Deep Enzymology Studies on DNA Methyltransferases Reveal Novel Connections between Flanking Sequences and Enzyme Activity

被引:17
|
作者
Jeltsch, Albert [1 ]
Adam, Sabrina [1 ]
Dukatz, Michael [1 ]
Emperle, Max [1 ]
Bashtrykov, Pavel [1 ]
机构
[1] Univ Stuttgart, Inst Biochem & Tech Biochem, Dept Biochem, D-70569 Stuttgart, Germany
关键词
EMBRYONIC STEM-CELLS; MOLECULAR ENZYMOLOGY; METHYLATION PATTERNS; BISULFITE CONVERSION; ICF SYNDROME; DNMT3A; MOUSE; MUTATIONS; RECOGNITION; PREFERENCE;
D O I
10.1016/j.jmb.2021.167186
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
DNA interacting enzymes recognize their target sequences embedded in variable flanking sequence context. The influence of flanking sequences on enzymatic activities of DNA methyltransferases (DNMTs) can be systematically studied with "deep enzymology" approaches using pools of double-stranded DNA substrates, which contain target sites in random flanking sequence context. After incubation with DNMTs and bisulfite conversion, the methylation states and flanking sequences of individual DNA molecules are determined by NGS. Deep enzymology studies with different human and mouse DNMTs revealed strong influences of flanking sequences on their CpG and non-CpG methylation activity and the structures of DNMTDNA complexes. Differences in flanking sequence preferences of DNMT3A and DNMT3B were shown to be related to the prominent role of DNMT3B in the methylation of human SATII repeat elements. Mutational studies in DNMT3B discovered alternative interaction networks between the enzyme and the DNA leading to a partial equalization of the effects of different flanking sequences. Structural studies in DNMT1 revealed striking correlations between enzymatic activities and flanking sequence dependent conformational changes upon DNA binding. Correlation of the biochemical data with cellular methylation patterns demonstrated that flanking sequence preferences are an important parameter that influences genomic DNA methylation patterns together with other mechanisms targeting DNMTs to genomic sites. (C) 2021 Elsevier Ltd. All rights reserved.
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页数:15
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