Dataset Information

Linking epigenetic function to electrostatics: The DNMT2 structural model example.

ABSTRACT: The amino acid sequence of DNMT2 is very similar to the catalytic domains of bacterial and eukaryotic proteins. However, there is great variability in the region of recognition of the target sequence. While bacterial DNMT2 acts as a DNA methyltransferase, previous studies have indicated low DNA methylation activity in eukaryotic DNMT2, with preference by tRNA methylation. Drosophilids are known as DNMT2-only species and the DNA methylation phenomenon is a not elucidated case yet, as well as the ontogenetic and physiologic importance of DNMT2 for this species group. In addition, more recently study showed that methylation in the genome in Drosophila melanogaster is independent in relation to DNMT2. Despite these findings, Drosophilidae family has more than 4,200 species with great ecological diversity and historical evolution, thus we, therefore, aimed to examine the drosophilids DNMT2 in order to verify its conservation at the physicochemical and structural levels in a functional context. We examined the twenty-six DNMT2 models generated by molecular modelling and five crystallographic structures deposited in the Protein Data Bank (PDB) using different approaches. Our results showed that despite sequence and structural similarity between species close related, we found outstanding differences when they are analyzed in the context of surface distribution of electrostatic properties. The differences found in the electrostatic potentials may be linked with different affinities and processivity of DNMT2 for its different substrates (DNA, RNA or tRNA) and even for interactions with other proteins involved in the epigenetic mechanisms.

SUBMITTER: Vieira GC

PROVIDER: S-EPMC5456315 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Linking epigenetic function to electrostatics: The DNMT2 structural model example.

Vieira Gilberto Cavalheiro GC Vieira Gustavo Fioravanti GF Sinigaglia Marialva M Silva Valente Vera Lúcia da VLD

PloS one 20170602 6

The amino acid sequence of DNMT2 is very similar to the catalytic domains of bacterial and eukaryotic proteins. However, there is great variability in the region of recognition of the target sequence. While bacterial DNMT2 acts as a DNA methyltransferase, previous studies have indicated low DNA methylation activity in eukaryotic DNMT2, with preference by tRNA methylation. Drosophilids are known as DNMT2-only species and the DNA methylation phenomenon is a not elucidated case yet, as well as the ...[more]

PMID: 28575027

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Project description:Recently, a molecular pathway linking inflammation to cell transformation has been discovered. This molecular pathway rests on a positive inflammatory feedback loop between NF-?B, Lin28, Let-7 microRNA and IL6, which leads to an epigenetic switch allowing cell transformation. A transient activation of an inflammatory signal, mediated by the oncoprotein Src, activates NF-?B, which elicits the expression of Lin28. Lin28 decreases the expression of Let-7 microRNA, which results in higher level of IL6 than achieved directly by NF-?B. In turn, IL6 can promote NF-?B activation. Finally, IL6 also elicits the synthesis of STAT3, which is a crucial activator for cell transformation. Here, we propose a computational model to account for the dynamical behavior of this positive inflammatory feedback loop. By means of a deterministic model, we show that an irreversible bistable switch between a transformed and a non-transformed state of the cell is at the core of the dynamical behavior of the positive feedback loop linking inflammation to cell transformation. The model indicates that inhibitors (tumor suppressors) or activators (oncogenes) of this positive feedback loop regulate the occurrence of the epigenetic switch by modulating the threshold of inflammatory signal (Src) needed to promote cell transformation. Both stochastic simulations and deterministic simulations of a heterogeneous cell population suggest that random fluctuations (due to molecular noise or cell-to-cell variability) are able to trigger cell transformation. Moreover, the model predicts that oncogenes/tumor suppressors respectively decrease/increase the robustness of the non-transformed state of the cell towards random fluctuations. Finally, the model accounts for the potential effect of competing endogenous RNAs, ceRNAs, on the dynamics of the epigenetic switch. Depending on their microRNA targets, the model predicts that ceRNAs could act as oncogenes or tumor suppressors by regulating the occurrence of cell transformation.

Dataset Information

Linking epigenetic function to electrostatics: The DNMT2 structural model example.

Publications

Linking epigenetic function to electrostatics: The DNMT2 structural model example.

Similar Datasets

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