Ecological epigenetics fact sheets

Fact sheets

• Fact sheet 1: DNA Methylation

  María-Estefanía López

  DNA methylation plays a crucial role in the regulation of gene expression, in the activity of transposable elements, in the defense against foreign DNA, and even in the inheritance of specific gene expression patterns (Xu, Tanino, & Robinson, 2016; Finnegan, Genger, Peacock, & Dennis, 1998; Xu, Tanino, Horner, & Robinson, 2016). DNA methylation refers to cytosine methylation process through the covalent enzyme-catalyzed transfer of methyl group from S-adenosylmethionine to 5’ position of cytosine, thus converting cytosine to 5-methylcytosine (5mC) (Pikaard et al., 2014; Sahu et al., 2013). DNA methylation in plants is species-, tissue-, organelle-, and age-specific. It is controlled by phytohormones, changes during plant development, and under biotic and abiotic stress conditions (Finnegan et al., 1998). This epigenetic mark can be accumulated during plant vegetative phases and be passed on to the next generations by germline cells. DNA cytosine methylation appears in three contexts, CG, CHG, and CHH, where H can be A, C, or T (Sahu et al., 2013). It predominantly occurs on transposons and other repetitive DNA elements in the genome. DNA methylation patterns must be stably maintained in order to ensure that transposons remain in a silenced state and to preserve cell type identity. DNA methylation is maintained by three different pathways: CG methylation by DNA METHYLTRANSFERASE 1 (MET1), CHG methylation by CHROMOMETHYLASE (CMT3), a plant specific DNA methyltransferase, and asymmetric CHH methylation through de novo methylation by DOMAINS REARRANGED METHYLTRANSFERASE 2 (DRM2) (Law & Jacobsen, 2011).  Although in most cases DNA methylation is a stable epigenetic mark, reduced levels of methylation are observed during plant development. The loss of methylation can either occur passively, via replication in the absence of functional maintenance methylation pathways, or actively by removing methylated cytosines with DNA glycosylase activity. The symmetrical CG or CHG methylation is inherited during the DNA replication in the form of hemimethylated sequences. It provides the memory of methylation imprint present in the parental DNA suggesting their role in stress protection memory(Suzuki & Bird, 2008). On the contrary, the asymmetrical cytosine methylation must be reestablished de novo after each replication cycle. Even though genes involved in DNA repair or epigenetic regulation of transcription have been studied extensively in plants evidence for components linking DNA repair and epigenetic inheritance is poorly known. DNA methylation in plants is closely associated with histone modifications and it affects binding of specific proteins to DNA and formation of respective transcription complexes in chromatin (Pikaard et al., 2014; Zamir, 2001). For this reason, it has been proposed that MET1 and DDM1 are involved in DNA damage response (Shaked, Avivi-ragolsky, & Levy, 2006). Showing that DDM1 mutations, generates a strong alteration in nuclear organization and chromatin structure, particularly in the centromeric and pericentromeric regions resulting in the impediment of DNA repair machinery to not have access to the damaged sequences. This fact emphasizes the broad involvement of recombination and repair DNA proteins in genome maintenance and link between epigenetic and genetic processes.

   

  Finnegan, E. J., Genger, R. K., Peacock, W. J., & Dennis, E. S. (1998). DNA METHYLATION IN PLANTS.

  Law, J. A., & Jacobsen, S. E. (2011). patterns in plants and animals, 11(3), 204–220. https://doi.org/10.1038/nrg2719.Establishing

  Pikaard, C. S., Scheid, O. M., Kingston, R. E., Tamkun, J. W., Baulcombe, D. C., & Dean, C. (2014). Epigenetic Regulation in Plants Epigenetic Regulation in Plants, 1–31. https://doi.org/10.1101/cshperspect.a019315

  Sahu, P. P., Pandey, G., Sharma, N., Puranik, S., Muthamilarasan, M., & Prasad, M. (2013). Epigenetic mechanisms of plant stress responses and adaptation. Plant Cell Reports, 32(8), 1151–1159. https://doi.org/10.1007/s00299-013-1462-x

  Shaked, H., Avivi-ragolsky, N., & Levy, A. A. (2006). Involvement of the Arabidopsis SWI2/SNF2 Chromatin Remodeling Gene Family in DNA Damage Response and Recombination, 2(June), 985–994. https://doi.org/10.1534/genetics.105.051664

  Suzuki, M. M., & Bird, A. (2008). DNA methylation landscapes: provocative insights from epigenomics, 9(June), 465–476. https://doi.org/10.1038/nrg2341

  Xu, J., Tanino, K. K., Horner, K. N., & Robinson, S. J. (2016). Quantitative trait variation is revealed in a novel hypomethylated population of woodland strawberry (Fragaria vesca). BMC Plant Biology, 16(1), 1–17. https://doi.org/10.1186/s12870-016-0936-8

  Xu, J., Tanino, K. K., & Robinson, S. J. (2016). Stable Epigenetic Variants Selected from an Induced Hypomethylated Fragaria vesca Population. Frontiers in Plant Science, 7(November), 1–14. https://doi.org/10.3389/fpls.2016.01768

  Zamir, D. (2001). Improving plant breeding with exotic genetic libraries. Nature Reviews Genetics, 2(12), 983–989. https://doi.org/10.1038/35103590

• Fact sheet 2: Histone modifications and Chromatin dynamics

  Daniela Ramos-Cruz

  In eukaryotes, DNA is tightly packed in the nucleus in a specific chromatin arrangement formed of nucleosomes. A nucleosome is the basic unit of DNA compaction and consists of 150 bp DNA wrapped around an octamer of histone proteins. Each octamer is composed of two copies of histones H2A, H2B, H3 and H4 and an intervening histone‐free linker DNA H1 (Luger et al., 1997). The level of chromatin compaction affects cellular processes such as transcription, replication or DNA repair by allowing the corresponding regulators to access the genetic information in response to developmental or environmental stimulus (Zhong et al., 2013).

  Accessibility of chromatin is regulated by a set of protein complexes grouped in two classes, histone-modifying enzymes and ATP-dependent chromatin remodeling complexes. In this chapter we will describe the current knowledge about histone modifications and chromatin remodelers in the regulation of chromatin dynamics to control cellular responses.

  Histone-modifying enzymes, post-translationally modify the N-terminal tails of histone proteins through acetylation, phosphorylation, ubiquitination, ADP-ribosylation and methylation. Histone modifications can be reversed by a set of antagonistic enzymes catalyzing the addition or removal of chemical modifications. The combinatorial arrangement of histone marks defines and partition the genome into functional domains, such as transcriptionally silent heterochromatin and transcriptionally active euchromatin. For example, histone acetylation has been linked to transcriptional activation, whereas histone ubiquitination and methylation has been observed in transcriptional activation and silencing. The inter-related collection of histone modifications forms the “histone code”, which is interpreted by effector proteins or “readers” that recognize and bind to modifications through specific domains, to further direct the different cellular responses dictated by the chromatin structure (Berr et al., 2011; Zhong et al, 2013; Strahl., 2000).

  The second class of chromatin-modifying factors are the ATP-dependent chromatin-remodeling enzymes, which alter nucleosomal structure and DNA accessibility. ATP-dependent chromatin remodelers mediate gene activation by reposition (slide, twist, or loop) of nucleosomes along the DNA, by eviction of histones from DNA or by facilitating exchange of histone variants. Every chromatin remodeling complex contains a conserved ATPase subunit. Based on the similarity of the ATPase subunit and the presence of unique domains, such as cromodomains, bromodomains or PHD domains, chromatin remodelers are grouped into 4 different families (INO80/SWR1, CHD, ISWI and SNF/SNF) (Jarillo et al., 2009, Han et al., 2015). Remodelers of each family differ in their biochemical activity. For example, ISWI and CHD family participate in nucleosome spacing in chromatin assembly after replication (Corona and Tamkun, 2004); SWI/SNF subfamilies are important for nucleosomal disassembly (Whitehouse et al., 1999; Phelan et al., 2000), whereas INO80 and SWR1 complexes have opposite roles in histone variant exchange (Mizuguchi et al., 2004; Papamichos‐Chronakis et al., 2011).

  Berr, Alexandre, Sarfraz Shafiq, and Wen-Hui Shen. 2011. “Histone Modifications in Transcriptional Activation during Plant Development.” Biochimica et Biophysica Acta 1809 (10): 567–76

  Corona, Davide F. V., and John W. Tamkun. 2004. “Multiple Roles for ISWI in Transcription, Chromosome Organization and DNA Replication.” Biochimica et Biophysica Acta 1677 (1-3): 113–19.

  Han, Soon-Ki, Miin-Feng Wu, Sujuan Cui, and Doris Wagner. 2015. “Roles and Activities of Chromatin Remodeling ATPases in Plants.” The Plant Journal: For Cell and Molecular Biology 83 (1): 62–77.

  Jarillo, José A., Manuel Piñeiro, Pilar Cubas, and José M. Martínez-Zapater. 2009. “Chromatin Remodeling in Plant Development.” The International Journal of Developmental Biology 53 (8-10): 1581–96.

  Luger, K., A. W. Mäder, R. K. Richmond, D. F. Sargent, and T. J. Richmond. 1997. “Crystal Structure of the Nucleosome Core Particle at 2.8 A Resolution.” Nature 389 (6648): 251–60.

  Mizuguchi, Gaku, Xuetong Shen, Joe Landry, Wei-Hua Wu, Subhojit Sen, and Carl Wu. 2004. “ATP-Driven Exchange of Histone H2AZ Variant Catalyzed by SWR1 Chromatin Remodeling Complex.” Science 303 (5656): 343–48.

  Papamichos-Chronakis, Manolis, Shinya Watanabe, Oliver J. Rando, and Craig L. Peterson. 2011. “Global Regulation of H2A.Z Localization by the INO80 Chromatin-Remodeling Enzyme Is Essential for Genome Integrity.” Cell 144 (2): 200–213.

  Phelan, M. L., G. R. Schnitzler, and R. E. Kingston. 2000. “Octamer Transfer and Creation of Stably Remodeled Nucleosomes by Human SWI-SNF and Its Isolated ATPases.” Molecular and Cellular Biology 20 (17): 6380–89.

  Strahl, B. D., and C. D. Allis. 2000. “The Language of Covalent Histone Modifications.” Nature 403 (6765): 41–45.

  Whitehouse, I., A. Flaus, B. R. Cairns, M. F. White, J. L. Workman, and T. Owen-Hughes. 1999. “Nucleosome Mobilization Catalysed by the Yeast SWI/SNF Complex.” Nature 400 (6746): 784–87.

  Zhong, Yong, Cynthia Kanagaratham, and Danuta Radzioch. 2013. “Chromatin Remodelling During Host-Bacterial Pathogen Interaction.” In Chromatin Remodelling, edited by Danuta Radzioch. InTech.

• Fact Sheet 3: Small RNAs and other non-coding RNAs

  Adrián Contreras Garrido

  Small RNAs (sRNAs) are a category of non-coding RNAs characterized for its short sequence (<200 nucleotide length) that are widespread in various eukaryotes [1]. Because of its arbitrary categorization by length, they form a heterogeneous group of short transcripts that is better understood when subcategorized in different groups. This subcategorization is accomplished by their specific role and their biogenesis [2].

   Although the first role assigned to this sRNAs was in the post transcriptional gene silencing (PTGS), it has been shown that sRNAs participate also in genome stability, heterochromatin formation, developmental gene regulation, transposon regulation and in the epigenetic silencing of repetitive regions and other genomic regions [3]. Overall, we can say that sRNAs (and other non-coding RNAs) establish a layer of regulation of coding and non-coding genomic regions of the eukaryotic cell [4]. sRNAs are unable to perform their biological role by themselves and they need to associate with proteins to perform their task [5]. Inside this protein-RNA complexes, sRNAs usually functions as targeting machinery of the complex, guiding the complex to its loci of action (which usually is the biogenesis loci of a particular sRNA but there are exceptions like the phasiRNAs [6].

  In this chapter we will focus in the major subgroup of sRNAs, the small interference RNAs (siRNAs) because of its role in epigenetics by the establishment of the de novo DNA methylation in plants [7]. Although it was first discovered in tobacco, the molecular mechanism has been well characterized in Arabidopsis thaliana [8]. We will describe at the molecular level the canonical and non-canonical [9] RNA directed DNA methylation pathways (RdDM) that exists in A. thaliana and its biological implications: The RdDM pathway has been implicated in transposon silencing, pathogen defense, stress responses, reproduction, and interallelic and intercellular communication [8]. We will also summarize the current knowledge that we have in this pathway in other plant organisms, specially maize [10] and how generalized it is.

  Last, we will go briefly through other non-coding, epigenetic related, RNAs, specially those who are proposed as a link between environmental stimuli and changes in gene expression [11]. Among them, long non coding RNAs (lncRNAs)  can function as cis and trans-regulators of gene expression, mainly through the recruitment of chromatin remodeling complexes to loci they scaffold in but also by mimicking miRNAs such as the lncRNA INDUCED BY PHOSPHATE STARVATION 1 (IPS1) [12].

  Our knowledge of the existence of biologically relevant ncRNAs  has been increasing since the implementation of the Next generation sequence (NGS) techniques but there are still several areas poorly understood about ncRNAs. We know its role in Eukaryotic organisms as gene expression regulators working in conjunction with protein complexes and guiding them to their specific target loci. Their relevance in stress responses and in the developmental stages of plants, yet it is poorly understood how this ncRNAs are selectively produced from their own loci and their regulation pathways.

  1.    Storz, G. An expanding universe of noncoding RNAs. Science 296, 1260–1263 (2002).

  2.    Axtell, M. J. Classification and comparison of small RNAs from plants. Annu. Rev. Plant Biol. 64, 137–159 (2013).

  3.    Castel, S. E. & Martienssen, R. A. RNA interference in the nucleus: roles for small RNAs in transcription, epigenetics and beyond. Nat. Rev. Genet. 14, 100–112 (2013).

  4.    Jarroux, J., Morillon, A. & Pinskaya, M. History, Discovery, and Classification of lncRNAs. Adv. Exp. Med. Biol. 1008, 1–46 (2017).

  5.    Bologna, N. G. & Voinnet, O. The diversity, biogenesis, and activities of endogenous silencing small RNAs in Arabidopsis. Annu. Rev. Plant Biol. 65, 473–503 (2014).

  6.    Fei, Q., Xia, R. & Meyers, B. C. Phased, secondary, small interfering RNAs in posttranscriptional regulatory networks. Plant Cell 25, 2400–2415 (2013).

  7.    Wassenegger, M., Heimes, S., Riedel, L. & Sänger, H. L. RNA-directed de novo methylation of genomic sequences in plants. Cell 76, 567–576 (1994).

  8.    Matzke, M. A. & Mosher, R. A. RNA-directed DNA methylation: an epigenetic pathway of increasing complexity. Nat. Rev. Genet. 15, 394–408 (2014).

  9.    Cuerda-Gil, D. & Slotkin, R. K. Non-canonical RNA-directed DNA methylation. Nat Plants 2, 16163 (2016).

  10.    Li, Q. et al. RNA-directed DNA methylation enforces boundaries between heterochromatin and euchromatin in the maize genome. Proc. Natl. Acad. Sci. U. S. A. 112, 14728–14733 (2015).

  11.    Peschansky, V. J. & Wahlestedt, C. Non-coding RNAs as direct and indirect modulators of epigenetic regulation. Epigenetics 9, 3–12 (2014).

  12.    Franco-Zorrilla, J. M. et al. Target mimicry provides a new mechanism for regulation of microRNA activity. Nat. Genet. 39, 1033–1037 (2007).

• Fact sheet 4: TEs and mobilomes

  Panpan Zhang

  In this chapter, we will study transposable elements (TEs) in plants which includes structure and type of TEs in eukaryotes, composition and differences of TEs in plant genomes, epigenetic regulation of TEs in plants and regulation of TEs in plant genome.

  A TE is a piece of DNA that can be replicated or fragmented from a chromosomal DNA, and then inserted into another site, thereby affecting gene regulation at the insertion site [1,2]. According to the transposition mechanism of TEs, TEs can be divided into two categories: RNA-type transposons and DNA-type transposons [3]. RNA-type transposons are also called retrotransposons. The splicing is first transcribed into RNA, and then transposed into DNA by reverse transcription. The whole process is transposed in the form of "DNA-RNA-DNA", so the RNA-based transposon is "copy-paste". The DNA type of the transposon "cut-paste" forms the transposition, which is cut from one position and inserted into another position under the action of a transposase [3,4].

  Although TEs are abundant in plant species and account for greater than 50% of some genomes [5], epigenetic modifications are involved in maintaining their silent state [6]. Among them, DNA methylation, histone modification and small RNA are important ways to inhibit transposition, usually in transcription or post-level transcription silencing [6]. The quantitative expansion and contraction of TEs are accompanied by the defense mechanism of the host genome, thereby not only changing the overall structure of the genome, but also causing changes in gene expression and function [7,8]. The effects of TEs on the insertion site genes are mainly characterized by mutations in the gene's own functions and new functionalization, genetic structural variations, reprogramming of nucleic acid sequences and epigenetic modifications, which may ultimately result in phenotypic variations [9].

  With the deepening of epigenetic research, people have a clearer understanding of the role of TEs in eukaryotes: from the initial structural features, transposition mechanisms to subsequent apparent inhibition modifications and further understanding of their regulation of gene expression. In higher plants, due to these characteristics of TEs, we can see them as hubs linking genomic variation and epigenomic variation, that is, they will also carry modification information while being subjected to apparent inhibition. Passing to adjacent regions induces the formation of epiallele, which can be further transmitted in the offspring , being a treasure for studying plant epigenetics [10].

  1. Rebollo, Rita, Mark T. Romanish, and Dixie L. Mager. "Transposable elements: an abundant and natural source of regulatory sequences for host genes." Annual review of genetics46 (2012): 21-42.

  2. Lisch, Damon. "How important are transposons for plant evolution?." Nature Reviews Genetics 14.1 (2013): 49.

  3. Wicker, Thomas, et al. "A unified classification system for eukaryotic transposable elements." Nature Reviews Genetics8.12 (2007): 973.

  4. Lee, Sung-Il, and Nam-Soo Kim. "Transposable elements and genome size variations in plants." Genomics & informatics 12.3 (2014): 87-97.

  5. Tenaillon, Maud I., Jesse D. Hollister, and Brandon S. Gaut. "A triptych of the evolution of plant transposable elements." Trends in plant science 15.8 (2010): 471-478.

  6. Lisch, Damon. "Epigenetic regulation of transposable elements in plants." Annual review of plant biology 60 (2009): 43-66.

  7. Chuong, Edward B., Nels C. Elde, and Cédric Feschotte. "Regulatory activities of transposable elements: from conflicts to benefits." Nature Reviews Genetics 18.2 (2017): 71.

  8. Rebollo, Rita, Mark T. Romanish, and Dixie L. Mager. "Transposable elements: an abundant and natural source of regulatory sequences for host genes." Annual review of genetics46 (2012): 21-42.

  9. Cui, Xiekui, and Xiaofeng Cao. "Epigenetic regulation and functional exaptation of transposable elements in higher plants." Current opinion in plant biology 21 (2014): 83-88.

  10. Mirouze, Marie, and Clémentine Vitte. "Transposable elements, a treasure trove to decipher epigenetic variation: insights from Arabidopsis and crop epigenomes." Journal of experimental botany 65.10 (2014): 2801-2812.

• Fact sheet 5: Life history strategies

  Morgane van Antro

  In ecology, the life history of a species refers to its demography features and include traits such as time of first reproduction, number of offspring per reproduction and in total, generation time and lifespan1.  These life histories are shaped by natural selection and reflects how a member of a species needs to distribute their resources among growth, survival and reproduction. In a natural environment, however, the resources needed are limited in supply and thus a trade-off on how the resources are distributed between growth, body maintenance and reproduction is needed in order to maximise fitness. How a species distributes its resources is known a life-history strategy and is the collection of life-history traits which are adapted to the environment a species finds itself in [1]. An important trade-off made by many animals and plants is between growth and reproduction. In most organisms, an increase in growth is countered by a change in the present reproduction capabilities, and vice-versa [2]. In plants, germination is the sprouting of a seed and is the earliest growth phase of a plant. Often germination occurs after a phase known as seed dormancy, which allows seeds to remain inactive until environmental conditions are optimal for growth to start. The transition from dormancy to germinations depends on both genetic and epigenetic factors. For example, in Arabidopsis thaliana the deacetylation of histones leads to the silencing of genes that controls hormones such as ethylene, abscisic acid and gibberellin, which are important to maintain dormancy [3,4]. The next pivotal step in angiosperm development is flowering. Understanding the evolution of time-to-reproduction is a key area in life-history research, and any direct or indirect selection pressures could induce changes in reproduction times during an individual’s lifetime [4,5]. In plants, this is measured via plant flowering time and vary widely among taxa. Some plants are annuals that flower only once and complete their life-cycle after reproduction in that year. Others are perennial that live for many years and flower repeatedly. Understanding why and how such variation in flowering behaviour emerged is a central interest in both ecology and evolution. Arabidopsis thaliana, an annual herb, provides the opportunity to determine the (epi)genetic basis that contributed to natural variations in the timing of flowering3,6. Comparing these finding to a perennial plant such as Arabis aplina, a close relative of A. thaliana, allows to determine which genetic and epigenetic marks (regulation of the FRIGIDA  FLOWERING LOCUS C genes as well as rate at which histones turn from an activated to a repressed state after vernalisation) are key factors in explaining natural variation of flowering times both in annual and perennial plants [6]. While all angiosperms flower, not all of them depend on sexual reproduction to propagate. Indeed, another important life history trait is the mode of reproduction. Plant can reproduce sexually and/or asexually. Depending on the mode of reproduction, the importance of epigenetic mechanisms will vary. This is particularly true when it comes to epigenetic reprogramming which occur during the formation of the germline [7]. This reprogramming is important for transmitting epigenetic information between cells/generations but more importantly, is essential for resetting epigenetic marks in order to reduce the risk of maintaining and transmitting dangerous epigenetic alleles [7,8]. It also, however, reduces the chances of transmitting novel and adaptive epigenetic alleles to the next generation. In general, epigenetic modifications of the genome are generally stable in somatic cells. In germs cells and early embryos, however, epigenetic reprogramming occurs on a genome-wide scale, and includes demethylation and remethylation of DNA and remodeling of histones [7]. In contrast to mammals, plants epigenetic reprogramming/resetting is classified as incomplete as the germline originates from adult somatic cells [8]. This means that the cells have gone through meiosis and thus environmental imprinting of the cells that will develop into a new individual is possible. Differences in the intensity of the epigenetic reprogramming can also be expected between plants with different reproductive modes [9]. Indeed, for epigenetic reprogramming to occur, a germline and thus sexual reproduction must occur. Yet some plants reproduce asexually (clonal or apomixes) where germline formation is incomplete or inexistent and thus epigenetic reprogramming, or at least resetting, could be bypassed [9,10]. This could in turn allow for a higher chance of transgenerational epigenetic inheritance to occur.  Inherited epigenetic marks have the potential to allow for populations to adapt to a changing environment. This might be particularly true for plants that reproduce asexually (including invasive plants) and to a lesser extend in apomicts. Theoretically, asexual plants are evolutionary dead end due their lower evolutionary capabilities compared to sexual species [11,12]. Perhaps, stably inherited epigenetic marks would allow for asexually reproducing plants to outweigh these evolutionary disadvantages. All in all, more and more studies are demonstrating the role of epigenetics when it comes to specific life-history strategies. These findings could change our way of seeing how these strategies have evolved and how they are controlled.

  1. Roff, D. A. The evolution of life histories: theory and analysis. Univ. Wales, Aberystwyth 384‑385 (1992). doi:10.1007/BF00043394
  2. Roff, D. A., Heibo, E. & VØllestad, L. A. The importance of growth and mortality costs in the evolution of the optimal life history. J. Evol. Biol. 19, 1920‑1930 (2006).
  3. Ríos, G., Leida, C., Conejero, A. & Badenes, M. L. Epigenetic regulation of bud dormancy events in perennial plants. Front. Plant Sci. 5, 1‑6 (2014).
  4. Xu, Y., Zhang, L. & Wu, G. Epigenetic Regulation of Juvenile-to-Adult Transition in Plants. Front. Plant Sci. 9, 1‑8 (2018).
  5. Ojolo, S. P. et al. Regulation of Plant Growth and Development: A Review From a Chromatin Remodeling Perspective. Front. Plant Sci. 9, 1‑13 (2018).
  6. Yaish, M. W., Colasanti, J. & Rothstein, S. J. The role of epigenetic processes in controlling flowering time in plants exposed to stress. J. Exp. Bot. 62, 3727‑3735 (2011).
  7. Feng, S., Jacobsen, S. E. & Reik, W. Epigenetic Reprogramming in Plant and Animal Development. Science (80-. ). 330, 622‑627 (2010).
  8. Hackett, J. A. & Surani, M. A. Beyond DNA: Programming and inheritance of parental methylomes. Cell 153, 737‑739 (2013).
  9. Verhoeven, K. J. F. & Preite, V. Epigenetic variation in asexually reproducing organisms. Evolution (N. Y). 68, 644‑655 (2014).
  10. Wu, S. C. & Zhang, Y. Active DNA demethylation: many roads lead to Rome. Nat. Rev. Mol. Cell Biol. 11, 607‑620 (2013).
  11. Massicotte, R. & Angers, B. General-Purpose Genotype or How Epigenetics Extend the Flexibility of a Genotype. Genet. Res. Int. 2012, 1‑7 (2012).
  12. Zhang, Y. & Zhang, D. Asexual and sexual reproductive strategies in clonal plants. Front. Biol. China 2, 256‑262 (2007).

• Fact sheet 6: Plant defense response

  Anupoma Niloya Troyee

  Starting with general aspects of plant stress, this chapter focuses on plant’s defense response to biotic and abiotic stresses, resistance traits, crosstalk in response(s) to stress, phases of plant stress and recently studied correlation of stress to epigenetics.

  In the natural environment, plants as sessile organisms are constantly exposed to a wide range of stresses. In biological context, stress can be denoted as any unfavorable condition that exerts a disadvantageous influence on the metabolism, growth or development of an organism [1]. Factors that induce stress in plants can have multiple physical, chemical or biological origins, such as extreme temperatures, drought, limited availability of light, non-optimal mineral composition or soil contamination, pathogen attack, lack of symbiotic partners, interactions with other plants, parasites or herbivores are among them. Stress(es) for plants have been classified also in varied ways, namely, according to the type of factors that cause the stress (biotic or abiotic), effect of the stress (positive or negative), or persistence of the stress (short or long term). For combating stress plants have developed a plethora of protective mechanisms or defensive responses including developmental and morphological adaptations, specific signaling and defense pathways, as well as innate and acquired immunity. Plant stress responses have also different phases and usually involve complex physiological, biochemical and molecular level reactions [2, 3]. Since stress is a major driver of evolution in plants and may have a huge impact on plant breeding and cultivation, it has become important to understand the plant stress responses.

  As one of the most significant defense responses, a broad  group of structural, chemical, and indirect resistance traits are observed through evolutionary race between plants and herbivores (i.e., ‘coevolution')[4, 5]. For instance, morphological features like waxes, trichomes, spinescence, raphids, pubescence, sceleropylly are well known physical resistance traits that have evolved in many different plants as a stress response to biotic stress like herbivore attack [8]. These resistance traits are costly implying frequently a reduction in growth and reproduction, a trade-off with critical consequences for plants that have evolved sophisticated mechanisms to balance it. [6, 7]. For adapting unfavorable environmental conditions, plants develop crosstalk among signaling networks during specific stress responses that activate ion channels, kinase cascades, production of reactive oxygen species (ROS), accumulation of hormones such as salicylic acid (SA), ethylene (ET), jasmonic acid (JA) and abscisic acid (ABA) or signal transduction by protein [9, 10]. Epigenetic factors have emerged also as key regulators of the defense response in plants and several research reports have demonstrated, for instance, the important role of small non-coding RNAs in the post transcriptional gene regulatory networks in response to biotic and abiotic stress[11, 13]. Also, chromatin regulators also have been reported to be involved in the regulation of stress-responsive gene networks under different abiotic stress conditions, e.g. histone modification on the drought-inducible genes are changed in response to drought stress [16]. Increasing evidences suggest that heritable variation in DNA methylation and histone modification can cause significant variation in plant defense responses [14, 15]. Additionally, activity of small RNAs and its role in environmental stress plants to modify respective gene is also taken to discussion as a constituent of plant stress responses [11].With the progression of time, the integration of epigenetics with genetics studies has revealed new areas of interactions and epigenetic mechanisms are suggested as one of the as possible mechanisms for ecological stress memory  [12]. So, in a stress-directed way epigenetic marks may control gene evolution that possibly permit initiation of new adaptive alleles on genetic and epigenetic levels. Although current data leave no doubt that throughout plant’s life cycle, it is able to perceive, to process, and to translate different stressful stimuli into adaptive defense responses, we are still far from understanding the underlying epigenetic and genetic role in the physiological and molecular mechanisms involved in them. As a final point, more focus should be given on filling the gaps of epigenetics study related to diverse plant stresses and its response.

  1. Kranner I, Minibayeva F V., Beckett RP, Seal CE. What is stress? Concepts, definitions and applications in seed science. New Phytol. 2010;188:655‑73.

  2. Shao HB, Guo QJ, Chu LY, Zhao XN, Su ZL, Hu YC, et al. Understanding molecular mechanism of higher plant plasticity under abiotic stress. Colloids Surfaces B Biointerfaces. 2007;54:37‑45.

  3. Mosa KA, Ismail A, Helmy M. Plant Stress Tolerance An Integrated Omics Approach. 2017. https://link.springer.com/content/pdf/10.1007%2F978-3-319-59379-1.pdf.

  4. Rasmann S, Agrawal AA. Plant defense against herbivory: progress in identifying synergism, redundancy, and antagonism between resistance traits. Curr Opin Plant Biol. 2009;12:473‑8.

  5. All AM, Terms J. Butterflies and Plants : A Study in Coevolution Author ( s ): Paul R . Ehrlich and Peter H . Raven Reviewed work ( s ): Published by : Society for the Study of Evolution Stable URL : http://www.jstor.org/stable/2406212 . 2013;18:586‑608.

  6. Strauss SY, Rudgers JA, Lau JA, Irwin RE. Direct and ecological costs of resistance to herbivory. Trends Ecol Evol. 2002;17:278‑85.

  7. Coley PD, Bryant JP, Chapin FS. Resource availability and plant antiherbivore defense. Science1985;230:895‑9.

  8. Hanley ME, Lamont BB, Fairbanks MM, Rafferty CM. Plant structural traits and their role in anti-herbivore defence. Perspect Plant Ecol Evol Syst. 2007;8:157‑78.

  9. Fraire-Velázquez S, Rodríguez-Guerra R, Sánchez-Calderón. Abiotic and Biotic Stress Response Crosstalk in Plants. In: Abiotic Stress Response in Plants - Physiological, Biochemical and Genetic Perspectives, eds. A. Shanker and B. Venkateswarlu. IntechOpen. 2011. ISBN: 978-953-307-672-0. Available from: http://www.intechopen.com/books/abiotic-stress-response-in-plants-physio...

  10. Hakim, Ullah A, Hussain A, Shaban M, Khan AH, Alariqi M, et al. Osmotin: A plant defense tool against biotic and abiotic stresses. Plant Physiol Biochem. 2018;123:149‑59. doi:10.1016/j.plaphy.2017.12.012.

  11. Khraiwesh B, Zhu J-K, Zhu J. Role of miRNAs and siRNAs in biotic and abiotic stress responses of plants. Biochim Biophys Acta. 2012;1819:137‑48. doi:10.1016/j.bbagrm.2011.05.001.Role.

  12. Bruce TJA, Matthes MC, Napier JA, Pickett JA. Stressful “memories” of plants: Evidence and possible mechanisms. Plant Sci. 2007;173:603‑8.

  13. Sagar Banerjeeab, Anil Sirohia, Abid A. Ansaric, Sarvajeet Singh Gillb. Role of small RNAs in abiotic stress responses in plants. Plant Gene 2017; 11(B): 180-189.

  14. Vít Latzel  Yuanye Zhang  Kim Karlsson Moritz  Markus Fischer Oliver Bossdorf. Epigenetic variation in plant responses to defence hormones. Annals of Botany, Volume 110, Issue 7, 1 November 2012, Pages 1423‑1428

  15. Trung Viet Hoang, Kieu Thi Xuan Vo, Woo-Jong Hong, Ki-Hong Jung and Jong-Seong Jeon.
   Defense Response to Pathogens Through Epigenetic Regulation in Rice. J. Plant Biol. 2018; 61:1-10

  16. Kim, J.M., et al., Chromatin changes in response to drought, salinity, heat, and cold stresses in plants. Front Plant Sci, 2015. 6: p. 114.

• Fact sheet 7: Plasticity and adaptation

  Iris Sammarco

  Plants are continuously exposed to fluctuating environmental conditions. Since they are sessile organisms, they have a restricted capacity to select the features of their environment; hence, it is crucial for them to successfully respond to environmental changes. Phenotypic plasticity is considered one of the major means by which plants can cope with environmental factor variability. Phenotypic plasticity is the ability of one genotype to produce more than one phenotype when exposed to different environments [1]. It includes all types of environmentally induced changes, such as behavioral, physiological, morphological and life-historical traits, and can be expressed either within the lifespan of a single individual, i.e. intragenerational plasticity [2] or across generations, i.e. intergenerational plasticity [3].

  Different types of plasticity can be found in nature, such as continuous or discrete, reversible or irreversible, and adaptive or non-adaptive. Plasticity is discrete if it results in alternative phenotypes referred to as polyphenisms, whereas it is continuous when it is genetically controlled, known as genetic polymorphism. It is reversible if it occurs within a single generation, whereas it is transgenerational when the conditions experienced in one generation interact with conditions experienced by subsequent generations [4]. Plasticity can be adaptive when it provides a fitness benefit, non-adaptive if it is an inevitable response to a physical process or resource limitations [5, 6].

  The mechanisms underlying phenotypic plasticity are still poorly understood. At the most fundamental level, all plastic responses originate at the level of individual cells, which receive and process signals from their environment. Alterations in gene expression in response to environmental changes produce the broad variations in physiology, morphology, behavior, etc., thus regulating phenotypic plasticity. Recent studies suggest that phenotypic plasticity can be mediated through epigenetic mechanisms [7, 8, 9, 10, 11, 12], which can be also involved in transgenerational inheritance [13, 14, 15, 16, 17, 18, 19]. The most studied epigenetic mechanism is DNA methylation which has been shown to alternatively increase variation in response to stressful conditions [13, 20] and has known effects on ecologically important phenotypes [21, 8, 11, 22]. Epigenetic effects could provide a rapid source of phenotypic variation without any change in genetic variation [23, 24], thus representing a potential mechanism for rapid adaptive responses to heterogeneous conditions.

  [1] Pigliucci M, Murren CJ, Schlichting CD. Phenotypic plasticity and evolution by genetic assimilation. J Exp Biol. 2006; 209(Pt 12):2362-7.

  [2] Young TP, Stanton ML, Christian CE. Effects of natural and simulated herbivory on spine lengths of Acacia drepanolobium in Kenya. Oikos, 101, 2003; pp. 171-179.

  [3] Agrawal AA, Larorsch C, Tollrian R. Transgenerational induction of defences in plants and animals. Nature, 401, 1999; pp. 60-63.

  [4] Salinas S, Brown SC, Mangel M, Munch SB. Non‐genetic inheritance and changing environments. 2013; Non‐Genetic Inheritance, 1, 38–50.

  [5] Weiner J. Allocation, plasticity and allometry in plants. Pers. Plant Ecol. Evol. And Syst., 6, 2004; pp. 207-215.

  [6] Kleunen MV, Fischer M. Constraints on the evolution of adaptive phenotypic plasticity in plants. New Phytologist, 166, 2005; pp. 49-60.

  [7] Richards CL, Walls RL, Bailey JP, Parameswaran R, George T, Pigliucci M. Plasticity in salt tolerance traits allows for invasion of novel habitat by Japanese knotweed s. l. (Fallopia japonica and F-bohemica, Polygonaceae). Am. J. Bot. 2008; 95:931–942.

  [8] Bossdorf O, Arcuri D, Richards CL, Pigliucci M. Experimental alteration of DNA methylation affects the phenotypic plasticity of ecologically relevant traits in Arabidopsis thaliana. Evol. Ecol. 2010; 24:541–553.

  [9] Scoville AG, Barnett LL, Bodbyl-Roels S, Kelly JK, Hileman LC. Differential regulation of a MYB transcription factor is correlated with transgenerational epigenetic inheritance of trichome density in Mimulus guttatus. New Phytol. 2011; 191:251–263.

  [10] Herrera CM, and Bazaga P. Epigenetic differentiation and relationship to adaptive genetic divergence in discrete populations of the violet Viola cazorlensis. New Phytol. 2010; 187:867–876.

  [11] Zhang YY, Fischer M, Colot V, and Bossdorf O. Epigenetic variation creates potential for evolution of plant phenotypic plasticity. New Phytol. 2013; 197:314–322.

  [12] Herman JJ, Spencer HG, Donohue K, Sultan SE. How stable ‘should’ epigenetic modifications be? Insights from adaptive plasticity and bet hedging. Evolution. 2014;  68:632–643.

  [13] Verhoeven KJ, van Dijk PJ, Biere A. Changes in genomic methylation patterns during the formation of triploid asexual dandelion lineages. Mol. Ecol. 2010; 19:315–324.

  [14] Verhoeven KJ, van Gurp TP. Transgenerational effects of stress exposure on offspring phenotypes in apomictic dandelion.PLoS One. 2012; 7: e38605.

  [15] Luna E, Ton J. The epigenetic machinery controlling transgenerational systemic acquired resistance. Plant Signal. Behav. 2012; 7: 615–618.

  [16] Rasmann S, De Vos M, Casteel CL, Tian D, Halitschke R, et al. Herbivory in the previous generation primes plants for enhanced insect resistance. Plant Physiol. 2012; 158: 854–863.

  [17] Ou X, Zhang Y, Xu C, Lin X, Zang Q, et al. Transgenerational inheritance of modified DNA methylation patterns and enhanced tolerance induced by heavy metal stress in rice (Oryza sativa L.). PLoS One. 2012; 7: e41143.

  [18] Öst A, Lempradl A, Casas E, Weigert M, Tiko T, et al. Paternal diet defines offspring chromatin state and intergenerational obesity. Cell. 2014; 159: 1352–1364.

  [19] Siklenka K, Erkek S, Godmann M, Lambrot R, McGraw S, et al. Disruption of histone methylation in developing sperm impairs offspring health transgenerationally. Science. 2015; 350: aab2006.

  [20] Dowen RH, Pelizzola M, Schmitz RJ, Lister R, Dowen JM, Nery JR, et al. Widespread dynamic DNA methylation in response to biotic stress. Proc. Natl Acad. Sci. USA. 2012; 109:E2183–E2191.

  [21] Johannes F, Porcher E, Teixeira FK, Saliba-Colombani V, Simon M, Agier N, et al. Assessing the impact of transgenerational epigenetic variation on complex traits. PLoS Genet. 2009; 5:e1000530. doi:10.1371/journal.pgen.1000530.

  [22] Cortijo S, Wardenaar R, Colome-Tatche M, Gilly A, Etcheverry M, Labadie K, et al. Mapping the epigenetic basis of complex traits. Science. 2014 Mar 7; 343(6175):1145–8. doi: 10.1126/science.1248127. Epub 2014 Feb 6.

  [23] Rapp RA, and Wendel JF. Epigenetics and plant evolution. New Phytol. 2005;168:81–91.

  [24] Bossdorf O, Richards CL, Pigliucci M. Epigenetics for ecologists. Ecol. Lett. 2008; 11:106–115.

• Fact sheet 8: Epigenetics in evolution

  Dario Galanti, Morgane van Antro, Anupoma Troyee

  History and current status of Evolutionary theory

  Since Charles Darwin and Alfred Russel Wallace came up with the fundamental and enlightening idea of evolution being the result of natural selection [1, 2], several steps have been taken through a more accurate and universal theory of evolution. Subsequent advances in fields like population genetics and more recently evolutionary and developmental biology (Evo-Devo), kept updating evolutionary theories until the present day. During the 20th Century, Darwin’s basic ideas [3] and Gregor Mendel’s studies on genetic inheritance [4] were joint in a unifying theory. This theory was named “Modern Synthesis” by Julian Huxley [5] and although it was implemented with some differences by its founders (Ernst Mayr, G. Ledyard Stebbins and Theodosius Dobzhansky), the basic principle was common: natural selection is acting on heritable genetic variation generated by mutations. Population genetics, focusing on the variation in allele frequencies and distribution of populations, was initially developed by Sewall Wright, J. B. S. Haldane and Ronald Fisher and offered the mathematical framework for the “Modern Synthesis” [6].

  At its latest status, the basic principles of the “Modern Synthesis”, i.e. the basic mechanisms driving evolution, can be summarized as follows [7].

  • Mutations are the ultimate source generating genetic variation
  • Evolutionary mechanisms such as Natural selection, Genetic drift and few others (Genetic hitchhiking, Epistatic effects…) further shape this variation causing changes in allele frequencies.
  • Sexual recombination (when present) reshuffles the distribution of alleles between individuals.
  • In addition, landscape is playing a crucial role by causing population subdivision and therefore affecting the occurrence of gene flow and migration.

  Despite the accuracy of this theory in describing the genetic implications of evolution, further advances were made in the last 20 years, posing the question whether additional drivers should be included in the evolutionary theory [8]. This is where transgenerational epigenetics comes into play, being one of the most important candidates to be included. Answering to this situation, Massimo Pigliucci and Gerd B. Müller renovated previous forms in a new “Extended Evolutionary Synthesis”, which included additional evolutionary drivers such as multilevel selection, transgenerational epigenetics, niche construction, evolvability and others [9, 10]. Nevertheless the question whether these additional drivers are significantly increasing the accuracy and power of the evolutionary theory is still under debate [9, 11‑13]. It is therefore crucial to determine the relative contribution of epigenetics to evolution in order to correctly implement it in an eventual future evolutionary theory.

  The role of Epigenetics

  In order to determine the role of epigenetics in evolution, it is crucial to understand to which extent different epigenetics marks are stably inherited across generations. While histone modifications seem to revert and not to be transgenerationally inherited by the offspring [14], there is evidence that DNA methylation variation is at least partially heritable [15‑18].

  An important complicating factor, hindering advances in determining the importance of epigenetics in evolution, is posed by the tight link between genetics and epigenetics. When a genetic polymorphism is controlling an epigenetic pattern, the latter will seem to be heritable and under selection, while in reality this is only true for the causal genetic polymorphism. Nevertheless different tools were developed to disentangle this aspect and it has been shown that epigenetic variation can, at least in some cases, be independent from DNA sequence variation [15, 18‑20].

  Given these basic requirements of being heritable and independent of DNA sequence variation, epigenetic variation could potentially play a role in evolution. This could specifically happen in two ways: 1) Epimutations, arising stochastically, at a higher rate than genetic mutations [21], could be under natural selection; 2) Environmentally induced epigenetic variation could provide a mean of rapid evolution [22, 23].

  1. Darwin C. On the Tendency of Varieties to Depart Indefinitely from the Original Type Chapter II. Proc Linn Soc London. 1958;3:50‑3.

  2. Wallace AR. On the Tendency of Varieties to Depart Indefinitely From the Original Type. Proc Linn Soc London. 1858;3 July:53‑62.

  3. Darwin C. On the Origin of Species. On the Origin of Species. 1859;:83‑5.

  4. Mendel G. Versuche über Pflanzen-Hybriden. Verhandlungen des naturforschenden Vereines Brünn. 1865;4:3‑47.

  5. Huxley J. Evolution: The Modern Synthesis. London Georg Allen Unwin. 1942;:609.

  6. Provine WB. The role of mathematical population geneticists in the evolutionary synthesis of the 1930s and 1940s. Stud Hist Biol. 1978;2:167‑92.

  7. Mayr E, Provine WB. The evolutionary synthesis: Perspectives on hte Unification of Biology. Harvard University Press; 1980.

  8. Wilkins AS. Waddington’s Unfinished Critique of Neo-Darwinian Genetics: Then and Now. Biol Theory. 2008;3:224‑32.

  9. Mesoudi A, Blanchet S, Charmantier A, Danchin É, Fogarty L, Jablonka E, et al. Is Non-genetic Inheritance Just a Proximate Mechanism? A Corroboration of the Extended Evolutionary Synthesis. Biol Theory. 2013;7:189‑95. doi:10.1007/s13752-013-0091-5.

  10. Pigliucci M. An extended synthesis for evolutionary biology. Ann N Y Acad Sci. 2009;1168:218‑28.

  11. Dickins TE, Barton RA. Reciprocal causation and the proximate-ultimate distinction. Biol Philos. 2013;28:747‑56.

  12. Haig D. Weismann Rules! OK? Epigenetics and the Lamarckian temptation. Biol Philos. 2007;22:415‑28.

  13. Pigliucci M, Finkelman L. The extended (Evolutionary) synthesis debate: Where science meets philosophy. Bioscience. 2014;64:511‑6.

  14. Pecinka A, Mittelsten Scheid O. Stress-induced chromatin changes: A critical view on their heritability. Plant Cell Physiol. 2012;53:801‑8.

  15. Cubas P, Vincent C, Coen E. An epigenetic mutation responsible for natural variation in floral symmetry. Nature. 1999;401:157‑61.

  16. Mittelsten Scheid O, Afsar K, Paszkowski J. Formation of stable epialleles and their paramutation-like interaction in tetraploid Arabidopsis thaliana. Nat Genet. 2003;34:450‑4.

  17. Rangwala SH, Elumalai R, Vanier C, Ozkan H, Galbraith DW, Richards EJ. Meiotically stable natural epialleles of Sadhu, a novel arabidopsis retroposon. PLoS Genet. 2006;2:0270‑81.

  18. Vaughn MW, Tanurdžić M, Lippman Z, Jiang H, Carrasquillo R, Rabinowicz PD, et al. Epigenetic natural variation in Arabidopsis thaliana. PLoS Biol. 2007;5:1617‑29.

  19. Riddle NC, Richards EJ. The control of natural variation in cytosine methylation in Arabidopsis. Genetics. 2002;162:355‑63.

  20. Shindo C, Lister C, Crevillen P, Nordborg M, Dean C. Variation in the epigenetic silencing of FLC contributesto natural variationin Arabidopsis vernalization response. Genes Dev. 2006;20:3079‑83. doi:10.1101/gad.405306.

  21. Becker C, Hagmann J, Müller J, Koenig D, Stegle O, Borgwardt K, et al. Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature. 2011;480:245‑9.

  22. Richards EJ. Inherited epigenetic variation - Revisiting soft inheritance. Nat Rev Genet. 2006;7:395‑401.

  23. Whitelaw NC, Whitelaw E. How lifetimes shape epigenotype within and across generations. Hum Mol Genet. 2006;15 SUPPL. 2:131‑7.

• Fact sheet 9: Epigenetics at the community level

  Bárbara Díez Rodríguez

  The emerging field of community and ecosystem genetics has shown that genetic diversity, defined as any measure that quantifies the magnitude of genetic variability within a population, has a strong impact on populations, communities and ecosystems both at the same and at different trophic levels. [1, 2] For instance, it has been shown that higher genetic diversity has a positive correlation on species diversity in a given grassland community [3]. Genetic diversity of the host plant can influence arthropod communities. For example, susceptibility to certain herbivores was shown to be influenced by host genotype in poplar [4]. A study by Johnson et al. [5] showed that the number of plant genotypes affected the arthropod community found on Oenethera biennis, translating into positive effects on total arthropod species richness, and in a study by Robinson et al. [6] genetic variation in functional traits was found to affect arthropod community composition in aspen trees (P. tremulae). However, there are also reports, where the host genotype does not show any influence on associated arthropod communities, e.g. in oak [7]. Genetic diversity of dominant plant species can also affect nutrient flux. For example, genotypic variation in aspen can have a strong effect on some processes, like litter decomposition [8]. Generally speaking, intraspecific trait variability contributes to amplify the functional diversity of plant communities, a key component of biodiversity with important implications for species coexistence and ecosystem functioning [9]. Until not so long ago, the underlying DNA sequence was thought to be the main source of biodiversity and to provide the baseline for evolution by natural selection and genetic diversity [10], but more recent studies show that heritable epigenetic variation can have adaptive effects on populations [11], has a bigger role in plasticity than previously thought, and that genetic mutations are not the only source of phenotypic variation [12, 13]. For instance, it has been shown that epigenetic mechanisms have a role in allelopathy and that epigenetic changes might be more determinant than genetic variability in the success of plant invasions [14, 15]. Also, epigenetic variation has a role on how plants respond to environmental stress conditions [16]. Therefore, an epigenomics approach to study ecosystem and community processes becomes especially important because these processes represent the combined effects of interactions among multiple species, environmental variation and complex feedback mechanisms that can be difficult to understand in an evolutionary context.

  1. Rapp RA, Wendel JF. Epigenetics and plant evolution. New Phytol. 2005;168:81‑91.

  2. Kagiya S, Yasugi M, Kudoh H, Nagano AJ, Utsumi S. Does genomic variation in a foundation species predict arthropod community structure in a riparian forest? Mol Ecol. 2018;27:1284‑95.

  3. Booth RE, Grime J. Effects of genetic impoverishment on plant community diversity. J Ecol. 2011;91:721‑30.

  4. Whitham T, FiFazio S, Schweitzer J, Shuster S, Allan G, Bailey J, et al. Extending Genomics to Natural. 2008;320 April:492‑5.

  5. Johnson MTJ, Lajeunesse MJ, Agrawal AA. Additive and interactive effects of plant genotypic diversity on arthropod communities and plant fitness. Ecol Lett. 2006;9:24‑34.

  6. Robinson KM, Ingvarsson PK, Jansson S, Albrectsen BR. Genetic variation in functional traits influences arthropod community composition in aspen (populus tremula L.). PLoS One. 2012;7:1‑12.

  7. Gossner MM, Brändle M, Brandl R, Bail J, Müller J, Opgenoorth L. Where is the extended phenotype in the wild? The community composition of arthropods on mature oak trees does not depend on the oak genotype. PLoS One. 2015;10.

  8. Bandau F, Albrectsen BR, Julkunen-Tiitto R, Gundale MJ. Genotypic variability in Populus tremula L. affects how anthropogenic nitrogen enrichment influences litter decomposition. Plant Soil. 2017;410:467‑81. doi:10.1007/s11104-016-3033-8.

  9. Medrano M, Herrera CM, Bazaga P. Epigenetic variation predicts regional and local intraspecific functional diversity in a perennial herb. Mol Ecol. 2014;23:4926‑38.

  10. Hughes AR, Inouye BD, Johnson MTJ, Underwood N, Vellend M. Ecological consequences of genetic diversity. Ecol Lett. 2008;11:609‑23.

  11. Latzel V, Allan E, Bortolini Silveira A, Colot V, Fischer M, Bossdorf O. Epigenetic diversity increases the productivity and stability of plant populations. Nat Commun. 2013;4:1‑7.

  12. Richards CL, Alonso C, Becker C, Bossdorf O, Bucher E, Colomé-Tatché M, et al. Ecological plant epigenetics: Evidence from model and non-model species, and the way forward. Ecol Lett. 2017;20.

  13. Heer K, Mounger J, Boquete MT, Richards CL, Opgenoorth L. The diversifying field of plant epigenetics. New Phytol. 2018;217.

  14. Pérez JE, Alfonsi C, Ramos C, Gómez JA, Muñoz C. How Some Alien Species Become Invasive.Some Ecological,Genetic and Epigenetic Basis for Bioinvasions. Interciencia. 2012;37:238‑44.

  15. Slotkin RK. Plant epigenetics: from genotype to phenotype and back again. Genome Biol. 2016;17:57. doi:10.1186/s13059-016-0920-5.

  16. Kinoshita T, Seki M. Epigenetic memory for stress response and adaptation in plants. Plant Cell Physiol. 2014;55:1859‑63.

• Fact sheet 10: Experimental design for epigenetic studies

  Bárbara Díez Rodríguez

  Common gardens

  To separate heritable epigenetic variation from non-heritable epigenetic variation (resulting from developmental plasticity in response to different environments) it is necessary to study the progeny of different natural populations and/or maternal families in a common environment, and to use the resemblance of epigenetic patterns among relatives as indication or epigenetic inheritance. To link epigenetic variation to functional phenotypic traits, it is necessary to control somehow for genetic variation. Only traits that ultimately affect fitness are relevant to the ecology and evolution of natural populations. [1]

  Working with clones

  Given the necessity of getting rid of genotypic variation, working with clonally reproducing plants can be a great tool to study epigenetic variation. Although some evidence suggests that changes in DNA methylation patterns can occur as a result of clonal propagation (i.e: tissue culture), in most situations these methylation patterns are inherited quite stably. By working with clones, it is easy to disregard the underlying genotypic structure, which should be the same in every individual, and focus only on the epigenetic variation. Because only stable and heritable epigenetic modifications are of interest to ecologists, small variations between clones can be accounted for by pooling samples from various individuals that are genotypically identical.[2]

  1. Bossdorf O, Richards CL, Pigliucci M. Epigenetics for ecologists. Ecol Lett. 2008;11:106‑15.

  2. Physiology C, Access A, Author T. Heritable epigenomic changes to the maize methylome resulting from tissue culture. Plant Cell Physiol. 2008.

• Fact sheet 11: NGS analyses

  Samar Fatma and Paloma Perez-Bello Gil

  Next Generation Sequencing describes DNA sequencing technology which revolutionized biological research. Presently, a number of different NGS platforms are available using different and improved sequencing technologies to overcome bottleneck in whole genome sequencing. Output of these sequencing machine are sequences of nucleotides called ‘Reads’ corresponding to all or a part of a DNA fragment. Length of theses reads vary for different sequencing technologies ranging from ~150 to ~1000 base pairs. Millions of reads are generated for a single input sample depending upon the size of the genome and the technology used. To proceed with the analysis, it is necessary to stitch together sequence reads to a long continuous sequence called ‘contig’. This process of piecing together these reads is called assembly. To handle the immense amount of sequenced data, different algorithms with different paradigms are used in genome assemblers. These can be divided into two main kinds: comparative or reference based assembly and de novo assembly. To unravel the potential of genome sequences, annotation is needed to extract relevant information ranging from gene models and functional information to microRNA and epigenetic modification. For non-model species annotation is generally confined to protein-coding sequence. Therefore, a series of steps are involved in genome annotation using different bioinformatics tools.

  Bioinformatic analysis of Next Generation Sequencing data is a broader term covering variation in genomic sequence to structural and functional analysis of proteins. Looking into the importance of variation in genomic sequences and their association with traits or diseases, Genome Wide Association Studies (GWAS) are done across the population or set of individuals. In plants, it provides insights into the gene level by associating phenotypic variation with single nucleotide polymorphisms (SNPs) to identify often small haplotype blocks that are significantly correlated with quantitative trait variation. Phenotypic changes that are not based on DNA sequence variation but chemical modifications that influence gene activity and expression are called epigenetic changes. Thus, Epigenetic Wide Association Studies (EWAS) observe genome – wide set of these epigenetic marks in different individuals and infer association between epigenetic variation and identifiable phenotype/trait. For further reading please refer to [1–6].

  1. Behjati S, Tarpey PS. What is next generation sequencing? Arch Dis Child Educ Pract Ed. 2013;98:236–8.

  2. Wajid B, Serpedin E. Review of General Algorithmic Features for Genome Assemblers for Next Generation Sequencers. Genomics, Proteomics Bioinforma. 2012;10:58–73. doi:10.1016/j.gpb.2012.05.006.

  3. Martin JA, Wang Z. Next-generation transcriptome assembly. Nat Rev Genet. 2011;12:671–82. doi:10.1038/nrg3068.

  4. Brachi B, Morris GP, Borevitz JO. Genome-wide association studies in plants: The missing heritability is in the field. Genome Biol. 2011;12.

  5. Ekblom R, Wolf JBW. A field guide to whole-genome sequencing, assembly and annotation. Evol Appl. 2014;7:1026–42.

  6. Richards CL, Alonso C, Becker C, Bossdorf O, Bucher E, Colomé-Tatché M, et al. Ecological plant epigenetics: Evidence from model and non-model species, and the way forward. Ecol Lett. 2017;20:1576–90.

• Fact sheet 12: Bisulfite Sequencing Methods

  Adam Nunn & Samar Fatma

  Though it is by no means the only biological mechanism within the domain of epigenetics, DNA methylation is among the most prevalent and the most studied throughout the field. But how exactly do we detect differences in methylation within species? One technique that has emerged at the forefront of epigenetic research is bisulfite sequencing: a distinct adaption of next-generation sequencing technology which brings genome-wide methylation profiles into nucleotide-level resolution.

  The technique, as refined by Lister et al. [1] and Cokus et al. [2], involves the treatment of extracted DNA from test samples with sodium bisulfite, a deamination agent which mediates the conversion of unmethylated cytosine nucleotides into uracil. Cytosine bases which carry methyl groups (e.g. 5-methylcytosine, 5-hydroxymethylcytosine) are left unaffected by the treatment and remain in their original, unconverted state. As uracil residues are subsequently interpreted during standard DNA sequencing as thymine, these bisulfite-treated samples can be subjected to standard sequencing protocols and used to generate FastQ reads which carry epigenetic information. Once generated, the reads effectively move the question from a biological one to a computational, algorithmic one.

  In standard sequencing the next step is usually to follow protocols for read alignment of the generated reads to a reference genome assembly. This presents some issues when handling bisulfite data, as thymine residues can no longer be considered as entirely independent entities to cytosine. Read alignment algorithms usually operate on the basis of some kind of scoring matrix, which assigns an overall probability for the alignment of two sequences based on the number and position of matches, mismatches, insertions and deletions between nucleotides. The problem arises in that reference cytosines can conceptually match with thymines in bisulfite-treated reads, but not vice versa (with the exception of mutations e.g. single nucleotide polymorphisms). Existing algorithms are often not built to handle this asymmetry between bases, so the solution is either to further adapt these tools in some way or to operate specifically with algorithms designed for bisulfite data. Several tools now exist in representation of either category, including notably Bismark [3] and BWA-meth [4], which adapt the popular standard aligners Bowtie [5] and BWA [6], and software such as Segemehl [7] or ERNE [8] which are capable of interpreting bisulfite reads in their own right.

  The principles of bisulfite sequencing notwithstanding, another important consideration when designing such an experiment involves the chosen strategy for library preparation. In the previous chapter we discussed sequencing depth and coverage; this applies here as we seek to maximize sequencing coverage with regards to the scope of the questions we are looking to answer, and the practical limitations of the study such as cost and time. Will your study look to investigate genome-wide methylation patterns, or is it enough to focus on a reduced subset of the DNA? Herein we will consider the applications of Reduced-Representation Bisulfite Sequencing (RRBS), Whole-Genome Bisulfite Sequencing (WGBS), and target enrichment methods such as SeqCapEpi. In particular, what implications do such protocols have on the robustness of the data, and what should we adjust for in terms of quality control during the downstream analyses?

  By the end of the chapter we will have covered the various technical concerns of bisulfite sequencing from DNA extraction and library preparation through to the sequencing itself and the downstream extraction of methylated positions. The bioinformatics principles determine the validity of the data in answering the questions posed by the study, and a priori consideration is therefore fundamental to the successful outcome of any such experiment. Finally, we will discuss the hard limitations of bisulfite sequencing and give brief suggestions for alternative methods that might be used to address these issues.

  1. Lister R, Malley RCO, Tonti-filippini J, Gregory BD, Berry CC, Millar  a H, et al. NIH Public Access. 2009;133:523–36.

  2. Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD, et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature. 2008;452:215–9.

  3. Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. bioinformatics. 2011 Apr 14;27(11):1571-2.

  4. Pedersen BS, Eyring K, De S, Yang IV, Schwartz DA. Fast and accurate alignment of long bisulfite-seq reads. arXiv preprint arXiv:1401.1129. 2014 Jan 6.

  5. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nature methods. 2012 Apr;9(4):357.

  6. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. bioinformatics. 2009 Jul 15;25(14):1754-60.

  7. Otto C, Stadler PF, Hoffmann S. Fast and sensitive mapping of bisulfite-treated sequencing data. Bioinformatics. 2012 May 10;28(13):1698-704.

  8. Prezza N, Del Fabbro C, Vezzi F, De Paoli E, Policriti A. ERNE-BS5: aligning BS-treated sequences by multiple hits on a 5-letters alphabet. InProceedings of the ACM conference on bioinformatics, computational biology and biomedicine 2012 Oct 7 (pp. 12-19). ACM.

• Fact sheet 13: Transcriptomics

  Bhumika Dubay and Adam Nunn

  Transcriptomics is the study of RNAs and their functions in the cell and because of the versatility in application it plays a crucial role in epigenetic studies. Basic transcriptomics studies comprise data accumulation, data preprocessing and data analysis as any other omics study. There are multiple ways of generating data for transcriptomic analyses depending on its application. Microarray and RNAseq are two major and widely studied methods among them but recent advances in the latter have made it a more preferable method for gene expression profiling [1].

  Expression quantification and determining the differential expression between the sample and the control is one of the major steps in transcriptomic studies. Expression is usually measured by the number of reads mapped to each locus in the transcriptome assembly step of RNAseq analyses [2]. HTSeq, FeatureCounts, Rcount, maxcounts, FIXSEQ, and Cuffquant are some of the tools that can measure the expression [2]. After quantifying the gene expression we can compare gene expression between conditions, such as a drug treatment vs. non-treated, methylation vs. no methylation and so on, and find out which genes are up- or down-regulated in each condition. Differentially expressed genes can be identified using tools that count the sequencing reads per gene and compare them between samples. Two of the most commonly used tools for this kind of analyses are DESeq and edgeR, packages from Bioconductor which are based on the negative binomial distribution [2].

  Coexpression networks are data-derived representations of genes behaving in a similar way in the differential expression analysis [3]. They are used to infer genes involved in specific pathways based on Pearson correlation and one of the examples is the weighted gene co-expression network analysis that has been successfully used to identify co-expression modules and intramodular hub genes based on RNAseq data.

  Gene set enrichment analysis (GSEA) is the next step which is a method that identify classes of genes that are over-represented in a large set of genes, and may have an association with specific phenotypic condition. The method uses statistical approaches to identify significantly enriched or depleted groups of genes [4]. There are many websites and downloadable programs that provide data sets and run the analyses such as PlantRegMap, Broad Institute, Enrichr, GeneSCF, DAVID, AmiGO 2, ToppGene Suite etc.

  Diverse classes of RNA, ranging from small to long non-coding RNAs, have emerged as key regulators of gene expression, genome stability and defense against foreign genetic elements and this is why it is important to include them in the transcriptomics studies for epigenetic regulations [5]. The same techniques that are used for the transcriptomic studies of coding RNA can be used to analyse the non-coding RNAs as well.

  Measuring the expression of an organism’s genes in different tissues or conditions, or at different times, gives information on how genes are regulated and reveal details of an organism’s biology. It can also be used to infer the functions of previously unannotated genes. Transcriptome analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human disease. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays.

  1. McGettigan PA. Transcriptomics in the RNA-seq era. Curr Opin Chem Biol. 2013;17:4–11.
  2. Greenbaum D, Colangelo C, Williams K, Gerstein M (2003). "Comparing protein abundance and mRNA expression levels on a genomic scale". Genome Biology. 4 (9): 117. doi:10.1186/gb-2003-4-9-117. PMC 193646 Freely accessible. PMID 12952525.
  3. Griffith M, Walker JR, Spies NC, Ainscough BJ, Griffith OL. Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud. PLoS Comput Biol. 2015;11:1–20.
  4. Marcotte EM, Pellegrini M, Thompson MJ, Yeates TO, Eisenberg D. A combined algorithm for genome-wide prediction of protein function. Nature. 1999;402:83–6.
  5. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102:15545–50. doi:10.1073/pnas.0506580102.
  6. Holoch D, Moazed D. RNA-mediated epigenetic regulation of gene expression. Nat Rev Genet. 2015;16:71–84. doi:10.1038/nrg3863.

• Fact sheet 14: Epigenetic data visualization

  Paloma Perez-Bello Gil and Nilay Can

  Proper data visualization is crucial for the analysis of experimental results from NGS technologies, and particularly in epigenomics where multiple molecular events are relevant. Being able to effectively represent epigenetic properties is useful not only for the extraction of meaningful information from experiments such as the identification of significant genomic regions involved in a epigenetic process but also in the initial phases of exploratory data analysis, when the visualization of data in the correct context can lead to the formulation of biological questions and statistical models [1].

  As the first step of DNA methylation analysis, it is useful to inspect a selection of genomic regions visually in locally-operated genome browsers such as IGV (Integrative Genomics Viewer) or web-based portals like the UCSC genome browser [2]. At this stage, it is possible to obtain a glimpse of the distribution of epigenetic marks across different genome features, such as genes, promoters, transposable elements and major genomic domains. In addition, several R/Bioconductor packages are available for data visualization. Some of them are quite general in scope, being designed for the processing of NGS data, whereas others are specialized in the presentation of data obtained from specific epigenomics methodologies [1].

  Complementary to these general purpose visualization tools, various types of statistical graphics approaches can be used to obtain a more informative view of DNA methylation data. For example, the Hilbert curve method compresses genome-wide maps into compact, two-dimensional diagrams, and these are useful for detecting spatial patterns in the distribution of DNA methylation. Location of DNA methylation in the genome is critical for the interpretation of its regulatory role as this epigenetic mark can have both neutral (or possibly even positive) and negative effects on gene expression depending on the genome features that are affected (e.g. gene body methylation vs transposon silencing). However, It should be noted that DNA methylation alone, albeit crucial for regulation of gene expression, provides partial information on epigenetic states, whereas chromatin dynamics is determined by a combination of several factors including histone modifications, non- histone proteins and non-coding RNAs (ncRNAs) that define chromatin structure and accessibility [3].

  The high dimensionality of the data sets describing all these properties makes them too complex to be handled from a classical point of view. It is, then, necessary to address the problem from a number of different perspectives at once. This approach leads to the concept of spatially-defined chromatin states that are contributed by multiple epigenetic marks enriched in the same genomic regions. To analyse chromatin modifications in a combinatorial manner, there are various methods using NGS available that combine multiple genome-wide epigenomic maps and use combinatorial and spatial mark patterns to infer a complete epigenetic annotation of the genome. One example is the Hidden Markov Model based algorithm used by the ChromHMM software, which learns chromatin-states using chIP-seq data of various histone modifications, although more comprehensive approaches taking to account DNA methylation as well might became available in the future. Recognizing chromatin states and identifying their genomic occurrences in the genome provides a systematic annotation of DNA elements and regulatory control regions, some of which can be involved in development, in cell differentiation as well as in the interaction between genome and environment [4].

  1. Bayón, G., Fernández, A. and Fraga, M. (2018). Bioinformatics Tools in Epigenomics Studies.
  2. Bock, Christoph. (2012). Analysing and Interpreting DNA Methylation Data. Nature Reviews Genetics, vol. 13, no. 10, pp. 705–719.
  3. Zhang, H., Lang, Z. and Zhu, J. (2018). Dynamics and function of DNA methylation in plants. Nature Reviews Molecular Cell Biology, 19(8), pp.489-506.
  4. Ernst, J. and Kellis, M. (2017). Chromatin-state discovery and genome annotation with ChromHMM. Nature Protocols, 12(12), pp.2478-2492.

• Fact sheet 15: Methylation extraction and variant calling

  Sultan Nilay Can and Bhumika Dubay

  DMP/DMR Calling in terms of context (CHH, CHG, CG)

  Cytosine carbon 5 position (5meC) methylation of DNA is an important epigenetic mechanism mostly found in CpG or CpHpG (H = A,C,T) sequence contexts to control gene expression (transcription), chromosome stability, genomic imprinting and silencing of transposons in plants. Therefore, the production of high quality whole genome methylation maps of single cytosine is an important attitude for understanding of how DNA methylation play a role in regulation of gene expression or the generation of abnormal phenotype. Moreover, we can get an impression about the patterns of methylation establishment.

  Whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) have been widely used to measure DNA methylation at a single CpG resolution. These collected methylation information needs to be analyzed and related with a conclusion to assign a biological meaning to it. That’s why some statistical analyses needed to differentiate methylated regions from unmethylated ones. Differentially methylated regions (DMRs) is one important term shows the contiguous genomic regions whose DNA methylation character shows difference between two groups of samples and they are composed of Differentially Methylated Positions (DMPs). DMRs have been used to characterize cell-type or condition specific DNA methylation. The detection of DMRs is a necessary condition to characterize different epigenetic states. For now, this is an important bottleneck in current methylome analysis [7].

  The main problem of detecting DMRs has two levels: The first level is to find a genomic region, in the second level, the individuals of two groups are significantly differed in their methylation levels. Some current solutions were provided such as using pooled data and employ some statistical distributions or suitable regression models fitted to single CpG methylation rates. After testing single CpGs for differential methylation (DMC), significant DMCs are merged to DMRs using däfferent methods (. There are several tools able to detect DMRs, but most designed only for mammalian systems and, thus, they were designed to primarily call CpG methylation. The methylation level of each cytosine was calculated as (#of methylated reads ) /  (#of methylated reads + #of unmethylated reads ). [1]

  In Arabidopsis, CHG and CHH methylations have different sequence preferences. Most of mammalian and plant DNA methylation is restricted to symmetrical CG sequences, but plants also have significant levels of cytosine methylation in their symmetric context CHG (where H is A, C or T) and even in asymmetric sequence [6].

  Recent study [5] results showed that CpG methylation is always symmetrically methylated, whereas non-CpG sites are strand-specifically methylated in introns, SINE elements and LINE elements. Even though hydroxymethylcytosine (hmC) could not be distinguished from methylcytosine by the current bisulfite conversion method showed that hmC is unlikely to occur in non-CpG sites; thus, we do not expect hmC to influence our main conclusions. We also showed that the skew of non-CpG methylation in intron is more pronounced at the boundaries and more significant for highly expressed genes.

  Variant Calling from “Bisulfite Sequencing”

  SNP identification is important to identify of allele-specific epigenetic events like, gamete specific and genetic imprinting. However, SNP calling from BS-Seq data has been shown to be complicated [1]. One problem is that reads from two genomic strands are not complementary at methylated loci. Two methods were widely used to solve this problem. First option is to  align the reads in a three-letter space; the other is a wildcard algorithm which accounts for the C/T conversions [3]. There exist many software packages use these two methods. Bismark , MethylCoder and BS Seeker are based on the Burrows–Wheeler transform. Bismark and BS Seeker use Bowtie  to align the reads in a three-letter space [4]. BSMAP  uses a wildcard algorithm. Also a tool called as Biscuit is quite common for variant detection from bisulfite data [12].

  Two steps are implemented to obtain the final SNP set. These are “Dynamic matrix algorithm” and “Approximate Bayesian modeling” [8][9].

  Different statistical models (HMM,Non Linear / Linear, Bimodal etc.)

  Every DMR tool has its own algorithm to detect and collect methylated regions and they prefer different statistical algorithms in their method. Also, some of the tools are mostly R executable but they can be executed in bash also. Each algorithm has their own advantages and disadvantages in it [10]. It would be better to take a look at commonly used couple of tools to analyze their algorithms [13].

   

  [1] Barturen, G., Rueda, A., Oliver, J. L., & Hackenberg, M. (2013). MethylExtract: High-Quality methylation maps and SNV calling from whole genome bisulfite sequencing data. F1000Research, 2, 217. http://doi.org/10.12688/f1000research.2-217.v2

  [2] Catoni, M., Tsang, J. M., Greco, A. P., & Zabet, N. R. (n.d.). DMRcaller: A versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/29986099

  [3] Chen P.Y., et al. (2010) BS Seeker: precise mapping for bisulfite sequencing. BMC Bioinformatics, 11, 203.

  [4] Krueger F., Andrews S.R. (2011) Bismark: a flexible aligner and methylation caller for bisulfite-seq applications. Bioinformatics, 27, 1571–1572.

  [5] Langmead B., Salzberg S.L. (2012) Fast gapped-read alignment with Bowtie 2, Nat. Methods, 9, 357–359.

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

  [7] Luo, S., & Preuss, D. (2003). Strand-biased DNA methylation associated with centromeric regions in Arabidopsis. Proceedings of the National Academy of Sciences of the United States of America, 100(19), 11133–11138. http://doi.org/10.1073/pnas.1831011100

  [8] Meissner, A., Gnirke, A., Bell, G. W., Ramsahoye, B., Lander, E. S., & Jaenisch, R. (2005). Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Research, 33(18), 5868–5877. http://doi.org/10.1093/nar/gki901

  [9] Pedersen B., et al. (2011) MethylCoder: software pipeline for bisulfite-treated sequences. Bioinformatics, 27, 2435–2436.

  [10] Robinson, M. D., Kahraman, A., Law, C. W., Lindsay, H., Nowicka, M., Weber, L. M., & Zhou, X. (2014). Statistical methods for detecting differentially methylated loci and regions. Frontiers in Genetics, 5, 324. http://doi.org/10.3389/fgene.2014.00324

  [11] Wilkins J.F. (2005) Genomic imprinting and methylation: epigenetic canalization and conflict. Trends Genet., 21, 356–365.

  [12] Xi Y., Li W. (2009) BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics, 10, 232.

  [13] https://omictools.com/snp-detection-category