Control of DNA methylation level is critical for gene regulation, and the factors that govern hypomethylation at CpG islands (CGIs) are still being uncovered. Here, we provide evidence that G-quadruplex (G4) DNA secondary structures are genomic features that influence methylation at CGIs. We show that the presence of G4 structure is tightly associated with CGI hypomethylation in the human genome. Surprisingly, we find that these G4 sites are enriched for DNA methyltransferase 1 (DNMT1) occupancy, which is consistent with our biophysical observations that DNMT1 exhibits higher binding affinity for G4s as compared to duplex, hemi-methylated, or single-stranded DNA. The biochemical assays also show that the G4 structure itself, rather than sequence, inhibits DNMT1 enzymatic activity. Based on these data, we propose that G4 formation sequesters DNMT1 thereby protecting certain CGIs from methylation and inhibiting local methylation.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173298PMC
http://dx.doi.org/10.1038/s41594-018-0131-8DOI Listing

Publication Analysis

Top Keywords

dna
6
dna g-quadruplex
4
g-quadruplex structures
4
structures mold
4
mold dna
4
dna methylome
4
methylome control
4
control dna
4
methylation
4
dna methylation
4

Similar Publications

Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs).

View Article and Find Full Text PDF

The diversity and heterogeneity of biomarkers has made the development of general methods for single-step quantification of analytes difficult. For individual biomarkers, electrochemical methods that detect a conformational change in an affinity binder upon analyte binding have shown promise. However, because the conformational change must operate within a nanometer-scale working distance, an entirely new sensor, with a unique conformational change, must be developed for each analyte.

View Article and Find Full Text PDF

PsDMAP1/PsTIP60-regulated H4K16ac is required for ROS-dependent virulence adaptation of on host plants.

Proc Natl Acad Sci U S A

January 2025

Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China.

Host plants and various fungicides inhibit plant pathogens by inducing the release of excessive reactive oxygen species (ROS) and causing DNA damage, either directly or indirectly leading to cell death. The mechanisms by which the oomycete manages ROS stress resulting from plant immune responses and fungicides remains unclear. This study elucidates the role of histone acetylation in ROS-induced DNA damage responses (DDR) to adapt to stress.

View Article and Find Full Text PDF

ANAC044 orchestrates mitochondrial stress signaling to trigger iron-induced stem cell death in root meristems.

Proc Natl Acad Sci U S A

January 2025

Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China.

While iron (Fe) is essential for life and plays important roles for almost all growth related processes, it can trigger cell death in both animals and plants. However, the underlying mechanisms for Fe-induced cell death in plants remain largely unknown. S-nitrosoglutathione reductase (GSNOR) has previously been reported to regulate nitric oxide homeostasis to prevent Fe-induced cell death within root meristems.

View Article and Find Full Text PDF

The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.

PLoS Comput Biol

January 2025

Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.

Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!