msgbsR: An R package for analysing methylation-sensitive restriction enzyme sequencing data

Unravelling DNA methylation dynamics during developmental stages in Quercus ilex subsp. ballota [Desf.] Samp

BACKGROUND

DNA methylation is a critical factor influencing plant growth, adaptability, and phenotypic plasticity. While extensively studied in model and crop species, it remains relatively unexplored in holm oak and other non-domesticated forest trees. This study conducts a comprehensive in-silico mining of DNA methyltransferase and demethylase genes within the holm oak genome to enhance our understanding of this essential process in these understudied species. The expression levels of these genes in adult and seedling leaves, as well as embryos, were analysed using quantitative real-time PCR (qRT-PCR). Global DNA methylation patterns were assessed through methylation-sensitive amplified polymorphism (MSAP) techniques. Furthermore, specific methylated genomic sequences were identified via MSAP sequencing (MSAP-Seq).

RESULT

A total of 13 DNA methyltransferase and three demethylase genes were revealed in the holm oak genome. Expression levels of these genes varied significantly between organs and developmental stages. MSAP analyses revealed a predominance of epigenetic over genetic variation among organs and developmental stages, with significantly higher global DNA methylation levels observed in adult leaves. Embryos exhibited frequent demethylation events, while de novo methylation was prevalent in seedling leaves. Approximately 35% of the genomic sequences identified by MSAP-Seq were methylated, predominantly affecting nuclear genes and intergenic regions, as opposed to repetitive sequences and chloroplast genes. Methylation was found to be more pronounced in the exonic regions of nuclear genes compared to their promoter and intronic regions. The methylated genes were predominantly associated with crucial biological processes such as photosynthesis, ATP synthesis-coupled electron transport, and defence response.

CONCLUSION

This study opens a new research direction in analysing variability in holm oak by evaluating the epigenetic events and mechanisms based on DNA methylation. It sheds light on the enzymatic machinery governing DNA (de)methylation, and the changes in the expression levels of methylases and demethylases in different organs along the developmental stages. The expression level was correlated with the DNA methylation pattern observed, showing the prevalence of de novo methylation and demethylation events in seedlings and embryos, respectively. Several methylated genes involved in the regulation of transposable element silencing, lipid biosynthesis, growth and development, and response to biotic and abiotic stresses are highlighted. MSAP-seq integrated with whole genome bisulphite sequencing and advanced sequencing technologies, such as PacBio or Nanopore, will bring light on epigenetic mechanisms regulating the expression of specific genes and its correlation with the phenotypic variability and the differences in the response to environmental cues, especially those related to climate change.

PSTVd infection in Nicotiana benthamiana plants has a minor yet detectable effect on CG methylation

Viroids are small circular RNAs infecting a wide range of plants. They do not code for any protein or peptide and therefore rely on their structure for their biological cycle. Observed phenotypes of viroid infected plants are thought to occur through changes at the transcriptional/translational level of the host. A mechanism involved in such changes is RNA-directed DNA methylation (RdDM). Till today, there are contradictory works about viroids interference of RdDM. In this study, we investigated the epigenetic effect of viroid infection in Nicotiana benthamiana plants. Using potato spindle tuber viroid (PSTVd) as the triggering pathogen and via bioinformatic analyses, we identified endogenous gene promoters and transposable elements targeted by 24 nt host siRNAs that differentially accumulated in PSTVd-infected and healthy plants. The methylation status of these targets was evaluated following digestion with methylation-sensitive restriction enzymes coupled with PCR amplification, and bisulfite sequencing. In addition, we used Methylation Sensitive Amplification Polymorphism (MSAP) followed by sequencing (MSAP-seq) to study genomic DNA methylation of 5-methylcytosine (5mC) in CG sites upon viroid infection. In this study we identified a limited number of target loci differentially methylated upon PSTVd infection. These results enhance our understanding of the epigenetic host changes as a result of pospiviroid infection.

Computational Protocol for DNA Methylation Profiling in Plants Using Restriction Enzyme-Based Genome Reduction

Epigenetics can be described as heritable phenotype changes that do not involve alterations in the underlying DNA sequence. Having widespread implications in fundamental biological phenomena, there is an increased interest in characterizing epigenetic modifications and studying their functional implications. DNA methylation, particularly 5-methylcytosine (5mC), stands out as the most studied epigenetic mark and several methodologies have been created to investigate it. With the development of next-generation sequencing technologies, several approaches to DNA methylation profiling were conceived, with differences in resolution and genomic scope. Besides the gold standard whole-genome bisulfite sequencing, which is costly for population-scale studies, genomic reduced representation methods emerged as viable alternatives to investigate methylation loci. Whole-genome bisulfite sequencing provides single-base methylation resolution but is costly for population-scale studies. Genomic reduction methods emerged as viable alternatives to investigate a fraction of methylated loci. One of such approaches uses double digestion with the restriction enzymes PstI and one of the isoschizomers, MspI and HpaII, with differential sensitivity to 5mC at the restriction site. Statistical comparison of sequencing reads counts obtained from the two libraries for each sample (PstI-MspI and PstI-HpaII) is used to infer the methylation status of thousands of cytosines. Here, we describe a general overview of the technique and a computational protocol to process the generated data to provide a medium-scale inventory of methylated sites in plant genomes. The software is available at https://github.com/wendelljpereira/DArTseqMet .

Additive and non-additive epigenetic signatures of natural hybridisation between fish species with different mating systems

Hybridization is a major source of evolutionary innovation. In plants, epigenetic mechanisms can help to stabilize hybrid genomes and contribute to reproductive isolation, but the relationship between genetic and epigenetic changes in animal hybrids is unclear. We analysed the relationship between genetic background and methylation patterns in natural hybrids of two genetically divergent fish species with different mating systems, Kryptolebias hermaphroditus (self-fertilizing) and K. ocellatus (outcrossing). Co-existing parental species displayed highly distinct genetic (SNPs) and methylation patterns (37,000 differentially methylated cytosines). Hybrids had predominantly intermediate methylation patterns (88.5% of the sites) suggesting additive effects, as expected from hybridization between genetically distant species. The large number of differentially methylated cytosines between hybrids and parental species (n = 5,800) suggests that hybridization may play a role in increasing genetic and epigenetic variation. Although most of the observed epigenetic variation was additive and had a strong genetic component, we also found a small percentage of non-additive, potentially stochastic, methylation differences that might act as an evolutionary bet-hedging strategy and increase fitness under environmental instability.

Computational challenges in detection of cancer using cell-free DNA methylation

Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.

Current Advances in DNA Methylation Analysis Methods

DNA methylation is one of the epigenetic changes, which plays a major role in regulating gene expression and, thus, many biological processes and diseases. There are several methods for determining the methylation of DNA samples. However, selecting the most appropriate method for answering biological questions appears to be a challenging task. The primary methods in DNA methylation focused on identifying the state of methylation of the examined genes and determining the total amount of 5-methyl cytosine. The study of DNA methylation at a large scale of genomic levels became possible following the use of microarray hybridization technology. The new generation of sequencing platforms now allows the preparation of genomic maps of DNA methylation at the single-open level. This review includes the majority of methods available to date, introducing the most widely used methods, the bisulfite treatment, biological identification, and chemical cutting along with their advantages and disadvantages. The techniques are then scrutinized according to their robustness, high throughput capabilities, and cost.

A cost-effective approach to DNA methylation detection by Methyl Sensitive DArT sequencing

Several studies suggest the relation of DNA methylation to diseases in humans and important phenotypes in plants drawing attention to this epigenetic mark as an important source of variability. In the last decades, several methodologies were developed to assess the methylation state of a genome. However, there is still a lack of affordable and precise methods for genome wide analysis in large sample size studies. Methyl sensitive double digestion MS-DArT sequencing method emerges as a promising alternative for methylation profiling. We developed a computational pipeline for the identification of DNA methylation using MS-DArT-seq data and carried out a pilot study using the Eucalyptus grandis tree sequenced for the species reference genome. Using a statistic framework as in differential expression analysis, 72,515 genomic sites were investigated and 5,846 methylated sites identified, several tissue specific, distributed along the species 11 chromosomes. We highlight a bias towards identification of DNA methylation in genic regions and the identification of 2,783 genes and 842 transposons containing methylated sites. Comparison with WGBS, DNA sequencing after treatment with bisulfite, data demonstrated a precision rate higher than 95% for our approach. The availability of a reference genome is useful for determining the genomic context of methylated sites but not imperative, making this approach suitable for any species. Our approach provides a cost effective, broad and reliable examination of DNA methylation profile on MspI/HpaII restriction sites, is fully reproducible and the source code is available on GitHub (https://github.com/wendelljpereira/ms-dart-seq).

A cost-effective approach to DNA methylation detection by Methyl Sensitive DArT sequencing

Several studies suggest the relation of DNA methylation to diseases in humans and important phenotypes in plants drawing attention to this epigenetic mark as an important source of variability. In the last decades, several methodologies were developed to assess the methylation state of a genome. However, there is still a lack of affordable and precise methods for genome wide analysis in large sample size studies. Methyl sensitive double digestion MS-DArT sequencing method emerges as a promising alternative for methylation profiling. We developed a computational pipeline for the identification of DNA methylation using MS-DArT-seq data and carried out a pilot study using the Eucalyptus grandis tree sequenced for the species reference genome. Using a statistic framework as in differential expression analysis, 72,515 genomic sites were investigated and 5,846 methylated sites identified, several tissue specific, distributed along the species 11 chromosomes. We highlight a bias towards identification of DNA methylation in genic regions and the identification of 2,783 genes and 842 transposons containing methylated sites. Comparison with WGBS, DNA sequencing after treatment with bisulfite, data demonstrated a precision rate higher than 95% for our approach. The availability of a reference genome is useful for determining the genomic context of methylated sites but not imperative, making this approach suitable for any species. Our approach provides a cost effective, broad and reliable examination of DNA methylation profile on MspI/HpaII restriction sites, is fully reproducible and the source code is available on GitHub (https://github.com/wendelljpereira/ms-dart-seq).

DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis

Sequencing technologies have changed not only our approaches to classical genetics, but also the field of epigenetics. Specific methods allow scientists to identify novel genome-wide epigenetic patterns of DNA methylation down to single-nucleotide resolution. DNA methylation is the most researched epigenetic mark involved in various processes in the human cell, including gene regulation and development of diseases, such as cancer. Increasing numbers of DNA methylation sequencing datasets from human genome are produced using various platforms-from methylated DNA precipitation to the whole genome bisulfite sequencing. Many of those datasets are fully accessible for repeated analyses. Sequencing experiments have become routine in laboratories around the world, while analysis of outcoming data is still a challenge among the majority of scientists, since in many cases it requires advanced computational skills. Even though various tools are being created and published, guidelines for their selection are often not clear, especially to non-bioinformaticians with limited experience in computational analyses. Separate tools are often used for individual steps in the analysis, and these can be challenging to manage and integrate. However, in some instances, tools are combined into pipelines that are capable to complete all the essential steps to achieve the result. In the case of DNA methylation sequencing analysis, the goal of such pipeline is to map sequencing reads, calculate methylation levels, and distinguish differentially methylated positions and/or regions. The objective of this review is to describe basic principles and steps in the analysis of DNA methylation sequencing data that in particular have been used for mammalian genomes, and more importantly to present and discuss the most pronounced computational pipelines that can be used to analyze such data. We aim to provide a good starting point for scientists with limited experience in computational analyses of DNA methylation and hydroxymethylation data, and recommend a few tools that are powerful, but still easy enough to use for their own data analysis.