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Methylation of Endogenous Human Retroelements in Health and Disease

  • W. A. SchulzEmail author
  • C. Steinhoff
  • A. R. Florl
Part of the Current Topics in Microbiology and Immunology book series (CT MICROBIOLOGY, volume 310)

Abstract

Retroelements constitute approximately 45% of the human genome. Long interspersed nuclear element (LINE) autonomous retrotransposons are predominantly represented by LINE-1, nonautonomous small interspersed nuclear elements (SINEs) are primarily represented by ALUs, and LTR retrotransposons by several families of human endogenous retroviruses (HERVs). The vast majority of LINE and HERV elements are densely methylated in normal somatic cells and contained in inactive chromatin. Methylation and chromatin structure together ensure a stable equilibrium between retroelements and their host. Hypomethylation and expression in developing germ cells opens a “window of opportunity” for retrotransposition and recombination that contribute to human evolution, but also inherited disease. In somatic cells, the presence of retroelements may be exploited to organize the genome into active and inactive regions, to separate domains and functional regions within one chromatin domain, to suppress transcriptional noise, and to regulate transcript stability. Retroelements, particularly ALUs, may also fulfill physiological roles during responses to stress and infections. Reactivation and hypomethylation of LINEs and HERVs may be important in the pathophysiology of cancer and various autoimmune diseases, contributing to chromosomal instability and chronically aberrant immune responses. The emerging insights into the pathophysiological importance of endogenous retroelements accentuate the gaps in our knowledge of how these elements are controlled in normal developing and mature cells.

Keywords

Germ Cell Germ Cell Cancer Develop Germ Cell Endogenous Human Endogenous Retroelements 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.Urologische KlinikHeinrich Heine UniversitätDüsseldorfGermany
  2. 2.Computational Molecular BiologyMax Planck Institute for Molecular GeneticsBerlinGermany

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