Aliaga, B. | Search bioRxiv

bioRxiv2023

Chromatin network rearrangements in differentiation and cancer reveal connections between evolutionary processes and gene regulation

Cancer cells are highly plastic, allowing them to adapt to changing conditions. Genes related to basic cellular processes evolved in ancient species, while more specialized genes appeared later with multicellularity (metazoan genes) or even after mammals evolved. Transcriptomic analyses have shown that ancient genes are up-regulated in cancer, while metazoan-origin genes are inactivated. Despite the importance of these observations, the underlying mechanisms remain unexplored. Here, we study local and global epigenomic mechanisms that may regulate genes from specific evolutionary periods. Using evolutionary gene age data, we characterize the epigenomic landscape, gene expression regulation, and chromatin organization in three cell types: human embryonic stem cells, normal B-cells, and primary cells from Chronic Lymphocytic Leukemia, a B-cell malignancy. We identify topological changes in chromatin organization during differentiation observing patterns in Polycomb repression and RNA Polymerase II pausing, which are reversed during oncogenesis. Beyond the non-random organization of genes and chromatin features in the 3D epigenome, we suggest that these patterns lead to preferential interactions among ancient, intermediate, and recent genes, mediated by RNA Polymerase II, Polycomb, and the lamina, respectively. Our findings shed light on gene regulation according to evolutionary age and suggest this organization changes across differentiation and oncogenesis.

cancer biology↗

bioRxiv2017

Notos - a Galaxy tool to analyze CpN observed expected ratios for inferring DNA methylation types

BackgroundDNA methylation patterns store epigenetic information in the vast majority of eukaryotic species. The relatively high costs and technical challenges associated with the detection of DNA methylation however have created a bias in the number of methylation studies towards model organisms. Consequently, it remains challenging to infer kingdom-wide general rules about the functions and evolutionary conservation of DNA methylation. Methylated cytosine is often found in specific CpN dinucleotides, and the frequency distributions of, for instance, CpG observed/expected (CpG o/e) ratios have been used to infer DNA methylation types based on higher mutability of methylated CpG.\n\nResultsPredominantly model-based approaches essentially founded on mixtures of Gaussian distributions are currently used to investigate questions related to the number and position of modes of CpG o/e ratios. These approaches require the selection of an appropriate criterion for determining the best model and will fail if empirical distributions are complex or even merely moderately skewed. We use a kernel density estimation (KDE) based technique for robust and precise characterization of complex CpN o/e distributions without a priori assumptions about the underlying distributions.\n\nConclusionsWe show that KDE delivers robust descriptions of CpN o/e distributions. For straightforward processing, we have developed a Galaxy tool, called Notos and available at the ToolShed, that calculates these ratios of input FASTA files and fits a density to their empirical distribution. Based on the estimated density the number and shape of modes of the distribution is determined, providing a rational for the prediction of the number and the types of different methylation classes. Notos is written in R and Perl.

bioinformatics↗