1
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Li T, Cheng C, Liu J. Chemical and Enzyme-Mediated Chemical Reactions for Studying Nucleic Acids and Their Modifications. Chembiochem 2024; 25:e202400220. [PMID: 38742371 DOI: 10.1002/cbic.202400220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/16/2024]
Abstract
Nucleic acids are genetic information-carrying molecules inside cells. Apart from basic nucleotide building blocks, there exist various naturally occurring chemical modifications on nucleobase and ribose moieties, which greatly increase the encoding complexity of nuclei acids, contribute to the alteration of nucleic acid structures, and play versatile regulation roles in gene expression. To study the functions of certain nucleic acids in various biological contexts, robust tools to specifically label and identify these macromolecules and their modifications, and to illuminate their structures are highly necessary. In this review, we summarize recent technique advances of using chemical and enzyme-mediated chemical reactions to study nucleic acids and their modifications and structures. By highlighting the chemical principles of these techniques, we aim to present a perspective on the advancement of the field as well as to offer insights into developing specific chemical reactions and precise enzyme catalysis utilized for nucleic acids and their modifications.
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Affiliation(s)
- Tengwei Li
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Yuhangtang Road 866, Hangzhou, 310058, Zhejiang Province, China
| | - Chongguang Cheng
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Yuhangtang Road 866, Hangzhou, 310058, Zhejiang Province, China
| | - Jianzhao Liu
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Yuhangtang Road 866, Hangzhou, 310058, Zhejiang Province, China
- Life Sciences Institute, Zhejiang University, Yuhangtang Road 866, Hangzhou, 310058, Zhejiang Province, China
- State Key Laboratory of Transvascular Implantation Devices, Hangzhou, 310009, Zhejiang Province, China
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2
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Ye R, Zhao H, Wang X, Xue Y. Technological advancements in deciphering RNA-RNA interactions. Mol Cell 2024:S1097-2765(24)00543-4. [PMID: 39047724 DOI: 10.1016/j.molcel.2024.06.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/11/2024] [Accepted: 06/28/2024] [Indexed: 07/27/2024]
Abstract
RNA-RNA interactions (RRIs) can dictate RNA molecules to form intricate higher-order structures and bind their RNA substrates in diverse biological processes. To elucidate the function, binding specificity, and regulatory mechanisms of various RNA molecules, especially the vast repertoire of non-coding RNAs, advanced technologies and methods that globally map RRIs are extremely valuable. In the past decades, many state-of-the-art technologies have been developed for this purpose. This review focuses on those high-throughput technologies for the global mapping of RRIs. We summarize the key concepts and the pros and cons of different technologies. In addition, we highlight the novel biological insights uncovered by these RRI mapping methods and discuss the future challenges for appreciating the crucial roles of RRIs in gene regulation across bacteria, viruses, archaea, and mammals.
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Affiliation(s)
- Rong Ye
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Hailian Zhao
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xi Wang
- State Key Laboratory of Female Fertility Promotion, Clinical Stem Cell Research Center, Peking University Third Hospital, Beijing 100191, China
| | - Yuanchao Xue
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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3
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Si Y, Zou J, Gao Y, Chuai G, Liu Q, Chen L. Foundation models in molecular biology. BIOPHYSICS REPORTS 2024; 10:135-151. [PMID: 39027316 PMCID: PMC11252241 DOI: 10.52601/bpr.2024.240006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/04/2024] [Indexed: 07/20/2024] Open
Abstract
Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.
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Affiliation(s)
- Yunda Si
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Jiawei Zou
- Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Yicheng Gao
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Guohui Chuai
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Qi Liu
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Luonan Chen
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
- Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
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4
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Yang S, Kim SH, Yang E, Kang M, Joo JY. Molecular insights into regulatory RNAs in the cellular machinery. Exp Mol Med 2024; 56:1235-1249. [PMID: 38871819 PMCID: PMC11263585 DOI: 10.1038/s12276-024-01239-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning.
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Affiliation(s)
- Sumin Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea
| | - Sung-Hyun Kim
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea
| | - Eunjeong Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea
| | - Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV, 89154, USA
| | - Jae-Yeol Joo
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea.
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5
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Singh S, Shyamal S, Das A, Panda AC. Global identification of mRNA-interacting circular RNAs by CLiPPR-Seq. Nucleic Acids Res 2024; 52:e29. [PMID: 38324478 PMCID: PMC11014417 DOI: 10.1093/nar/gkae058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 01/04/2024] [Accepted: 01/18/2024] [Indexed: 02/09/2024] Open
Abstract
Although the functional role of circular RNA (circRNA) interaction with microRNAs and proteins has been studied extensively, circRNA interactions with the protein-coding mRNAs in intact cells remain largely unknown. Here, by employing AMT-mediated proximity ligation of RNA-RNA duplexes followed by circRNA enrichment and deep sequencing, we report a novel Cross-Linking Poly(A) Pulldown RNase R Sequencing (CLiPPR-seq) technology which identified hundreds of mRNA-interacting circRNAs in three different cell types, including βTC6, C2C12 and HeLa cells. Furthermore, CLiPP-seq without RNase R treatment was also performed to identify the mRNA expression in these cells. BLAST analysis of circRNAs in CLiPPR-seq sample with the mRNAs in CLiPP-seq samples determined their potential complementary sequences for circRNA-mRNA interaction. Pulldown of circRNAs and poly(A) RNAs confirmed the direct interaction of circRNAs with target mRNAs. Silencing of mRNA-interacting circRNAs led to the altered expression of target mRNAs in βTC6 cells, suggesting the role of direct interaction of circRNAs with mRNAs in gene expression regulation. CLiPPR-seq thus represents a novel method for illuminating the myriad of uncharacterized circRNA-mRNA hybrids that may regulate gene expression.
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Affiliation(s)
- Suman Singh
- Institute of Life Sciences, Nalco Square, Bhubaneswar, Odisha 751023, India
- Regional Center for Biotechnology, Faridabad, Haryana 121001, India
| | | | - Arundhati Das
- Institute of Life Sciences, Nalco Square, Bhubaneswar, Odisha 751023, India
| | - Amaresh C Panda
- Institute of Life Sciences, Nalco Square, Bhubaneswar, Odisha 751023, India
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6
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Verma SK, Kuyumcu-Martinez MN. RNA binding proteins in cardiovascular development and disease. Curr Top Dev Biol 2024; 156:51-119. [PMID: 38556427 DOI: 10.1016/bs.ctdb.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Congenital heart disease (CHD) is the most common birth defect affecting>1.35 million newborn babies worldwide. CHD can lead to prenatal, neonatal, postnatal lethality or life-long cardiac complications. RNA binding protein (RBP) mutations or variants are emerging as contributors to CHDs. RBPs are wizards of gene regulation and are major contributors to mRNA and protein landscape. However, not much is known about RBPs in the developing heart and their contributions to CHD. In this chapter, we will discuss our current knowledge about specific RBPs implicated in CHDs. We are in an exciting era to study RBPs using the currently available and highly successful RNA-based therapies and methodologies. Understanding how RBPs shape the developing heart will unveil their contributions to CHD. Identifying their target RNAs in the embryonic heart will ultimately lead to RNA-based treatments for congenital heart disease.
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Affiliation(s)
- Sunil K Verma
- Department of Molecular Physiology and Biological Physics, University of Virginia School of Medicine Charlottesville, VA, United States.
| | - Muge N Kuyumcu-Martinez
- Department of Molecular Physiology and Biological Physics, University of Virginia School of Medicine Charlottesville, VA, United States; Robert M. Berne Cardiovascular Research Center, University of Virginia School of Medicine, Charlottesville, VA, United States; University of Virginia Cancer Center, Charlottesville, VA, United States.
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7
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Wang S, Xu Y. RNA structure promotes liquid-to-solid phase transition of short RNAs in neuronal dysfunction. Commun Biol 2024; 7:137. [PMID: 38287096 PMCID: PMC10824717 DOI: 10.1038/s42003-024-05828-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/16/2024] [Indexed: 01/31/2024] Open
Abstract
In nucleotide expansion disorders, RNA foci are reportedly associated with neurodegenerative disease pathogeneses. Characteristically, these RNAs exhibit long poly-RNA repeats, such as 47 × CAG, 47 × CUG, or 29 × GGGGCC, usually becoming abnormal pathological aggregations above a critical number of nucleotide repeats. However, it remains unclear whether short, predominantly cellular RNA molecules can cause phase transitions to induce RNA foci. Herein, we demonstrated that short RNAs even with only two repeats can aggregate into a solid-like state via special RNA G-quadruplex structures. In human cells, these solid RNA foci could not dissolve even when using agents that disrupt RNA gelation. The aggregation of shorter RNAs can be clearly observed in vivo. Furthermore, we found that RNA foci induce colocalization of the RNA-binding protein Sam68, a protein commonly found in patients with fragile X-associated tremor/ataxia syndrome, suppressing cell clonogenicity and eventually causing cell death. Our results suggest that short RNA gelation promoted by specific RNA structures contribute to the neurological diseases, which disturb functional cellular processes.
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Affiliation(s)
- Shiyu Wang
- Division of Chemistry, Department of Medical Sciences, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan
| | - Yan Xu
- Division of Chemistry, Department of Medical Sciences, Faculty of Medicine, University of Miyazaki, 5200 Kihara, Kiyotake, Miyazaki, 889-1692, Japan.
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8
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Wu T, Cheng AY, Zhang Y, Xu J, Wu J, Wen L, Li X, Liu B, Dou X, Wang P, Zhang L, Fei J, Li J, Ouyang Z, He C. KARR-seq reveals cellular higher-order RNA structures and RNA-RNA interactions. Nat Biotechnol 2024:10.1038/s41587-023-02109-8. [PMID: 38238480 PMCID: PMC11255127 DOI: 10.1038/s41587-023-02109-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 12/15/2023] [Indexed: 02/12/2024]
Abstract
RNA fate and function are affected by their structures and interactomes. However, how RNA and RNA-binding proteins (RBPs) assemble into higher-order structures and how RNA molecules may interact with each other to facilitate functions remain largely unknown. Here we present KARR-seq, which uses N3-kethoxal labeling and multifunctional chemical crosslinkers to covalently trap and determine RNA-RNA interactions and higher-order RNA structures inside cells, independent of local protein binding to RNA. KARR-seq depicts higher-order RNA structure and detects widespread intermolecular RNA-RNA interactions with high sensitivity and accuracy. Using KARR-seq, we show that translation represses mRNA compaction under native and stress conditions. We determined the higher-order RNA structures of respiratory syncytial virus (RSV) and vesicular stomatitis virus (VSV) and identified RNA-RNA interactions between the viruses and the host RNAs that potentially regulate viral replication.
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Affiliation(s)
- Tong Wu
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Anthony Youzhi Cheng
- Department of Genetics and Genome Sciences and Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Yuexiu Zhang
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Jiayu Xu
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Jinjun Wu
- Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA
| | - Li Wen
- Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA
| | - Xiao Li
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Bei Liu
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Xiaoyang Dou
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Pingluan Wang
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Linda Zhang
- Department of Chemistry, University of Chicago, Chicago, IL, USA
- Howard Hughes Medical Institute, Chicago, IL, USA
| | - Jingyi Fei
- Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA
| | - Jianrong Li
- Department of Veterinary Biosciences, College of Veterinary Medicine, The Ohio State University, Columbus, OH, USA
| | - Zhengqing Ouyang
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA.
| | - Chuan He
- Department of Chemistry, University of Chicago, Chicago, IL, USA.
- Howard Hughes Medical Institute, Chicago, IL, USA.
- Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA.
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9
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Bose R, Saleem I, Mustoe AM. Causes, functions, and therapeutic possibilities of RNA secondary structure ensembles and alternative states. Cell Chem Biol 2024; 31:17-35. [PMID: 38199037 PMCID: PMC10842484 DOI: 10.1016/j.chembiol.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/21/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024]
Abstract
RNA secondary structure plays essential roles in encoding RNA regulatory fate and function. Most RNAs populate ensembles of alternatively paired states and are continually unfolded and refolded by cellular processes. Measuring these structural ensembles and their contributions to cellular function has traditionally posed major challenges, but new methods and conceptual frameworks are beginning to fill this void. In this review, we provide a mechanism- and function-centric compendium of the roles of RNA secondary structural ensembles and minority states in regulating the RNA life cycle, from transcription to degradation. We further explore how dysregulation of RNA structural ensembles contributes to human disease and discuss the potential of drugging alternative RNA states to therapeutically modulate RNA activity. The emerging paradigm of RNA structural ensembles as central to RNA function provides a foundation for a deeper understanding of RNA biology and new therapeutic possibilities.
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Affiliation(s)
- Ritwika Bose
- Therapeutic Innovation Center (THINC), Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX, USA
| | - Irfana Saleem
- Therapeutic Innovation Center (THINC), Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX, USA
| | - Anthony M Mustoe
- Therapeutic Innovation Center (THINC), Department of Biochemistry and Molecular Pharmacology, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
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10
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Srivastava M, Dukeshire MR, Mir Q, Omoru OB, Manzourolajdad A, Janga SC. Experimental and computational methods for studying the dynamics of RNA-RNA interactions in SARS-COV2 genomes. Brief Funct Genomics 2024; 23:46-54. [PMID: 36752040 PMCID: PMC10799312 DOI: 10.1093/bfgp/elac050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/24/2022] [Accepted: 11/11/2022] [Indexed: 02/09/2023] Open
Abstract
Long-range ribonucleic acid (RNA)-RNA interactions (RRI) are prevalent in positive-strand RNA viruses, including Beta-coronaviruses, and these take part in regulatory roles, including the regulation of sub-genomic RNA production rates. Crosslinking of interacting RNAs and short read-based deep sequencing of resulting RNA-RNA hybrids have shown that these long-range structures exist in severe acute respiratory syndrome coronavirus (SARS-CoV)-2 on both genomic and sub-genomic levels and in dynamic topologies. Furthermore, co-evolution of coronaviruses with their hosts is navigated by genetic variations made possible by its large genome, high recombination frequency and a high mutation rate. SARS-CoV-2's mutations are known to occur spontaneously during replication, and thousands of aggregate mutations have been reported since the emergence of the virus. Although many long-range RRIs have been experimentally identified using high-throughput methods for the wild-type SARS-CoV-2 strain, evolutionary trajectory of these RRIs across variants, impact of mutations on RRIs and interaction of SARS-CoV-2 RNAs with the host have been largely open questions in the field. In this review, we summarize recent computational tools and experimental methods that have been enabling the mapping of RRIs in viral genomes, with a specific focus on SARS-CoV-2. We also present available informatics resources to navigate the RRI maps and shed light on the impact of mutations on the RRI space in viral genomes. Investigating the evolution of long-range RNA interactions and that of virus-host interactions can contribute to the understanding of new and emerging variants as well as aid in developing improved RNA therapeutics critical for combating future outbreaks.
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Affiliation(s)
- Mansi Srivastava
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
- Department of Biology, Indiana University, 1001 East 3 St, Bloomington, Indiana 47405, USA
| | - Matthew R Dukeshire
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
| | - Quoseena Mir
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
| | - Okiemute Beatrice Omoru
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
| | - Amirhossein Manzourolajdad
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
- Department of Computer Science, Colgate University, Hamilton, NY, USA
| | - Sarath Chandra Janga
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, 975 West Walnut Street, Indianapolis, Indiana 46202, USA
- Centre for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), 410 West 10th Street, Indianapolis, Indiana 46202, USA
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11
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Das G, Das T, Parida S, Ghosh Z. LncRTPred: Predicting RNA-RNA mode of interaction mediated by lncRNA. IUBMB Life 2024; 76:53-68. [PMID: 37606159 DOI: 10.1002/iub.2778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 07/19/2023] [Indexed: 08/23/2023]
Abstract
Long non-coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA-mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real-time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML-based lncRNA-mRNA target prediction model- 'LncRTPred'. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non-interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease-specific lncRNA-mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA-mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred.
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Affiliation(s)
- Gourab Das
- Division of Bioinformatics, Bose Institute, Kolkata, India
| | - Troyee Das
- Division of Bioinformatics, Bose Institute, Kolkata, India
| | - Sibun Parida
- Division of Bioinformatics, Bose Institute, Kolkata, India
| | - Zhumur Ghosh
- Division of Bioinformatics, Bose Institute, Kolkata, India
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12
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Tieng FYF, Abdullah-Zawawi MR, Md Shahri NAA, Mohamed-Hussein ZA, Lee LH, Mutalib NSA. A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools. Brief Bioinform 2023; 25:bbad421. [PMID: 38040490 PMCID: PMC10753535 DOI: 10.1093/bib/bbad421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/16/2023] [Accepted: 10/26/2023] [Indexed: 12/03/2023] Open
Abstract
RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.
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Affiliation(s)
- Francis Yew Fu Tieng
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia
| | | | - Nur Alyaa Afifah Md Shahri
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of Systems Biology (INBIOSIS), UKM, Selangor 43600, Malaysia
- Department of Applied Physics, Faculty of Science and Technology, UKM, Selangor 43600, Malaysia
| | - Learn-Han Lee
- Sunway Microbiomics Centre, School of Medical and Life Sciences, Sunway University, Sunway City 47500, Malaysia
- Novel Bacteria and Drug Discovery Research Group, Microbiome and Bioresource Research Strength, Jeffrey Cheah School of Medicine and Health Sciences, Monash University of Malaysia, Selangor 47500, Malaysia
| | - Nurul-Syakima Ab Mutalib
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur 56000, Malaysia
- Novel Bacteria and Drug Discovery Research Group, Microbiome and Bioresource Research Strength, Jeffrey Cheah School of Medicine and Health Sciences, Monash University of Malaysia, Selangor 47500, Malaysia
- Faculty of Health Sciences, UKM, Kuala Lumpur 50300, Malaysia
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13
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Tao S, Hou Y, Diao L, Hu Y, Xu W, Xie S, Xiao Z. Long noncoding RNA study: Genome-wide approaches. Genes Dis 2023; 10:2491-2510. [PMID: 37554208 PMCID: PMC10404890 DOI: 10.1016/j.gendis.2022.10.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 10/09/2022] [Accepted: 10/23/2022] [Indexed: 11/30/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.
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Affiliation(s)
- Shuang Tao
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Yarui Hou
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Liting Diao
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Yanxia Hu
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Wanyi Xu
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Shujuan Xie
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
- Institute of Vaccine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Zhendong Xiao
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
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14
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Ripin N, Parker R. Formation, function, and pathology of RNP granules. Cell 2023; 186:4737-4756. [PMID: 37890457 PMCID: PMC10617657 DOI: 10.1016/j.cell.2023.09.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/28/2023] [Accepted: 09/07/2023] [Indexed: 10/29/2023]
Abstract
Ribonucleoprotein (RNP) granules are diverse membrane-less organelles that form through multivalent RNA-RNA, RNA-protein, and protein-protein interactions between RNPs. RNP granules are implicated in many aspects of RNA physiology, but in most cases their functions are poorly understood. RNP granules can be described through four key principles. First, RNP granules often arise because of the large size, high localized concentrations, and multivalent interactions of RNPs. Second, cells regulate RNP granule formation by multiple mechanisms including posttranslational modifications, protein chaperones, and RNA chaperones. Third, RNP granules impact cell physiology in multiple manners. Finally, dysregulation of RNP granules contributes to human diseases. Outstanding issues in the field remain, including determining the scale and molecular mechanisms of RNP granule function and how granule dysfunction contributes to human disease.
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Affiliation(s)
- Nina Ripin
- Department of Biochemistry and Howard Hughes Medical Institute, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Roy Parker
- Department of Biochemistry and Howard Hughes Medical Institute, University of Colorado Boulder, Boulder, CO 80303, USA.
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15
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Hu Y, Hao T, Yu H, Miao W, Zheng Y, Tao W, Zhuang J, Wang J, Fan Y, Jia S. lhCLIP reveals the in vivo RNA-RNA interactions recognized by hnRNPK. PLoS Genet 2023; 19:e1011006. [PMID: 37851698 PMCID: PMC10635571 DOI: 10.1371/journal.pgen.1011006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 11/09/2023] [Accepted: 10/05/2023] [Indexed: 10/20/2023] Open
Abstract
RNA-RNA interactions play a crucial role in regulating gene expression and various biological processes, but identifying these interactions on a transcriptomic scale remains a challenge. To address this, we have developed a new biochemical technique called pCp-biotin labelled RNA hybrid and ultraviolet crosslinking and immunoprecipitation (lhCLIP) that enables the transcriptome-wide identification of intra- and intermolecular RNA-RNA interactions mediated by a specific RNA-binding protein (RBP). Using lhCLIP, we have uncovered a diverse landscape of intermolecular RNA interactions recognized by hnRNPK in human cells, involving all major classes of noncoding RNAs (ncRNAs) and mRNA. Notably, hnRNPK selectively binds with snRNA U4, U11, and U12, and shapes the secondary structure of these snRNAs, which may impact RNA splicing. Our study demonstrates the potential of lhCLIP as a user-friendly and widely applicable method for discovering RNA-RNA interactions mediated by a particular protein of interest and provides a valuable tool for further investigating the role of RBPs in gene expression and biological processes.
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Affiliation(s)
- Yuanlang Hu
- Department of General Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, People’s Republic of China
- Ministry of Science and Education, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, People’s Republic of China
- College of basic medical sciences, Three Gorges University, Yichang, People’s Republic of China
| | - Tao Hao
- Department of General Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, People’s Republic of China
- The Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, Jinan University, Guangzhou, People’s Republic of China
| | - Hanwen Yu
- Key Laboratory for Stem Cells and Tissue Engineering (Sun Yat-sen University), Ministry of Education, Guangzhou, People’s Republic of China
| | - Wenbin Miao
- Ministry of Science and Education, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, People’s Republic of China
| | - Yi Zheng
- Ministry of Science and Education, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, People’s Republic of China
| | - Weihua Tao
- Department of General Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, People’s Republic of China
- The Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, Jinan University, Guangzhou, People’s Republic of China
| | - Jingshen Zhuang
- Department of General Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, People’s Republic of China
| | - Jichang Wang
- Key Laboratory for Stem Cells and Tissue Engineering (Sun Yat-sen University), Ministry of Education, Guangzhou, People’s Republic of China
| | - Yujuan Fan
- Ministry of Science and Education, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, People’s Republic of China
| | - Shiqi Jia
- Department of General Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, People’s Republic of China
- The Guangdong-Hong Kong-Macao Joint University Laboratory of Metabolic and Molecular Medicine, Jinan University, Guangzhou, People’s Republic of China
- Key Lab of Guangzhou Basic and Translational Research of Pan-vascular Diseases, Guangzhou, People’s Republic of China
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16
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Wen X, Zhong S. Alu transposable elements rewire enhancer-promoter network through RNA pairing. Mol Cell 2023; 83:3234-3235. [PMID: 37738962 PMCID: PMC11075628 DOI: 10.1016/j.molcel.2023.08.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/24/2023]
Abstract
A recent study by Liang et al.1 reveals that interacting enhancer RNAs (eRNAs) and promoter-transcribed upstream antisense RNAs (uaRNAs) can identify enhancer-promoter interactions. Complementary sequences within the interacting eRNAs and uaRNAs, predominantly Alu sequences, confer the specificity for eRNA-uaRNA pairing and hence enhancer-promoter recognition.
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Affiliation(s)
- Xingzhao Wen
- Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sheng Zhong
- Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA 92093, USA; Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.
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17
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Barbagallo C, Stella M, Ferrara C, Caponnetto A, Battaglia R, Barbagallo D, Di Pietro C, Ragusa M. RNA-RNA competitive interactions: a molecular civil war ruling cell physiology and diseases. EXPLORATION OF MEDICINE 2023:504-540. [DOI: 10.37349/emed.2023.00159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/02/2023] [Indexed: 09/02/2023] Open
Abstract
The idea that proteins are the main determining factors in the functioning of cells and organisms, and their dysfunctions are the first cause of pathologies, has been predominant in biology and biomedicine until recently. This protein-centered view was too simplistic and failed to explain the physiological and pathological complexity of the cell. About 80% of the human genome is dynamically and pervasively transcribed, mostly as non-protein-coding RNAs (ncRNAs), which competitively interact with each other and with coding RNAs generating a complex RNA network regulating RNA processing, stability, and translation and, accordingly, fine-tuning the gene expression of the cells. Qualitative and quantitative dysregulations of RNA-RNA interaction networks are strongly involved in the onset and progression of many pathologies, including cancers and degenerative diseases. This review will summarize the RNA species involved in the competitive endogenous RNA network, their mechanisms of action, and involvement in pathological phenotypes. Moreover, it will give an overview of the most advanced experimental and computational methods to dissect and rebuild RNA networks.
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Affiliation(s)
- Cristina Barbagallo
- Section of Biology and Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Michele Stella
- Section of Biology and Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | | | - Angela Caponnetto
- Section of Biology and Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Rosalia Battaglia
- Section of Biology and Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Davide Barbagallo
- Section of Biology and Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Cinzia Di Pietro
- Section of Biology and Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Marco Ragusa
- Section of Biology and Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
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18
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Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets. Methods Mol Biol 2023; 2586:163-173. [PMID: 36705904 DOI: 10.1007/978-1-0716-2768-6_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The computational prediction of RNA-RNA interactions has long been studied in RNA informatics. Most of the existing approaches focused on the interaction prediction of short RNAs in small datasets. However, in recent years, two fast prediction methods, RIsearch2 and RIblast, have been developed to predict transcriptome-scale interactions or long RNA interactions. The key idea of the software acceleration of these tools was the integration of a seed-and-extend method, which is used in fast sequence alignment tools, into RNA-RNA interaction prediction. As a result, the two software programs were ten to a thousand times faster than the existing tools; because of this acceleration, detection of genome-wide microRNA target sites or interaction partners of function-unknown long noncoding RNAs has become possible. In this review, we describe the basic concept of the algorithm, its applications, and the future perspectives of the fast RNA-RNA interaction prediction tools.
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19
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Web Services for RNA-RNA Interaction Prediction. Methods Mol Biol 2023; 2586:175-195. [PMID: 36705905 DOI: 10.1007/978-1-0716-2768-6_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.
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20
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Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets. Funct Integr Genomics 2023; 23:33. [PMID: 36625940 PMCID: PMC9838419 DOI: 10.1007/s10142-022-00947-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023]
Abstract
Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.
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21
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Kumar D, Sahoo SS, Chauss D, Kazemian M, Afzali B. Non-coding RNAs in immunoregulation and autoimmunity: Technological advances and critical limitations. J Autoimmun 2023; 134:102982. [PMID: 36592512 PMCID: PMC9908861 DOI: 10.1016/j.jaut.2022.102982] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/11/2022] [Accepted: 12/15/2022] [Indexed: 01/02/2023]
Abstract
Immune cell function is critically dependent on precise control over transcriptional output from the genome. In this respect, integration of environmental signals that regulate gene expression, specifically by transcription factors, enhancer DNA elements, genome topography and non-coding RNAs (ncRNAs), are key components. The first three have been extensively investigated. Even though non-coding RNAs represent the vast majority of cellular RNA species, this class of RNA remains historically understudied. This is partly because of a lag in technological and bioinformatic innovations specifically capable of identifying and accurately measuring their expression. Nevertheless, recent progress in this domain has enabled a profusion of publications identifying novel sub-types of ncRNAs and studies directly addressing the function of ncRNAs in human health and disease. Many ncRNAs, including circular and enhancer RNAs, have now been demonstrated to play key functions in the regulation of immune cells and to show associations with immune-mediated diseases. Some ncRNAs may function as biomarkers of disease, aiding in diagnostics and in estimating response to treatment, while others may play a direct role in the pathogenesis of disease. Importantly, some are relatively stable and are amenable to therapeutic targeting, for example through gene therapy. Here, we provide an overview of ncRNAs and review technological advances that enable their study and hold substantial promise for the future. We provide context-specific examples by examining the associations of ncRNAs with four prototypical human autoimmune diseases, specifically rheumatoid arthritis, psoriasis, inflammatory bowel disease and multiple sclerosis. We anticipate that the utility and mechanistic roles of these ncRNAs in autoimmunity will be further elucidated in the near future.
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Affiliation(s)
- Dhaneshwar Kumar
- Immunoregulation Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), NIH, Bethesda, MD, USA
| | - Subhransu Sekhar Sahoo
- Departments of Biochemistry and Computer Science, Purdue University, West Lafayette, IN, USA
| | - Daniel Chauss
- Immunoregulation Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), NIH, Bethesda, MD, USA
| | - Majid Kazemian
- Departments of Biochemistry and Computer Science, Purdue University, West Lafayette, IN, USA
| | - Behdad Afzali
- Immunoregulation Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), NIH, Bethesda, MD, USA.
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22
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Krohmaly KI, Freishtat RJ, Hahn AL. Bioinformatic and experimental methods to identify and validate bacterial RNA-human RNA interactions. J Investig Med 2023; 71:23-31. [PMID: 36162901 DOI: 10.1136/jim-2022-002509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/31/2022] [Indexed: 01/21/2023]
Abstract
Ample evidence supports the importance of the microbiota on human health and disease. Recent studies suggest that extracellular vesicles are an important means of bacterial-host communication, in part via the transport of small RNAs (sRNAs). Bacterial sRNAs have been shown to co-precipitate with human and mouse RNA-induced silencing complex, hinting that some may regulate gene expression as eukaryotic microRNAs do. Bioinformatic tools, including those that can incorporate an sRNA's secondary structure, can be used to predict interactions between bacterial sRNAs and human messenger RNAs (mRNAs). Validation of these potential interactions using reproducible experimental methods is essential to move the field forward. This review will cover the evidence of interspecies communication via sRNAs, bioinformatic tools currently available to identify potential bacterial sRNA-host (specifically, human) mRNA interactions, and experimental methods to identify and validate those interactions.
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Affiliation(s)
- Kylie I Krohmaly
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, District of Columbia, USA.,Institute for Biomedical Sciences, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Robert J Freishtat
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, District of Columbia, USA.,Division of Emergency Medicine, Children's National Hospital, Washington, District of Columbia, USA.,Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA
| | - Andrea L Hahn
- Center for Genetic Medicine Research, Children's National Research Institute, Washington, District of Columbia, USA.,Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, USA.,Division of Infectious Diseases, Children's National Hospital, Washington, District of Columbia, USA
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23
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Yang W, Lyu Y, Xiang R, Yang J. Long Noncoding RNAs in the Pathogenesis of Insulin Resistance. Int J Mol Sci 2022; 23:ijms232416054. [PMID: 36555704 PMCID: PMC9785789 DOI: 10.3390/ijms232416054] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/10/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Insulin resistance (IR), designated as the blunted response of insulin target tissues to physiological level of insulin, plays crucial roles in the development and progression of diabetes, nonalcoholic fatty liver disease (NAFLD) and other diseases. So far, the distinct mechanism(s) of IR still needs further exploration. Long non-coding RNA (lncRNA) is a class of non-protein coding RNA molecules with a length greater than 200 nucleotides. LncRNAs are widely involved in many biological processes including cell differentiation, proliferation, apoptosis and metabolism. More recently, there has been increasing evidence that lncRNAs participated in the pathogenesis of IR, and the dysregulated lncRNA profile played important roles in the pathogenesis of metabolic diseases including obesity, diabetes and NAFLD. For example, the lncRNAs MEG3, H19, MALAT1, GAS5, lncSHGL and several other lncRNAs have been shown to regulate insulin signaling and glucose/lipid metabolism in various tissues. In this review, we briefly introduced the general features of lncRNA and the methods for lncRNA research, and then summarized and discussed the recent advances on the roles and mechanisms of lncRNAs in IR, particularly focused on liver, skeletal muscle and adipose tissues.
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Affiliation(s)
- Weili Yang
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Yixiang Lyu
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
- Key Laboratory of Cardiovascular Science of the Ministry of Education, Center for Non-Coding RNA Medicine, Beijing 100191, China
| | - Rui Xiang
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
- Key Laboratory of Cardiovascular Science of the Ministry of Education, Center for Non-Coding RNA Medicine, Beijing 100191, China
| | - Jichun Yang
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
- Key Laboratory of Cardiovascular Science of the Ministry of Education, Center for Non-Coding RNA Medicine, Beijing 100191, China
- Correspondence:
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24
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Ding T, Zhang H. Novel biological insights revealed from the investigation of multiscale genome architecture. Comput Struct Biotechnol J 2022; 21:312-325. [PMID: 36582436 PMCID: PMC9791078 DOI: 10.1016/j.csbj.2022.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
Gene expression and cell fate determination require precise and coordinated epigenetic regulation. The complex three-dimensional (3D) genome organization plays a critical role in transcription in myriad biological processes. A wide range of architectural features of the 3D genome, including chromatin loops, topologically associated domains (TADs), chromatin compartments, and phase separation, together regulate the chromatin state and transcriptional activity at multiple levels. With the help of 3D genome informatics, recent biochemistry and imaging approaches based on different strategies have revealed functional interactions among biomacromolecules, even at the single-cell level. Here, we review the occurrence, mechanistic basis, and functional implications of dynamic genome organization, and outline recent experimental and computational approaches for profiling multiscale genome architecture to provide robust tools for studying the 3D genome.
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Affiliation(s)
| | - He Zhang
- Corresponding author at: School of Life Science and Technology, Tongji University, Shanghai 200092, PR China.
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25
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Zhang J, Fei Y, Sun L, Zhang QC. Advances and opportunities in RNA structure experimental determination and computational modeling. Nat Methods 2022; 19:1193-1207. [PMID: 36203019 DOI: 10.1038/s41592-022-01623-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 11/09/2022]
Abstract
Beyond transferring genetic information, RNAs are molecules with diverse functions that include catalyzing biochemical reactions and regulating gene expression. Most of these activities depend on RNAs' specific structures. Therefore, accurately determining RNA structure is integral to advancing our understanding of RNA functions. Here, we summarize the state-of-the-art experimental and computational technologies developed to evaluate RNA secondary and tertiary structures. We also highlight how the rapid increase of experimental data facilitates the integrative modeling approaches for better resolving RNA structures. Finally, we provide our thoughts on the latest advances and challenges in RNA structure determination methods, as well as on future directions for both experimental approaches and artificial intelligence-based computational tools to model RNA structure. Ultimately, we hope the technological advances will deepen our understanding of RNA biology and facilitate RNA structure-based biomedical research such as designing specific RNA structures for therapeutics and deploying RNA-targeting small-molecule drugs.
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Affiliation(s)
- Jinsong Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China.,Tsinghua-Peking Center for Life Sciences, Beijing, China
| | - Yuhan Fei
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China.,Tsinghua-Peking Center for Life Sciences, Beijing, China
| | - Lei Sun
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China. .,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China. .,Tsinghua-Peking Center for Life Sciences, Beijing, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China. .,Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing, China. .,Tsinghua-Peking Center for Life Sciences, Beijing, China.
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26
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Singh S, Shyamal S, Panda AC. Detecting RNA-RNA interactome. WILEY INTERDISCIPLINARY REVIEWS. RNA 2022; 13:e1715. [PMID: 35132791 DOI: 10.1002/wrna.1715] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/27/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
The last decade has seen a robust increase in various types of novel RNA molecules and their complexity in gene regulation. RNA molecules play a critical role in cellular events by interacting with other biomolecules, including protein, DNA, and RNA. It has been established that RNA-RNA interactions play a critical role in several biological processes by regulating the biogenesis and function of RNA molecules. Interestingly, RNA-RNA interactions regulate the biogenesis of diverse RNA molecules, including mRNAs, microRNAs, tRNAs, and circRNAs, through splicing or backsplicing. Structured RNAs like rRNA, tRNA, and snRNAs achieve their functional conformation by intramolecular RNA-RNA interactions. In addition, functional consequences of many intermolecular RNA-RNA interactions have been extensively studied in the regulation of gene expression. Hence, it is essential to understand the mechanism and functions of RNA-RNA interactions in eukaryotes. Conventionally, RNA-RNA interactions have been identified through diverse biochemical methods for decades. The advent of high-throughput RNA-sequencing technologies has revolutionized the identification of global RNA-RNA interactome in cells and their importance in RNA structure and function in gene expression regulation. Although these technologies revealed tens of thousands of intramolecular and intermolecular RNA-RNA interactions, we further look forward to future unbiased and quantitative high-throughput technologies for detecting transcriptome-wide RNA-RNA interactions. With the ability to detect RNA-RNA interactome, we expect that future studies will reveal the higher-order structures of RNA molecules and multi-RNA hybrids impacting human health and diseases. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.
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Affiliation(s)
- Suman Singh
- Institute of Life Sciences, Nalco Square, Bhubaneswar, India
- Regional Center for Biotechnology, Faridabad, India
| | | | - Amaresh C Panda
- Institute of Life Sciences, Nalco Square, Bhubaneswar, India
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27
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Xu B, Zhu Y, Cao C, Chen H, Jin Q, Li G, Ma J, Yang SL, Zhao J, Zhu J, Ding Y, Fang X, Jin Y, Kwok CK, Ren A, Wan Y, Wang Z, Xue Y, Zhang H, Zhang QC, Zhou Y. Recent advances in RNA structurome. SCIENCE CHINA. LIFE SCIENCES 2022; 65:1285-1324. [PMID: 35717434 PMCID: PMC9206424 DOI: 10.1007/s11427-021-2116-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/01/2022] [Indexed: 12/27/2022]
Abstract
RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.
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Affiliation(s)
- Bingbing Xu
- MOE Laboratory of Biosystems Homeostasis & Protection, Innovation Center for Cell Signaling Network, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yanda Zhu
- MOE Laboratory of Biosystems Homeostasis & Protection, Innovation Center for Cell Signaling Network, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Changchang Cao
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Hao Chen
- Life Sciences Institute, Zhejiang University, Hangzhou, 310058, China
| | - Qiongli Jin
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Guangnan Li
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, 430072, China
| | - Junfeng Ma
- Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Siwy Ling Yang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Jieyu Zhao
- Department of Chemistry, and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Jianghui Zhu
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China
- Tsinghua-Peking Center for Life Sciences, Beijing, 100084, China
| | - Yiliang Ding
- Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, United Kingdom.
| | - Xianyang Fang
- Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
| | - Yongfeng Jin
- MOE Laboratory of Biosystems Homeostasis & Protection, Innovation Center for Cell Signaling Network, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Chun Kit Kwok
- Department of Chemistry, and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China.
- Shenzhen Research Institute of City University of Hong Kong, Shenzhen, 518057, China.
| | - Aiming Ren
- Life Sciences Institute, Zhejiang University, Hangzhou, 310058, China.
| | - Yue Wan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore.
| | - Zhiye Wang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Yuanchao Xue
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100101, China.
| | - Huakun Zhang
- Key Laboratory of Molecular Epigenetics of the Ministry of Education, Northeast Normal University, Changchun, 130024, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
- Tsinghua-Peking Center for Life Sciences, Beijing, 100084, China.
| | - Yu Zhou
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
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28
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Dai X, Shen L. Advances and Trends in Omics Technology Development. Front Med (Lausanne) 2022; 9:911861. [PMID: 35860739 PMCID: PMC9289742 DOI: 10.3389/fmed.2022.911861] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/09/2022] [Indexed: 12/11/2022] Open
Abstract
The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.
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29
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Aviran S, Incarnato D. Computational approaches for RNA structure ensemble deconvolution from structure probing data. J Mol Biol 2022; 434:167635. [PMID: 35595163 DOI: 10.1016/j.jmb.2022.167635] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/29/2022] [Accepted: 05/05/2022] [Indexed: 12/15/2022]
Abstract
RNA structure probing experiments have emerged over the last decade as a straightforward way to determine the structure of RNA molecules in a number of different contexts. Although powerful, the ability of RNA to dynamically interconvert between, and to simultaneously populate, alternative structural configurations, poses a nontrivial challenge to the interpretation of data derived from these experiments. Recent efforts aimed at developing computational methods for the reconstruction of coexisting alternative RNA conformations from structure probing data are paving the way to the study of RNA structure ensembles, even in the context of living cells. In this review, we critically discuss these methods, their limitations and possible future improvements.
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Affiliation(s)
- Sharon Aviran
- Biomedical Engineering Department and Genome Center, University of California, Davis, CA, USA.
| | - Danny Incarnato
- Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, the Netherlands.
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30
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Zhang M, Hwang IT, Li K, Bai J, Chen JF, Weissman T, Zou JY, Lu Z. Classification and clustering of RNA crosslink-ligation data reveal complex structures and homodimers. Genome Res 2022; 32:968-985. [PMID: 35332099 PMCID: PMC9104705 DOI: 10.1101/gr.275979.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 01/11/2022] [Indexed: 12/04/2022]
Abstract
The recent development and application of methods based on the general principle of "crosslinking and proximity ligation" (crosslink-ligation) are revolutionizing RNA structure studies in living cells. However, extracting structure information from such data presents unique challenges. Here, we introduce a set of computational tools for the systematic analysis of data from a wide variety of crosslink-ligation methods, specifically focusing on read mapping, alignment classification, and clustering. We design a new strategy to map short reads with irregular gaps at high sensitivity and specificity. Analysis of previously published data reveals distinct properties and bias caused by the crosslinking reactions. We perform rigorous and exhaustive classification of alignments and discover eight types of arrangements that provide distinct information on RNA structures and interactions. To deconvolve the dense and intertwined gapped alignments, we develop a network/graph-based tool Crosslinked RNA Secondary Structure Analysis using Network Techniques (CRSSANT), which enables clustering of gapped alignments and discovery of new alternative and dynamic conformations. We discover that multiple crosslinking and ligation events can occur on the same RNA, generating multisegment alignments to report complex high-level RNA structures and multi-RNA interactions. We find that alignments with overlapped segments are produced from potential homodimers and develop a new method for their de novo identification. Analysis of overlapping alignments revealed potential new homodimers in cellular noncoding RNAs and RNA virus genomes in the Picornaviridae family. Together, this suite of computational tools enables rapid and efficient analysis of RNA structure and interaction data in living cells.
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Affiliation(s)
- Minjie Zhang
- Department of Pharmacology and Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089, USA
| | - Irena T Hwang
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
| | - Kongpan Li
- Department of Pharmacology and Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089, USA
| | - Jianhui Bai
- Department of Pharmacology and Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089, USA
| | - Jian-Fu Chen
- Center for Craniofacial Molecular Biology, University of Southern California (USC), Los Angeles, California 90033, USA
| | - Tsachy Weissman
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
| | - James Y Zou
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
- Department of Biomedical Data Science and Chan-Zuckerberg Biohub, Stanford University, Palo Alto, California 94305, USA
| | - Zhipeng Lu
- Department of Pharmacology and Pharmaceutical Sciences, University of Southern California, Los Angeles, California 90089, USA
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31
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Tay DJW, Lew ZZR, Chu JJH, Tan KS. Uncovering Novel Viral Innate Immune Evasion Strategies: What Has SARS-CoV-2 Taught Us? Front Microbiol 2022; 13:844447. [PMID: 35401477 PMCID: PMC8984613 DOI: 10.3389/fmicb.2022.844447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 02/17/2022] [Indexed: 11/13/2022] Open
Abstract
The ongoing SARS-CoV-2 pandemic has tested the capabilities of public health and scientific community. Since the dawn of the twenty-first century, viruses have caused several outbreaks, with coronaviruses being responsible for 2: SARS-CoV in 2007 and MERS-CoV in 2013. As the border between wildlife and the urban population continue to shrink, it is highly likely that zoonotic viruses may emerge more frequently. Furthermore, it has been shown repeatedly that these viruses are able to efficiently evade the innate immune system through various strategies. The strong and abundant antiviral innate immunity evasion strategies shown by SARS-CoV-2 has laid out shortcomings in our approach to quickly identify and modulate these mechanisms. It is thus imperative that there be a systematic framework for the study of the immune evasion strategies of these viruses, to guide development of therapeutics and curtail transmission. In this review, we first provide a brief overview of general viral evasion strategies against the innate immune system. Then, we utilize SARS-CoV-2 as a case study to highlight the methods used to identify the mechanisms of innate immune evasion, and pinpoint the shortcomings in the current paradigm with its focus on overexpression and protein-protein interactions. Finally, we provide a recommendation for future work to unravel viral innate immune evasion strategies and suitable methods to aid in the study of virus-host interactions. The insights provided from this review may then be applied to other viruses with outbreak potential to remain ahead in the arms race against viral diseases.
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Affiliation(s)
- Douglas Jie Wen Tay
- Biosafety Level 3 Core Facility, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Infectious Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhe Zhang Ryan Lew
- Infectious Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Otolaryngology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Justin Jang Hann Chu
- Biosafety Level 3 Core Facility, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Infectious Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Collaborative and Translation Unit for Hand, Foot and Mouth Disease (HFMD), Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
| | - Kai Sen Tan
- Biosafety Level 3 Core Facility, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Infectious Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Otolaryngology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- *Correspondence: Kai Sen Tan,
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32
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Cao H, Kapranov P. Methods to Analyze the Non-Coding RNA Interactome—Recent Advances and Challenges. Front Genet 2022; 13:857759. [PMID: 35368711 PMCID: PMC8969105 DOI: 10.3389/fgene.2022.857759] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 02/15/2022] [Indexed: 12/03/2022] Open
Abstract
Most of the human genome is transcribed to generate a multitude of non-coding RNAs. However, while these transcripts have generated an immense amount of scientific interest, their biological function remains a subject of an intense debate. Understanding mechanisms of action of non-coding RNAs is a key to addressing the issue of biological relevance of these transcripts. Based on some well-understood non-coding RNAs that function inside the cell by interacting with other molecules, it is generally believed many other non-coding transcripts could also function in a similar fashion. Therefore, development of methods that can map RNA interactome is the key to understanding functionality of the extensive cellular non-coding transcriptome. Here, we review the vast progress that has been made in the past decade in technologies that can map RNA interactions with different sites in DNA, proteins or other RNA molecules; the general approaches used to validate the existence of novel interactions; and the challenges posed by interpreting the data obtained using the interactome mapping methods.
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33
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Yang SY, Monchaud D, Wong JMY. Global mapping of RNA G-quadruplexes (G4-RNAs) using G4RP-seq. Nat Protoc 2022; 17:870-889. [PMID: 35140410 DOI: 10.1038/s41596-021-00671-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 11/25/2021] [Indexed: 11/09/2022]
Abstract
Guanine-rich RNAs can fold into four-stranded structures, termed G-quadruplexes (G4-RNAs), and participate in a wide range of biological processes. Here we describe in detail a G4-RNA-specific precipitation (G4RP) protocol, which enables the transcriptomic profiling of G4-RNAs. The G4RP protocol consists of a chemical cross-linking step, followed by affinity capture with a G4-specific probe, BioTASQ. G4RP can be coupled with sequencing to capture a comprehensive global snapshot of folded G4-RNAs. This method can also be used to profile induced changes (i.e., through G4 ligand treatments) within the G4-RNA transcriptome. The entire protocol can be completed in 1-2 weeks and can be scaled up or down depending on the specific experimental goals. In addition to the protocol details, we also provide here a guide for optimization in different laboratory setups.
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Affiliation(s)
- Sunny Y Yang
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - David Monchaud
- Institut de Chimie Moléculaire, ICMUB CNRS UMR 6302, UBFC, Dijon, France
| | - Judy M Y Wong
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
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34
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Wang D, Ye R, Cai Z, Xue Y. Emerging roles of RNA-RNA interactions in transcriptional regulation. WILEY INTERDISCIPLINARY REVIEWS. RNA 2022; 13:e1712. [PMID: 35042277 DOI: 10.1002/wrna.1712] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 12/07/2021] [Accepted: 12/16/2021] [Indexed: 12/26/2022]
Abstract
Pervasive transcription of the human genome generates a massive amount of noncoding RNAs (ncRNAs) that lack protein-coding potential but play crucial roles in development, differentiation, and tumorigenesis. To achieve these biological functions, ncRNAs must first fold into intricate structures via intramolecular RNA-RNA interactions (RRIs) and then interact with different RNA substrates via intermolecular RRIs. RRIs are usually facilitated, stabilized, or mediated by RNA-binding proteins. With this guiding principle, several protein-based high-throughput methods have been developed for unbiased mapping of defined or all RNA-binding protein-mediated RRIs in various species and cell lines. In addition, some chemical-based approaches are also powerful to detect RRIs globally based on the fact that RNA duplex can be cross-linked by psoralen or its derivative 4'-aminomethyltrioxsalen. These efforts have significantly expanded our understanding of RRIs in determining the specificity and variability of gene regulation. Here, we review the current knowledge of the regulatory roles of RRI, focusing on their emerging roles in transcriptional regulation and nuclear body formation. This article is categorized under: RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry.
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Affiliation(s)
- Di Wang
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Rong Ye
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhaokui Cai
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Yuanchao Xue
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
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35
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Wu WS, Brown JS, Chen PH, Shiue SC, Lee DE, Lee HC. CLASH Analyst: A Web Server to Identify In Vivo RNA–RNA Interactions from CLASH Data. Noncoding RNA 2022; 8:ncrna8010006. [PMID: 35076587 PMCID: PMC8788457 DOI: 10.3390/ncrna8010006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/04/2022] [Accepted: 01/07/2022] [Indexed: 12/24/2022] Open
Abstract
Non-coding RNAs, such as miRNAs and piRNAs, play critical roles in gene regulation through base-pairing interactions with their target molecules. The recent development of the crosslinking, ligation, and sequencing of hybrids (CLASH) method has allowed scientists to map transcriptome-wide RNA–RNA interactions by identifying chimeric reads consisting of fragments from regulatory RNAs and their targets. However, analyzing CLASH data requires scientists to use advanced bioinformatics, and currently available tools are limited for users with little bioinformatic experience. In addition, many published CLASH studies do not show the full scope of RNA–RNA interactions that were captured, highlighting the importance of reanalyzing published data. Here, we present CLASH Analyst, a web server that can analyze raw CLASH data within a fully customizable and easy-to-use interface. CLASH Analyst accepts raw CLASH data as input and identifies the RNA chimeras containing the regulatory and target RNAs according to the user’s interest. Detailed annotation of the captured RNA–RNA interactions is then presented for the user to visualize within the server or download for further analysis. We demonstrate that CLASH Analyst can identify miRNA- and piRNA-targeting sites reported from published CLASH data and should be applicable to analyze other RNA–RNA interactions. CLASH Analyst is freely available for academic use.
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Affiliation(s)
- Wei-Sheng Wu
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan; (W.-S.W.); (P.-H.C.); (S.-C.S.); (D.-E.L.)
| | - Jordan S. Brown
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637, USA;
| | - Pin-Hao Chen
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan; (W.-S.W.); (P.-H.C.); (S.-C.S.); (D.-E.L.)
| | - Sheng-Cian Shiue
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan; (W.-S.W.); (P.-H.C.); (S.-C.S.); (D.-E.L.)
| | - Dong-En Lee
- Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan; (W.-S.W.); (P.-H.C.); (S.-C.S.); (D.-E.L.)
| | - Heng-Chi Lee
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637, USA;
- Correspondence:
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Abstract
Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.
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37
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Zeng C, Takeda A, Sekine K, Osato N, Fukunaga T, Hamada M. Bioinformatics Approaches for Determining the Functional Impact of Repetitive Elements on Non-coding RNAs. Methods Mol Biol 2022; 2509:315-340. [PMID: 35796972 DOI: 10.1007/978-1-0716-2380-0_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With a large number of annotated non-coding RNAs (ncRNAs), repetitive sequences are found to constitute functional components (termed as repetitive elements) in ncRNAs that perform specific biological functions. Bioinformatics analysis is a powerful tool for improving our understanding of the role of repetitive elements in ncRNAs. This chapter summarizes recent findings that reveal the role of repetitive elements in ncRNAs. Furthermore, relevant bioinformatics approaches are systematically reviewed, which promises to provide valuable resources for studying the functional impact of repetitive elements on ncRNAs.
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Affiliation(s)
- Chao Zeng
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan.
- AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), Tokyo, Japan.
| | - Atsushi Takeda
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Kotaro Sekine
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Naoki Osato
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Tsukasa Fukunaga
- Waseda Institute for Advanced Study, Waseda University, Tokyo, Japan
| | - Michiaki Hamada
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan.
- AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), Tokyo, Japan.
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38
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Xue Y. Architecture of RNA-RNA interactions. Curr Opin Genet Dev 2021; 72:138-144. [PMID: 34954430 DOI: 10.1016/j.gde.2021.11.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/04/2021] [Accepted: 11/23/2021] [Indexed: 11/30/2022]
Abstract
RNA molecules tend to form intricate tertiary structures via intramolecular RNA-RNA interactions (RRIs) to regulate transcription, RNA processing, and translation processes. In these biological processes, RNAs, especially noncoding RNAs, usually achieve their regulatory specificity through intermolecular RNA-RNA base pairing and execute their regulatory outcomes via associated RNA-binding proteins. Decoding intramolecular and intermolecular RRIs is a prerequisite for understanding the architecture of various RNA molecules and their regulatory roles in development, differentiation, and disease. Many sequencing-based methods have recently been invented and have revealed extraordinarily complicated RRIs in mammalian cells. Here, we discuss the technical advances and limitations of various methodologies developed for studying cellular RRIs, with a focus on the emerging architectural roles of RRIs in gene regulation.
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Affiliation(s)
- Yuanchao Xue
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Identifying proximal RNA interactions from cDNA-encoded crosslinks with ShapeJumper. PLoS Comput Biol 2021; 17:e1009632. [PMID: 34905538 PMCID: PMC8670686 DOI: 10.1371/journal.pcbi.1009632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 11/11/2021] [Indexed: 01/07/2023] Open
Abstract
SHAPE-JuMP is a concise strategy for identifying close-in-space interactions in RNA molecules. Nucleotides in close three-dimensional proximity are crosslinked with a bi-reactive reagent that covalently links the 2'-hydroxyl groups of the ribose moieties. The identities of crosslinked nucleotides are determined using an engineered reverse transcriptase that jumps across crosslinked sites, resulting in a deletion in the cDNA that is detected using massively parallel sequencing. Here we introduce ShapeJumper, a bioinformatics pipeline to process SHAPE-JuMP sequencing data and to accurately identify through-space interactions, as observed in complex JuMP datasets. ShapeJumper identifies proximal interactions with near-nucleotide resolution using an alignment strategy that is optimized to tolerate the unique non-templated reverse-transcription profile of the engineered crosslink-traversing reverse-transcriptase. JuMP-inspired strategies are now poised to replace adapter-ligation for detecting RNA-RNA interactions in most crosslinking experiments.
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40
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Silveira GO, Coelho HS, Amaral MS, Verjovski-Almeida S. Long non-coding RNAs as possible therapeutic targets in protozoa, and in Schistosoma and other helminths. Parasitol Res 2021; 121:1091-1115. [PMID: 34859292 DOI: 10.1007/s00436-021-07384-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 11/14/2021] [Indexed: 12/26/2022]
Abstract
Long non-coding RNAs (lncRNAs) emerged in the past 20 years due to massive amounts of scientific data regarding transcriptomic analyses. They have been implicated in a plethora of cellular processes in higher eukaryotes. However, little is known about lncRNA possible involvement in parasitic diseases, with most studies only detecting their presence in parasites of human medical importance. Here, we review the progress on lncRNA studies and their functions in protozoans and helminths. In addition, we show an example of knockdown of one lncRNA in Schistosoma mansoni, SmLINC156349, which led to in vitro parasite adhesion, motility, and pairing impairment, with a 20% decrease in parasite viability and 33% reduction in female oviposition. Other observed phenotypes were a decrease in the proliferation rate of both male and female worms and their gonads, and reduced female lipid and vitelline droplets that are markers for well-developed vitellaria. Impairment of female worms' vitellaria in SmLINC156349-silenced worms led to egg development deficiency. All those results demonstrate the great potential of the tools and methods to characterize lncRNAs as potential new therapeutic targets. Further, we discuss the challenges and limitations of current methods for studying lncRNAs in parasites and possible solutions to overcome them, and we highlight the future directions of this exciting field.
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Affiliation(s)
- Gilbert O Silveira
- Laboratório de Parasitologia, Instituto Butantan, São Paulo, SP, 05503-900, Brazil.,Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, São Paulo, SP, 05508-900, Brazil
| | - Helena S Coelho
- Laboratório de Parasitologia, Instituto Butantan, São Paulo, SP, 05503-900, Brazil
| | - Murilo S Amaral
- Laboratório de Parasitologia, Instituto Butantan, São Paulo, SP, 05503-900, Brazil.
| | - Sergio Verjovski-Almeida
- Laboratório de Parasitologia, Instituto Butantan, São Paulo, SP, 05503-900, Brazil. .,Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, São Paulo, SP, 05508-900, Brazil.
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41
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Micheel J, Safrastyan A, Wollny D. Advances in Non-Coding RNA Sequencing. Noncoding RNA 2021; 7:70. [PMID: 34842804 PMCID: PMC8628893 DOI: 10.3390/ncrna7040070] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022] Open
Abstract
Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.
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Affiliation(s)
| | | | - Damian Wollny
- RNA Bioinformatics/High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University, 07743 Jena, Germany; (J.M.); (A.S.)
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42
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Johnson KL, Qi Z, Yan Z, Wen X, Nguyen TC, Zaleta-Rivera K, Chen CJ, Fan X, Sriram K, Wan X, Chen ZB, Zhong S. Revealing protein-protein interactions at the transcriptome scale by sequencing. Mol Cell 2021; 81:4091-4103.e9. [PMID: 34348091 PMCID: PMC8500946 DOI: 10.1016/j.molcel.2021.07.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/12/2021] [Accepted: 07/08/2021] [Indexed: 02/07/2023]
Abstract
We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.
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Affiliation(s)
- Kara L Johnson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhijie Qi
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhangming Yan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xingzhao Wen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tri C Nguyen
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kathia Zaleta-Rivera
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chien-Ju Chen
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xiaochen Fan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kiran Sriram
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Xueyi Wan
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhen Bouman Chen
- Department of Diabetes Complications and Metabolism, Beckman Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Sheng Zhong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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43
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Learning noncoding RNA biology from viruses. Mamm Genome 2021; 33:412-420. [PMID: 34491378 DOI: 10.1007/s00335-021-09915-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/02/2021] [Indexed: 10/20/2022]
Abstract
Insights into interactions between viral factors and the cellular machinery usually lead to discoveries concerning host cell biology. Thus, the gene expression field has historically relied on viral model systems to discover mechanisms underlying different cellular processes. In recent years, the functional characterization of the small nuclear noncoding RNAs expressed by the oncogenic Herpesvirus saimiri, called HSURs, resulted in the discovery of two mechanisms for the regulation of gene expression. HSUR1 and HSUR2 associate with host microRNAs, which are small noncoding RNAs that broadly regulate gene expression by binding to messenger RNAs. HSUR1 provided the first example of a process known as target-directed miRNA degradation that operates in cells to regulate miRNA populations. HSUR2 functions as a miRNA adaptor, uncovering an entirely new, indirect mechanism by which miRNAs can inhibit mRNA function. Here, I review the path that led to these discoveries and their implications and postulate new exciting questions about the functions of these fascinating viral noncoding RNAs.
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44
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Predicting RNA Scaffolds with a Hybrid Method of Vfold3D and VfoldLA. Methods Mol Biol 2021. [PMID: 34086269 DOI: 10.1007/978-1-0716-1499-0_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
The ever-increasing discoveries of noncoding RNA functions draw a strong demand for RNA structure determination from the sequence. In recently years, computational studies for RNA structures, at both the two-dimensional and the three-dimensional levels, led to several highly promising new developments. In this chapter, we describe a hybrid method, which combines the motif template-based Vfold3D model and the loop template-based VfoldLA model, to predict RNA 3D structures. The main emphasis is placed on the definition of motifs and loops, the treatment of no-template motifs, and the 3D structure assembly from templates of motifs and loops. For illustration, we use the ZIKV xrRNA1 as an example to show the template-based prediction of RNA 3D structures from the 2D structure. The web server for the hybrid model is freely accessible at http://rna.physics.missouri.edu/vfold3D2 .
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45
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Chowdhary A, Satagopam V, Schneider R. Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer. Front Genet 2021; 12:649619. [PMID: 34276764 PMCID: PMC8281131 DOI: 10.3389/fgene.2021.649619] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/20/2021] [Indexed: 01/09/2023] Open
Abstract
Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).
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Affiliation(s)
- Anshika Chowdhary
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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46
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Wang XW, Liu CX, Chen LL, Zhang QC. RNA structure probing uncovers RNA structure-dependent biological functions. Nat Chem Biol 2021; 17:755-766. [PMID: 34172967 DOI: 10.1038/s41589-021-00805-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 04/23/2021] [Indexed: 01/22/2023]
Abstract
RNA molecules fold into complex structures that enable their diverse functions in cells. Recent revolutionary innovations in transcriptome-wide RNA structural probing of living cells have ushered in a new era in understanding RNA functions. Here, we summarize the latest technological advances for probing RNA secondary structures and discuss striking discoveries that have linked RNA regulation and biological processes through interrogation of RNA structures. In particular, we highlight how different long noncoding RNAs form into distinct secondary structures that determine their modes of interactions with protein partners to realize their unique functions. These dynamic structures mediate RNA regulatory functions through altering interactions with proteins and other RNAs. We also outline current methodological hurdles and speculate about future directions for development of the next generation of RNA structure-probing technologies of higher sensitivity and resolution, which could then be applied in increasingly physiologically relevant studies.
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Affiliation(s)
- Xi-Wen Wang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.,Tsinghua-Peking Center for Life Sciences, Beijing, China
| | - Chu-Xiao Liu
- State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ling-Ling Chen
- State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,School of Life Science and Technology, ShanghaiTech University, Shanghai, China. .,School of Life Sciences, Hangzhou Institute for Advanced Study, University of the Chinese Academy of Sciences, Hangzhou, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China. .,Tsinghua-Peking Center for Life Sciences, Beijing, China.
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47
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Global in situ profiling of RNA-RNA spatial interactions with RIC-seq. Nat Protoc 2021; 16:2916-2946. [PMID: 34021296 DOI: 10.1038/s41596-021-00524-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 02/18/2021] [Indexed: 02/04/2023]
Abstract
Emerging evidence has demonstrated that RNA-RNA interactions are vital in controlling diverse biological processes, including transcription, RNA splicing and protein translation. RNA in situ conformation sequencing (RIC-seq) is a technique for capturing protein-mediated RNA-RNA proximal interactions globally in living cells at single-base resolution. Cells are first treated with formaldehyde to fix all the protein-mediated RNA-RNA interactions in situ. After cell permeabilization and micrococcal nuclease digestion, the proximally interacting RNAs are 3' end-labeled with pCp-biotin and subsequently ligated using T4 RNA ligase. The chimeric RNAs are then enriched and converted into libraries for paired-end sequencing. After deep sequencing, computational analysis yields interaction strength scores for every base on proximally interacting RNAs in the starting populations. The whole experimental procedure is designed to be completed within 6 d, followed by an additional 8 d for computational analysis. RIC-seq technology can unbiasedly detect intra- and intermolecular RNA-RNA interactions, thereby rendering it useful for reconstructing RNA higher-order structures and identifying direct noncoding RNA targets.
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48
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Decoding LncRNAs. Cancers (Basel) 2021; 13:cancers13112643. [PMID: 34072257 PMCID: PMC8199187 DOI: 10.3390/cancers13112643] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/23/2021] [Accepted: 05/25/2021] [Indexed: 02/07/2023] Open
Abstract
Non-coding RNAs (ncRNAs) have been considered as unimportant additions to the transcriptome. Yet, in light of numerous studies, it has become clear that ncRNAs play important roles in development, health and disease. Long-ignored, long non-coding RNAs (lncRNAs), ncRNAs made of more than 200 nucleotides have gained attention due to their involvement as drivers or suppressors of a myriad of tumours. The detailed understanding of some of their functions, structures and interactomes has been the result of interdisciplinary efforts, as in many cases, new methods need to be created or adapted to characterise these molecules. Unlike most reviews on lncRNAs, we summarize the achievements on lncRNA studies by taking into consideration the approaches for identification of lncRNA functions, interactomes, and structural arrangements. We also provide information about the recent data on the involvement of lncRNAs in diseases and present applications of these molecules, especially in medicine.
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49
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Nair RR, Zabezhinsky D, Gelin-Licht R, Haas BJ, Dyhr MC, Sperber HS, Nusbaum C, Gerst JE. Multiplexed mRNA assembly into ribonucleoprotein particles plays an operon-like role in the control of yeast cell physiology. eLife 2021; 10:66050. [PMID: 33942720 PMCID: PMC8137142 DOI: 10.7554/elife.66050] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/02/2021] [Indexed: 02/02/2023] Open
Abstract
Prokaryotes utilize polycistronic messages (operons) to co-translate proteins involved in the same biological processes. Whether eukaryotes achieve similar regulation by selectively assembling and translating monocistronic messages derived from different chromosomes is unknown. We employed transcript-specific RNA pulldowns and RNA-seq/RT-PCR to identify yeast mRNAs that co-precipitate as ribonucleoprotein (RNP) complexes. Consistent with the hypothesis of eukaryotic RNA operons, mRNAs encoding components of the mating pathway, heat shock proteins, and mitochondrial outer membrane proteins multiplex in trans, forming discrete messenger ribonucleoprotein (mRNP) complexes (called transperons). Chromatin capture and allele tagging experiments reveal that genes encoding multiplexed mRNAs physically interact; thus, RNA assembly may result from co-regulated gene expression. Transperon assembly and function depends upon histone H4, and its depletion leads to defects in RNA multiplexing, decreased pheromone responsiveness and mating, and increased heat shock sensitivity. We propose that intergenic associations and non-canonical histone H4 functions contribute to transperon formation in eukaryotic cells and regulate cell physiology.
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Affiliation(s)
- Rohini R Nair
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Dmitry Zabezhinsky
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Rita Gelin-Licht
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Brian J Haas
- Broad Institute of MIT and Harvard, Cambridge, United States
| | - Michael Ca Dyhr
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Hannah S Sperber
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | - Chad Nusbaum
- Broad Institute of MIT and Harvard, Cambridge, United States
| | - Jeffrey E Gerst
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
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50
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Bergalet J, Patel D, Legendre F, Lapointe C, Benoit Bouvrette LP, Chin A, Blanchette M, Kwon E, Lécuyer E. Inter-dependent Centrosomal Co-localization of the cen and ik2 cis-Natural Antisense mRNAs in Drosophila. Cell Rep 2021; 30:3339-3352.e6. [PMID: 32160541 DOI: 10.1016/j.celrep.2020.02.047] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 12/24/2019] [Accepted: 02/10/2020] [Indexed: 11/30/2022] Open
Abstract
Overlapping genes are prevalent in most genomes, but the extent to which this organization influences regulatory events operating at the post-transcriptional level remains unclear. Studying the cen and ik2 genes of Drosophila melanogaster, which are convergently transcribed as cis-natural antisense transcripts (cis-NATs) with overlapping 3' UTRs, we found that their encoded mRNAs strikingly co-localize to centrosomes. These transcripts physically interact in a 3' UTR-dependent manner, and the targeting of ik2 requires its 3' UTR sequence and the presence of cen mRNA, which serves as the main driver of centrosomal co-localization. The cen transcript undergoes localized translation in proximity to centrosomes, and its localization is perturbed by polysome-disrupting drugs. By interrogating global fractionation-sequencing datasets generated from Drosophila and human cellular models, we find that RNAs expressed as cis-NATs tend to co-localize to specific subcellular fractions. This work suggests that post-transcriptional interactions between RNAs with complementary sequences can dictate their localization fate in the cytoplasm.
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Affiliation(s)
- Julie Bergalet
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada
| | - Dhara Patel
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Département de Biochimie et Médecine Moléculaire and Programme de Biologie Moléculaire, Université de Montréal, Montréal, QC, Canada
| | - Félix Legendre
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Département de Biochimie et Médecine Moléculaire and Programme de Biologie Moléculaire, Université de Montréal, Montréal, QC, Canada
| | - Catherine Lapointe
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada
| | - Louis Philip Benoit Bouvrette
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Département de Biochimie et Médecine Moléculaire and Programme de Biologie Moléculaire, Université de Montréal, Montréal, QC, Canada
| | - Ashley Chin
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Division of Experimental Medicine, McGill University, Montréal, QC, Canada
| | | | - Eunjeong Kwon
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada
| | - Eric Lécuyer
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Département de Biochimie et Médecine Moléculaire and Programme de Biologie Moléculaire, Université de Montréal, Montréal, QC, Canada; Division of Experimental Medicine, McGill University, Montréal, QC, Canada.
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