Diana Buitrago

Determining the effect of DNA methylation on chromatin structure and function in higher organisms is challenging due to the extreme complexity of epigenetic regulation. We studied a simpler model system, budding yeast, that lacks DNA methylation machinery making it a perfect model system to study the intrinsic role of DNA methylation in chromatin structure and function. We expressed the murine DNA methyltransferases in Saccharomyces cerevisiae and analyzed the correlation between DNA methylation, nucleosome positioning, gene expression and 3D genome organization. Despite lacking the machinery for positioning and reading methylation marks, induced DNA methylation follows a conserved pattern with low methylation levels at the 5' end of the gene increasing gradually toward the 3' end, with concentration of methylated DNA in linkers and nucleosome free regions, and with actively expressed genes showing low and high levels of methylation at transcription start and terminating sites respectively, mimicking the patterns seen in mammals. We also see that DNA methylation increases chromatin condensation in peri-centromeric regions, decreases overall DNA flexibility, and favors the heterochromatin state. Taken together, these results demonstrate that methylation intrinsically modulates chromatin structure and function even in the absence of cellular machinery evolved to recognize and process the methylation signal.

We report here the solution structure of several repetitive DNA sequences containing d(TCGTTCCGT) and related repeats. At physiological pH, these sequences fold into i-motif like quadruplexes in which every two repeats a globular structure is stabilized by two hemiprotonated C:C+ base pairs, flanked by two minor groove tetrads resulting from the association of G:C or G:T base pairs. The interaction between the minor groove tetrads and the nearby C:C+ base pairs affords a strong stabilization, which results in effective pHT values above 7.5. Longer sequences with more than two repeats are able to fold in tandem, forming a rosary bead-like structure. Bioinformatics analysis shows that these sequences are prevalent in the human genome, and are present in development-related genes.

We used extensive molecular dynamics simulations to study the structural and dynamic properties of the central d(TpA) step in the highly polymorphic d(CpTpApG) tetranucleotide. Contrary to the assumption of the dinucleotide-model and its nearest neighbours (tetranucleotide-model), the properties of the central d(TpA) step change quite significantly dependent on the next-to-nearest (hexanucleotide) sequence context and in a few cases are modulated by even remote neighbours (beyond next-to-nearest from the central TpA). Our results highlight the existence of previously undescribed dynamical mechanisms for the transmission of structural information into the DNA and demonstrate the existence of certain sequences with special physical properties that can impact on the global DNA structure and dynamics.

We present Nucleosome Dynamics, a suite of programs integrated into a virtual research environment and created to define nucleosome architecture and dynamics from noisy experimental data. The package allows both the definition of nucleosome architectures and the detection of changes in nucleosomal organization due to changes in cellular conditions. Results are displayed in the context of genomic information thanks to different visualizers and browsers, allowing the user a holistic, multidimensional view of the genome/transcriptome. The package shows good performance for both locating equilibrium nucleosome architecture and nucleosome dynamics and provides abundant useful information in several test cases, where experimental data on nucleosome position (and for some cases expression level) have been collected for cells under different external conditions (cell cycle phase, yeast metabolic cycle progression, changes in nutrients or difference in MNase digestion level). Nucleosome Dynamics is a free software and is provided under several distribution models.