Biology subjects

Osmala, M.

Publications and source records attributed to Osmala, M..

3 recordsLinked to original sources

bioRxiv2024

Oriented nucleosome binding by the pioneer factor ELF2

Pioneer transcription factors are DNA-binding proteins that can bind to nucleosomes in closed chromatin regions, exposing enhancers and promoters of genes for transcription. The action of these factors underpin stem cell pluripotency, cell reprogramming and differentiation. ELF2 is an ETS family pioneer factor that has a strong preference for oriented binding on nucleosomes at a composite head-to-tail dimeric sequence motif with a 2 bp spacing between the GGAA core elements. In this study, we investigated the interaction between ELF2 and a nucleosome using single-particle cryo-electron microscopy (cryo-EM). The ELF2-nucleosome structure shows two ELF2 proteins bound to the nucleosome cooperatively at superhelical location +4. The recognition [a]4 helices of both ELF2 monomers dock into the major groove at the core GGAA motifs and unwrap approximately four helical turns of the DNA from the surface of the nucleosome. The unwrapping almost completely exposes one histone H2A:H2B dimer, which dissociates in a subset of particles to form an ELF2-bound hexasome. ChIP-seq combined with genome-wide motif mapping indicates that all unmethylated high-affinity ELF2 double motifs are occupied, indicating that ELF2 is able to use its binding energy to function as a pioneer factor both in vitro and in vivo. ELF2 binding is highly enriched downstream of transcription start sites (TSS) of highly expressed genes, with the motifs oriented in such a way that the TSS becomes accessible upon ELF2 binding. This mechanism is essential for cells, as mutation of ELF2 motifs result in decreased cell proliferation. Taken together, our results indicate that ELF2 can use its binding energy to open chromatin in an oriented fashion, facilitating nucleosome positioning and transcription initiation at transcription start sites.

biochemistry↗

bioRxiv2022

ChromDMM: A Dirichlet-Multinomial Mixture Model For Clustering Heterogeneous Epigenetic Data

MotivationResearch on epigenetic modifications and other chromatin features at genomic regulatory elements elucidates essential biological mechanisms including the regulation of gene expression. Despite the growing number of epigenetic datasets, new tools are still needed to discover novel distinctive patterns of heterogeneous epigenetic signals at regulatory elements. ResultsWe introduce ChromDMM, a product Dirichlet-multinomial mixture model for clustering genomic regions that are characterised by multiple chromatin features. ChromDMM extends the mixture model framework by profile shifting and flipping that can probabilistically account for inaccuracies in the position and strand-orientation of the genomic regions. Owing to hyper-parameter optimisation, ChromDMM can also regularise the smoothness of the epigenetic profiles across the consecutive genomic regions. With simulated data, we demonstrate that ChromDMM clusters, shifts, and strand-orients the profiles more accurately than previous methods. With ENCODE data, we show that the clustering of enhancer regions in the human genome reveals distinct patterns in several chromatin features. We further validate the enhancer clusters by their enrichment for transcriptional regulatory factor binding sites. AvailabilityThe software is available at https://github.com/MariaOsmala/ChromDMM

bioinformatics↗

bioRxiv2019

Enhancer prediction in the human genome by probabilistic modeling of the chromatin feature patterns

BackgroundThe binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq). The resulting chromatin feature data has been successfully adopted for genome-wide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type and do not utilise the pattern of the ChIP-seq coverage profiles efficiently. ResultsIn this work, we propose a PRobabilistic Enhancer PRedictIoN Tool (PREPRINT) that assumes characteristic coverage patterns of chromatin features at enhancers and employs a statistical model to account for their variability. PREPRINT defines probabilistic distance measures to quantify the similarity of the genomic query regions and the characteristic coverage patterns. The probabilistic scores of the enhancer and non-enhancer samples are utilised to train a kernel-based classifier. The performance of the method is demonstrated on ENCODE data for two cell lines. The predicted enhancers are computationally validated based on the transcriptional regulatory protein binding sites and compared to the predictions obtained by state-of-the-art methods. ConclusionPREPRINT performs favorably to the state-of-the-art methods, especially when requiring the methods to predict a larger set of enhancers. PREPRINT generalises successfully to data from cell type not utilised for training, and often the PREPRINT performs better than the previous methods. The PREPRINT enhancers are less sensitive to the choice of prediction threshold. PREPRINT identifies biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies. Availabilityhttps://github.com/MariaOsmala/preprint Contactmaria.osmala@aalto.fi

bioinformatics↗