of DMMs among three barley tissues. (a) Number of DMMs between roots and leaf blades (root vs. blade) and roots and sheaths (roots vs. sheaths). Histogram colour indicates whether the DMMs are hypomethylated (blue) or hypermethylated (red) in leaf parts compared to roots. (b and c) Venn diagram showing the number of DMMs stable between root and blade tissues (b) and between root and sheath tissues (c). (d) Number of DMMs from pairwise comparison between leaf blades 1–3 and sheaths 1–3. Histogram colour indicates whether the DMMs are hypomethylated (blue) or hypermethylated (red) in sheaths compared with blades. (e) Venn diagram showing the number of DMMs common in pairwise comparisons between leaf blades 1–3 and sheaths 1–2. Tissue samples were collected from seedlings at the three-leaf stage of five barley varieties grown in five replicates for 21 days after sowing. Blade 1–3 and sheath 1–3 indicate the rank of the organ type; first, second and third, respectively, on seedlings. DMMs were selected based on the significance of the false discovery rate, FDR, <0.05. DMMs present in both sheaths and blades when compared with roots, have* 

*Hierarchical clustering analysis of the DMMs. (a) The 3266 common DMMs between roots and all leaf parts (sheath 1–3, blade 1–3). The colours in the heat map indicate whether the DMM is hypomethylated (blue) or hypermethylated (red) in leaf parts compared to roots. (b) Hierarchical clustering of the 20 stable DMMs between blades and sheaths. In this heat map the red colour shows hypermethylation of DMMs in sheaths compared with blades. Blade and sheath samples were collected from seedlings at three-leaf stage of five barley varieties grown in five replicates for 21 days after sowing. Blade 1–3 and sheath 1–3 indicate the rank of the leaf on seedlings, first, second and third, respectively. The first number of the marker label on the y axis indicates the chromosome number on which the marker is located.*

#### **Figure 4.**

*Directionality of the methylation in tissue-specific DNA methylation markers. (a) Boxplots showing the spread of the fold-change of locus read counts between blades and sheaths, roots and blades, and roots and sheaths. (b) Detail of boxplots, highlighting the median of methylation fold-change of all loci in each comparison. The fold-change of DNA methylation was estimated by computing 2( log2FC), with* log*2FC = logarithm 2 of fold-change in read counts for each sequenced locus between pairwise comparisons of tissues collected from three-leaf stage barley seedlings. Leaf blades were the reference state for blade-sheath comparison, whereas roots were the reference for root-blade and root-sheath comparisons. Negative and positive values on the y axis indicate respectively, hypermethylation and hypomethylation of the tissue that is compared to the reference. Locus coverage was estimated for each tissue by using 25 replicates for roots and 75 for blades and sheaths (5 plants from each of the 5 varieties included in the study (DNA was extracted from 1 single root and from 3 independent leaves per plant)).*

#### **Figure 5.**

*Distribution of tissue-specific differentially methylated markers (DMMs) around genes. (a) DMMs between roots and leaves, present in both blades and sheaths as in* **Figure 2b** *and* **c***; (b) blade-specific DMMs between roots and leaves and (c) sheath-specific DMMs between roots and leaves. The y axis indicates the distance to genes in kilo base pairs (kb) on both flanking regions. Negative and positive values indicate upstream and downstream of genes, respectively. DMMs overlapping with genes are considered as changes in gene-body methylation (body). The x axis shows the number of DMMs per 1 kb window.*

2391 DMMs, 0.5%) (**Figure 5c**). The majority of these (10 out of 13 DMMs) were sited within 3 kb of a gene, and no DMMs were present 3–5 kb from transcription margins (**Figure 5c**). Of 37 gene-body DMMs detected across all comparisons (**Figure 5a–c**), 27 overlapped with an exon and the remaining 10 markers were in intronic regions, 70–604 bp upstream of exons, except 1 DMM, which was 62 bp downstream an exon (Appendix A).

#### **3.6 Distribution of tissue-specific DMMs near repeat regions**

Many more tissue-specific DMMs were detected near repeats than near genes. The DMMs around repeat regions (as defined in the Ensembl database (http:// plants.ensembl.org/biomart/martview/)) were concentrated either within the repeats or within 1 kb of their margins (**Figure 6a**). A similar distribution pattern was obtained with both blade-specific and sheath-specific DMMs when contrasted with roots, with more DMMs overlapping with the repeats themselves than in the 1 kb stretches flanking their margins (**Figure 6b** and **c**). The few markers that were differentially methylated between blades and sheaths (20 DMMs in total) were all located within 1 kb of a repeat (**Figure 6d**). Therefore, stable tissue-specific DMMs appeared to occur preferentially within repeats and 1 kb flanking regions, with higher frequency within 1 kb downstream than within 1 kb upstream, regardless of tissue types (**Figure 6a–d**).

#### **3.7 Distribution of genes around differentially methylated (DM) repeats**

To investigate a possible interaction between differentially methylated (DM) repeats and genes, the distance of genes from DM repeats between root and leaf samples was evaluated. In this way, we found 105 genes near repeats (up to 5 kb either side), of which 37 overlapped with a repeat and the remaining genes were scattered up- and downstream from the repeat (**Figure 7**). The number of DM repeats surrounded by genes thus represented only a tiny proportion of the total repeats that were differentially methylated between roots and leaves (105 out of 3266 DM repeats, 3.21%). About half of genes near DM repeats (52 of 105 genes) were also differentially methylated, whereas the remainder (53 genes) were not.

**129**

**Figure 7.**

*window.*

**Figure 6.**

*shows the number of DMMs per 1 kb window.*

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley…*

*Distribution of tissue-specific differentially methylated markers (DMMs) around repeats. (a) DMMs between roots and leaves, present in both blades and sheaths as in* **Figure 2b** *and* **c***; (b) blade-specific DMMs between roots and leaves; (c) sheath-specific DMMs between roots and leaves; (d) DMMs between blades and sheaths. The x axis indicates the distance to repeats in kilo base pairs (kb) on both flanking regions. Negative and positive values indicate upstream and downstream repeat regions, respectively. RR: repeat regions. The y axis* 

*Distribution of genes around differentially methylated repeat regions. The x axis indicates the distance to repeats in kilo base pairs (kb) on both flanking regions. Negative and positive values indicate upstream and downstream repeat regions, respectively. RR, repeat regions. The y axis shows the number of genes per 1 kb* 

*DOI: http://dx.doi.org/10.5772/intechopen.90886*

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley… DOI: http://dx.doi.org/10.5772/intechopen.90886*

#### **Figure 6.**

*DNA Methylation Mechanism*

**Figure 5.**

downstream an exon (Appendix A).

tissue types (**Figure 6a–d**).

2391 DMMs, 0.5%) (**Figure 5c**). The majority of these (10 out of 13 DMMs) were sited within 3 kb of a gene, and no DMMs were present 3–5 kb from transcription margins (**Figure 5c**). Of 37 gene-body DMMs detected across all comparisons (**Figure 5a–c**), 27 overlapped with an exon and the remaining 10 markers were in intronic regions, 70–604 bp upstream of exons, except 1 DMM, which was 62 bp

*Distribution of tissue-specific differentially methylated markers (DMMs) around genes. (a) DMMs between roots and leaves, present in both blades and sheaths as in* **Figure 2b** *and* **c***; (b) blade-specific DMMs between roots and leaves and (c) sheath-specific DMMs between roots and leaves. The y axis indicates the distance to genes in kilo base pairs (kb) on both flanking regions. Negative and positive values indicate upstream and downstream of genes, respectively. DMMs overlapping with genes are considered as changes in gene-body* 

Many more tissue-specific DMMs were detected near repeats than near genes. The DMMs around repeat regions (as defined in the Ensembl database (http:// plants.ensembl.org/biomart/martview/)) were concentrated either within the repeats or within 1 kb of their margins (**Figure 6a**). A similar distribution pattern was obtained with both blade-specific and sheath-specific DMMs when contrasted with roots, with more DMMs overlapping with the repeats themselves than in the 1 kb stretches flanking their margins (**Figure 6b** and **c**). The few markers that were differentially methylated between blades and sheaths (20 DMMs in total) were all located within 1 kb of a repeat (**Figure 6d**). Therefore, stable tissue-specific DMMs appeared to occur preferentially within repeats and 1 kb flanking regions, with higher frequency within 1 kb downstream than within 1 kb upstream, regardless of

**3.7 Distribution of genes around differentially methylated (DM) repeats**

To investigate a possible interaction between differentially methylated (DM) repeats and genes, the distance of genes from DM repeats between root and leaf samples was evaluated. In this way, we found 105 genes near repeats (up to 5 kb either side), of which 37 overlapped with a repeat and the remaining genes were scattered up- and downstream from the repeat (**Figure 7**). The number of DM repeats surrounded by genes thus represented only a tiny proportion of the total repeats that were differentially methylated between roots and leaves (105 out of 3266 DM repeats, 3.21%). About half of genes near DM repeats (52 of 105 genes) were also differentially methylated, whereas the remainder (53 genes) were not.

**3.6 Distribution of tissue-specific DMMs near repeat regions**

*methylation (body). The x axis shows the number of DMMs per 1 kb window.*

**128**

*Distribution of tissue-specific differentially methylated markers (DMMs) around repeats. (a) DMMs between roots and leaves, present in both blades and sheaths as in* **Figure 2b** *and* **c***; (b) blade-specific DMMs between roots and leaves; (c) sheath-specific DMMs between roots and leaves; (d) DMMs between blades and sheaths. The x axis indicates the distance to repeats in kilo base pairs (kb) on both flanking regions. Negative and positive values indicate upstream and downstream repeat regions, respectively. RR: repeat regions. The y axis shows the number of DMMs per 1 kb window.*

#### **Figure 7.**

*Distribution of genes around differentially methylated repeat regions. The x axis indicates the distance to repeats in kilo base pairs (kb) on both flanking regions. Negative and positive values indicate upstream and downstream repeat regions, respectively. RR, repeat regions. The y axis shows the number of genes per 1 kb window.*

#### **4. Discussion**

#### **4.1 Extensive epigenetic differentiation between roots and leaves**

In this study, we detected large numbers of DDMs between roots and leaves that were conserved across a diverse array of barley genotypes, and so were deemed far more likely to be organ-specific than genotype-dependent. Of these, hypomethylation of the mCCGG motif predominated in leaves (**Figures 2b** and **c**, **3b** and **4a**). More surprisingly, we also detected similarly conserved DMMs between leaf-blades and leaf-sheaths (**Figures 2e** and **4b**). The number of conserved DMMs between blades and sheaths (20 DMMs), all hypermethylated in sheaths, was relatively consistent with the closeness of these structures in position and function. These findings are broadly congruent with previous studies, which reported differential DNA methylation between variable tissues (e.g. endosperm, pollen, leaves, and roots) in diverse plant species [7–10], but additionally hint that the developmental closeness of structures being compared may also be reflected in the distinctiveness of their methylation profiles. However, controversy over the extent and validity of organ-specific DMMs [9, 10, 21–23] could cast doubt over their utility for organ diagnosis or as a tool to gain greater insight into the genes responsible for organ development/identity. Here, we sought to mitigate against the possibility of type I errors in DMM assignment through the unprecedented use of five diverse varieties and five biological replicates of each variety in the identification of these marks. In contrast to our findings, previous workers have reported little difference in the methylation levels of both mCG and mCHG motifs between roots and leaves in Arabidopsis [9] and sorghum [10]. Further, no significant difference was detected at all for mCG and mCHG methylation levels between tissues in cotton [37]. These divergences may simply reflect genuine biological differences between taxonomic groups. However, it is also important to recognise that such differences may also arise from the approach used to identify organ-specific DMMs. Variability in the techniques used to assess plant methylation profiles may introduce different forms of bias and preclude or complicate comparison among studies. DMM detection can be influenced by factors such as (1) the genome coverage of the methylation profiling method (low coverage methods such as MSAP are likely to miss many markers) [7], and (2) the data analysis approach used, which can compare either global methylation levels (e.g. percent methylation) [9] or methylated loci (e.g. DMMs) [28]. We contend that relying solely on global methylation levels can be misleading in comparing tissue profiles, because similar methylation levels may show completely different patterns and so vital information content is lost.

The current study revealed that tissue-specific DNA methylation occurred abundantly in the mCHG context (in particular mCCGGs) (**Figure** 2a and **c**). This concurs with reports of the CHG context similarly dominating differential DNA methylation between organs in *Brachypodium distachyon* [8] and sorghum [10]. Although tissuespecific methylation also occurs in other cytosine contexts [10], our results and other studies [10, 22] suggest that mCCGG is a primary motif of epigenetic distinctiveness of plant organs. However, since *Msp*I activity is affected by the presence of cytosine hydroxymethylation on its recognition sequence [38], some of the markers identified here as being cytosine methylation induced, could be due to the presence of (de) hydroxymethylation events instead. Additionally, while tissue-specific DMMs were mostly hypomethylated in leaves compared to roots in the present study (**Figure 3b**), in Arabidopsis, Widman et al. [9] found that hypermethylation prevailed in leaves compared with roots. This apparent contradiction in the directionality of methylation in DMMs between roots and leaves may be a reflection a difference in the polarity of early divisions in the monocotyledonous barley and the dicotyledonous Arabidopsis embryos or else the methylation profiling method implemented.

**131**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley…*

In addition to tissue-specificity of methylation profiles, one notable finding in the current study was that leaf cohorts exhibited a strong tendency to co-cluster. This suggests that the nature of methylation divergence between organs is not absolutely fixed and instead appears to change with developmental progression. This observation accords with previous reports that genome-wide methylation patterns are not static during plant development [39]. Additionally, a considerable portion of DMMs between roots and leaves was also specific to the leaf rank, due to the steady decrease in the number of DMMs between roots and leaf blades with the rank of the latter (**Figure 2a–c**). In this case, therefore, the slow but progressive accumulation of additional methylation marks in the leaves increases their divergence from root profiles and enables the separation of leaf cohorts. However, the small number of DMMs distinguishing between leaf blades and leaf sheaths ran counter to this trend such that there were no DMMs capable of discrimination between these leaf parts among the oldest cohort studied (leaf 1) (**Figure 2d** and **Table 2**). It seems intuitively improbable that older cohorts of leaves would simply lose differentiation between structurally distinct parts, especially if these marks had a functional role in defining function. Perhaps the most plausible biological explanation for the apparent erosion of divergence lies in the different chronological ages of the leaf cohorts that were sampled. Put simply, the third leaves were the least mature of the three cohorts collected and so it is entirely possible that the blade-sheath differential marks had yet to appear in these samples. Thus, it is important to consider the developmental and ageing progression chronology when assigning DMMs and that some organ- or structure-specific marks may only become organ-specific late in their development. Such late-emerging developmental DMMs should mean that the cumulative number of tissue-specific markers increases and so the organs or structures become more distinct, through leaf growth stages [40], each of which may carry a specific epigenetic profile. Certainly, others have noted that methylation profiles vary progressively as the organ develops [3, 41, 42] before reaching, at maturity, a "default" methylome, which may be conserved across varieties [24]. These results suggest that, once leaves are differentiated and mature, they do not show significant differences in DNA methylation profiles, regardless of their rank of appearance. Additionally, the location of half of the 20 common DMMs between blades and sheaths on chromosome 5H implies that this chromosome carries loci

**4.2 DNA methylation flux is tissue-specific during barley seedlings** 

*DOI: http://dx.doi.org/10.5772/intechopen.90886*

important for blade and sheath identities.

**of the barley genome**

**4.3 Tissue-specific DNA methylation preferably occurs in repeat regions** 

further investigation [46]. However, it is already well-established that tissuespecific DMMs can influence gene expression by enhancing gene transcription [9]

Organ-specific DMMs identified here were primarily associated with repeat regions. No significant difference was observed between the frequency of CCGG sites in and around genes and repeats. However, 84% of the barley genome is comprised of mobile elements or other repeat structures [25, 43], indicating that the fact that the majority of detected DMMs are located within or in the proximity of a repeat is due to the intrinsic repetitive nature of the studied genome. Nevertheless, the fact that 27 DMMs overlapped with exons and 10 were located in introns (Appendix A) contradicts previous claims that CHG methylation marks are exclusively restricted to repeat regions and intergenic regions [20, 21, 44, 45]. The possible regulatory significance of such gene body CHG methylation marks requires

**development**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley… DOI: http://dx.doi.org/10.5772/intechopen.90886*

#### **4.2 DNA methylation flux is tissue-specific during barley seedlings development**

In addition to tissue-specificity of methylation profiles, one notable finding in the current study was that leaf cohorts exhibited a strong tendency to co-cluster. This suggests that the nature of methylation divergence between organs is not absolutely fixed and instead appears to change with developmental progression. This observation accords with previous reports that genome-wide methylation patterns are not static during plant development [39]. Additionally, a considerable portion of DMMs between roots and leaves was also specific to the leaf rank, due to the steady decrease in the number of DMMs between roots and leaf blades with the rank of the latter (**Figure 2a–c**). In this case, therefore, the slow but progressive accumulation of additional methylation marks in the leaves increases their divergence from root profiles and enables the separation of leaf cohorts. However, the small number of DMMs distinguishing between leaf blades and leaf sheaths ran counter to this trend such that there were no DMMs capable of discrimination between these leaf parts among the oldest cohort studied (leaf 1) (**Figure 2d** and **Table 2**). It seems intuitively improbable that older cohorts of leaves would simply lose differentiation between structurally distinct parts, especially if these marks had a functional role in defining function. Perhaps the most plausible biological explanation for the apparent erosion of divergence lies in the different chronological ages of the leaf cohorts that were sampled. Put simply, the third leaves were the least mature of the three cohorts collected and so it is entirely possible that the blade-sheath differential marks had yet to appear in these samples. Thus, it is important to consider the developmental and ageing progression chronology when assigning DMMs and that some organ- or structure-specific marks may only become organ-specific late in their development. Such late-emerging developmental DMMs should mean that the cumulative number of tissue-specific markers increases and so the organs or structures become more distinct, through leaf growth stages [40], each of which may carry a specific epigenetic profile. Certainly, others have noted that methylation profiles vary progressively as the organ develops [3, 41, 42] before reaching, at maturity, a "default" methylome, which may be conserved across varieties [24]. These results suggest that, once leaves are differentiated and mature, they do not show significant differences in DNA methylation profiles, regardless of their rank of appearance. Additionally, the location of half of the 20 common DMMs between blades and sheaths on chromosome 5H implies that this chromosome carries loci important for blade and sheath identities.

#### **4.3 Tissue-specific DNA methylation preferably occurs in repeat regions of the barley genome**

Organ-specific DMMs identified here were primarily associated with repeat regions. No significant difference was observed between the frequency of CCGG sites in and around genes and repeats. However, 84% of the barley genome is comprised of mobile elements or other repeat structures [25, 43], indicating that the fact that the majority of detected DMMs are located within or in the proximity of a repeat is due to the intrinsic repetitive nature of the studied genome. Nevertheless, the fact that 27 DMMs overlapped with exons and 10 were located in introns (Appendix A) contradicts previous claims that CHG methylation marks are exclusively restricted to repeat regions and intergenic regions [20, 21, 44, 45]. The possible regulatory significance of such gene body CHG methylation marks requires further investigation [46]. However, it is already well-established that tissuespecific DMMs can influence gene expression by enhancing gene transcription [9]

*DNA Methylation Mechanism*

**4.1 Extensive epigenetic differentiation between roots and leaves**

In this study, we detected large numbers of DDMs between roots and leaves that were conserved across a diverse array of barley genotypes, and so were deemed far more likely to be organ-specific than genotype-dependent. Of these, hypomethylation of the mCCGG motif predominated in leaves (**Figures 2b** and **c**, **3b** and **4a**). More surprisingly, we also detected similarly conserved DMMs between leaf-blades and leaf-sheaths (**Figures 2e** and **4b**). The number of conserved DMMs between blades and sheaths (20 DMMs), all hypermethylated in sheaths, was relatively consistent with the closeness of these structures in position and function. These findings are broadly congruent with previous studies, which reported differential DNA methylation between variable tissues (e.g. endosperm, pollen, leaves, and roots) in diverse plant species [7–10], but additionally hint that the developmental closeness of structures being compared may also be reflected in the distinctiveness of their methylation profiles. However, controversy over the extent and validity of organ-specific DMMs [9, 10, 21–23] could cast doubt over their utility for organ diagnosis or as a tool to gain greater insight into the genes responsible for organ development/identity. Here, we sought to mitigate against the possibility of type I errors in DMM assignment through the unprecedented use of five diverse varieties and five biological replicates of each variety in the identification of these marks. In contrast to our findings, previous

workers have reported little difference in the methylation levels of both mCG and mCHG motifs between roots and leaves in Arabidopsis [9] and sorghum [10]. Further, no significant difference was detected at all for mCG and mCHG methylation levels between tissues in cotton [37]. These divergences may simply reflect genuine biological differences between taxonomic groups. However, it is also important to recognise that such differences may also arise from the approach used to identify organ-specific DMMs. Variability in the techniques used to assess plant methylation profiles may introduce different forms of bias and preclude or complicate comparison among studies. DMM detection can be influenced by factors such as (1) the genome coverage of the methylation profiling method (low coverage methods such as MSAP are likely to miss many markers) [7], and (2) the data analysis approach used, which can compare either global methylation levels (e.g. percent methylation) [9] or methylated loci (e.g. DMMs) [28]. We contend that relying solely on global methylation levels can be misleading in comparing tissue profiles, because similar methylation levels may show

The current study revealed that tissue-specific DNA methylation occurred abundantly in the mCHG context (in particular mCCGGs) (**Figure** 2a and **c**). This concurs with reports of the CHG context similarly dominating differential DNA methylation between organs in *Brachypodium distachyon* [8] and sorghum [10]. Although tissuespecific methylation also occurs in other cytosine contexts [10], our results and other studies [10, 22] suggest that mCCGG is a primary motif of epigenetic distinctiveness of plant organs. However, since *Msp*I activity is affected by the presence of cytosine hydroxymethylation on its recognition sequence [38], some of the markers identified here as being cytosine methylation induced, could be due to the presence of (de) hydroxymethylation events instead. Additionally, while tissue-specific DMMs were mostly hypomethylated in leaves compared to roots in the present study (**Figure 3b**), in Arabidopsis, Widman et al. [9] found that hypermethylation prevailed in leaves compared with roots. This apparent contradiction in the directionality of methylation in DMMs between roots and leaves may be a reflection a difference in the polarity of early divisions in the monocotyledonous barley and the dicotyledonous Arabidopsis embryos or else the methylation profiling method implemented.

completely different patterns and so vital information content is lost.

**4. Discussion**

**130**

and alternative splicing [47] or through repression due to immediate proximity to transcription start site [48].

The predominance of DMMs around and within repeats leads us to speculate that they could play an important role in defining organ identity in barley, and accords with previous findings in *Brachypodium distachyon* [8]. This flux of DNA methylation patterns in repeats [8, 42, 49] has been proposed to regulate [44] developmental shifts during plant growth and development [11, 39]. Nevertheless, the association between DMMs in/around repeat regions and organ identity described here does not establish a causal link between the two. However, there are grounds for reasoning that this may be the case and that the possibility warrants further study. First, repeat regions were previously proposed to be involved in alternative promoters, a substantial proportion of which (>40%) was reported to shape tissue differentiation [16]. Therefore, tissue-specific DMMs in repeats may contribute to alternative promoters, and thus determine organ identity. Second, differential gene expression between roots and leaves [25, 50] implies a firm regulatory system, including epigenetic mechanisms to guarantee tissue-specific cell development. Tissue-specific DMMs in repeats show that repeats are not the so-called "selfish parasites" of the genome [51], but can directly or indirectly affect tissue-specific gene expression [42, 52, 53]. Finally, it has been suggested that transposons coordinate splice variants, a genomic event that occurs in more than 60% of plant genes [54, 55], thus generating multiple mRNA transcripts from a single gene [56, 57]. Many splice variants are tissue-specific [58], suggesting that it is entirely possible that tissue-specific DMMs in repeats affect alternative splicing and subsequent gene expression. Also, some DM genes might potentially be regulated simultaneously by their own methylation and that of repeats [53, 59], due to proximity with DM repeats.

#### **5. Conclusions**

This study provides a comprehensive set of robust tissue specific epimarkers which were conserved in all barley genotypes tested and can therefore be considered genotype independent. Such markers have potential to be converted into locus-specific methylation sensitive cleaved amplified polymorphic sequence markers (ms-CAPS) to be used as diagnostic of sample origin. Moreover, these markers provide a basis for the understanding of the role of DNA methylation in plant organ differentiation and development. Our data illustrates that during tissue development, DNA methylation evolves to reach a default profile once the tissue is completely differentiated at maturity. It is possible that the plant organ formation and maturation is under at least partial control of DNA methylation changes. In addition, repeats could play an important role in tissue definition. The existence of tissue-specific mCCGG sites suggests that this context carries important factors of tissue differentiation. Expression analysis of tissue samples would conclusively demonstrate the role of tissue-specific DMMs in gene regulation. These markers will provide a basis for future studies of the role of DNA methylation in plant organ differentiation and development.

#### **Acknowledgements**

The authors are grateful to the Australian Agency for International Development (AusAID) for supporting MK with an Australian Awards Scholarship. MJW was paid by the BBSRC (BBS/E/W/0012843C). This work is supported by the National

**133**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley…*

number 2352987000, and award number 2019-67013-29168.

"The authors declare no conflict of interest."

Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch Program

MK conceived and performed the experiments, analysed the data and wrote the manuscript; BJM performed ms-GBS data alignments; MJW, ESS, BB, CMRL conceived the experiments and supervised the work. All authors read and com-

**Chrom. Exons DMMs Tissue Start End ID Rank Start End bp to** 

**exon**

*DOI: http://dx.doi.org/10.5772/intechopen.90886*

**Conflict of interest**

**Author contributions**

mented on the manuscript.

**Appendix** 

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley… DOI: http://dx.doi.org/10.5772/intechopen.90886*

Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch Program number 2352987000, and award number 2019-67013-29168.

#### **Conflict of interest**

*DNA Methylation Mechanism*

transcription start site [48].

and alternative splicing [47] or through repression due to immediate proximity to

The predominance of DMMs around and within repeats leads us to speculate that they could play an important role in defining organ identity in barley, and accords with previous findings in *Brachypodium distachyon* [8]. This flux of DNA methylation patterns in repeats [8, 42, 49] has been proposed to regulate [44] developmental shifts during plant growth and development [11, 39]. Nevertheless, the association between DMMs in/around repeat regions and organ identity described here does not establish a causal link between the two. However, there are grounds for reasoning that this may be the case and that the possibility warrants further study. First, repeat regions were previously proposed to be involved in alternative promoters, a substantial proportion of which (>40%) was reported to shape tissue differentiation [16]. Therefore, tissue-specific DMMs in repeats may contribute to alternative promoters, and thus determine organ identity. Second, differential gene expression between roots and leaves [25, 50] implies a firm regulatory system, including epigenetic mechanisms to guarantee tissue-specific cell development. Tissue-specific DMMs in repeats show that repeats are not the so-called "selfish parasites" of the genome [51], but can directly or indirectly affect tissue-specific gene expression [42, 52, 53]. Finally, it has been suggested that transposons coordinate splice variants, a genomic event that occurs in more than 60% of plant genes [54, 55], thus generating multiple mRNA transcripts from a single gene [56, 57]. Many splice variants are tissue-specific [58], suggesting that it is entirely possible that tissue-specific DMMs in repeats affect alternative splicing and subsequent gene expression. Also, some DM genes might potentially be regulated simultaneously by their own methylation and that of repeats [53, 59], due to proximity with DM

This study provides a comprehensive set of robust tissue specific epimarkers which were conserved in all barley genotypes tested and can therefore be considered genotype independent. Such markers have potential to be converted into locus-specific methylation sensitive cleaved amplified polymorphic sequence markers (ms-CAPS) to be used as diagnostic of sample origin. Moreover, these markers provide a basis for the understanding of the role of DNA methylation in plant organ differentiation and development. Our data illustrates that during tissue development, DNA methylation evolves to reach a default profile once the tissue is completely differentiated at maturity. It is possible that the plant organ formation and maturation is under at least partial control of DNA methylation changes. In addition, repeats could play an important role in tissue definition. The existence of tissue-specific mCCGG sites suggests that this context carries important factors of tissue differentiation. Expression analysis of tissue samples would conclusively demonstrate the role of tissue-specific DMMs in gene regulation. These markers will provide a basis for future studies of the role of DNA methylation in plant organ

The authors are grateful to the Australian Agency for International Development

(AusAID) for supporting MK with an Australian Awards Scholarship. MJW was paid by the BBSRC (BBS/E/W/0012843C). This work is supported by the National

**132**

repeats.

**5. Conclusions**

differentiation and development.

**Acknowledgements**

"The authors declare no conflict of interest."

#### **Author contributions**

MK conceived and performed the experiments, analysed the data and wrote the manuscript; BJM performed ms-GBS data alignments; MJW, ESS, BB, CMRL conceived the experiments and supervised the work. All authors read and commented on the manuscript.


#### **Appendix**


#### **Table A1.**

*List of differentially methylated exons. Bolded value is the only first exon methylated upstream 452 bp from a transcription start.*

**135**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley…*

*DOI: http://dx.doi.org/10.5772/intechopen.90886*

**Author details**

Bettina Berger5

Australia

KY, USA

Moumouni Konate1

Farako-Ba, Burkina Faso

, Mike J. Wilkinson<sup>2</sup>

Rural Sciences, Aberystwyth, Ceredigion, UK

of Adelaide, Glen Osmond, SA, Australia

\*Address all correspondence to: mjw19@aber.ac.uk

Glen Osmond, SA, Australia

and carlos.rodriguezlopez@uky.edu

provided the original work is properly cited.

and Carlos M. Rodríguez López6

1 Institut de l'Environnement et de Recherche Agricole (INERA), Station de

2 Pwllpeiran Upland Research Centre, Institute of Biological, Environmental and

3 Robinson Research Institute, School of Medicine, The University of Adelaide, SA,

4 School of Agriculture, Food and Wine, Waite Research Institute, The University

5 The Plant Accelerator, Australian Plant Phenomics Facility, School of Agriculture,

6 Environmental Epigenetics and Genetics Group, Department of Horticulture, College of Agriculture, Food and Environment, University of Kentucky, Lexington,

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Food and Wine, Waite Research Institute, The University of Adelaide,

\*, Benjamin T. Mayne3

\*

, Eileen S. Scott4

,

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley… DOI: http://dx.doi.org/10.5772/intechopen.90886*

#### **Author details**

*DNA Methylation Mechanism*

**Table A1.**

*transcription start.*

**Chrom. Exons DMMs Tissue Start End ID Rank Start End bp to** 

7H 96049105 96050237 exon:MLOC\_36488.1:3 3 96049134 96049134 0 leaf 3H 516390233 516390451 exon:MLOC\_37766.1:4 4 516390244 516390244 0 blade 4H 434415593 434415838 exon:MLOC\_58529.1:4 4 434415773 434415773 0 blade 2H 578608506 578608551 exon:MLOC\_54514.1:5 5 578608549 578608549 0 blade 5H 484203288 484203413 exon:MLOC\_73139.2:5 5 484203386 484203386 0 blade 2H 2183704 2183865 exon:MLOC\_57446.2:9 9 2183753 2183753 0 leaf 7H 41386814 41387497 exon:MLOC\_57450.2:9 9 41387134 41387134 0 leaf 4H 434420196 434420586 exon:MLOC\_58529.6:13 13 434420355 434420355 0 blade 3H 541205210 541205401 exon:MLOC\_37244.3:16 16 541205351 541205351 0 leaf 7H 570620131 570620572 exon:MLOC\_14604.2:16 16 570620258 570620258 0 blade 7H 583930566 583930636 exon:MLOC\_62970.1:2 2 583930697 583930697 62 leaf

*List of differentially methylated exons. Bolded value is the only first exon methylated upstream 452 bp from a* 

*DMMs: differentially methylated markers; Chrom: chromosome; bp: base pair*

**exon**

**134**

Moumouni Konate1 , Mike J. Wilkinson<sup>2</sup> \*, Benjamin T. Mayne3 , Eileen S. Scott4 , Bettina Berger5 and Carlos M. Rodríguez López6 \*

1 Institut de l'Environnement et de Recherche Agricole (INERA), Station de Farako-Ba, Burkina Faso

2 Pwllpeiran Upland Research Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth, Ceredigion, UK

3 Robinson Research Institute, School of Medicine, The University of Adelaide, SA, Australia

4 School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, Glen Osmond, SA, Australia

5 The Plant Accelerator, Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, Waite Research Institute, The University of Adelaide, Glen Osmond, SA, Australia

6 Environmental Epigenetics and Genetics Group, Department of Horticulture, College of Agriculture, Food and Environment, University of Kentucky, Lexington, KY, USA

\*Address all correspondence to: mjw19@aber.ac.uk and carlos.rodriguezlopez@uky.edu

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[37] Osabe K, Clement JD, Bedon F, Pettolino FA, Ziolkowski L, Llewellyn DJ, et al. Genetic and DNA methylation changes in cotton (Gossypium) genotypes and tissues. PLoS One. 2014;**9**:e86049

[38] Ichiyanagi K. Inhibition of MspI cleavage activity by hydroxymethylation of the CpG site: A concern for DNA modification studies using restriction endonucleases. Epigenetics. 2012;**7**(2):131-136. DOI: 10.4161/ epi.7.2.18909

[39] Zhong S, Fei Z, Chen Y-R, Zheng Y, Huang M, Vrebalov J, et al. Single-base resolution methylomes of tomato fruit development reveal epigenome modifications associated with ripening. Nature Biotechnology. 2013;**31**:154-159

[40] Candaele J, Demuynck K, Mosoti D, Beemster GTS, Inze D, Nelissen H. Differential methylation during maize leaf growth targets

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[41] Brandeis M, Ariel M, Cedar H. Dynamics of DNA methylation during development. BioEssays. 1993;**15**:709-713

[42] Zhang X. The epigenetic landscape of plants. Science. 2008;**320**:489-492

[43] Wicker T et al. A whole-genome snapshot of 454 sequences exposes the composition of the barley genome and provides evidence for parallel evolution of genome size in wheat and barley. Plant Journal. 2009;**59**:712-722

[44] Bewick AJ, Ji L, Niederhuth CE, Willing E-M, Hofmeister BT, Shi X, et al. On the origin and evolutionary consequences of gene body DNA methylation. Proceedings of the National Academy of Sciences. 2016;**113**:9111-9116

[45] Deaton AM, Bird A. CpG islands and the regulation of transcription. Genes & Development. 2011;**25**:1010-1022

[46] Xie H, Konate M, Sai N, Tesfamicael KG, Cavagnaro T, Gilliham M, et al. Global DNA methylation patterns can play a role in defining terroir in grapevine (*Vitis vinifera* cv. Shiraz). Frontiers in Plant Science. 2017;**8**:1860

[47] Li-Byarlay H, Li Y, Stroud H, Feng S, Newman TC, Kaneda M, et al. RNA interference knockdown of DNA methyl-transferase 3 affects gene alternative splicing in the honey bee. Proceedings of the National Academy of Sciences. 2013;**110**:12750-12755

[48] Jones PA. Functions of DNA methylation: Islands, start sites, gene bodies and beyond. Nature Reviews Genetics. 2012;**13**:484-492

[49] Hollister JD, Gaut BS. Epigenetic silencing of transposable elements:

**139**

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley…*

[58] Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ. Deep surveying of alternative splicing complexity in the human transcriptome by highthroughput sequencing. Nature Genetics. 2008;**40**:1413-1415

[59] Lorincz MC, Dickerson DR, Schmitt M, Groudine M. Intragenic DNA methylation alters chromatin structure and elongation efficiency in mammalian cells. Nature Structural Molecular Biology. 2004;**11**:1068-1075

*DOI: http://dx.doi.org/10.5772/intechopen.90886*

transposition and deleterious effects on neighboring gene expression. Genome

[50] Druka A, Muehlbauer G, Druka I, Caldo R, Baumann U, Rostoks N, et al. An atlas of gene expression from seed to seed through barley development. Functional & Integrative Genomics.

[51] Orgel LE, Crick FHC. Selfish DNA: The ultimate parasite. Nature.

[52] Lister R, O'Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell.

[53] Hirsch CD, Springer NM. Transposable element influences on gene expression in plants. Biochimica et Biophysica Acta—Gene Regulatory Mechanisms. 1860;**2016**:157-165

[54] Marquez Y, Brown JWS, Simpson C, Barta A, Kalyna M. Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Research.

[55] Simpson CG, Lewandowska D, Fuller J, Maronova M, Kalyna M, Davidson D, et al. Alternative splicing

[56] Barbazuk WB, Fu Y, McGinnis KM. Genome-wide analyses of alternative splicing in plants: Opportunities and challenges. Genome Research.

[57] Warf MB, Berglund JA. The role of RNA structure in regulating premRNA splicing. Trends in Biochemical

in plants. Biochemical Society Transactions. 2008;**36**:508-510

A trade-off between reduced

Research. 2009;**19**:1419-1428

2006;**6**:202-211

1980;**284**:604-607

2008;**133**:523-536

2012;**22**:1184-1195

2008;**18**:1381-1392

Sciences. 2010;**35**:169-178

*Atlas of Age- and Tissue-Specific DNA Methylation during Early Development of Barley… DOI: http://dx.doi.org/10.5772/intechopen.90886*

A trade-off between reduced transposition and deleterious effects on neighboring gene expression. Genome Research. 2009;**19**:1419-1428

*DNA Methylation Mechanism*

to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological). 1995:289-300

developmentally regulated genes. Plant

[42] Zhang X. The epigenetic landscape of plants. Science. 2008;**320**:489-492

[43] Wicker T et al. A whole-genome snapshot of 454 sequences exposes the composition of the barley genome and provides evidence for parallel evolution of genome size in wheat and barley. Plant Journal. 2009;**59**:712-722

[44] Bewick AJ, Ji L, Niederhuth CE, Willing E-M, Hofmeister BT, Shi X, et al. On the origin and evolutionary consequences of gene body DNA methylation. Proceedings of the National Academy of Sciences.

Physiology. 2014;**164**:1350-1364

development. BioEssays.

1993;**15**:709-713

2016;**113**:9111-9116

2011;**25**:1010-1022

[45] Deaton AM, Bird A. CpG islands and the regulation of

[46] Xie H, Konate M, Sai N,

[47] Li-Byarlay H, Li Y, Stroud H, Feng S, Newman TC, Kaneda M, et al. RNA interference knockdown of DNA methyl-transferase 3 affects gene alternative splicing in the honey bee. Proceedings of the National Academy of

Sciences. 2013;**110**:12750-12755

[48] Jones PA. Functions of DNA methylation: Islands, start sites, gene bodies and beyond. Nature Reviews

[49] Hollister JD, Gaut BS. Epigenetic silencing of transposable elements:

Genetics. 2012;**13**:484-492

transcription. Genes & Development.

Tesfamicael KG, Cavagnaro T, Gilliham M, et al. Global DNA methylation patterns can play a role in defining terroir in grapevine (*Vitis vinifera* cv. Shiraz). Frontiers in Plant Science. 2017;**8**:1860

[41] Brandeis M, Ariel M, Cedar H. Dynamics of DNA methylation during

[33] Dunn OJ. Multiple comparisons among means. Journal of the American Statistical Association. 1961;**56**:52-64

[34] Rodríguez López CM, Morán P, Lago F, Espiñeira M, Beckmann M, Consuegra S. Detection and quantification of tissue of origin in salmon and veal products using methylation sensitive AFLPs. Food Chemistry. 2012;**131**:1493-1498

[35] Quinlan AR, Hall IM. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics.

[36] Brockman W, Alvarez P, Young S, Garber M, Giannoukos G, Lee WL, et al. Quality scores and SNP detection in sequencing-by-synthesis systems. Genome Research. 2008;**18**:763-770

[37] Osabe K, Clement JD, Bedon F, Pettolino FA, Ziolkowski L, Llewellyn DJ, et al. Genetic and DNA methylation changes in cotton (Gossypium) genotypes and tissues.

[38] Ichiyanagi K. Inhibition of MspI cleavage activity by hydroxymethylation

[39] Zhong S, Fei Z, Chen Y-R, Zheng Y, Huang M, Vrebalov J, et al. Single-base resolution methylomes of tomato fruit development reveal epigenome modifications associated with ripening. Nature Biotechnology. 2013;**31**:154-159

of the CpG site: A concern for DNA modification studies using restriction endonucleases. Epigenetics. 2012;**7**(2):131-136. DOI: 10.4161/

[40] Candaele J, Demuynck K, Mosoti D, Beemster GTS, Inze D, Nelissen H. Differential methylation during maize leaf growth targets

PLoS One. 2014;**9**:e86049

epi.7.2.18909

2010;**26**:841-842

**138**

[50] Druka A, Muehlbauer G, Druka I, Caldo R, Baumann U, Rostoks N, et al. An atlas of gene expression from seed to seed through barley development. Functional & Integrative Genomics. 2006;**6**:202-211

[51] Orgel LE, Crick FHC. Selfish DNA: The ultimate parasite. Nature. 1980;**284**:604-607

[52] Lister R, O'Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell. 2008;**133**:523-536

[53] Hirsch CD, Springer NM. Transposable element influences on gene expression in plants. Biochimica et Biophysica Acta—Gene Regulatory Mechanisms. 1860;**2016**:157-165

[54] Marquez Y, Brown JWS, Simpson C, Barta A, Kalyna M. Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Research. 2012;**22**:1184-1195

[55] Simpson CG, Lewandowska D, Fuller J, Maronova M, Kalyna M, Davidson D, et al. Alternative splicing in plants. Biochemical Society Transactions. 2008;**36**:508-510

[56] Barbazuk WB, Fu Y, McGinnis KM. Genome-wide analyses of alternative splicing in plants: Opportunities and challenges. Genome Research. 2008;**18**:1381-1392

[57] Warf MB, Berglund JA. The role of RNA structure in regulating premRNA splicing. Trends in Biochemical Sciences. 2010;**35**:169-178

[58] Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ. Deep surveying of alternative splicing complexity in the human transcriptome by highthroughput sequencing. Nature Genetics. 2008;**40**:1413-1415

[59] Lorincz MC, Dickerson DR, Schmitt M, Groudine M. Intragenic DNA methylation alters chromatin structure and elongation efficiency in mammalian cells. Nature Structural Molecular Biology. 2004;**11**:1068-1075

**Chapter 8**

**Abstract**

**1. Introduction**

**141**

Plant Genomes

Library Preparation for Whole

*Kendall R. Corbin and Carlos M. Rodriguez Lopez*

**Keywords:** whole genome bisulfite sequencing, methylome analysis,

Plants being sessile have developed strategies to adapt to their environment, specifically via epigenetic modification of their genome [1, 2]. Epigenetic mechanisms, both heritable and reversible, allow an organism to respond to its environment through changes in gene expression, without changing the underlying genome [3–6]. One of the most widely studied epigenetic mechanisms is cytosine methylation (5mC), which is the result of a methyl group replacing a hydrogen in the cyclic carbon-5 of cytosines. In plants, methylation of cytosine bases can occur in three contexts (DNA base sequences) CG, CHG or CHH, where H is any nucleotide other than G [7]. Plant nuclear genomes are known to contain more extensive and expansive DNA methylation than that found in animals [8]. DNA methylation has been identified in a range of plants and plays a role in a wide variety of biological processes from plant development and organ differentiation to response to stress [9–20].

Due to the functional importance of DNA methylation in many species, a plethora of DNA methylation analysis approaches has been developed in recent years. These can be mainly grouped into three functional types that (1) indicate the methylation status of a specific sequence; (2) reveal the degree and patterning of DNA methylation across partly characterized genomes; or (3) facilitate the discovery and sequencing of new epialleles [7]. From a technical point of view, such methodologies can be grouped into those using global estimation of all nucleic base species (e.g. HPLC and LC-MS/MS),

DNA methylation, epigenetic modifications, *Vitis vinifera*

Genome Bisulfite Sequencing of

Epigenetic mechanisms are a key interface between the environment and the genotype. These mechanisms regulate gene expression in response to plant development and environmental stimuli, which ultimately affects the plant's phenotype. DNA methylation, in particular cytosine methylation, is probably the best studied epigenetic modification in eukaryotes. It has been associated to the regulation of gene expression in response to cell/tissue differentiation, organism development and adaptation to changing environments. Whole genome bisulfite sequencing (WGBS) is considered the gold standard to study DNA methylation at a genome level. Here we present a protocol for the preparation of whole genome bisulfite sequencing libraries from plant samples (grapevine leaves) which includes detailed instructions for sample collection and DNA extraction, sequencing library preparation and bisulfite treatment.

#### **Chapter 8**

## Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes

*Kendall R. Corbin and Carlos M. Rodriguez Lopez*

#### **Abstract**

Epigenetic mechanisms are a key interface between the environment and the genotype. These mechanisms regulate gene expression in response to plant development and environmental stimuli, which ultimately affects the plant's phenotype. DNA methylation, in particular cytosine methylation, is probably the best studied epigenetic modification in eukaryotes. It has been associated to the regulation of gene expression in response to cell/tissue differentiation, organism development and adaptation to changing environments. Whole genome bisulfite sequencing (WGBS) is considered the gold standard to study DNA methylation at a genome level. Here we present a protocol for the preparation of whole genome bisulfite sequencing libraries from plant samples (grapevine leaves) which includes detailed instructions for sample collection and DNA extraction, sequencing library preparation and bisulfite treatment.

**Keywords:** whole genome bisulfite sequencing, methylome analysis, DNA methylation, epigenetic modifications, *Vitis vinifera*

#### **1. Introduction**

Plants being sessile have developed strategies to adapt to their environment, specifically via epigenetic modification of their genome [1, 2]. Epigenetic mechanisms, both heritable and reversible, allow an organism to respond to its environment through changes in gene expression, without changing the underlying genome [3–6]. One of the most widely studied epigenetic mechanisms is cytosine methylation (5mC), which is the result of a methyl group replacing a hydrogen in the cyclic carbon-5 of cytosines. In plants, methylation of cytosine bases can occur in three contexts (DNA base sequences) CG, CHG or CHH, where H is any nucleotide other than G [7]. Plant nuclear genomes are known to contain more extensive and expansive DNA methylation than that found in animals [8]. DNA methylation has been identified in a range of plants and plays a role in a wide variety of biological processes from plant development and organ differentiation to response to stress [9–20].

Due to the functional importance of DNA methylation in many species, a plethora of DNA methylation analysis approaches has been developed in recent years. These can be mainly grouped into three functional types that (1) indicate the methylation status of a specific sequence; (2) reveal the degree and patterning of DNA methylation across partly characterized genomes; or (3) facilitate the discovery and sequencing of new epialleles [7]. From a technical point of view, such methodologies can be grouped into those using global estimation of all nucleic base species (e.g. HPLC and LC-MS/MS),

methylation-sensitive restriction enzymes [18, 21, 22], high-resolution melting analysis [10, 23, 24], methylcytosine-specific antibodies and methylated DNA-binding domains [25, 26], bisulfite conversion of DNA, and third-generation DNA sequencing technologies, including single molecule real-time (SMRT) sequencing and nanopore sequencing (for extensive reviews in these methodologies, see [27–29]).

**2. Procedure**

**2.1 Equipment**

i. Ultralow freezer (80°C)

*DOI: http://dx.doi.org/10.5772/intechopen.90716*

ii. Mortar and pestle—used to grind leaf samples prior to DNA extraction. Use a clean set for each sample to avoid cross-contamination. Wash both parts using warm water and soap, air-dry, wrap in aluminium foil and autoclave.

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes*

iii. NanoDrop™ 2000 Spectrophotometer—this UV-Vis spectrophotometer has the capability to quantify and assess the purity of small volumes of DNA (0.5 μL). The sample may be pipetted directly onto the optical measurement surface. Additional information regarding DNA

quantification and quality assessment using NanoDrop can be found at https://www.thermofisher.com/us/en/home/industrial/spectroscopy-eleme ntal-isotope-analysis/molecular-spectroscopy/ultraviolet-visible-visiblespectrophotometry-uv-vis-vis/uv-vis-vis-instruments/nanodrop-mic rovolume-spectrophotometers/nanodrop-nucleic-acid-quantification.html

iv. Thermocycler (PCR machine)—thermocyclers amplify segments of nucleic acid following a series of temperature-controlled enzymatic reactions.

v. Covaris M220 Focused-Ultrasonicator™ and MicroTUBE-50 (Covaris, catalog number: 520166) (or equivalent models and parts)—sonicators are

vi. Magnetic rack for 1.5 mL tubes—magnetic racks are used for separation and purification of nucleic acids in combination with paramagnetic beads

vii. Qubit Fluorometric Quantification and Qubit dsDNA HS (High Sensitivity) Assay Kit (Thermo Fisher Scientific, catalog number: Q32854). Qubit assays accurately quantify nucleic acids quickly and require small volumes

viii. Agilent Fragment Analyzer, Agilent Bioanalyzer (Agilent Technologies) or

i. Sterile microcentrifuge tubes 1.5 mL (Eppendorf® Safe-Lock™)

ii. 15 mL polypropylene centrifuge tubes (Laboratory Product Sales)

used for shearing DNA to a desired size.

the Bio-Rad Experion (Bio-Rad Laboratories).

(e.g. AMPure XP beads).

of sample.

**2.2 Consumables**

**143**

ix. High-speed centrifuge.

iii. Filtered pipette tips

vi. Sterile 500 μL tubes

iv. Wide-bore pipette tips

v. Sterile 200 μL PCR tubes

Of all these techniques currently available, only bisulfite conversion of DNA and third-generation DNA sequencing provide a single-base resolution view of methylated cytosines across the selected target sequence. This approach is not limited by genome size and may be applied to a relatively small fraction of a genome or a whole genome. More recently developed techniques are capable of reading 5mC, and other DNA modifications, without the need for any chemical alteration of the target DNA molecule. However, their throughput, accuracy and affordability are still not sufficient for routine use. Bisulfite conversion of DNA, in turn, is based on the selective chemical modification of unmethylated cytosines (C) into uracils (U) (which are read as thymines (T) by DNA polymerases during PCR amplification) (**Figure 1**), while leaving unchanged 5mC (**Figure 2**). Due to its high throughput, reliability and low cost, bisulfite conversion is considered the "gold standard" DNA methylation analysis. Next-generation sequencing (NGS) allows the rapid sequencing of whole genomes. Combined with bisulfite conversion of the target DNA, it also permits the identification of methylated cytosines at a single-base resolution of whole genomes (i.e. whole genome bisulfite sequencing (WGBS)).

**Figure 1.**

*Bisulfite conversion of unmethylated cytosines. Bisulfite conversion reaction starts with the addition of a sodium bisulfite group (sulphonation step) to the pyrimidine ring double bond between carbons 5 and 6 to form a 5,6 dihydrocytosine-6-sulphonate. Next, spontaneous and irreversible hydrolytic deamination results in a 5,6 dihydrouracil-6-sulphonate (deamination step). Finally, high pH conditions favor the loss of the sulphonate group (desulphonation step) to form uracil. Only unmethylated cytosines are susceptible to the bisulfite reaction. Methylated (5mC and 5-hmC) cytosines do not undergo conversion.*


#### **Figure 2.**

*Bisulfite conversion of a sample DNA sequence. Nucleotides highlighted in blue (methylated cytosines) are protected from bisulfite conversion and are maintained as cytosines. Unmethylated cytosines are converted to uracils. Loss of the original base pairing will yield two different PCR products from each DNA fragment.*

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes DOI: http://dx.doi.org/10.5772/intechopen.90716*

### **2. Procedure**

methylation-sensitive restriction enzymes [18, 21, 22], high-resolution melting analysis

Of all these techniques currently available, only bisulfite conversion of DNA and third-generation DNA sequencing provide a single-base resolution view of methylated cytosines across the selected target sequence. This approach is not limited by genome size and may be applied to a relatively small fraction of a genome or a whole genome. More recently developed techniques are capable of reading 5mC, and other DNA modifications, without the need for any chemical alteration of the target DNA molecule. However, their throughput, accuracy and affordability are still not sufficient for routine use. Bisulfite conversion of DNA, in turn, is based on the selective chemical modification of unmethylated cytosines (C) into uracils (U) (which are read as thymines (T) by DNA polymerases during PCR amplification) (**Figure 1**), while leaving unchanged 5mC (**Figure 2**). Due to its high throughput, reliability and low cost, bisulfite conversion is considered the "gold standard" DNA methylation analysis. Next-generation sequencing (NGS) allows the rapid sequencing of whole genomes. Combined with bisulfite conversion of the target DNA, it also permits the identification of methylated cytosines at a single-base resolution of

[10, 23, 24], methylcytosine-specific antibodies and methylated DNA-binding domains [25, 26], bisulfite conversion of DNA, and third-generation DNA sequencing technologies, including single molecule real-time (SMRT) sequencing and nanopore

sequencing (for extensive reviews in these methodologies, see [27–29]).

*DNA Methylation Mechanism*

whole genomes (i.e. whole genome bisulfite sequencing (WGBS)).

*Methylated (5mC and 5-hmC) cytosines do not undergo conversion.*

*Bisulfite conversion of unmethylated cytosines. Bisulfite conversion reaction starts with the addition of a sodium bisulfite group (sulphonation step) to the pyrimidine ring double bond between carbons 5 and 6 to form a 5,6 dihydrocytosine-6-sulphonate. Next, spontaneous and irreversible hydrolytic deamination results in a 5,6 dihydrouracil-6-sulphonate (deamination step). Finally, high pH conditions favor the loss of the sulphonate group (desulphonation step) to form uracil. Only unmethylated cytosines are susceptible to the bisulfite reaction.*

*Bisulfite conversion of a sample DNA sequence. Nucleotides highlighted in blue (methylated cytosines) are protected from bisulfite conversion and are maintained as cytosines. Unmethylated cytosines are converted to uracils. Loss of the original base pairing will yield two different PCR products from each DNA fragment.*

**Figure 1.**

**Figure 2.**

**142**

#### **2.1 Equipment**


#### **2.2 Consumables**


#### **2.3 Chemicals and reagents**

i. Molecular biology grade ethanol (MilliporeSigma or Fisher BioReagents)

your choice (sequences are provided below<sup>2</sup>

*DOI: http://dx.doi.org/10.5772/intechopen.90716*

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes*

each cycle. Store double-stranded adapters at �20°C.

ACACTCTTTCCCTACACGACGCTCTTCCGATC\*T-30

TCCAGTCAC[i7]ATCTCGTATGCCGTCTTCTGCTTG-3<sup>0</sup>

oligonucleotides with standard desalting from the provider of your choice (sequences are provided below). To prepare the primers, resuspend both oligonucleotides with TE buffer to a final concentration of 100 μM. (This is your stock solution. Store at �20°C.) To prepare the Forward and Reverse

i. Label all tubes prior to starting any of the described protocols to reduce the

<sup>2</sup> [i7] index sequences can be found at https://support.illumina.com/content/dam/illumina-support/ documents/documentation/chemistry\_documentation/experiment-design/illumina-adapter-sequences-

xx. Library amplification primers: primers are ordered as lyophilized

Primer Mix, mix 10 μL from each in a new tube, and add 80 μL of molecular grade water to achieve a final concentration of 10 μM.

I.TruSeq universal adapter: 50

II.TruSeq INDEX adapter: 50

I.Forward primer: 5<sup>0</sup>

II.Reverse primer: 5<sup>0</sup>

i. Insulated polystyrene box

**2.4 Additional items required**

ii. Pipettes

**3. Set-up**

1000000002694-09.pdf

**145**

iii. Water bath

iv. Liquid nitrogen

TTTCCCTACACGA-3<sup>0</sup>

v. Refrigerator (4°C) and freezer (�20°C)

likeliness of downstream errors.

resuspend both oligonucleotides with TE buffer to a final concentration of 200 μM. Then add 75 μL from each into a 200 μL sterile PCR tube. To allow annealing of the complementary sections of the oligos, heat the mixture using a thermocycler to 95°C for 1 min, and then slowly lower the temperature to 30°C at a rate of 1°C/min. This can be accomplished by programming your thermocycler with a single step PCR cycle at 95°C for 1 min followed by 65 cycles during which the temperature is reduced by 1°C

). To prepare the adapters,






<sup>1</sup> Order the oligonucleotides with standard desalting. Request that all cytosines are methylated. This will allow the sequence integrity of the adapters to be maintained after bisulfite treatment. Also, order the indexed adapter with a 5<sup>0</sup> phosphate group and TruSeq Universal Adapter with phosphorothioate bond between the 3<sup>0</sup> end C and T nucleotides.

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes DOI: http://dx.doi.org/10.5772/intechopen.90716*

> your choice (sequences are provided below<sup>2</sup> ). To prepare the adapters, resuspend both oligonucleotides with TE buffer to a final concentration of 200 μM. Then add 75 μL from each into a 200 μL sterile PCR tube. To allow annealing of the complementary sections of the oligos, heat the mixture using a thermocycler to 95°C for 1 min, and then slowly lower the temperature to 30°C at a rate of 1°C/min. This can be accomplished by programming your thermocycler with a single step PCR cycle at 95°C for 1 min followed by 65 cycles during which the temperature is reduced by 1°C each cycle. Store double-stranded adapters at �20°C.

I.TruSeq universal adapter: 50 -AATGATACGGCGACCACCGAGATCT ACACTCTTTCCCTACACGACGCTCTTCCGATC\*T-30

II.TruSeq INDEX adapter: 50 -P\*GATCGGAAGAGCACACGTCTGAAC TCCAGTCAC[i7]ATCTCGTATGCCGTCTTCTGCTTG-3<sup>0</sup>

xx. Library amplification primers: primers are ordered as lyophilized oligonucleotides with standard desalting from the provider of your choice (sequences are provided below). To prepare the primers, resuspend both oligonucleotides with TE buffer to a final concentration of 100 μM. (This is your stock solution. Store at �20°C.) To prepare the Forward and Reverse Primer Mix, mix 10 μL from each in a new tube, and add 80 μL of molecular grade water to achieve a final concentration of 10 μM.

> I.Forward primer: 5<sup>0</sup> -AATGATACGGCGACCACCGAGATCTACACTC TTTCCCTACACGA-3<sup>0</sup>

II.Reverse primer: 5<sup>0</sup> -CAAGCAGAAGACGGCATACGAGAT-3<sup>0</sup>

#### **2.4 Additional items required**


#### **3. Set-up**

**2.3 Chemicals and reagents**

*DNA Methylation Mechanism*

i. Molecular biology grade ethanol (MilliporeSigma or Fisher BioReagents)

xii. Agencourt AMPure XP magnetic beads (Beckman Coulter, catalog number:

xiii. Q5® High-Fidelity 2� Master Mix (New England Biolabs, catalog number:

xiv. 10� End Repair Buffer (New England Biolabs, catalog number: B6052S)

xv. End Repair Enzyme Mix (New England Biolabs, catalog number: E6051)

xvi. 10� dA-Tailing Reaction Buffer (New England Biolabs, catalog number:

xvii. A-tailing Enzyme (e.g. Klenow Fragment (3<sup>0</sup> ! 5<sup>0</sup> exo-) (New England

xviii. 10� T4 DNA Ligation Buffer and T4 DNA Ligase (New England Biolabs,

oligonucleotides with the specified modifications<sup>1</sup> from the provider of

<sup>1</sup> Order the oligonucleotides with standard desalting. Request that all cytosines are methylated. This will allow the sequence integrity of the adapters to be maintained after bisulfite treatment. Also, order the indexed adapter with a 5<sup>0</sup> phosphate group and TruSeq Universal Adapter with phosphorothioate bond

xix. TruSeq Sequencing adapters: adapters are ordered as lyophilized

ii. Molecular biology grade water (MilliporeSigma)

xi. RNAse A (Sigma-Aldrich, catalog number: R4642)

iii. Cetyltrimethylammonium bromide (CTAB)

iv. Ethylenediaminetetraacetic acid (EDTA)

v. Tris hydrochloride (Tris-HCl)

vii. Polyvinylpolypyrrolidone (PVP)

vi. Hydrochloric acid (HCl)

x. Sodium chloride (NaCl)

viii. Chloroform

A63880)

M0492S)

B6059S)

between the 3<sup>0</sup> end C and T nucleotides.

**144**

Biolabs, catalog number: M0212S)

catalog numbers: B0202S and M0202)

ix. Octane

i. Label all tubes prior to starting any of the described protocols to reduce the likeliness of downstream errors.

<sup>2</sup> [i7] index sequences can be found at https://support.illumina.com/content/dam/illumina-support/ documents/documentation/chemistry\_documentation/experiment-design/illumina-adapter-sequences-1000000002694-09.pdf

ii. Use sterilized tools (scissors, knives, tweezers, etc.) for harvesting plant material, and clean utensils thoroughly between samples using 70% (v/v) ethanol.

*Note*: By immediately snap-freezing the samples, changes in DNA methylation

profiles induced during harvesting and cell death will be minimized.

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes*

of material transport from the field to laboratory.

*DOI: http://dx.doi.org/10.5772/intechopen.90716*

**3.2 Recipes for buffers, solutions and reagents**

**4. Protocol**

fragments.

**147**

sterile spatula.

(added to original extract).

**4.1 DNA extraction**

iii. Store all samples at 80°C until required for DNA extraction. *Note*: Storage of samples at ultralow temperatures will minimize DNA degradation. Avoid unnecessary freeze-thawing cycles, including during the period

i. Ethanol (70, 80 and 95% v/v). Store at room temperature.

iii. Chloroform-octanol 24:1 (v/v). Store at room temperature.

adjust pH to 8.0 and autoclave. Store at room temperature

DNA extraction is carried out following a modified CTAB protocol [33].

*Note*: The mortar should be fully cooled in liquid nitrogen prior to and during usage. In addition, the sample must remain frozen during the grinding process.

ii. Grind 500 mg of leaf material in a mortar and pestle. Continue to add liquid

iii. Add 5 mL of CTAB extraction buffer to the ground leaves and mix with a

iv. Transfer the slurry to a 15 mL polypropylene centrifuge tube. Rinse the mortar and pestle with 1 mL of extraction buffer, and add to the tube

v. Add 50 mg polyvinylpolypyrrolidone (PVP), screw the cap on the tube

tightly, and invert the tube several times to mix thoroughly.

and then adjust the volume to 1 L with water.

i. Pour liquid nitrogen on to a mortar and pestle.

nitrogen to ensure the equipment remains cold.

*Note*: Over grinding of plant biomass will cause DNA shearing, which results in lower yields after bisulfite treatment due to degradation of small DNA

Accidental thawing may result in DNA degradation.

ii. CTAB DNA extraction buffer (per 100 mL): 20 mM sodium EDTA (1 mL of 0.5 M stock) and 100 mM Tris-HCl (10 mL of 1 M stock), adjust pH to 8.0 with HCl; add 1.4 M NaCl (8.2 g), 1% (w/v) PVP (1.0 g), and 2.0% (w/v)

iv. 5 M sodium chloride (NaCl)—dissolve 292 g of NaCl in 800 mL of water,

v. 1 Tris-EDTA buffer (TE buffer)—10 mM Tris-HCl and 1 mM EDTA,

CTAB (2.0 g). Dissolve CTAB by heating to 60°C. Store at 37°C.


#### v. General safety notes.


#### **3.1 Collection of plant material**

i. Collect three individual leaves at bud burst (E-L 7 [30]) from the number of desired grapevines. The rationale for using immature vegetative tissue (leaves) is that cell number is fixed very early during development; thus the number of genome copies per gram of tissue is higher in younger leaves relative to older leaves. It is also advantageous to use younger plant material as some plant species accumulate secondary metabolites (such as alkaloids and flavonoids) as their tissues age. High levels of these metabolites can impede DNA extraction or PCR amplification [31].

*Note*: DNA methylation has been shown to change with the plant's circadian cycle [32] and during plant development [19]. Thus, when collecting samples for DNA methylation analysis from more than one plant, it is extremely important to harvest all plant tissue at approximately the same time of day and at the same developmental stage in order to minimize unwanted variability in DNA methylation.

ii. Immediately upon harvesting the leaves, put the material in a pre-labelled 1.5 mL centrifuge tube. Place the tubes in an insulated container (i.e. polystyrene box) and cover with dry ice (solid CO2).

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes DOI: http://dx.doi.org/10.5772/intechopen.90716*

*Note*: By immediately snap-freezing the samples, changes in DNA methylation profiles induced during harvesting and cell death will be minimized.

iii. Store all samples at 80°C until required for DNA extraction.

*Note*: Storage of samples at ultralow temperatures will minimize DNA degradation. Avoid unnecessary freeze-thawing cycles, including during the period of material transport from the field to laboratory.

#### **3.2 Recipes for buffers, solutions and reagents**


#### **4. Protocol**

ii. Use sterilized tools (scissors, knives, tweezers, etc.) for harvesting plant material, and clean utensils thoroughly between samples using 70% (v/v)

iii. Gloves should be worn at all times while handling samples to minimize

iv. DNA extractions, next-generation sequencing library preparations and bisulfite treatments should be carried out in a PCR cabinet or similar to

from your local Office of Environmental Health and Safety.

• Follow safe operating procedures when handling cryogenic products (dry ice and liquid nitrogen). Prior to usage (and transport) of

cryogenic products, a risk assessment should be conducted to evaluate hazards and identify control measures that may be implemented to minimize the level of risk. Additional information about cryogenic materials precautions and safe handling procedures may be available

• β-Mercaptoethanol (also known as 2-hydroxyethylmercaptan, BME or thioethylene glycol) is a toxic chemical that should be handled with extreme caution. Exposure to this product may cause respiratory issues, vomiting or skin irritation. Long-term exposure to this product can result in death. Personal protective equipment should be worn when handling this product and all experimental work conducted in a fume hood. Hazard control measures include wearing nitrile laboratory gloves (if gloves get splashed or tear, change immediately), safety glasses, closed toe shoes, a laboratory coat, and if spills are possible, a face shield. Safety documentation about this product, including information relevant to storage, transport and disposal, may be found

i. Collect three individual leaves at bud burst (E-L 7 [30]) from the number of desired grapevines. The rationale for using immature vegetative tissue (leaves) is that cell number is fixed very early during development; thus the number of genome copies per gram of tissue is higher in younger leaves relative to older leaves. It is also advantageous to use younger plant material as some plant species accumulate secondary metabolites (such as alkaloids and flavonoids) as their tissues age. High levels of these metabolites can

*Note*: DNA methylation has been shown to change with the plant's circadian cycle [32] and during plant development [19]. Thus, when collecting samples for DNA methylation analysis from more than one plant, it is extremely important to harvest all plant tissue at approximately the same time of day and at the same developmental

ii. Immediately upon harvesting the leaves, put the material in a pre-labelled

1.5 mL centrifuge tube. Place the tubes in an insulated container (i.e. polystyrene box) and cover with dry ice (solid CO2).

cross-contamination (change gloves as needed).

ethanol.

*DNA Methylation Mechanism*

minimize contamination.

on manufacturers Website.

impede DNA extraction or PCR amplification [31].

stage in order to minimize unwanted variability in DNA methylation.

**3.1 Collection of plant material**

**146**

v. General safety notes.

#### **4.1 DNA extraction**

DNA extraction is carried out following a modified CTAB protocol [33].

i. Pour liquid nitrogen on to a mortar and pestle.

*Note*: The mortar should be fully cooled in liquid nitrogen prior to and during usage. In addition, the sample must remain frozen during the grinding process. Accidental thawing may result in DNA degradation.

ii. Grind 500 mg of leaf material in a mortar and pestle. Continue to add liquid nitrogen to ensure the equipment remains cold.

*Note*: Over grinding of plant biomass will cause DNA shearing, which results in lower yields after bisulfite treatment due to degradation of small DNA fragments.


*Note*: PVP is added at a concentration of 100 mg PVP/g leaf tissue used in step ii.

*Note*: TE buffer should be used as the reference blank.

*DOI: http://dx.doi.org/10.5772/intechopen.90716*

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes*

**4.2 DNA shearing**

tube.

enzyme.

**149**

**4.3 Sheared DNA end repair**

xx. Normalize DNA concentrations to 20 ng/μL using molecular grade water.

xxi. Store DNA samples at 20°C (short-term) or 80°C (long-term).

i. Aliquot 1 μg of genomic DNA (equivalent to 50 μL of DNA with a

ii. Shear DNA to 200 bp fragments using the Covaris M220 Focused-

Duration, 90 s; peak power, 75 W; duty factor, 25%; cycles per burst, 1000

iii. Transfer 50 μL of the fragmented DNA to a clean, pre-labelled 200 μL PCR

i. Prepare End Repair Master Mix containing 8 μL molecular grade water, 7

*Note*: When preparing Master Mixes, prepare 10% extra to account for pipetting errors, and allow enough reaction mix for all sample. For example, for 10 samples, prepare enough Master Mix for those samples plus one extra (11 in total): combine 88 μL molecular grade water, 77 μL of 10 end repair buffer and 55 μL end repair

ii. Add 20 μL of End Repair Master Mix to each of the sheared samples.

*Note*: At this point remove AMPure XP beads from the refrigerator and allow the

iv. Capture DNA by adding 120 μL of AMPure XP beads, pipette up and down to achieve a homogenous mixture, and incubate at room temperature for 5 min.

vii. Keep the tube on the magnetic rack and remove the supernatant using a

bottle to reach room temperature before use. Immediately before pipetting, resuspend the beads by vortexing vigorously. The AMPure purification system selectively binds DNA fragments to paramagnetic beads, allowing the removal of excess primers, nucleotides, salts and enzymes during a simple washing step. These clean-up steps result in a more purified PCR product. For further information about using AMPure XP for PCR purification, please refer to the manufacturer's manual.

v. Transfer the beads with captured DNA to a 1.5 mL tube.

vi. Place the tube on a magnetic rack for 2 min.

pipette. Do not disturb the beads.

μL of 10 end repair buffer and 5 μL end repair enzyme.

Ultrasonicator™, using the following specifications:

*Note*: Label the top and the side of the PCR tubes.

iii. Incubate in a thermocycler at 20°C for 30 min.

concentration of 20 ng/μL) into a Covaris MicroTUBE-50, and add 5 μL of molecular biology water. The final volume in the microtube is 55 μL.

vi. Incubate the tube in a water bath set at 60°C for 25 min. Carefully remove the tube from the bath and cool to room temperature.

*Note*: Take care when removing the sample from the water bath, wear personal protective equipment (laboratory jacket, safety glasses and heat-resistant gloves).


*Note*: An RNAse treatment step is included to enzymatically digest RNA in the material, minimizing the amount of RNA extracted with the DNA. Contaminating RNA will result in the overestimation of DNA quantity.


*Note*: The solution should be left for at least 15 min but can stay refrigerated for longer if necessary.


*Note*: Differential centrifugation steps aid in keeping the DNA at the bottom of the tube.


*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes DOI: http://dx.doi.org/10.5772/intechopen.90716*

*Note*: TE buffer should be used as the reference blank.

xx. Normalize DNA concentrations to 20 ng/μL using molecular grade water.

xxi. Store DNA samples at 20°C (short-term) or 80°C (long-term).

#### **4.2 DNA shearing**

*Note*: PVP is added at a concentration of 100 mg PVP/g leaf tissue used in step ii.

vi. Incubate the tube in a water bath set at 60°C for 25 min. Carefully remove

*Note*: Take care when removing the sample from the water bath, wear personal protective equipment (laboratory jacket, safety glasses and heat-resistant gloves).

vii. Centrifuge the homogenate for 5 min at 14,000 *g* (room temperature),

viii. Treat with 1 μL RNase A per 100 μL DNA solution and incubate at 37°C for

*Note*: An RNAse treatment step is included to enzymatically digest RNA in the material, minimizing the amount of RNA extracted with the DNA. Contaminating

ix. Add 6 mL of chloroform-octanol, and mix gently by inverting the tube

x. Spin at 14,000 *g* for 15 min in a centrifuge (room temperature).

xi. Using a wide-bore pipette tip, transfer the top aqueous phase to a new 15 mL tube. A second chloroform-octanol extraction may be performed if the aqueous phase is cloudy due to the presence of PVP (repeat steps

xii. Add 3 mL of 5 M NaCl to the aqueous solution and mix well (invert gently

xiii. Add two volumes of cold (20°C) 95% (v/v) ethanol and refrigerate

*Note*: The solution should be left for at least 15 min but can stay refrigerated for

xv. Increase the speed of the centrifuge to 14,000 *g*. Spin samples for an

*Note*: Differential centrifugation steps aid in keeping the DNA at the bottom of

xvii. Remove ethanol by pipetting—do not disturb the DNA pellet. Air-dry the remaining ethanol by leaving the tubes uncovered at room temperature for

xvi. Carefully pour off supernatant and wash pellet with 1 mL of chilled

(4–6°C) until DNA strands begin to appear.

xiv. Spin at 10,000 *g* for 3 min (room temperature).

xviii. Solubilize the DNA pellet in 200–300 μL TE buffer.

xix. Quantify isolated DNA using the NanoDropTM 2000.

the tube from the bath and cool to room temperature.

and transfer the supernatant to a clean 1.5 mL tube.

RNA will result in the overestimation of DNA quantity.

20–25 times to form an emulsion.

15 min.

*DNA Methylation Mechanism*

ix to xi).

by hand).

additional 3 min.

10 min.

(0–4°C) 70% (v/v) ethanol.

longer if necessary.

the tube.

**148**

	- iii. Transfer 50 μL of the fragmented DNA to a clean, pre-labelled 200 μL PCR tube.

*Note*: Label the top and the side of the PCR tubes.

#### **4.3 Sheared DNA end repair**

i. Prepare End Repair Master Mix containing 8 μL molecular grade water, 7 μL of 10 end repair buffer and 5 μL end repair enzyme.

*Note*: When preparing Master Mixes, prepare 10% extra to account for pipetting errors, and allow enough reaction mix for all sample. For example, for 10 samples, prepare enough Master Mix for those samples plus one extra (11 in total): combine 88 μL molecular grade water, 77 μL of 10 end repair buffer and 55 μL end repair enzyme.

ii. Add 20 μL of End Repair Master Mix to each of the sheared samples.

iii. Incubate in a thermocycler at 20°C for 30 min.

*Note*: At this point remove AMPure XP beads from the refrigerator and allow the bottle to reach room temperature before use. Immediately before pipetting, resuspend the beads by vortexing vigorously. The AMPure purification system selectively binds DNA fragments to paramagnetic beads, allowing the removal of excess primers, nucleotides, salts and enzymes during a simple washing step. These clean-up steps result in a more purified PCR product. For further information about using AMPure XP for PCR purification, please refer to the manufacturer's manual.


*Note*: The aim of this step is to remove the AMPure XP buffer. At this stage the DNA is captured by the beads which are kept in the tube by the magnet. The buffer can be discarded.

viii. Keep the tube on the magnetic rack and add 200 μL of 80% (v/v) ethanol.

iv. Capture DNA by adding 90 μL of AMPure XP beads, pipette up and down to achieve a homogenous mix, and leave at room temperature for

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes*

v. Transfer the beads with the capture DNA to a clean 1.5 mL tube.

vii. Keep the tube on the magnetic rack and remove the supernatant without

*Note:* The aim of this step is to remove the AMPure XP buffer. At this stage the DNA is captured by the beads which are kept in the tube by the magnet. The buffer

viii. Keep the tube on the magnetic rack and add 200 μL of 80% (v/v) ethanol.

ix. Incubate for 30 s on the magnetic rack and use a pipette to remove the

xi. Evaporate ethanol by leaving the tube open on the magnetic rack for 5 min.

xii. Remove the tube from the magnetic rack and resuspend the beads by adding 32 μL of molecular grade water and pipette up and down.

xiii. Leave the tube at room temperature for 5 min to allow the DNA to be

xiv. Place the tube in the magnetic rack and leave at room temperature for 2

xv. Transfer 30 μL of the supernatant to a clean 200 μL PCR tube. Do not

μL T4 DNA Ligase and 7.5 μL molecular grade water.

tube and mix by pipetting up and down.

iv. Incubate in a thermocycler at 20°C for 15 min.

i. Prepare the Ligation Master Mix containing 5 μL of 10 Ligation Buffer, 2.5

ii. Add 5 μL of TruSeq Adapter to each of the samples in a 200 μL PCR tube. *Note*: Add 5 μL of adapters (10 μM) for every 1 μg of starting DNA. If you are planning to multiplex more than one sample in each sequencing lane, use adapters

iii. Add 15 μL of Ligation Master Mix to each of the samples in the 200 μL PCR

v. Capture DNA by adding 90 μL of AMPure XP beads, pipette up and down to achieve a homogenous mix. Leave at room temperature for 5 min.

vi. Place the tube on a magnetic rack for 2 min.

disturbing the beads using a pipette.

5 min.

*DOI: http://dx.doi.org/10.5772/intechopen.90716*

can be safely discarded.

ethanol.

min.

transfer beads.

with different index sequences.

**151**

**4.5 Ligation of sequencing adapters**

x. Repeat steps viii and ix.

released from the AMPure beads.

*Note*: Due to the different evaporation rates of H20 and ethanol, it is important to use freshly prepared ethanol.


*Note*: After the second ethanol wash, remove as much ethanol as possible using a 10 μL pipette. These wash steps are important to remove any remains of the End Repair Master Mix. At this stage the DNA is captured by the AMPure beads which are kept in the tube by the magnet.

xi. Remove residual ethanol by leaving the tube open on the magnetic rack for 5 min (air-dry).

*Note*: Do not over dry the beads as it will lower DNA yields. Appearance of cracks on the bead pellet is indicative of over drying.


*Note*: At this stage the DNA is resuspended in the water. Beads can be safely discarded. Do not attempt to pipette the entire volume in the tube (42 μL) as some of the AMPure beads may be transferred which could affect later reactions. If beads are disturbed during pipetting, simply put the whole volume back in the tube and proceed from step xiv.

#### **4.4 Fragmented DNA A-tailing**

i. Prepare the A-tailing Master Mix containing 2 μL molecular grade water, 5 μL of 10 A-tailing buffer and 3 μL A-tailing enzyme.

*Note*: When preparing Master Mixes, prepare 10% extra to account for pipetting errors and allow enough reaction mix for all samples.


*Note:* The aim of this step is to remove the AMPure XP buffer. At this stage the DNA is captured by the beads which are kept in the tube by the magnet. The buffer can be safely discarded.

	- ix. Incubate for 30 s on the magnetic rack and use a pipette to remove the ethanol.
	- x. Repeat steps viii and ix.

*Note*: The aim of this step is to remove the AMPure XP buffer. At this stage the DNA is captured by the beads which are kept in the tube by the magnet. The buffer

viii. Keep the tube on the magnetic rack and add 200 μL of 80% (v/v) ethanol.

*Note*: Due to the different evaporation rates of H20 and ethanol, it is important to

ix. Incubate for 30 s on the magnetic rack and use a pipette to remove the

*Note*: After the second ethanol wash, remove as much ethanol as possible using a 10 μL pipette. These wash steps are important to remove any remains of the End Repair Master Mix. At this stage the DNA is captured by the AMPure beads which

xi. Remove residual ethanol by leaving the tube open on the magnetic rack for

*Note*: Do not over dry the beads as it will lower DNA yields. Appearance of

xii. Remove the tube from the magnetic rack, add 42 μL of molecular grade

xiii. Leave the tube at room temperature for 5 min to allow the DNA to be

xiv. Place the tube in the magnetic rack and leave at room temperature for

*Note*: At this stage the DNA is resuspended in the water. Beads can be safely discarded. Do not attempt to pipette the entire volume in the tube (42 μL) as some of the AMPure beads may be transferred which could affect later reactions. If beads are disturbed during pipetting, simply put the whole volume back in the tube and

i. Prepare the A-tailing Master Mix containing 2 μL molecular grade water,

*Note*: When preparing Master Mixes, prepare 10% extra to account for pipetting

ii. Add 10 μL of A-tailing Master Mix to each of the samples (200 μL PCR

xv. Transfer 40 μL of the supernatant to a clean 200 μL PCR tube.

5 μL of 10 A-tailing buffer and 3 μL A-tailing enzyme.

errors and allow enough reaction mix for all samples.

iii. Incubate in a thermocycler at 30°C for 30 min.

water, and pipette up and until beads are fully resuspended.

can be discarded.

use freshly prepared ethanol.

*DNA Methylation Mechanism*

x. Repeat steps viii and ix.

are kept in the tube by the magnet.

5 min (air-dry).

2 min.

proceed from step xiv.

tube).

**150**

**4.4 Fragmented DNA A-tailing**

cracks on the bead pellet is indicative of over drying.

released from the AMPure beads.

ethanol.


#### **4.5 Ligation of sequencing adapters**


*Note*: Add 5 μL of adapters (10 μM) for every 1 μg of starting DNA. If you are planning to multiplex more than one sample in each sequencing lane, use adapters with different index sequences.


vi. Keep the tube on the magnetic rack, and remove the supernatant without

vii. Keep the tube on the magnetic rack and add 200 μL of 80% (v/v) ethanol.

x. Evaporate ethanol by leaving the tube open on the magnetic rack for 5 min.

adding 22 μL of molecular grade water and pipette up and down until beads

viii. Incubate for 30 s on the magnetic rack and use a pipette to remove the

xi. Remove the tube from the magnetic rack and resuspend the beads by

xii. Leave the tube at room temperature for 5 min to allow the DNA to be

xiii. Place the tube in the magnetic rack and leave at room temperature for

xiv. Transfer 20 μL of the supernatant to a clean 200 μL PCR tube. Make sure

DNA samples are bisulfite converted using the EZ DNA Methylation-Lightning

ii. Add 130 μL of Lightning Conversion Reagent to the tube containing the 20

*Note*: Mix and then centrifuge briefly to ensure there are no droplets in the cap or

iii. Place the PCR tube in a thermal cycler and incubate using the following

<sup>3</sup> High temperature is used to achieve complete denaturation of the double stranded DNA molecule and

i. Thaw samples completely (if stored in the freezer prior to bisulfite treatment), and centrifuge to bring droplets to the bottom.

*Storage*: At this stage the size-selected samples can be stored until required for bisulfite treatment. For short-term storage keep at 20°C, for long-term store at 80°C.

*Note*: In this case a ratio of 0.88 (82 μL AMPure XP buffer/93 μL DNA) will capture fragments above 100 bp. The supernatant, containing unligated TruSeq

adapters or DNA fragments below that size can be safely discarded.

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes*

disturbing the beads using a pipette.

*DOI: http://dx.doi.org/10.5772/intechopen.90716*

ethanol.

2 min.

Kit (Zymo Research).

sides of the tube.

**153**

programme:

ix. Repeat steps vii and viii.

are fully resuspended.

not to transfer the beads.

μL size-selected library.

a. 98°C for 8 min<sup>3</sup>

to favor the forward reaction during the reversible sulphonation step.

released from the AMPure beads.

**4.7 Bisulfite conversion of size-selected library**

*Note*: The aim of this step is to remove the AMPure XP buffer. At this stage the DNA is captured by the beads which are kept in the tube by the magnet. The buffer can be safely discarded.


#### **4.6 Sequencing library fragment size selection**

i. Add 60μL of AMPure beads to capture DNA fragments>450 bp, pipette up and down to achieve a homogenous mix, and leave at room temperature for 5 min.

*Note*: Beads preferentially capture larger fragments of DNA. The size range that the beads capture is determined by the volume to volume ratio of AMPure XP buffer and DNA aqueous solution. In this case a ratio of 0.6 (60 μL AMPure XP buffer/100 μL DNA) will capture fragments above 450 bp.


*Note*: Do not discard the supernatant in this case. The supernatant contains the fragment size range required for sequencing, while larger, unwanted fragments are still captured by the beads. At this stage the beads and the tube containing them can be discarded.


vi. Keep the tube on the magnetic rack, and remove the supernatant without disturbing the beads using a pipette.

*Note*: In this case a ratio of 0.88 (82 μL AMPure XP buffer/93 μL DNA) will capture fragments above 100 bp. The supernatant, containing unligated TruSeq adapters or DNA fragments below that size can be safely discarded.


vi. Transfer the beads with the captured DNA to a clean 1.5 mL tube.

viii. Keep the tube on the magnetic rack, and remove the supernatant without

*Note*: The aim of this step is to remove the AMPure XP buffer. At this stage the DNA is captured by the beads which are kept in the tube by the magnet. The buffer

ix. Keep the tube on the magnetic rack and add 200 μL of 80% (v/v) ethanol.

x. Incubate for 30 s on the magnetic rack and use a pipette to remove the ethanol.

xii. Evaporate ethanol by leaving the tube open on the magnetic rack for 5 min.

xv. Place the tube in the magnetic rack and leave at room temperature for 2 min.

xvi. Transfer 100 μL of the supernatant to a clean 1.5 mL tube. Do not transfer

i. Add 60μL of AMPure beads to capture DNA fragments>450 bp, pipette up and down to achieve a homogenous mix, and leave at room temperature for 5 min.

*Note*: Beads preferentially capture larger fragments of DNA. The size range that

iii. With the tube on the magnetic rack, transfer 155 μL of supernatant to a new

iv. Add 20 μL of beads to the 155 μL of supernatant collected in step iii, pipette up and down to achieve a homogenous mix, and leave at room temperature

*Note*: Do not discard the supernatant in this case. The supernatant contains the fragment size range required for sequencing, while larger, unwanted fragments are still captured by the beads. At this stage the beads and the tube containing them can

the beads capture is determined by the volume to volume ratio of AMPure XP buffer and DNA aqueous solution. In this case a ratio of 0.6 (60 μL AMPure XP

xiii. Remove the tube from the magnetic rack, add 105 μL of molecular grade water, and pipette up and down until beads are resuspended.

xiv. Leave the tube at room temperature for 5 min to allow the DNA to be

vii. Place the tube on a magnetic rack for 2 min.

disturbing the beads using a pipette.

released from the AMPure beads.

**4.6 Sequencing library fragment size selection**

buffer/100 μL DNA) will capture fragments above 450 bp.

ii. Place the tube on a magnetic rack for 2 min.

v. Place the tube on a magnetic rack for 2 min.

tube without disturbing the beads.

can be safely discarded.

*DNA Methylation Mechanism*

beads.

be discarded.

**152**

for 5 min.

xi. Repeat steps ix and x.


*Storage*: At this stage the size-selected samples can be stored until required for bisulfite treatment. For short-term storage keep at 20°C, for long-term store at 80°C.

#### **4.7 Bisulfite conversion of size-selected library**

DNA samples are bisulfite converted using the EZ DNA Methylation-Lightning Kit (Zymo Research).


*Note*: Mix and then centrifuge briefly to ensure there are no droplets in the cap or sides of the tube.

	- a. 98°C for 8 min<sup>3</sup>

<sup>3</sup> High temperature is used to achieve complete denaturation of the double stranded DNA molecule and to favor the forward reaction during the reversible sulphonation step.

b. 54°C for 60 min<sup>4</sup>


xi. Add 200 μL of M-Wash Buffer to the column. Centrifuge at full speed for 30 s. Discard the flow-through and collection tube. Keep the column

*Note*: These are wash steps. At this stage the DNA is still captured in the column

xii. Place the column into a 1.5 mL microcentrifuge tube, and add 12 μL of M-Elution Buffer directly to the column matrix. Centrifuge for 30 s at

*Storage*: Ideally use bisulfite-treated DNA immediately after treatment. After bisulfite conversion of non-methylated cytosines into uracils, genomic DNA does not maintain its original base pairing. This typically leads to single-stranded A-, U-, and T-rich DNA that is more susceptible to degradation. Long-term storage of bisulfite-converted DNA will lead to loss of sample concentration. If long-term

i. Prepare the PCR Master Mix: 25 <sup>μ</sup>L Q5® High-Fidelity 2 Master Mix, 2.5 μL Forward and Reverse Library Amplification Primer Mix at 10 μM and

*Note*: When preparing Master Mixes, prepare 10% extra to account for pipetting

ii. Thaw samples completely (if stored prior to bisulfite treatment) and

v. Place the PCR tube/tubes in a thermal cycler and incubate using the

vi. Centrifuge the PCR tube for a few seconds to ensure there are no droplets in the cap or sides of the tube due to condensation generated during PCR

<sup>7</sup> Maintain the number of cycles as low as possible to minimize DNA polymerase base substitution

<sup>8</sup> After PCR amplification, bisulfite-treated DNA recovers its base pairing. This stabilizes the DNA

iii. Transfer 10 μL of the bisulfite-treated library to a new 200 μL PCR tube.

matrix.

matrix, and the flow-through can be safely discarded.

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes*

storage is required, place in an ultralow freezer (80°C).

**4.8 PCR amplification of bisulfite-converted library**

12.5 μL molecular grade water.

errors and allow enough reaction mix for all samples.

centrifuge to bring droplets to the bottom.

iv. Add 40 μL of PCR Master Mix to each tube.

following program:

Go to step 2: 7–12 times<sup>7</sup>

98°C for 30 s 98°C for 30 s 60°C for 30 s

72°C for 4 min 72°C for 10 min 4°C hold<sup>8</sup>

amplification.

molecule making long-term storage possible.

errors.

**155**

full speed to elute the DNA.

*DOI: http://dx.doi.org/10.5772/intechopen.90716*

*Note*: Do not touch the bottom of the column with a pipette tip; this may damage the filtering matrix.

v. Load the sample (from step iii) into the Zymo-SpinTM IC Column containing the M-Binding Buffer. Close the cap and mix by inverting the column 10 times.

*Note*: Do not touch the bottom of the column with a pipette tip; this may damage the filtering matrix.

vi. Centrifuge at full speed (>10,000 *g*) for 30 s. Discard the flow-through.

*Note*: At this stage the DNA is captured in the column matrix and the flowthrough liquid can be safely discarded.

vii. Add 100 μL of M-Wash Buffer<sup>6</sup> to the column. Centrifuge at full speed (>10,000 *g*) for 30 s in benchtop centrifuge. Discard the flow-through.

*Note*: This is a wash step. At this stage, the DNA is still captured in the column matrix and the flow-through can be safely discarded.

viii. Add 200 μL of L-desulphonation buffer to the column, and leave at room temperature (20–30°C) for 15–20 min.

*Note*: This is an alkali desulphonation step that chemically removes the SO3<sup>2</sup> group added to unmethylated cytosines during the sulphonation step (**Figure 1**). At the end of this stage, cytosines that were originally unmethylated will be converted to uracils.

ix. After the incubation period, centrifuge at full speed for 30 s. Discard the flow-through.

*Note*: The aim of this centrifugation step is to remove the L-desulphonation buffer. At this stage the DNA is still captured in the column matrix.

x. Add 200 μL of M-Wash Buffer to the column. Centrifuge at full speed for 30 s. Discard the flow-through.

<sup>4</sup> This step consists of two consecutive chemical reactions. First, a sulphonation step selectively adds a SO3 group to unmethylated cytosines leaving methylated cytosines unchanged. Then, a spontaneous hydrolytic deamination exchanges de amino group (NH2) for an oxygen atom in the sulfonated cytosines during the sulphonation step (**Figure 1**).

<sup>5</sup> The 4°C storage step is optional. Ideally continue with the rest of the protocol right after the incubation. Longer storage at 4°C could result in DNA degradation.

<sup>6</sup> Ensure that molecular grade 100% ethanol is added to the M-DNA Wash Buffer as recommended by the manufacturer. For example, add 24 mL of ethanol to the 6 mL M-Wash Buffer concentrate (D5030) or 96 mL to the 24 mL M-Wash Buffer concentrate (D5031). M-DNA Wash Buffer included with D5030S and D5030T kits is supplied ready-to-use and does not require the addition of ethanol.

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes DOI: http://dx.doi.org/10.5772/intechopen.90716*

xi. Add 200 μL of M-Wash Buffer to the column. Centrifuge at full speed for 30 s. Discard the flow-through and collection tube. Keep the column matrix.

*Note*: These are wash steps. At this stage the DNA is still captured in the column matrix, and the flow-through can be safely discarded.

xii. Place the column into a 1.5 mL microcentrifuge tube, and add 12 μL of M-Elution Buffer directly to the column matrix. Centrifuge for 30 s at full speed to elute the DNA.

*Storage*: Ideally use bisulfite-treated DNA immediately after treatment. After bisulfite conversion of non-methylated cytosines into uracils, genomic DNA does not maintain its original base pairing. This typically leads to single-stranded A-, U-, and T-rich DNA that is more susceptible to degradation. Long-term storage of bisulfite-converted DNA will lead to loss of sample concentration. If long-term storage is required, place in an ultralow freezer (80°C).

#### **4.8 PCR amplification of bisulfite-converted library**

i. Prepare the PCR Master Mix: 25 <sup>μ</sup>L Q5® High-Fidelity 2 Master Mix, 2.5 μL Forward and Reverse Library Amplification Primer Mix at 10 μM and 12.5 μL molecular grade water.

*Note*: When preparing Master Mixes, prepare 10% extra to account for pipetting errors and allow enough reaction mix for all samples.


98°C for 30 s 98°C for 30 s 60°C for 30 s Go to step 2: 7–12 times<sup>7</sup> 72°C for 4 min 72°C for 10 min 4°C hold<sup>8</sup>

vi. Centrifuge the PCR tube for a few seconds to ensure there are no droplets in the cap or sides of the tube due to condensation generated during PCR amplification.

b. 54°C for 60 min<sup>4</sup>

the filtering matrix.

*DNA Methylation Mechanism*

the filtering matrix.

to uracils.

SO3

**154**

flow-through.

during the sulphonation step (**Figure 1**).

column 10 times.

through liquid can be safely discarded.

matrix and the flow-through can be safely discarded.

temperature (20–30°C) for 15–20 min.

30 s. Discard the flow-through.

incubation. Longer storage at 4°C could result in DNA degradation.

c. 4°C storage for up to 20 h<sup>5</sup>

iv. Add 600 μL of M-Binding Buffer to a Zymo-SpinTM IC Column, and place

*Note*: Do not touch the bottom of the column with a pipette tip; this may damage

containing the M-Binding Buffer. Close the cap and mix by inverting the

*Note*: Do not touch the bottom of the column with a pipette tip; this may damage

vi. Centrifuge at full speed (>10,000 *g*) for 30 s. Discard the flow-through.

*Note*: At this stage the DNA is captured in the column matrix and the flow-

vii. Add 100 μL of M-Wash Buffer<sup>6</sup> to the column. Centrifuge at full speed (>10,000 *g*) for 30 s in benchtop centrifuge. Discard the flow-through.

*Note*: This is a wash step. At this stage, the DNA is still captured in the column

viii. Add 200 μL of L-desulphonation buffer to the column, and leave at room

ix. After the incubation period, centrifuge at full speed for 30 s. Discard the

x. Add 200 μL of M-Wash Buffer to the column. Centrifuge at full speed for

*Note*: The aim of this centrifugation step is to remove the L-desulphonation

<sup>4</sup> This step consists of two consecutive chemical reactions. First, a sulphonation step selectively adds a

<sup>6</sup> Ensure that molecular grade 100% ethanol is added to the M-DNA Wash Buffer as recommended by the manufacturer. For example, add 24 mL of ethanol to the 6 mL M-Wash Buffer concentrate (D5030) or 96 mL to the 24 mL M-Wash Buffer concentrate (D5031). M-DNA Wash Buffer included with D5030S and D5030T kits is supplied ready-to-use and does not require the addition of ethanol.

<sup>5</sup> The 4°C storage step is optional. Ideally continue with the rest of the protocol right after the

 group to unmethylated cytosines leaving methylated cytosines unchanged. Then, a spontaneous hydrolytic deamination exchanges de amino group (NH2) for an oxygen atom in the sulfonated cytosines

buffer. At this stage the DNA is still captured in the column matrix.

*Note*: This is an alkali desulphonation step that chemically removes the SO3<sup>2</sup> group added to unmethylated cytosines during the sulphonation step (**Figure 1**). At the end of this stage, cytosines that were originally unmethylated will be converted

the column into the collection tube (provided by supplier).

v. Load the sample (from step iii) into the Zymo-SpinTM IC Column

<sup>7</sup> Maintain the number of cycles as low as possible to minimize DNA polymerase base substitution errors.

<sup>8</sup> After PCR amplification, bisulfite-treated DNA recovers its base pairing. This stabilizes the DNA molecule making long-term storage possible.


*Note*: In this case a ratio of 0.9 (45 μL AMPure XP buffer/50 μL PCR product) will capture fragments above 100 bp. The supernatant containing unused PCR primers or DNA fragments below that size can be safely discarded.


**5. Data analysis and results**

*AMP XP beads size selection.*

**Figure 3.**

sequence.

**6. Conclusion**

**157**

i. Perform FastQC Analysis to remove low-quality sequences.

v. Remove PCR duplicates with Bismark Deduplicate function.

using the Bismark Methylation Extractor function.

iv. Map trimmed reads using Bismark aligner.

ii. Use Trim Galore! (http://www.bioinformatics.babraham.ac.uk/projects/ trim\_galore) to trim sequencing adapters and to remove low-quality

*Example electropherogram of successful WGBS library. Gel image on the left of the figure includes the gel images for (A1) the internal ladder and (B1) the WGBS library. The electropherogram on the right shows the lower and upper fragments of the internal ladder and the fragment size distribution for the WGBS (highlighted in blue in box B). The presence of peaks below 100 bp in the electropherogram is indicative of sequencing adapters or PCR primers. The presence of DNA fragments over 500 bp (Box C) indicates large fragments of DNA that could reduce the quality and output of the sequencing run. Both types of fragments should be removed using*

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes*

*DOI: http://dx.doi.org/10.5772/intechopen.90716*

iii. Perform FastQC Analysis to remove low-quality trimmed sequences.

vi. Obtain methylation calls and methylation percentages per each CpG site

By following the protocol described herein, you have have a single-base resolution methylome for your sample. The quality of this methylome will depend on two main factors: (a) the sequencing depth of the produced methylome and (b) the number of replicates included in your experiment. With this data, you can infer methylation density at different genomic levels (i.e. along chromosomes; in different genomic features like genes, transposable elements, etc.) and within specific genomic features like promoters and gene bodies. If you are trying to identify changes in DNA methylation associated to a specific variable (e.g. growing environment, stress, tissue/cell type, age, disease, etc.), then you can identify


*Note*: A good WGBS library should show a fragment distribution between 150 and 500 bp (**Figure 3** Box B). Smaller peaks in the electropherogram would be indicative of sequencing adapters or PCR primers (**Figure 3** Box A). The presence or primers will reduce the quality and yield of the sequencing run. If present, they can be removed by repeating the AMPure XP bead clean-up described in steps vii to xvii of the PCR amplification of bisulfite-converted library protocol. Make sure that molecular grade water is added to the library to adjust to a final volume of 50 μL before adding the 45 μL of AMPure beads. Once the library passes the QC, it can be stored until sequenced. For short-term storage, keep at 20°C, for longer-term keep at 80°C.

xix. Sequence the final library using the HiSeq Illumina platform.

xx. Analyse sequencing results.

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes DOI: http://dx.doi.org/10.5772/intechopen.90716*

#### **Figure 3.**

vii. Add 45μL of beads to the PCR product, pipette up and down to achieve a

ix. Keep the tube on the magnetic rack, and remove the supernatant without

x. Keep the tube on the magnetic rack and add 200 μL of 80% (v/v) ethanol.

xi. Incubate for 30 s on the magnetic rack and use a pipette to remove the

xiii. Air-dry any ethanol by leaving the tube open on the magnetic rack for

xiv. Remove the tube from the magnetic rack and resuspend the beads by

xv. Leave the tube at room temperature for 5 min to allow the DNA to be

xvi. Place the tube in the magnetic rack and leave at room temperature for

xvii. Transfer 20 μL of the supernatant to a clean 500 μL tube. Make sure not

xviii. Check sequencing library concentration using Qubit and fragment size distribution using the Agilent Fragment Analyzer, Agilent Bioanalyzer

*Note*: A good WGBS library should show a fragment distribution between 150 and 500 bp (**Figure 3** Box B). Smaller peaks in the electropherogram would be indicative of sequencing adapters or PCR primers (**Figure 3** Box A). The presence or primers will reduce the quality and yield of the sequencing run. If present, they can be removed by repeating the AMPure XP bead clean-up described in steps vii to xvii of the PCR amplification of bisulfite-converted library protocol. Make sure that molecular grade water is added to the library to adjust to a final volume of 50 μL before adding the 45 μL of AMPure beads. Once the library passes the QC, it can be stored until sequenced. For short-term storage, keep at 20°C, for longer-term

(Agilent Technologies) or the Bio-Rad Experion (Bio-Rad).

xix. Sequence the final library using the HiSeq Illumina platform.

adding 22 μL of molecular grade water and pipette up and down until beads

*Note*: In this case a ratio of 0.9 (45 μL AMPure XP buffer/50 μL PCR product) will capture fragments above 100 bp. The supernatant containing unused PCR

homogenous mix, and leave at room temperature for 5 min.

viii. Place the tube on a magnetic rack for 2 min.

primers or DNA fragments below that size can be safely discarded.

disturbing the beads using a pipette.

ethanol.

*DNA Methylation Mechanism*

5 min.

2 min.

keep at 80°C.

**156**

xii. Repeat steps ix and x.

are fully resuspended.

to transfer the beads.

xx. Analyse sequencing results.

released from the AMPure beads.

*Example electropherogram of successful WGBS library. Gel image on the left of the figure includes the gel images for (A1) the internal ladder and (B1) the WGBS library. The electropherogram on the right shows the lower and upper fragments of the internal ladder and the fragment size distribution for the WGBS (highlighted in blue in box B). The presence of peaks below 100 bp in the electropherogram is indicative of sequencing adapters or PCR primers. The presence of DNA fragments over 500 bp (Box C) indicates large fragments of DNA that could reduce the quality and output of the sequencing run. Both types of fragments should be removed using AMP XP beads size selection.*

#### **5. Data analysis and results**


#### **6. Conclusion**

By following the protocol described herein, you have have a single-base resolution methylome for your sample. The quality of this methylome will depend on two main factors: (a) the sequencing depth of the produced methylome and (b) the number of replicates included in your experiment. With this data, you can infer methylation density at different genomic levels (i.e. along chromosomes; in different genomic features like genes, transposable elements, etc.) and within specific genomic features like promoters and gene bodies. If you are trying to identify changes in DNA methylation associated to a specific variable (e.g. growing environment, stress, tissue/cell type, age, disease, etc.), then you can identify

differentially methylated cytosines (DMCs) or differentially methylated regions (DMRs) between groups of samples (i.e. control vs treatment). Methods such as Fisher's exact test can be used in the absence of replicates [34]. However, this approach does not consider the possibility of biological variability which is of great importance on a plastic trait such as DNA methylation. Linear or logistic regressionbased methods are better suited to capture biological variability since they can compare methylation levels between groups of samples. One example of linear regression method is BSmooth [35] which assumes that data follows a binomial distribution and uses linear regression and t-tests to identify methylation differences for each site. One issue with linear regression is overfitting of DNA methylation levels beyond the 0 to 1 range that methylation proportion/fraction values regenerate. Logistic regression methods, implemented by software such as methylKit can deal better with data restricted to a 0 to 1 range by correcting to data dispersion.

**References**

644-655

**7**:1354

236-248

[1] Verhoeven KJF, Preite V. Epigenetic variation in asexually reproducing organisms. Evolution. 2014;**68**(3):

*DOI: http://dx.doi.org/10.5772/intechopen.90716*

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes*

epigenetic correlates of long-term inequality in herbivory. Molecular Ecology. 2011;**20**(8):1675-1688

[11] Rico L, Ogaya R, Barbeta A, Peñuelas J. Changes in DNA

[12] Ashikawa I. Surveying CpG

of rice cultivars. Plant Molecular Biology. 2001;**45**(1):31-39

[13] Humbeck K. Epigenetic and small RNA regulation of senescence. Plant Molecular Biology. 2013;**82**(6):529-537

[14] Tricker PJ, López CMR, Gibbings G,

[15] Tricker P, Rodríguez López C, Hadley P, Wagstaff C, Wilkinson M. Pre-conditioning the epigenetic response to high vapor pressure deficit increases the drought tolerance of *Arabidopsis thaliana*. Plant Signaling &

[16] Amoah S, Kurup S, Rodriguez Lopez CM, Welham SJ, Powers SJ, Hopkins CJ, et al. A hypomethylated population of *Brassica rapa* for forward

Hadley P, Wilkinson MJ. Transgenerational, dynamic methylation of stomata genes in response to low relative humidity. International Journal of Molecular Sciences. 2013;**14**(4):6674-6689

Behavior. 2013;**8**(10)

methylation fingerprint of Quercus ilex trees in response to experimental field drought simulating projected climate change. Plant Biology. 2014;**16**(2):


3799-3813

419-427

methylation at 5<sup>0</sup>

[10] Tricker PJ, Gibbings JG, Rodríguez López CM, Hadley P, Wilkinson MJ. Low relative humidity triggers RNAdirected de novo DNA methylation and suppression of genes controlling stomatal development. Journal of Experimental Botany. 2012;**63**(10):

[2] Latzel V, Rendina González AP, Rosenthal J. Epigenetic memory as a basis for intelligent behavior in clonal plants. Frontiers in Plant Science. 2016;

[3] Verhoeven KJF, Jansen JJ, van Dijk PJ, Biere A. Stress-induced DNA methylation changes and their

heritability in asexual dandelions. New Phytologist. 2010;**185**(4):1108-1118

[4] Ocaña J, Walter B, Schellenbaum P. Stable MSAP markers for the distinction of *Vitis vinifera* cv Pinot noir clones. Molecular Biotechnology. 2013;**55**(3):

[5] Zhang Y-Y, Fischer M, Colot V, Bossdorf O. Epigenetic variation creates

phenotypic plasticity. New Phytologist.

[6] Baránek M, Čechová J, Raddová J, Holleinová V, Ondrušíková E, Pidra M. Dynamics and reversibility of the DNA methylation landscape of grapevine plants (*Vitis vinifera*) stressed by in vitro cultivation and thermotherapy. PLoS

[7] Rodríguez López CM, Wilkinson MJ.

[8] Vanyushin BF, Ashapkin VV. DNA methylation in higher plants: Past, present and future. Biochimica et Biophysica Acta. 2011;**1809**(8):360-368

[9] Herrera CM, Bazaga P. Untangling

individual variation in natural populations: Ecological, genetic and

**159**

potential for evolution of plant

2013;**197**(1):314-322

One. 2015;**10**(5):e0126638

Epi-fingerprinting and epiinterventions for improved crop production and food quality. Frontiers

in Plant Science. 2015;**6**:397

### **Acknowledgements**

This work is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch Program number 2352987000.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Kendall R. Corbin and Carlos M. Rodriguez Lopez\* Environmental Epigenetics and Genetics Group, Department of Horticulture, College of Agriculture, Food and Environment, University of Kentucky, Lexington, KY, USA

\*Address all correspondence to: cmro267@uky.edu

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Library Preparation for Whole Genome Bisulfite Sequencing of Plant Genomes DOI: http://dx.doi.org/10.5772/intechopen.90716*

#### **References**

differentially methylated cytosines (DMCs) or differentially methylated regions (DMRs) between groups of samples (i.e. control vs treatment). Methods such as Fisher's exact test can be used in the absence of replicates [34]. However, this approach does not consider the possibility of biological variability which is of great importance on a plastic trait such as DNA methylation. Linear or logistic regressionbased methods are better suited to capture biological variability since they can compare methylation levels between groups of samples. One example of linear regression method is BSmooth [35] which assumes that data follows a binomial distribution and uses linear regression and t-tests to identify methylation differences for each site. One issue with linear regression is overfitting of DNA methylation levels beyond the 0 to 1 range that methylation proportion/fraction values regenerate. Logistic regression methods, implemented by software such as

methylKit can deal better with data restricted to a 0 to 1 range by correcting to data

This work is supported by the National Institute of Food and Agriculture,

U.S. Department of Agriculture, Hatch Program number 2352987000.

The authors declare no conflict of interest.

Kendall R. Corbin and Carlos M. Rodriguez Lopez\*

\*Address all correspondence to: cmro267@uky.edu

provided the original work is properly cited.

Environmental Epigenetics and Genetics Group, Department of Horticulture, College of Agriculture, Food and Environment, University of Kentucky, Lexington,

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

dispersion.

**Acknowledgements**

*DNA Methylation Mechanism*

**Conflict of interest**

**Author details**

KY, USA

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[3] Verhoeven KJF, Jansen JJ, van Dijk PJ, Biere A. Stress-induced DNA methylation changes and their heritability in asexual dandelions. New Phytologist. 2010;**185**(4):1108-1118

[4] Ocaña J, Walter B, Schellenbaum P. Stable MSAP markers for the distinction of *Vitis vinifera* cv Pinot noir clones. Molecular Biotechnology. 2013;**55**(3): 236-248

[5] Zhang Y-Y, Fischer M, Colot V, Bossdorf O. Epigenetic variation creates potential for evolution of plant phenotypic plasticity. New Phytologist. 2013;**197**(1):314-322

[6] Baránek M, Čechová J, Raddová J, Holleinová V, Ondrušíková E, Pidra M. Dynamics and reversibility of the DNA methylation landscape of grapevine plants (*Vitis vinifera*) stressed by in vitro cultivation and thermotherapy. PLoS One. 2015;**10**(5):e0126638

[7] Rodríguez López CM, Wilkinson MJ. Epi-fingerprinting and epiinterventions for improved crop production and food quality. Frontiers in Plant Science. 2015;**6**:397

[8] Vanyushin BF, Ashapkin VV. DNA methylation in higher plants: Past, present and future. Biochimica et Biophysica Acta. 2011;**1809**(8):360-368

[9] Herrera CM, Bazaga P. Untangling individual variation in natural populations: Ecological, genetic and

epigenetic correlates of long-term inequality in herbivory. Molecular Ecology. 2011;**20**(8):1675-1688

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### *Edited by Metin Budak and Mustafa Yıldız*

#### *'Simplicity is the ultimate sophistication'* Leonardo Da Vinci

The methylation that occurs simply by attaching one or more methyl molecules to a DNA molecule continues to confuse the scientific world by creating highly complex molecular arrangements. Research on methylation mechanisms have discovered that this simple biochemical event (which adapts to the changing micro/macro environment of the organism, to diseases and even cancerous processes) has shown that it is actually not as simple as it seems. In the last 50 years, our efforts to understand these mechanisms and use them to benefit human beings have continued. With this book called "DNA methylation mechanism", in which we try to explain the effects on every stage of life, we hope that we have been able to create a resource book for everyone interested in this field, from students who are interested, to amateurs and professionals.

Published in London, UK © 2020 IntechOpen © Ugreen / iStock

DNA Methylation Mechanism

IntechOpen Book Series

Biochemistry, Volume 15

DNA Methylation Mechanism

*Edited by Metin Budak and Mustafa Yıldız*