Methylation Sequencing

The genetic code is comprised of the four bases adenine (A), guanine (G), thymine (T), and cytosine (C). Determining their sequence is done using whole genome sequencing. Whole genome sequencing can give insight into variations in the genetic code, including single nucleotide variants (SNVs), small insertions and deletions (indels), copy number variants (CNVs), and structural variants. These variations can play a pivotal role in diseases. Thus, whole genome sequencing can contribute, amongst others, to disease research, cancer studies, personalized medicine approaches, and translational medicine.

In Epigenomics, the changes in gene function are studied that are not due to changes in the genome sequence, e.g., due to mutations or recombination. These epigenetic changes in gene function are passed on to daughter cells. This is done, for example, by methylation of cytosines. Especially cytosines that are followed by guanines, so-called CpG regions, are modified. If the fifth carbon position is modified with a methyl group, a 5-methylcytosine (5mC) is generated. The oxidation of this 5mC results in the formation of 5-hydroxymethylcytosine (5hmC). This DNA methylation can be associated with altered gene expression. Methylated cytosines are, for example, present at transcription start sites of repressed genes. However, methylation can also be associated with gene activation, e.g., during development. Thus, revealing the methylation pattern of a cell can have pivotal implications for the understanding of biological processes, such as aging, but also for diagnosing diseases, such as cancer, atherosclerosis, cardiovascular diseases, or nervous disorders.

For this reason, the enzymatic methyl-sequencing (EM-Seq) was developed as one method to determine the methylation of the genome. In two enzymatic reactions, 5mC and 5hmC are detected. First, the two enzymes tet methylcytosine dioxygenase 2 (TET2) and T4 phage β-glucosyltransferase (T4-BGT) convert the methylated cytosines into products that cannot be deaminated. Next, apolipoprotein B mRNA editing enzyme catalytic subunit 3A (APOBEC3A) deaminates all cytosines that are not modified. With these three enzymes, methylated cytosines can be identified.

The enzymatic methyl-sequencing has several advantages compared to bisulfite sequencing:

• As the enzyme conversion is gentler than the bisulfite conversion, less DNA damage is observed in the enzyme methyl-seq. Lesser DNA damage results in libraries with longer inserts, facilitating the mapping of the reads.

• In addition to longer inserts, the gentler enzyme methyl-seq also enables higher quality of the libraries, which can be amplified more efficiently. The result of this efficient amplification is a higher yield with less PCR cycles. Additionally, enzyme methyl-seq shows constantly low duplicate values, independent of the amount of input material.

• In bisulfite sequencing, especially CpG regions suffer from the harsh conversion. In these CpG regions, more damage, breaks, and losses occur, leading to an underrepresentation of these regions in the bisulfite sequencing. This bias is not observable in the enzyme methyl-seq: EM-seq libraries show a consistent GC coverage.

Enzyme methyl-sequencing is a gentle and straight-forward way to analyze the methylation patterns of a genome. EM-seq opens new possibilities for research and clinical applications, increasing the understanding of epigenetics in the context of health and disease.

With PacBio’s long-read HiFi sequencing, CeGaT offers a second option to determine your sample’s methylation status. In contrast to whole genome bisulfite sequencing or enzymatic methyl-sequencing, no DNA modifications or conversion are required in this approach to detect methylation of 5-methylcytosines (5mC) in the context of CpG-regions. The fluorescence signal is detected during the sequencing process to determine the incorporated nucleotide accurately. In addition, the kinetics of the polymerase are measured. The base context and the epigenetic changes impact the nucleotide incorporation kinetics. Thus, additionally, detecting the kinetics gives valuable information about the methylation status without converting the bases before the sequencing. A convolutional neural network processes the measured kinetics to determine the methylation status of each CpG site. With this neural network, the probability of methylation for each CpG site is reported.