Decoding the genomic landscape of chromatin-associated biomolecular condensates
Year: 2024
Type: article
Abstract: Biomolecular condensates play a significant role in chromatin activities, primarily by concentrating and compartmentalizing proteins and/or nucleic acids. However, their genomic landscapes and compositions remain largely unexplored due to a lack of dedicated computational tools for systematic identification in vivo. To address this, we develop CondSigDetector, a computational framework designed to detect condensate-like chromatin-associated protein co-occupancy signatures (CondSigs), to predict genomic loci and component proteins of distinct chromatin-associated biomolecular condensates. Applying this framework to mouse embryonic stem cells (mESC) and human K562 cells enable us to depict the high-resolution genomic landscape of chromatin-associated biomolecular condensates, and uncover both known and potentially unknown biomolecular condensates. Multi-omics analysis and experimental validation further verify the condensation properties of CondSigs. Additionally, our investigation sheds light on the impact of chromatin-associated biomolecular condensates on chromatin activities. Collectively, CondSigDetector provides an approach to decode the genomic landscape of chromatin-associated condensates, facilitating a deeper understanding of their biological functions and underlying mechanisms in cells.
Source: Nature Communications
Authors: Zhaowei Yu, Qi Wang, Qichen Zhang, Yawen Tian, Guo Yan, Jidong Zhu, Guangya Zhu, Yong Zhang
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,790
Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, China Postdoctoral Science Foundation, Science and Technology Commission of Shanghai Municipality, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, Shanghai Advanced Research Institute, Chinese Academy of Sciences
OpenAlex ID: W4401534197
