Affiliation:
1. Federal Research Center “Fundamentals of Biotechnology” of the Russian Academy of Sciences
Abstract
Currently, active research is focused on investigating the mechanisms that regulate the development of various pathologies and their evolutionary dynamics. Epigenetic mechanisms, such as DNA methylation, play a significant role in evolutionary processes, as their changes have a faster impact on the phenotype compared to mutagenesis. In this study, we attempted to develop an algorithm for identifying differentially methylated regions associated with metabolic syndrome, which have undergone methylation changes in humans during the transition from a huntergatherer to a sedentary lifestyle. The application of existing wholegenome bisulfite sequencing methods is limited for ancient samples due to their low quality and fragmentation, and the approach to obtaining DNA methylation profiles differs significantly between ancient huntergatherer samples and modern tissues. In this study, we validated DamMet, an algorithm for reconstructing ancient methylomes. Application of DamMet to Neanderthal and Denisovan genomes showed a moderate level of correlation with previously published methylation profiles and demonstrated an underestimation of methylation levels in the reconstructed profiles by an average of 15–20 %. Additionally, we developed a new Pythonbased algorithm that allows for the comparison of methylomes in ancient and modern samples, despite the absence of methylation profiles in modern bone tissue within the context of obesity. This analysis involves a twostep data processing approach, where the first step involves the identification and filtration of tissuespecific methylation regions, and the second step focuses on the direct search for differentially methylated regions in specific areas associated with the researcher’s target condition. By applying this algorithm to test data, we identified 38 differentially methylated regions associated with obesity, the majority of which were located in promoter regions. The pipeline demonstrated sufficient efficiency in detecting these regions. These results confirm the feasibility of reconstructing DNA methylation profiles in ancient samples and comparing them with modern methylomes. Furthermore, possibilities for further methodological development and the implementation of a new step for studying differentially methylated positions associated with evolutionary processes are discussed.
Publisher
Institute of Cytology and Genetics, SB RAS
Subject
General Biochemistry, Genetics and Molecular Biology,General Agricultural and Biological Sciences
Reference41 articles.
1. Angermueller C., Lee H.J., Reik W., Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017;18(1):67. DOI 10.1186/s13059-017-1189-z
2. Bock C., Reither S., Mikeska T., Paulsen M., Walter J., Lengauer T. BiQ Analyzer: visualization and quality control for DNA methylation data from bisulfite sequencing. Bioinformatics. 2005;21(21): 4067-4068. DOI 10.1093/bioinformatics/bti652
3. Bolger A.M., Lohse M., Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114-2120. DOI 10.1093/bioinformatics/btu170
4. Briggs A.W., Stenzel U., Johnson P.L.F., Green R.E., Kelso J., Prüfer K., Meyer M., Krause J., Ronan M.T., Lachmann M., Pääbo S. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl. Acad. Sci. USA. 2007;104(37):14616-14621. DOI 10.1073/pnas.0704665104
5. Briggs A.W., Good J.M., Green R.E., Krause J., Maricic T., Stenzel U., Lalueza-Fox C., Rudan P., Brajković D., Kućan Ž., Gušić I., Schmitz R., Doronichev V.B., Golovanova L.V., de la Rasilla M.,