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37 Publications visible to you, out of a total of 37
Epigenetic Heterogeneity of B-Cell Lymphoma: Chromatin Modifiers.

Abstract (Expand)

We systematically studied the expression of more than fifty histone and DNA (de)methylating enzymes in lymphoma and healthy controls. As a main result, we found that the expression levels of nearly all … enzymes become markedly disturbed in lymphoma, suggesting deregulation of large parts of the epigenetic machinery. We discuss the effect of DNA promoter methylation and of transcriptional activity in the context of mutated epigenetic modifiers such as EZH2 and MLL2. As another mechanism, we studied the coupling between the energy metabolism and epigenetics via metabolites that act as cofactors of JmjC-type demethylases. Our study results suggest that Burkitt's lymphoma and diffuse large B-cell Lymphoma differ by an imbalance of repressive and poised promoters, which is governed predominantly by the activity of methyltransferases and the underrepresentation of demethylases in this regulation. The data further suggest that coupling of epigenetics with the energy metabolism can also be an important factor in lymphomagenesis in the absence of direct mutations of genes in metabolic pathways. Understanding of epigenetic deregulation in lymphoma and possibly in cancers in general must go beyond simple schemes using only a few modes of regulation.

Authors: Lydia Hopp, Lilit Nersisyan, Henry Löffler-Wirth, Arsen Arakelyan, Hans Binder

Date Published: 21st Oct 2015

Publication Type: Journal

A topological map of the genetic components of grapevine-Admixture meets SOMmelier machine learning

Abstract (Expand)

Inferring the genetic structure of populations at the subpopulation level is crucial for understanding the evolutionary forces and demographic histories that shape genetic diversity. Among the most … widely used approaches in population genetics are methods based on admixture and structure modeling—named after the respective software tools—which have become standard due to their intuitive, interpretable outputs. In this study, we address a key methodological question: how does traditional admixture-based decomposition of genetic components in multilocus population data relate to clustering approaches that leverage machine learning, specifically Self-Organizing Maps (SOMs)? We implemented this approach through our custom SOM-based tool, SOMmelier, which enables the portrayal of genetic structure by identifying modules of co-mutated SNPs and arranging them in a topology-aware genetic landscape. In this context, topology-awareness refers to the organization of genetic modules in a two-dimensional map, where their spatial proximity reflects mutual similarity. As a case study, we applied SOMmelier to investigate the population genetics of European grapevine. Based on prior literature, we considered up to six genetic components, which formed a genetic landscape that closely mirrors the geographic expanse of the classical Mediterranean world—from Western Asia through the Caucasus to Western Europe. The resulting topology reflects the dynamic spatial and temporal nature of grapevine domestication and diffusion. SOMmelier thus represents a complementary and powerful framework for genetic data analysis. By integrating the topological structure of SNP co-variation, it offers new perspectives on population structure, evolutionary history, and trait associations in grapevine—and has broader applicability to other species and systems in population genetics.

Author: Anush Baloyan, Tomas Konecny, Emma Hovhannisyan, Nate Zadirako, Maria Nikoghosyan, Hans Binder

Date Published: No date defined

Publication Type: Journal

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