Histone dynamics mediate DNA unwrapping and sliding in nucleosomes

Армеев, Г. А.; Kniazeva, Anastasiia S.; Komarova, Galina A.; Kirpichnikov, Mikhaǐl P.; Shaytan, Alexey K.

doi:10.1038/s41467-021-22636-9

Cited by 110 publications

(101 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Histone tails have a high degree of conformational flexibility and might protrude into the solvent and remain perpetually accessible for binding by chromatin factors 1 , 8 – 10 . However, there is growing evidence that histone tails can extensively interact with the nucleosomal and linker DNA 11 – 19 , which raises the possibility that tails may modulate the nucleosomal and linker DNA accessibility and regulate the nucleosome recognition by binding partners. It has been shown that despite the lower net negative charge of the nucleosome compared to the free DNA, nucleosomes are characterized by an enhanced negative charge density (so-called electrostatic focusing) even with the intact positively charged histone tails 20 .…”

Section: Introductionmentioning

confidence: 99%

Binding of regulatory proteins to nucleosomes is modulated by dynamic histone tails

Peng

Onufriev

et al. 2021

Nat Commun

View full text Add to dashboard Cite

Little is known about the roles of histone tails in modulating nucleosomal DNA accessibility and its recognition by other macromolecules. Here we generate extensive atomic level conformational ensembles of histone tails in the context of the full nucleosome, totaling 65 microseconds of molecular dynamics simulations. We observe rapid conformational transitions between tail bound and unbound states, and characterize kinetic and thermodynamic properties of histone tail-DNA interactions. Different histone types exhibit distinct binding modes to specific DNA regions. Using a comprehensive set of experimental nucleosome complexes, we find that the majority of them target mutually exclusive regions with histone tails on nucleosomal/linker DNA around the super-helical locations ± 1, ± 2, and ± 7, and histone tails H3 and H4 contribute most to this process. These findings are explained within competitive binding and tail displacement models. Finally, we demonstrate the crosstalk between different histone tail post-translational modifications and mutations; those which change charge, suppress tail-DNA interactions and enhance histone tail dynamics and DNA accessibility.

show abstract

Section: Introductionmentioning

confidence: 99%

Binding of regulatory proteins to nucleosomes is modulated by dynamic histone tails

Peng

Onufriev

et al. 2021

Nat Commun

View full text Add to dashboard Cite

show abstract

“…Because histone proteins are tightly wrapped by double-stranded DNA in the nucleus to compress DNA into chromatin, interaction between histones and DNA is crucial for gene activity [ 57 ]. In further detail, since DNA binds to an octamer structure of the histone complex, (H3, H4) 2 (H2A, H2B) 2 , histones may release or capture DNA to turn-on or turn-off gene expression, respectively.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

Nguyen

Kim

et al. 2021

Biomedicines

View full text Add to dashboard Cite

Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics.

show abstract

“…Unfortunately, there is a lack of studies investigating the function of this modification. The arginine residue 49 is thought to interact with the DNA and postulated to regulate gene expression by affecting H3K56ac (transcriptional activation) [ 68 – 70 ]. To a lesser extent, H3R42me2, linked to transcriptional activation, was increased under all treatments [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

Therapeutical interference with the epigenetic landscape of germ cell tumors: a comparative drug study and new mechanistical insights

et al. 2022

View full text Add to dashboard Cite

Background Type II germ cell tumors (GCT) are the most common solid cancers in males of age 15 to 35 years. Treatment of these tumors includes cisplatin-based therapy achieving high cure rates, but also leading to late toxicities. As mainly young men are suffering from GCTs, late toxicities play a major role regarding life expectancy, and the development of therapy resistance emphasizes the need for alternative therapeutic options. GCTs are highly susceptible to interference with the epigenetic landscape; therefore, this study focuses on screening of drugs against epigenetic factors as a treatment option for GCTs. Results We present seven different epigenetic inhibitors efficiently decreasing cell viability in GCT cell lines including cisplatin-resistant subclones at low concentrations by targeting epigenetic modifiers and interactors, like histone deacetylases (Quisinostat), histone demethylases (JIB-04), histone methyltransferases (Chaetocin), epigenetic readers (MZ-1, LP99) and polycomb-repressive complexes (PRT4165, GSK343). Mass spectrometry-based analyses of the histone modification landscape revealed effects beyond the expected mode-of-action of each drug, suggesting a wider spectrum of activity than initially assumed. Moreover, we characterized the effects of each drug on the transcriptome of GCT cells by RNA sequencing and found common deregulations in gene expression of ion transporters and DNA-binding factors. A kinase array revealed deregulations of signaling pathways, like cAMP, JAK-STAT and WNT. Conclusion Our study identified seven drugs against epigenetic modifiers to treat cisplatin-resistant GCTs. Further, we extensively analyzed off-target effects and modes-of-action, which are important for risk assessment of the individual drugs.

show abstract

Histone dynamics mediate DNA unwrapping and sliding in nucleosomes

Cited by 110 publications

References 80 publications

Binding of regulatory proteins to nucleosomes is modulated by dynamic histone tails

Binding of regulatory proteins to nucleosomes is modulated by dynamic histone tails

Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

Therapeutical interference with the epigenetic landscape of germ cell tumors: a comparative drug study and new mechanistical insights

Contact Info

Product

Resources

About