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Zheng M, Lin Y, Wang W, Zhao Y, Bao X. Application of nucleoside or nucleotide analogues in RNA dynamics and RNA-binding protein analysis. WILEY INTERDISCIPLINARY REVIEWS. RNA 2022; 13:e1722. [PMID: 35218164 DOI: 10.1002/wrna.1722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 01/07/2022] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
Cellular RNAs undergo dynamic changes during RNA biological processes, which are tightly orchestrated by RNA-binding proteins (RBPs). Yet, the investigation of RNA dynamics is hurdled by highly abundant steady-state RNAs, which make the signals of dynamic RNAs less detectable. Notably, the exert of nucleoside or nucleotide analogue-based RNA technologies has provided a remarkable platform for RNA dynamics research, revealing diverse unnoticed features in RNA metabolism. In this review, we focus on the application of two types of analogue-based RNA sequencing, antigen-/antibody- and click chemistry-based methodologies, and summarize the RNA dynamics features revealed. Moreover, we discuss emerging single-cell newly transcribed RNA sequencing methodologies based on nucleoside analogue labeling, which provides novel insights into RNA dynamics regulation at single-cell resolution. On the other hand, we also emphasize the identification of RBPs that interact with polyA, non-polyA RNAs, or newly transcribed RNAs and also their associated RNA-binding domains at genomewide level through ultraviolet crosslinking and mass spectrometry in different contexts. We anticipated that further modification and development of these analogue-based RNA and RBP capture technologies will aid in obtaining an unprecedented understanding of RNA biology. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Methods > RNA Analyses in Cells.
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Affiliation(s)
- Meifeng Zheng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yingying Lin
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- The Center for Infection and Immunity Study, School of Medicine, Sun Yat-sen University, Guangming Science City, Shenzhen, China
| | - Wei Wang
- Center for Biosafety, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yu Zhao
- Molecular Cancer Research Center, School of Medicine, Sun Yat-sen University, Shenzhen, China
| | - Xichen Bao
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
- Center for Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
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Wang Y, Chong TH, Unarta IC, Xu X, Suarez GD, Wang J, Lis JT, Huang X, Cheung PPH. EmPC-seq: Accurate RNA-sequencing and Bioinformatics Platform to Map RNA Polymerases and Remove Background Error. Bio Protoc 2021; 11:e3921. [PMID: 33732808 PMCID: PMC7952946 DOI: 10.21769/bioprotoc.3921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 12/24/2020] [Accepted: 12/27/2020] [Indexed: 11/02/2022] Open
Abstract
Transcription errors can substantially affect metabolic processes in organisms by altering the epigenome and causing misincorporations in mRNA, which is translated into aberrant mutant proteins. Moreover, within eukaryotic genomes there are specific Transcription Error-Enriched genomic Loci (TEELs) which are transcribed by RNA polymerases with significantly higher error rates and hypothesized to have implications in cancer, aging, and diseases such as Down syndrome and Alzheimer's. Therefore, research into transcription errors is of growing importance within the field of genetics. Nevertheless, methodological barriers limit the progress in accurately identifying transcription errors. Pro-Seq and NET-Seq can purify nascent RNA and map RNA polymerases along the genome but cannot be used to identify transcriptional mutations. Here we present background Error Model-coupled Precision nuclear run-on Circular-sequencing (EmPC-seq), a method combining a nuclear run-on assay and circular sequencing with a background error model to precisely detect nascent transcription errors and effectively discern TEELs within the genome.
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Affiliation(s)
- Yuqing Wang
- The Hong Kong University of Science and Technology -Shenzhen Research Institute, Shenzhen, China
- Bioengineering Graduate Program, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR
| | - Tin Hang Chong
- Department of Chemistry, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong SAR
| | - Ilona Christy Unarta
- Bioengineering Graduate Program, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR
| | - Xinzhou Xu
- Bioengineering Graduate Program, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR
| | - Gianmarco D. Suarez
- Department of Chemistry, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong SAR
| | - Jiguang Wang
- Bioengineering Graduate Program, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR
- Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong SAR
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong SAR
| | - John T. Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, USA
- The HKUST Jockey Club Institute for Advanced Study (IAS), The Hong Kong University of Science and Technology, Hong Kong SAR
| | - Xuhui Huang
- The Hong Kong University of Science and Technology -Shenzhen Research Institute, Shenzhen, China
- Bioengineering Graduate Program, Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR
- Department of Chemistry, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong SAR
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong SAR
| | - Peter Pak-Hang Cheung
- The Hong Kong University of Science and Technology -Shenzhen Research Institute, Shenzhen, China
- Department of Chemistry, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong SAR
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