1
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Bonilla SL, Jones AN, Incarnato D. Structural and biophysical dissection of RNA conformational ensembles. Curr Opin Struct Biol 2024; 88:102908. [PMID: 39146886 DOI: 10.1016/j.sbi.2024.102908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/23/2024] [Accepted: 07/23/2024] [Indexed: 08/17/2024]
Abstract
RNA's ability to form and interconvert between multiple secondary and tertiary structures is critical to its functional versatility and the traditional view of RNA structures as static entities has shifted towards understanding them as dynamic conformational ensembles. In this review we discuss RNA structural ensembles and their dynamics, highlighting the concept of conformational energy landscapes as a unifying framework for understanding RNA processes such as folding, misfolding, conformational changes, and complex formation. Ongoing advancements in cryo-electron microscopy and chemical probing techniques are significantly enhancing our ability to investigate multiple structures adopted by conformationally dynamic RNAs, while traditional methods such as nuclear magnetic resonance spectroscopy continue to play a crucial role in providing high-resolution, quantitative spatial and temporal information. We discuss how these methods, when used synergistically, can provide a comprehensive understanding of RNA conformational ensembles, offering new insights into their regulatory functions.
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Affiliation(s)
- Steve L Bonilla
- Laboratory of RNA Structural Biology and Biophysics, The Rockefeller University, 1230 York Ave, New York, NY 10065, USA.
| | - Alisha N Jones
- Department of Chemistry, New York University, 31 Washington Place, New York, NY 10003, USA.
| | - Danny Incarnato
- Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, the Netherlands.
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2
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Chauvier A, Walter NG. Beyond ligand binding: Single molecule observation reveals how riboswitches integrate multiple signals to balance bacterial gene regulation. Curr Opin Struct Biol 2024; 88:102893. [PMID: 39067113 DOI: 10.1016/j.sbi.2024.102893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 06/26/2024] [Accepted: 07/09/2024] [Indexed: 07/30/2024]
Abstract
Riboswitches are specialized RNA structures that orchestrate gene expression in response to sensing specific metabolite or ion ligands, mostly in bacteria. Upon ligand binding, these conformationally dynamic RNA motifs undergo structural changes that control critical gene expression processes such as transcription termination and translation initiation, thereby enabling cellular homeostasis and adaptation. Because RNA folds rapidly and co-transcriptionally, riboswitches make use of the low complexity of RNA sequences to adopt alternative, transient conformations on the heels of the transcribing RNA polymerase (RNAP), resulting in kinetic partitioning that defines the regulatory outcome. This review summarizes single molecule microscopy evidence that has begun to unveil a sophisticated network of dynamic, kinetically balanced interactions between riboswitch architecture and the gene expression machinery that, together, integrate diverse cellular signals.
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Affiliation(s)
- Adrien Chauvier
- Single Molecule Analysis Group and Center for RNA Biomedicine, Department of Chemistry, University of Michigan, Ann Arbor, MI, USA. https://twitter.com/adrienchauvier
| | - Nils G Walter
- Single Molecule Analysis Group and Center for RNA Biomedicine, Department of Chemistry, University of Michigan, Ann Arbor, MI, USA.
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3
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Hua C, Huang J, Sun Y, Wang T, Li Y, Cui Z, Deng X. Hfq mediates transcriptome-wide RNA structurome reprogramming under virulence-inducing conditions in a phytopathogen. Cell Rep 2024; 43:114544. [PMID: 39052478 DOI: 10.1016/j.celrep.2024.114544] [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: 01/12/2024] [Revised: 05/27/2024] [Accepted: 07/10/2024] [Indexed: 07/27/2024] Open
Abstract
Although RNA structures play important roles in regulating gene expression, the mechanism and function of mRNA folding in plant bacterial pathogens remain elusive. Therefore, we perform dimethyl sulfate sequencing (DMS-seq) on the Pseudomonas syringae under nutrition-rich and -deficient conditions, revealing that the mRNA structure changes substantially in the minimal medium (MM) that tunes global translation efficiency (TE), thereby inducing virulence. This process is led by the increased expression of hfq, which is directly activated by transcription regulators RpoS and CysB. The co-occurrence of Hfq and RpoS in diverse bacteria and the deep conservation of Hfq Y25 is critical for RNA-mediated regulation and implicates the wider biological importance of mRNA structure and feedback loops in the control of global gene expression.
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Affiliation(s)
- Canfeng Hua
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Jiadai Huang
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Yue Sun
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Tingting Wang
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Youyue Li
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Zining Cui
- National Key Laboratory of Green Pesticide, Integrative Microbiology Research Center, Guangdong Province Key Laboratory of Microbial Signals and Disease Control, College of Plant Protection, South China Agricultural University, Guangzhou, China
| | - Xin Deng
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guangdong, China; Tung Biomedical Sciences Center, City University of Hong Kong, Hong Kong, China.
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4
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Scholten NR, Haandrikman D, Tolhuis JO, Morandi E, Incarnato D. SHAPEwarp-web: sequence-agnostic search for structurally homologous RNA regions across databases of chemical probing data. Nucleic Acids Res 2024; 52:W362-W367. [PMID: 38709889 PMCID: PMC11223795 DOI: 10.1093/nar/gkae348] [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: 03/18/2024] [Revised: 04/08/2024] [Accepted: 04/19/2024] [Indexed: 05/08/2024] Open
Abstract
RNA molecules perform a variety of functions in cells, many of which rely on their secondary and tertiary structures. Chemical probing methods coupled with high-throughput sequencing have significantly accelerated the mapping of RNA structures, and increasingly large datasets of transcriptome-wide RNA chemical probing data are becoming available. Analogously to what has been done for decades in the protein world, this RNA structural information can be leveraged to aid the discovery of structural similarity to a known RNA (or RNA family), which, in turn, can inform about the function of transcripts. We have previously developed SHAPEwarp, a sequence-agnostic method for the search of structurally homologous RNA segments in a database of reactivity profiles derived from chemical probing experiments. In its original implementation, however, SHAPEwarp required substantial computational resources, even for moderately sized databases, as well as significant Linux command line know-how. To address these limitations, we introduce here SHAPEwarp-web, a user-friendly web interface to rapidly query large databases of RNA chemical probing data for structurally similar RNAs. Aside from featuring a completely rewritten core, which speeds up by orders of magnitude the search inside large databases, the web server hosts several high-quality chemical probing databases across multiple species. SHAPEwarp-web is available from https://shapewarp.incarnatolab.com.
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Affiliation(s)
- Niek R Scholten
- School for Life Science and Technology, Hanze University of Applied Sciences, 9747 AS Groningen, The Netherlands
| | - Dennis Haandrikman
- School for Life Science and Technology, Hanze University of Applied Sciences, 9747 AS Groningen, The Netherlands
| | - Joshua O Tolhuis
- School for Life Science and Technology, Hanze University of Applied Sciences, 9747 AS Groningen, The Netherlands
| | - Edoardo Morandi
- Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, 9747 AG Groningen, The Netherlands
| | - Danny Incarnato
- Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, 9747 AG Groningen, The Netherlands
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5
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Cao X, Zhang Y, Ding Y, Wan Y. Identification of RNA structures and their roles in RNA functions. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00748-6. [PMID: 38926530 DOI: 10.1038/s41580-024-00748-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/28/2024] [Indexed: 06/28/2024]
Abstract
The development of high-throughput RNA structure profiling methods in the past decade has greatly facilitated our ability to map and characterize different aspects of RNA structures transcriptome-wide in cell populations, single cells and single molecules. The resulting high-resolution data have provided insights into the static and dynamic nature of RNA structures, revealing their complexity as they perform their respective functions in the cell. In this Review, we discuss recent technical advances in the determination of RNA structures, and the roles of RNA structures in RNA biogenesis and functions, including in transcription, processing, translation, degradation, localization and RNA structure-dependent condensates. We also discuss the current understanding of how RNA structures could guide drug design for treating genetic diseases and battling pathogenic viruses, and highlight existing challenges and future directions in RNA structure research.
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Affiliation(s)
- Xinang Cao
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, Singapore, Singapore
| | - Yueying Zhang
- Department of Cell and Developmental Biology, John Innes Centre, Norwich, UK
| | - Yiliang Ding
- Department of Cell and Developmental Biology, John Innes Centre, Norwich, UK.
| | - Yue Wan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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6
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Rouse WB, Tompkins VS, O’Leary CA, Moss WN. The RNA secondary structure of androgen receptor-FL and V7 transcripts reveals novel regulatory regions. Nucleic Acids Res 2024; 52:6596-6613. [PMID: 38554103 PMCID: PMC11194067 DOI: 10.1093/nar/gkae220] [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: 11/21/2023] [Accepted: 03/18/2024] [Indexed: 04/01/2024] Open
Abstract
The androgen receptor (AR) is a ligand-dependent nuclear transcription factor belonging to the steroid hormone nuclear receptor family. Due to its roles in regulating cell proliferation and differentiation, AR is tightly regulated to maintain proper levels of itself and the many genes it controls. AR dysregulation is a driver of many human diseases including prostate cancer. Though this dysregulation often occurs at the RNA level, there are many unknowns surrounding post-transcriptional regulation of AR mRNA, particularly the role that RNA secondary structure plays. Thus, a comprehensive analysis of AR transcript secondary structure is needed. We address this through the computational and experimental analyses of two key isoforms, full length (AR-FL) and truncated (AR-V7). Here, a combination of in-cell RNA secondary structure probing experiments (targeted DMS-MaPseq) and computational predictions were used to characterize the static structural landscape and conformational dynamics of both isoforms. Additionally, in-cell assays were used to identify functionally relevant structures in the 5' and 3' UTRs of AR-FL. A notable example is a conserved stem loop structure in the 5'UTR of AR-FL that can bind to Poly(RC) Binding Protein 2 (PCBP2). Taken together, our results reveal novel features that regulate AR expression.
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Affiliation(s)
- Warren B Rouse
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Van S Tompkins
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Collin A O’Leary
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA
- Current Address: Departments of Biology and Chemistry, Cornell College, Mount Vernon, IA 52314, USA
| | - Walter N Moss
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA
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7
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He S, Huang R, Townley J, Kretsch RC, Karagianes TG, Cox DBT, Blair H, Penzar D, Vyaltsev V, Aristova E, Zinkevich A, Bakulin A, Sohn H, Krstevski D, Fukui T, Tatematsu F, Uchida Y, Jang D, Lee JS, Shieh R, Ma T, Martynov E, Shugaev MV, Bukhari HST, Fujikawa K, Onodera K, Henkel C, Ron S, Romano J, Nicol JJ, Nye GP, Wu Y, Choe C, Reade W, Das R. Ribonanza: deep learning of RNA structure through dual crowdsourcing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.24.581671. [PMID: 38464325 PMCID: PMC10925082 DOI: 10.1101/2024.02.24.581671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.
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Affiliation(s)
- Shujun He
- Department of Chemical Engineering, Texas A&M University, TX, USA
| | - Rui Huang
- Department of Biochemistry, Stanford CA, USA
| | | | | | | | - David B T Cox
- Department of Biochemistry, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
| | | | - Dmitry Penzar
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Valeriy Vyaltsev
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Elizaveta Aristova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Arsenii Zinkevich
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Artemy Bakulin
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Hoyeol Sohn
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Daniel Krstevski
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | | | | | | | - Donghoon Jang
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
| | | | - Roger Shieh
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Tom Ma
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Eduard Martynov
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
| | - Maxim V Shugaev
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
| | | | | | | | | | - Shlomo Ron
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Jonathan Romano
- Eterna Massive Open Laboratory
- Howard Hughes Medical Institute
| | | | - Grace P Nye
- Department of Biochemistry, Stanford CA, USA
| | - Yuan Wu
- Department of Biochemistry, Stanford CA, USA
- Howard Hughes Medical Institute
| | | | | | - Rhiju Das
- Department of Biochemistry, Stanford CA, USA
- Biophysics Program, Stanford CA, USA
- Howard Hughes Medical Institute
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8
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von Löhneysen S, Spicher T, Varenyk Y, Yao HT, Lorenz R, Hofacker I, Stadler PF. Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction. J Comput Biol 2024; 31:549-563. [PMID: 38935442 DOI: 10.1089/cmb.2024.0519] [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: 06/29/2024] Open
Abstract
Extrinsic, experimental information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies. Evolutionary conservation of RNA secondary structure elements is detectable in alignments of phylogenetically related sequences and provides evidence for the presence of certain base pairs that can also be converted into pseudo-energy contributions. We show that the centroid base pairs computed from a consensus folding model such as RNAalifold result in a substantial improvement of the prediction accuracy for single sequences. Evidence for specific base pairs turns out to be more informative than a position-wise profile for the conservation of the pairing status. A comparison with chemical probing data, furthermore, strongly suggests that phylogenetic base pairing data are more informative than position-specific data on (un)pairedness as obtained from chemical probing experiments. In this context we demonstrate, in addition, that the conversion of signal from probing data into pseudo-energies is possible using thermodynamic structure predictions as a reference instead of known RNA structures.
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Affiliation(s)
- Sarah von Löhneysen
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, Germany
| | - Thomas Spicher
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
- UniVie Doctoral School Computer Science (DoCS), University of Vienna, Vienna, Austria
| | - Yuliia Varenyk
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical, University of Vienna, Vienna, Austria
| | - Hua-Ting Yao
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Ronny Lorenz
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Ivo Hofacker
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, Germany
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia
- Santa Fe Institute, Santa Fe, New Mexico, USA
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9
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Bose E, Xiong S, Jones AN. Probing RNA structure and dynamics using nanopore and next generation sequencing. J Biol Chem 2024; 300:107317. [PMID: 38677514 PMCID: PMC11145556 DOI: 10.1016/j.jbc.2024.107317] [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] [Received: 07/03/2023] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024] Open
Abstract
It has become increasingly evident that the structures RNAs adopt are conformationally dynamic; the various structured states that RNAs sample govern their interactions with other nucleic acids, proteins, and ligands to regulate a myriad of biological processes. Although several biophysical approaches have been developed and used to study the dynamic landscape of structured RNAs, technical limitations have limited their application to all classes of RNA due to variable size and flexibility. Recent advances combining chemical probing experiments with next-generation- and direct sequencing have emerged as an alternative approach to exploring the conformational dynamics of RNA. In this review, we provide a methodological overview of the sequencing-based techniques used to study RNA conformational dynamics. We discuss how different techniques have enabled us to better understand the propensity of RNAs from a variety of different classes to sample multiple conformational states. Finally, we present examples of the ways these techniques have reshaped how we think about RNA structure.
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Affiliation(s)
- Emma Bose
- Department of Chemistry, New York University, New York, New York, USA
| | - Shengwei Xiong
- Department of Chemistry, New York University, New York, New York, USA
| | - Alisha N Jones
- Department of Chemistry, New York University, New York, New York, USA.
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10
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Kovachka S, Tong Y, Childs-Disney JL, Disney MD. Heterobifunctional small molecules to modulate RNA function. Trends Pharmacol Sci 2024; 45:449-463. [PMID: 38641489 DOI: 10.1016/j.tips.2024.03.006] [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] [Received: 03/02/2024] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/21/2024]
Abstract
RNA has diverse cellular functionality, including regulating gene expression, protein translation, and cellular response to stimuli, due to its intricate structures. Over the past decade, small molecules have been discovered that target functional structures within cellular RNAs and modulate their function. Simple binding, however, is often insufficient, resulting in low or even no biological activity. To overcome this challenge, heterobifunctional compounds have been developed that can covalently bind to the RNA target, alter RNA sequence, or induce its cleavage. Herein, we review the recent progress in the field of RNA-targeted heterobifunctional compounds using representative case studies. We identify critical gaps and limitations and propose a strategic pathway for future developments of RNA-targeted molecules with augmented functionalities.
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Affiliation(s)
- Sandra Kovachka
- Department of Chemistry, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology, 130 Scripps Way, Jupiter, FL 33458, USA
| | - Yuquan Tong
- Department of Chemistry, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology, 130 Scripps Way, Jupiter, FL 33458, USA; The Scripps Research Institute, 130 Scripps Way, Jupiter, FL 33458, USA
| | - Jessica L Childs-Disney
- Department of Chemistry, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology, 130 Scripps Way, Jupiter, FL 33458, USA
| | - Matthew D Disney
- Department of Chemistry, The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology, 130 Scripps Way, Jupiter, FL 33458, USA; The Scripps Research Institute, 130 Scripps Way, Jupiter, FL 33458, USA.
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11
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Zacco E, Broglia L, Kurihara M, Monti M, Gustincich S, Pastore A, Plath K, Nagakawa S, Cerase A, Sanchez de Groot N, Tartaglia GG. RNA: The Unsuspected Conductor in the Orchestra of Macromolecular Crowding. Chem Rev 2024; 124:4734-4777. [PMID: 38579177 PMCID: PMC11046439 DOI: 10.1021/acs.chemrev.3c00575] [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: 08/14/2023] [Revised: 01/12/2024] [Accepted: 01/18/2024] [Indexed: 04/07/2024]
Abstract
This comprehensive Review delves into the chemical principles governing RNA-mediated crowding events, commonly referred to as granules or biological condensates. We explore the pivotal role played by RNA sequence, structure, and chemical modifications in these processes, uncovering their correlation with crowding phenomena under physiological conditions. Additionally, we investigate instances where crowding deviates from its intended function, leading to pathological consequences. By deepening our understanding of the delicate balance that governs molecular crowding driven by RNA and its implications for cellular homeostasis, we aim to shed light on this intriguing area of research. Our exploration extends to the methodologies employed to decipher the composition and structural intricacies of RNA granules, offering a comprehensive overview of the techniques used to characterize them, including relevant computational approaches. Through two detailed examples highlighting the significance of noncoding RNAs, NEAT1 and XIST, in the formation of phase-separated assemblies and their influence on the cellular landscape, we emphasize their crucial role in cellular organization and function. By elucidating the chemical underpinnings of RNA-mediated molecular crowding, investigating the role of modifications, structures, and composition of RNA granules, and exploring both physiological and aberrant phase separation phenomena, this Review provides a multifaceted understanding of the intriguing world of RNA-mediated biological condensates.
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Affiliation(s)
- Elsa Zacco
- RNA
Systems Biology Lab, Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy
| | - Laura Broglia
- RNA
Systems Biology Lab, Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy
| | - Misuzu Kurihara
- RNA
Biology Laboratory, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo 060-0812, Japan
| | - Michele Monti
- RNA
Systems Biology Lab, Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy
| | - Stefano Gustincich
- Central
RNA Lab, Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy
| | - Annalisa Pastore
- UK
Dementia Research Institute at the Maurice Wohl Institute of King’s
College London, London SE5 9RT, U.K.
| | - Kathrin Plath
- Department
of Biological Chemistry, David Geffen School
of Medicine at the University of California Los Angeles, Los Angeles, California 90095, United States
| | - Shinichi Nagakawa
- RNA
Biology Laboratory, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo 060-0812, Japan
| | - Andrea Cerase
- Blizard
Institute,
Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 4NS, U.K.
- Unit
of Cell and developmental Biology, Department of Biology, Università di Pisa, 56123 Pisa, Italy
| | - Natalia Sanchez de Groot
- Unitat
de Bioquímica, Departament de Bioquímica i Biologia
Molecular, Universitat Autònoma de
Barcelona, 08193 Barcelona, Spain
| | - Gian Gaetano Tartaglia
- RNA
Systems Biology Lab, Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy
- Catalan
Institution for Research and Advanced Studies, ICREA, Passeig Lluís Companys 23, 08010 Barcelona, Spain
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12
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Sinzger-D'Angelo M, Hanst M, Reinhardt F, Koeppl H. Effects of mRNA conformational switching on translational noise in gene circuits. J Chem Phys 2024; 160:134108. [PMID: 38573847 DOI: 10.1063/5.0186927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 03/08/2024] [Indexed: 04/06/2024] Open
Abstract
Intragenic translational heterogeneity describes the variation in translation at the level of transcripts for an individual gene. A factor that contributes to this source of variation is the mRNA structure. Both the composition of the thermodynamic ensemble, i.e., the stationary distribution of mRNA structures, and the switching dynamics between those play a role. The effect of the switching dynamics on intragenic translational heterogeneity remains poorly understood. We present a stochastic translation model that accounts for mRNA structure switching and is derived from a Markov model via approximate stochastic filtering. We assess the approximation on various timescales and provide a method to quantify how mRNA structure dynamics contributes to translational heterogeneity. With our approach, we allow quantitative information on mRNA switching from biophysical experiments or coarse-grain molecular dynamics simulations of mRNA structures to be included in gene regulatory chemical reaction network models without an increase in the number of species. Thereby, our model bridges a gap between mRNA structure kinetics and gene expression models, which we hope will further improve our understanding of gene regulatory networks and facilitate genetic circuit design.
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Affiliation(s)
| | - Maleen Hanst
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Felix Reinhardt
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Heinz Koeppl
- Centre for Synthetic Biology, Technische Universität Darmstadt, Darmstadt, Germany
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13
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Irving PS, Weeks KM. RNAvigate: efficient exploration of RNA chemical probing datasets. Nucleic Acids Res 2024; 52:2231-2241. [PMID: 38348910 PMCID: PMC10954457 DOI: 10.1093/nar/gkae089] [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: 04/30/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 02/27/2024] Open
Abstract
Chemical probing technologies enable high-throughput examination of diverse structural features of RNA, including local nucleotide flexibility, RNA secondary structure, protein and ligand binding, through-space interaction networks, and multistate structural ensembles. Deep understanding of RNA structure-function relationships typically requires evaluating a system under structure- and function-altering conditions, linking these data with additional information, and visualizing multilayered relationships. Current platforms lack the broad accessibility, flexibility and efficiency needed to iterate on integrative analyses of these diverse, complex data. Here, we share the RNA visualization and graphical analysis toolset RNAvigate, a straightforward and flexible Python library that automatically parses 21 standard file formats (primary sequence annotations, per- and internucleotide data, and secondary and tertiary structures) and outputs 18 plot types. RNAvigate enables efficient exploration of nuanced relationships between multiple layers of RNA structure information and across multiple experimental conditions. Compatibility with Jupyter notebooks enables nonburdensome, reproducible, transparent and organized sharing of multistep analyses and data visualization strategies. RNAvigate simplifies and accelerates discovery and characterization of RNA-centric functions in biology.
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Affiliation(s)
- Patrick S Irving
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599-3290, USA
| | - Kevin M Weeks
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599-3290, USA
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14
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Wang H, Zhang Y, Du S. Integrated analysis of lactate-related genes identifies POLRMT as a novel marker promoting the proliferation, migration and energy metabolism of hepatocellular carcinoma via Wnt/β-Catenin signaling. Am J Cancer Res 2024; 14:1316-1337. [PMID: 38590398 PMCID: PMC10998737 DOI: 10.62347/zttg4319] [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: 10/14/2023] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is a prevalent and deadly form of cancer globally with typically unfavorable outcomes. Increasing research suggests that lactate serves as an important carbon contributor to cellular metabolism and holds a crucial part in the progression, sustenance, and treatment response of tumors. However, the contribution of lactate-related genes (LRGs) in HCC is still unclear. In this study, we analyzed TCGA datasets and screened 21 differentially expressed LRGs related to long-term survivals in HCC patients. Pan-cancer assays revealed that 21 LRGs expression exhibited a dysregulated level in man types of tumors and associated with clinical prognosis of tumor patients. The analysis of 21 LRGs successfully classified HCC samples into two molecular subtypes, and these two subtypes showed significant differences in clinical information, gene expression, and immune characteristics. Subsequently, based on the aforementioned 21 LRGs, a novel prognostic signature (DTYMK, IRAK1, POLRMT, MPV17, UQCRH, PDSS1, SLC16A3, SPP1 and LDHD) was generated by LASSO-Cox regression analysis. Survival assays demonstrated that the signature performed well in predicting the overall survival of patients with HCC. The results of Gene Set Variation Analysis indicated that the high GSVA scores were associated with poor prognosis. Moreover, we also investigated the correlation between GSVA scores and various signaling pathways in HCC. Among the nine prognostic genes, our attention focused on POLRMT which was highly expressed in HCC specimens based on TCGA datasets and several HCC cell lines. In addition, functional assays indicated that POLRMT distinctly promoted the proliferation, migration and energy metabolism of HCC cells via regulating Wnt/β-Catenin signaling. Overall, through the establishment of a novel prognostic signature, we have provided potential clinical value for assessing the prognosis of HCC patients. Furthermore, our study has identified the high expression of POLRMT in HCC and demonstrated its crucial role in HCC cell proliferation. These findings hold great importance in advancing our understanding of the pathophysiology of HCC, identifying new therapeutic targets, and improving patient survival rates.
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Affiliation(s)
- Huifen Wang
- Department of Gastroenterology, China-Japan Friendship Hospital Beijing 100029, P. R. China
| | - Yanli Zhang
- Department of Gastroenterology, China-Japan Friendship Hospital Beijing 100029, P. R. China
| | - Shiyu Du
- Department of Gastroenterology, China-Japan Friendship Hospital Beijing 100029, P. R. China
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15
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von Löhneysen S, Mörl M, Stadler PF. Limits of experimental evidence in RNA secondary structure prediction. FRONTIERS IN BIOINFORMATICS 2024; 4:1346779. [PMID: 38456157 PMCID: PMC10918467 DOI: 10.3389/fbinf.2024.1346779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/09/2024] [Indexed: 03/09/2024] Open
Affiliation(s)
- Sarah von Löhneysen
- Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
| | - Mario Mörl
- Institute for Biochemistry, Leipzig University, Leipzig, Germany
| | - Peter F. Stadler
- Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
- Competence Center for Scalable Data Analytics and Artificial Intelligence, School of Embedded and Compositive Artificial Intelligence (SECAI), Leipzig University, Leipzig, Germany
- Department of Theoretical Chemistry, University of Vienna, Wien, Austria
- Facultad de Ciencias, Universidad National de Colombia, Bogotá, Colombia
- Center for Non-Coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark
- Santa Fe Institute, Santa Fe, NM, United States
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16
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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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17
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Allouche D, De Bisschop G, Saaidi A, Hardouin P, du Moutier FXL, Ponty Y, Bruno S. RNA Secondary Structure Modeling Following the IPANEMAP Workflow. Methods Mol Biol 2024; 2726:85-104. [PMID: 38780728 DOI: 10.1007/978-1-0716-3519-3_4] [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: 05/25/2024]
Abstract
The structure of RNA molecules and their complexes are crucial for understanding biology at the molecular level. Resolving these structures holds the key to understanding their manifold structure-mediated functions ranging from regulating gene expression to catalyzing biochemical processes. Predicting RNA secondary structure is a prerequisite and a key step to accurately model their three dimensional structure. Although dedicated modelling software are making fast and significant progresses, predicting an accurate secondary structure from the sequence remains a challenge. Their performance can be significantly improved by the incorporation of experimental RNA structure probing data. Many different chemical and enzymatic probes have been developed; however, only one set of quantitative data can be incorporated as constraints for computer-assisted modelling. IPANEMAP is a recent workflow based on RNAfold that can take into account several quantitative or qualitative data sets to model RNA secondary structure. This chapter details the methods for popular chemical probing (DMS, CMCT, SHAPE-CE, and SHAPE-Map) and the subsequent analysis and structure prediction using IPANEMAP.
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Affiliation(s)
- Delphine Allouche
- CiTCOM, Cibles Thérapeutiques et conception de médicaments, UMR8038 CNRS, Université de PARIS, Paris, France
- Sanofi mRNA center of excellence 1541, Marcy-l'Etoile, France
| | - Grégoire De Bisschop
- CiTCOM, Cibles Thérapeutiques et conception de médicaments, UMR8038 CNRS, Université de PARIS, Paris, France
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada
| | - Afaf Saaidi
- Georgia Institute of Technology, School of Mathematics, Atlanta, GA, USA
| | - Pierre Hardouin
- CiTCOM, Cibles Thérapeutiques et conception de médicaments, UMR8038 CNRS, Université de PARIS, Paris, France
| | | | - Yann Ponty
- CNRS UMR 7161, LIX, Ecole Polytechnique, Palaiseau, France.
| | - Sargueil Bruno
- CiTCOM, Cibles Thérapeutiques et conception de médicaments, UMR8038 CNRS, Université de PARIS, Paris, France.
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18
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Rovira E, Moreno B, Razquin N, Blázquez L, Hernández-Alcoceba R, Fortes P, Pastor F. Engineering U1-Based Tetracycline-Inducible Riboswitches to Control Gene Expression in Mammals. ACS NANO 2023; 17:23331-23346. [PMID: 37971502 DOI: 10.1021/acsnano.3c01994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Synthetic riboswitches are promising regulatory devices due to their small size, lack of immunogenicity, and ability to fine-tune gene expression in the absence of exogenous trans-acting factors. Based on a gene inhibitory system developed at our lab, termed U1snRNP interference (U1i), we developed tetracycline (TC)-inducible riboswitches that modulate mRNA polyadenylation through selective U1 snRNP recruitment. First, we engineered different TC-U1i riboswitches, which repress gene expression unless TC is added, leading to inductions of gene expression of 3-to-4-fold. Second, we developed a technique called Systematic Evolution of Riboswitches by Exponential Enrichment (SEREX), to isolate riboswitches with enhanced U1 snRNP binding capacity and activity, achieving inducibilities of up to 8-fold. Interestingly, by multiplexing riboswitches we increased inductions up to 37-fold. Finally, we demonstrated that U1i-based riboswitches are dose-dependent and reversible and can regulate the expression of reporter and endogenous genes in culture cells and mouse models, resulting in attractive systems for gene therapy applications. Our work probes SEREX as a much-needed technology for the in vitro identification of riboswitches capable of regulating gene expression in vivo.
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Affiliation(s)
- Eric Rovira
- Department of Gene Therapy and Regulation of Gene Expression, Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona 31008, Spain
| | - Beatriz Moreno
- Department of Molecular Therapy, Aptamer Unit, Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona 31008, Spain
| | - Nerea Razquin
- Department of Gene Therapy and Regulation of Gene Expression, Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona 31008, Spain
| | - Lorea Blázquez
- Department of Neurosciences, Biodonostia Health Research Institute, 20014 San Sebastián, Spain
- CIBERNED, ISCIII (CIBER, Carlos III Institute, Spanish Ministry of Sciences and Innovation), 28031 Madrid, Spain
- Ikerbasque, Basque Foundation for Science, 48009 Bilbao, Spain
| | - Ruben Hernández-Alcoceba
- Department of Gene Therapy and Regulation of Gene Expression, Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona 31008, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona 31008, Spain
- Spanish Network for Advanced Therapies (TERAV ISCIII), Madrid 28029, Spain
| | - Puri Fortes
- Department of Gene Therapy and Regulation of Gene Expression, Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona 31008, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona 31008, Spain
- Spanish Network for Advanced Therapies (TERAV ISCIII), Madrid 28029, Spain
- Liver and Digestive Diseases Networking Biomedical Research Centre (CIBERehd), Madrid 28029, Spain
| | - Fernando Pastor
- Department of Molecular Therapy, Aptamer Unit, Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona 31008, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona 31008, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid 28029, Spain
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19
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Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. Proteins 2023; 91:1747-1770. [PMID: 37876231 PMCID: PMC10841292 DOI: 10.1002/prot.26602] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/21/2023] [Accepted: 09/07/2023] [Indexed: 10/26/2023]
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
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Affiliation(s)
- Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, CA USA
- Biophysics Program, Stanford University School of Medicine, CA USA
- Howard Hughes Medical Institute, Stanford University, CA USA
| | | | - Adam J. Simpkin
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Phillip Pham
- Department of Biochemistry, Stanford University School of Medicine, CA USA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of Medicine, CA USA
| | - Fan Bu
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230036, Anhui, China
| | - Ronan M. Keegan
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
- Life Science, Diamond Light Source, Harwell Science, UK
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Daniel J. Rigden
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, F-67084, Strasbourg, France
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20
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Irving PS, Weeks KM. RNAvigate: Efficient exploration of RNA chemical probing datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538311. [PMID: 37162917 PMCID: PMC10168276 DOI: 10.1101/2023.04.25.538311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Chemical probing technologies enable high-throughput examination of diverse structural features of RNA including local nucleotide flexibility, RNA secondary structure, protein- and ligand-binding, through-space interaction networks, and multi-state structural ensembles. Performing these experiments, by themselves, does not directly lead to biological insight. Instead, deep understanding of RNA structure-function relationships typically requires evaluating a system under structure- and function-altering conditions, linking these data with additional information, and visualizing multi-layered relationships. Current platforms lack the broad accessibility, flexibility, and efficiency needed to iterate on integrative analyses of these diverse, complex data. Here, we share the RNA visualization and graphical analysis toolset RNAvigate, a straightforward and flexible Python library. RNAvigate currently automatically parses twenty-one standard file formats (primary sequence annotations, per- and internucleotide data, and secondary and tertiary structures) and outputs eighteen plot types. These features enable efficient exploration of nuanced relationships between chemical probing data, RNA structure, and motif annotations across multiple experimental samples. Compatibility with Jupyter Notebooks enables non-burdensome, reproducible, transparent and organized sharing of multi-step analyses and data visualization strategies. RNAvigate simplifies examination of multi-layered RNA structure information and accelerates discovery and characterization of RNA-centric functions in biology.
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Affiliation(s)
- Patrick S. Irving
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599-3290
| | - Kevin M. Weeks
- Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599-3290
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21
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Szyjka CE, Strobel EJ. Observation of coordinated RNA folding events by systematic cotranscriptional RNA structure probing. Nat Commun 2023; 14:7839. [PMID: 38030633 PMCID: PMC10687018 DOI: 10.1038/s41467-023-43395-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 11/08/2023] [Indexed: 12/01/2023] Open
Abstract
RNA begins to fold as it is transcribed by an RNA polymerase. Consequently, RNA folding is constrained by the direction and rate of transcription. Understanding how RNA folds into secondary and tertiary structures therefore requires methods for determining the structure of cotranscriptional folding intermediates. Cotranscriptional RNA chemical probing methods accomplish this by systematically probing the structure of nascent RNA that is displayed from an RNA polymerase. Here, we describe a concise, high-resolution cotranscriptional RNA chemical probing procedure called variable length Transcription Elongation Complex RNA structure probing (TECprobe-VL). We demonstrate the accuracy and resolution of TECprobe-VL by replicating and extending previous analyses of ZTP and fluoride riboswitch folding and mapping the folding pathway of a ppGpp-sensing riboswitch. In each system, we show that TECprobe-VL identifies coordinated cotranscriptional folding events that mediate transcription antitermination. Our findings establish TECprobe-VL as an accessible method for mapping cotranscriptional RNA folding pathways.
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Affiliation(s)
- Courtney E Szyjka
- Department of Biological Sciences, The University at Buffalo, Buffalo, NY, 14260, USA
| | - Eric J Strobel
- Department of Biological Sciences, The University at Buffalo, Buffalo, NY, 14260, USA.
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22
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Feng HG, Wu CX, Zhong GC, Gong JP, Miao CM, Xiong B. Integrative analysis reveals that SLC38A1 promotes hepatocellular carcinoma development via PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism. J Cancer Res Clin Oncol 2023; 149:15879-15898. [PMID: 37673823 DOI: 10.1007/s00432-023-05360-3] [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] [Received: 07/31/2023] [Accepted: 08/27/2023] [Indexed: 09/08/2023]
Abstract
Although hepatocellular carcinoma (HCC) is rather frequent, little is known about the molecular pathways underlying its development, progression, and prognosis. In the current study, we comprehensively analyzed the deferentially expressed metabolism-related genes (MRGs) in HCC based on TCGA datasets attempting to discover the potentially prognostic genes in HCC. The up-regulated MRGs were further subjected to analyze their prognostic values and protein expressions. Twenty-seven genes were identified because their high expressions were significant in OS, PFS, DFS, DSS, and HCC tumor samples. They were then used for GO, KEGG, methylation, genetics changes, immune infiltration analyses. Moreover, we established a prognostic model in HCC using univariate assays and LASSO regression based on these MRGs. Additionally, we also found that SLC38A1, an amino acid metabolism closely related transporter, was a potential prognostic gene in HCC, and its function in HCC was further studied using experiments. We found that the knockdown of SLC38A1 notably suppressed the growth and migration of HCC cells. Further studies revealed that SLC38A1 modulated the development of HCC cells by regulating PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism. In conclusion, this study identified the potentially prognostic MRGs in HCC and uncovered that SLC38A1 regulated HCC development and progression by regulating PI3K/AKT/mTOR signaling via glutamine mediated energy metabolism, which might provide a novel marker and potential therapeutic target in HCC.
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Affiliation(s)
- Hua-Guo Feng
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China
| | - Chuan-Xin Wu
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China
| | - Guo-Chao Zhong
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China
| | - Jian-Ping Gong
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China
| | - Chun-Mu Miao
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China
| | - Bin Xiong
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, No. 74 Linjiang Road, Chongqing, China.
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23
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Wang Y, Zhang H, Xu Z, Zhang S, Guo R. TransUFold: Unlocking the structural complexity of short and long RNA with pseudoknots. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:19320-19340. [PMID: 38052602 DOI: 10.3934/mbe.2023854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The RNA secondary structure is like a blueprint that holds the key to unlocking the mysteries of RNA function and 3D structure. It serves as a crucial foundation for investigating the complex world of RNA, making it an indispensable component of research in this exciting field. However, pseudoknots cannot be accurately predicted by conventional prediction methods based on free energy minimization, which results in a performance bottleneck. To this end, we propose a deep learning-based method called TransUFold to train directly on RNA data annotated with structure information. It employs an encoder-decoder network architecture, named Vision Transformer, to extract long-range interactions in RNA sequences and utilizes convolutions with lateral connections to supplement short-range interactions. Then, a post-processing program is designed to constrain the model's output to produce realistic and effective RNA secondary structures, including pseudoknots. After training TransUFold on benchmark datasets, we outperform other methods in test data on the same family. Additionally, we achieve better results on longer sequences up to 1600 nt, demonstrating the outstanding performance of Vision Transformer in extracting long-range interactions in RNA sequences. Finally, our analysis indicates that TransUFold produces effective pseudoknot structures in long sequences. As more high-quality RNA structures become available, deep learning-based prediction methods like Vision Transformer can exhibit better performance.
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Affiliation(s)
- Yunxiang Wang
- School of Cyber Security and Computer, Hebei University, Baoding, Hebei, China
| | - Hong Zhang
- School of Cyber Security and Computer, Hebei University, Baoding, Hebei, China
| | - Zhenchao Xu
- School of Cyber Security and Computer, Hebei University, Baoding, Hebei, China
| | - Shouhua Zhang
- Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
| | - Rui Guo
- College of Life Sciences, Institute of Life Science and Green Development, Hebei University, Baoding, China
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24
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Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538330. [PMID: 37162955 PMCID: PMC10168427 DOI: 10.1101/2023.04.25.538330] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
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Affiliation(s)
- Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, CA USA
- Biophysics Program, Stanford University School of Medicine, CA USA
- Howard Hughes Medical Institute, Stanford University, CA USA
| | | | - Adam J. Simpkin
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV)
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Phillip Pham
- Department of Biochemistry, Stanford University School of Medicine, CA USA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of Medicine, CA USA
| | - Fan Bu
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China
- Division of Life Sciences and Medicine,University of Science and Technology of China, Hefei 230036, Anhui, China
| | - Ronan M. Keegan
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
- Life Science, Diamond Light Source, Harwell Science, UK
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV)
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Daniel J. Rigden
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, F-67084, Strasbourg, France
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25
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Huiskes FG, Creemers EE, Brundel BJJM. Dissecting the Molecular Mechanisms Driving Electropathology in Atrial Fibrillation: Deployment of RNA Sequencing and Transcriptomic Analyses. Cells 2023; 12:2242. [PMID: 37759465 PMCID: PMC10526291 DOI: 10.3390/cells12182242] [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] [Received: 07/15/2023] [Revised: 08/30/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
Despite many efforts to treat atrial fibrillation (AF), the most common progressive and age-related cardiac tachyarrhythmia in the Western world, the efficacy is still suboptimal. A plausible reason for this is that current treatments are not directed at underlying molecular root causes that drive electrical conduction disorders and AF (i.e., electropathology). Insights into AF-induced transcriptomic alterations may aid in a deeper understanding of electropathology. Specifically, RNA sequencing (RNA-seq) facilitates transcriptomic analyses and discovery of differences in gene expression profiles between patient groups. In the last decade, various RNA-seq studies have been conducted in atrial tissue samples of patients with AF versus controls in sinus rhythm. Identified differentially expressed molecular pathways so far include pathways related to mechanotransduction, ECM remodeling, ion channel signaling, and structural tissue organization through developmental and inflammatory signaling pathways. In this review, we provide an overview of the available human AF RNA-seq studies and highlight the molecular pathways identified. Additionally, a comparison is made between human RNA-seq findings with findings from experimental AF model systems and we discuss contrasting findings. Finally, we elaborate on new exciting RNA-seq approaches, including single-nucleotide variants, spatial transcriptomics and profiling of different populations of total RNA, small RNA and long non-coding RNA.
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Affiliation(s)
- Fabries G. Huiskes
- Department of Physiology, Amsterdam UMC, Location Vrije Universiteit, VUmc, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, 1081 HZ, Amsterdam, The Netherlands;
- Department of Experimental Cardiology, Amsterdam UMC, Location AMC, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, 1105 AZ Amsterdam, The Netherlands;
| | - Esther E. Creemers
- Department of Experimental Cardiology, Amsterdam UMC, Location AMC, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, 1105 AZ Amsterdam, The Netherlands;
| | - Bianca J. J. M. Brundel
- Department of Physiology, Amsterdam UMC, Location Vrije Universiteit, VUmc, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, 1081 HZ, Amsterdam, The Netherlands;
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26
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Yang E, Zhang H, Zang Z, Zhou Z, Wang S, Liu Z, Liu Y. GCNfold: A novel lightweight model with valid extractors for RNA secondary structure prediction. Comput Biol Med 2023; 164:107246. [PMID: 37487383 DOI: 10.1016/j.compbiomed.2023.107246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/26/2023]
Abstract
RNA secondary structure is essential for predicting the tertiary structure and understanding RNA function. Recent research tends to stack numerous modules to design large deep-learning models. This can increase the accuracy to more than 70%, as well as significant training costs and prediction efficiency. We proposed a model with three feature extractors called GCNfold. Structure Extractor utilizes a three-layer Graph Convolutional Network (GCN) to mine the structural information of RNA, such as stems, hairpin, and internal loops. Structure and Sequence Fusion embeds structural information into sequences with Transformer Encoders. Long-distance Dependency Extractor captures long-range pairwise relationships by UNet. The experiments indicate that GCNfold has a small number of parameters, a fast inference speed, and a high accuracy among all models with over 80% accuracy. Additionally, GCNfold-Small takes only 90ms to infer an RNA secondary structure and can achieve close to 90% accuracy on average. The GCNfold code is available on Github https://github.com/EnbinYang/GCNfold.
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Affiliation(s)
- Enbin Yang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Hao Zhang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; College of Software, Jilin University, Changchun, 130012, China
| | - Zinan Zang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Zhiyong Zhou
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Shuo Wang
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Zhen Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Graduate School of Engineering, Nagasaki Institute of Applied Science, 536 Aba-machi, Nagasaki 851-0193, Japan
| | - Yuanning Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; College of Software, Jilin University, Changchun, 130012, China.
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27
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Kolberg T, von Löhneysen S, Ozerova I, Wellner K, Hartmann R, Stadler P, Mörl M. Led-Seq: ligation-enhanced double-end sequence-based structure analysis of RNA. Nucleic Acids Res 2023; 51:e63. [PMID: 37114986 PMCID: PMC10287922 DOI: 10.1093/nar/gkad312] [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: 01/24/2023] [Revised: 03/21/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
Structural analysis of RNA is an important and versatile tool to investigate the function of this type of molecules in the cell as well as in vitro. Several robust and reliable procedures are available, relying on chemical modification inducing RT stops or nucleotide misincorporations during reverse transcription. Others are based on cleavage reactions and RT stop signals. However, these methods address only one side of the RT stop or misincorporation position. Here, we describe Led-Seq, a new approach based on lead-induced cleavage of unpaired RNA positions, where both resulting cleavage products are investigated. The RNA fragments carrying 2', 3'-cyclic phosphate or 5'-OH ends are selectively ligated to oligonucleotide adapters by specific RNA ligases. In a deep sequencing analysis, the cleavage sites are identified as ligation positions, avoiding possible false positive signals based on premature RT stops. With a benchmark set of transcripts in Escherichia coli, we show that Led-Seq is an improved and reliable approach based on metal ion-induced phosphodiester hydrolysis to investigate RNA structures in vivo.
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Affiliation(s)
- Tim Kolberg
- Institute for Biochemistry, Leipzig University, Brüderstr. 34, 04103 Leipzig, Germany
| | - Sarah von Löhneysen
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, Leipzig University, Härtelstr. 16–18, 04107 Leipzig, Germany
| | - Iuliia Ozerova
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, Leipzig University, Härtelstr. 16–18, 04107 Leipzig, Germany
| | - Karolin Wellner
- Institute for Biochemistry, Leipzig University, Brüderstr. 34, 04103 Leipzig, Germany
| | - Roland K Hartmann
- Institute for Pharmaceutical Chemistry, Philipps University Marburg, Marbacher Weg 6, 35037 Marburg, Germany
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, Leipzig University, Härtelstr. 16–18, 04107 Leipzig, Germany
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany
- Department of Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria
- Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia
- Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
| | - Mario Mörl
- Institute for Biochemistry, Leipzig University, Brüderstr. 34, 04103 Leipzig, Germany
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28
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Liu D, Xu C, Liu Y, Ouyang W, Lin S, Xu A, Zhang Y, Xie Y, Huang Q, Zhao W, Chen Z, Wang L, Chen S, Huang J, Wu ZB, Sun X. A systematic survey of LU domain-containing proteins reveals a novel human gene, LY6A, which encodes the candidate ortholog of mouse Ly-6A/Sca-1 and is aberrantly expressed in pituitary tumors. Front Med 2023; 17:458-475. [PMID: 36928550 DOI: 10.1007/s11684-022-0968-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/12/2022] [Indexed: 03/18/2023]
Abstract
The Ly-6 and uPAR (LU) domain-containing proteins represent a large family of cell-surface markers. In particular, mouse Ly-6A/Sca-1 is a widely used marker for various stem cells; however, its human ortholog is missing. In this study, based on a systematic survey and comparative genomic study of mouse and human LU domain-containing proteins, we identified a previously unannotated human gene encoding the candidate ortholog of mouse Ly-6A/Sca-1. This gene, hereby named LY6A, reversely overlaps with a lncRNA gene in the majority of exonic sequences. We found that LY6A is aberrantly expressed in pituitary tumors, but not in normal pituitary tissues, and may contribute to tumorigenesis. Similar to mouse Ly-6A/Sca-1, human LY6A is also upregulated by interferon, suggesting a conserved transcriptional regulatory mechanism between humans and mice. We cloned the full-length LY6A cDNA, whose encoded protein sequence, domain architecture, and exon-intron structures are all well conserved with mouse Ly-6A/Sca-1. Ectopic expression of the LY6A protein in cells demonstrates that it acts the same as mouse Ly-6A/Sca-1 in their processing and glycosylphosphatidylinositol anchoring to the cell membrane. Collectively, these studies unveil a novel human gene encoding a candidate biomarker and provide an interesting model gene for studying gene regulatory and evolutionary mechanisms.
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Affiliation(s)
- Dan Liu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chunhui Xu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yanting Liu
- Department of Neurosurgery, Center of Pituitary Tumor, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Wen Ouyang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shaojian Lin
- Department of Neurosurgery, Center of Pituitary Tumor, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Aining Xu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yuanliang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yinyin Xie
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qiuhua Huang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weili Zhao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhu Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lan Wang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Saijuan Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Jinyan Huang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Biomedical Big Data Center, First Affiliated Hospital, Zhejiang University School of Medicine, and Cancer Center, Zhejiang University, Hangzhou, 310000, China.
| | - Zhe Bao Wu
- Department of Neurosurgery, Center of Pituitary Tumor, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Department of Neurosurgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Xiaojian Sun
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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29
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Small I, Melonek J, Bohne AV, Nickelsen J, Schmitz-Linneweber C. Plant organellar RNA maturation. THE PLANT CELL 2023; 35:1727-1751. [PMID: 36807982 PMCID: PMC10226603 DOI: 10.1093/plcell/koad049] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/05/2023] [Accepted: 01/17/2023] [Indexed: 05/30/2023]
Abstract
Plant organellar RNA metabolism is run by a multitude of nucleus-encoded RNA-binding proteins (RBPs) that control RNA stability, processing, and degradation. In chloroplasts and mitochondria, these post-transcriptional processes are vital for the production of a small number of essential components of the photosynthetic and respiratory machinery-and consequently for organellar biogenesis and plant survival. Many organellar RBPs have been functionally assigned to individual steps in RNA maturation, often specific to selected transcripts. While the catalog of factors identified is ever-growing, our knowledge of how they achieve their functions mechanistically is far from complete. This review summarizes the current knowledge of plant organellar RNA metabolism taking an RBP-centric approach and focusing on mechanistic aspects of RBP functions and the kinetics of the processes they are involved in.
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Affiliation(s)
- Ian Small
- Australian Research Council Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Crawley 6009, Australia
| | - Joanna Melonek
- Australian Research Council Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Crawley 6009, Australia
| | | | - Jörg Nickelsen
- Department of Molecular Plant Sciences, LMU Munich, 82152 Martinsried, Germany
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30
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Bayraktar E, Bayraktar R, Oztatlici H, Lopez-Berestein G, Amero P, Rodriguez-Aguayo C. Targeting miRNAs and Other Non-Coding RNAs as a Therapeutic Approach: An Update. Noncoding RNA 2023; 9:ncrna9020027. [PMID: 37104009 PMCID: PMC10145226 DOI: 10.3390/ncrna9020027] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/29/2023] [Accepted: 04/07/2023] [Indexed: 04/28/2023] Open
Abstract
Since the discovery of the first microRNAs (miRNAs, miRs), the understanding of miRNA biology has expanded substantially. miRNAs are involved and described as master regulators of the major hallmarks of cancer, including cell differentiation, proliferation, survival, the cell cycle, invasion, and metastasis. Experimental data indicate that cancer phenotypes can be modified by targeting miRNA expression, and because miRNAs act as tumor suppressors or oncogenes (oncomiRs), they have emerged as attractive tools and, more importantly, as a new class of targets for drug development in cancer therapeutics. With the use of miRNA mimics or molecules targeting miRNAs (i.e., small-molecule inhibitors such as anti-miRS), these therapeutics have shown promise in preclinical settings. Some miRNA-targeted therapeutics have been extended to clinical development, such as the mimic of miRNA-34 for treating cancer. Here, we discuss insights into the role of miRNAs and other non-coding RNAs in tumorigenesis and resistance and summarize some recent successful systemic delivery approaches and recent developments in miRNAs as targets for anticancer drug development. Furthermore, we provide a comprehensive overview of mimics and inhibitors that are in clinical trials and finally a list of clinical trials based on miRNAs.
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Affiliation(s)
- Emine Bayraktar
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Recep Bayraktar
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hulya Oztatlici
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Histology and Embryology, Gaziantep University, Gaziantep 27310, Turkey
| | - Gabriel Lopez-Berestein
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Paola Amero
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Cristian Rodriguez-Aguayo
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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31
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Mustoe AM, Weidmann CA, Weeks KM. Single-Molecule Correlated Chemical Probing: A Revolution in RNA Structure Analysis. Acc Chem Res 2023; 56:763-775. [PMID: 36917683 PMCID: PMC10078950 DOI: 10.1021/acs.accounts.2c00782] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
RNA molecules convey biological information both in their linear sequence and in their base-paired secondary and tertiary structures. Chemical probing experiments, which involve treating an RNA with a reagent that modifies conformationally dynamic nucleotides, have broadly enabled examination of short- and long-range RNA structure in diverse contexts, including in living cells. For decades, chemical probing experiments have been interpreted in a per-nucleotide way, such that the reactivity measured at each nucleotide reports the average structure at a position over all RNA molecules within a sample. However, there are numerous important cases where per-nucleotide chemical probing falls short, including for RNAs that are bound by proteins, RNAs that form complex higher order structures, and RNAs that sample multiple conformations.Recent experimental and computational innovations have started a revolution in RNA structure analysis by transforming chemical probing into a massively parallel, single-molecule experiment. Enabled by a specialized reverse transcription strategy called mutational profiling (MaP), multiple chemical modification events can be measured within individual RNA molecules. Nucleotides that communicate structurally through direct base pairing or large-scale folding-unfolding transitions will react with chemical probes in a correlated manner, thereby revealing structural complexity hidden to conventional approaches. These single-molecule correlated chemical probing (smCCP) experiments can be interpreted to directly identify nucleotides that base pair (the PAIR-MaP strategy) and to reveal long-range, through-space structural communication (RING-MaP). Correlated probing can also define the thermodynamic populations of complex RNA ensembles (DANCE-MaP). Complex RNA-protein networks can be interrogated by cross-linking proteins to RNA and measuring correlations between cross-linked positions (RNP-MaP).smCCP thus visualizes RNA secondary and higher-order structure with unprecedented accuracy, defining novel structures, RNA-protein interaction networks, time-resolved dynamics, and allosteric structural switches. These strategies are not mutually exclusive; in favorable cases, multiple levels of RNA structure ─ base pairing, through-space structural communication, and equilibrium ensembles ─ can be resolved concurrently. The physical experimentation required for smCCP is profoundly simple, and experiments are readily performed in cells on RNAs of any size, including large noncoding RNAs and mRNAs. Single-molecule correlated chemical probing is paving the way for a new generation of biophysical studies on RNA in living systems.
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Affiliation(s)
- Anthony M. Mustoe
- Verna and Marrs McClean Department of Biochemistry and Molecular Biology, Department of Molecular and Human Genetics, and Therapeutic Innovation Center (THINC), One Baylor Plaza, Baylor College of Medicine, Houston, TX 77030
| | - Chase A. Weidmann
- Department of Biological Chemistry, Center for RNA Biomedicine, 1150 W. Medical Center Drive, University of Michigan Medical School, Ann Arbor, MI 48109
| | - Kevin M. Weeks
- Department of Chemistry, University of North Carolina, Chapel Hill NC 27599-3290
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32
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Banijamali E, Baronti L, Becker W, Sajkowska-Kozielewicz JJ, Huang T, Palka C, Kosek D, Sweetapple L, Müller J, Stone MD, Andersson ER, Petzold K. RNA:RNA interaction in ternary complexes resolved by chemical probing. RNA (NEW YORK, N.Y.) 2023; 29:317-329. [PMID: 36617673 PMCID: PMC9945442 DOI: 10.1261/rna.079190.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
RNA regulation can be performed by a second targeting RNA molecule, such as in the microRNA regulation mechanism. Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) probes the structure of RNA molecules and can resolve RNA:protein interactions, but RNA:RNA interactions have not yet been addressed with this technique. Here, we apply SHAPE to investigate RNA-mediated binding processes in RNA:RNA and RNA:RNA-RBP complexes. We use RNA:RNA binding by SHAPE (RABS) to investigate microRNA-34a (miR-34a) binding its mRNA target, the silent information regulator 1 (mSIRT1), both with and without the Argonaute protein, constituting the RNA-induced silencing complex (RISC). We show that the seed of the mRNA target must be bound to the microRNA loaded into RISC to enable further binding of the compensatory region by RISC, while the naked miR-34a is able to bind the compensatory region without seed interaction. The method presented here provides complementary structural evidence for the commonly performed luciferase-assay-based evaluation of microRNA binding-site efficiency and specificity on the mRNA target site and could therefore be used in conjunction with it. The method can be applied to any nucleic acid-mediated RNA- or RBP-binding process, such as splicing, antisense RNA binding, or regulation by RISC, providing important insight into the targeted RNA structure.
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Affiliation(s)
- Elnaz Banijamali
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Lorenzo Baronti
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Walter Becker
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Stockholm, Sweden
| | | | - Ting Huang
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Christina Palka
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, California 95064, USA
| | - David Kosek
- Department of Cell and Molecular Biology, Karolinska Institute, 17177 Stockholm, Sweden
| | - Lara Sweetapple
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Juliane Müller
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Stockholm, Sweden
| | - Michael D Stone
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, California 95064, USA
| | - Emma R Andersson
- Department of Cell and Molecular Biology, Karolinska Institute, 17177 Stockholm, Sweden
| | - Katja Petzold
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, 17177 Stockholm, Sweden
- Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch 7600, South Africa
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33
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Szyjka CE, Strobel EJ. Observation of coordinated cotranscriptional RNA folding events. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.21.529405. [PMID: 36865203 PMCID: PMC9980086 DOI: 10.1101/2023.02.21.529405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
RNA begins to fold as it is transcribed by an RNA polymerase. Consequently, RNA folding is constrained by the direction and rate of transcription. Understanding how RNA folds into secondary and tertiary structures therefore requires methods for determining the structure of cotranscriptional folding intermediates. Cotranscriptional RNA chemical probing methods accomplish this by systematically probing the structure of nascent RNA that is displayed from RNA polymerase. Here, we have developed a concise, high-resolution cotranscriptional RNA chemical probing procedure called Transcription Elongation Complex RNA structure probing-Multilength (TECprobe-ML). We validated TECprobe-ML by replicating and extending previous analyses of ZTP and fluoride riboswitch folding, and mapped the folding pathway of a ppGpp-sensing riboswitch. In each system, TECprobe-ML identified coordinated cotranscriptional folding events that mediate transcription antitermination. Our findings establish TECprobe-ML as an accessible method for mapping cotranscriptional RNA folding pathways.
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Affiliation(s)
- Courtney E. Szyjka
- Department of Biological Sciences, The University at Buffalo, Buffalo, NY 14260, USA
| | - Eric J. Strobel
- Department of Biological Sciences, The University at Buffalo, Buffalo, NY 14260, USA
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34
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Probing Transient Riboswitch Structures via Single Molecule Accessibility Analysis. Methods Mol Biol 2023; 2568:37-51. [PMID: 36227561 PMCID: PMC10078578 DOI: 10.1007/978-1-0716-2687-0_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Riboswitches are a class of RNA motifs in the untranslated regions of bacterial messenger RNAs (mRNAs) that can adopt different conformations to regulate gene expression. The binding of specific small molecule or ion ligands, or other RNAs, influences the conformation the riboswitch adopts. Single Molecule Kinetic Analysis of RNA Transient Structure (SiM-KARTS) offers an approach for probing this structural isomerization, or conformational switching, at the level of single mRNA molecules. SiM-KARTS utilizes fluorescently labeled, short, sequence-complementary DNA or RNA oligonucleotide probes that transiently access a specific RNA conformation over another. Binding and dissociation to a surface-immobilized target RNA of arbitrary length are monitored by Total Internal Reflection Fluorescence Microscopy (TIRFM) and quantitatively analyzed, via spike train and burst detection, to elucidate the rate constants of isomerization, revealing mechanistic insights into riboswitching.
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35
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Myatt DP, Wharram L, Graham C, Liddell J, Branton H, Pizzey C, Cowieson N, Rambo R, Shattock RJ. Biophysical characterization of the structure of a SARS-CoV-2 self-amplifying RNA (saRNA) vaccine. Biol Methods Protoc 2023; 8:bpad001. [PMID: 36915370 PMCID: PMC10008065 DOI: 10.1093/biomethods/bpad001] [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: 10/07/2022] [Revised: 01/13/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023] Open
Abstract
The current SARS-Covid-2 (SARS-CoV-2) pandemic has led to an acceleration of messenger ribonucleic acid (mRNA) vaccine technology. The development of production processes for these large mRNA molecules, especially self-amplifying mRNA (saRNA), has required concomitant development of analytical characterization techniques. Characterizing the purity, shape and structure of these biomolecules is key to their successful performance as drug products. This article describes the biophysical characterization of the Imperial College London Self-amplifying viral RNA vaccine (IMP-1) developed for SARS-CoV-2. A variety of analytical techniques have been used to characterize the IMP-1 RNA molecule. In this article, we use ultraviolet spectroscopy, dynamic light scattering, size-exclusion chromatography small-angle X-ray scattering and circular dichroism to determine key biophysical attributes of IMP-1. Each technique provides important information about the concentration, size, shape, structure and purity of the molecule.
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Affiliation(s)
- Daniel P Myatt
- The National Biologics Manufacturing Centre (NBMC), The Centre for Process Innovation, Darlington DL1 1GL, UK
| | - Lewis Wharram
- The National Biologics Manufacturing Centre (NBMC), The Centre for Process Innovation, Darlington DL1 1GL, UK
| | - Charlotte Graham
- The National Biologics Manufacturing Centre (NBMC), The Centre for Process Innovation, Darlington DL1 1GL, UK
| | - John Liddell
- The National Biologics Manufacturing Centre (NBMC), The Centre for Process Innovation, Darlington DL1 1GL, UK
| | - Harvey Branton
- The National Biologics Manufacturing Centre (NBMC), The Centre for Process Innovation, Darlington DL1 1GL, UK
| | - Claire Pizzey
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire, UK
| | - Nathan Cowieson
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire, UK
| | - Robert Rambo
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire, UK
| | - Robin J Shattock
- Department of Infectious Disease, Imperial College London, London W21PG, UK
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36
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Spitale RC, Incarnato D. Probing the dynamic RNA structurome and its functions. Nat Rev Genet 2023; 24:178-196. [PMID: 36348050 PMCID: PMC9644009 DOI: 10.1038/s41576-022-00546-w] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/06/2022] [Indexed: 11/09/2022]
Abstract
RNA is a key regulator of almost every cellular process, and the structures adopted by RNA molecules are thought to be central to their functions. The recent fast-paced evolution of high-throughput sequencing-based RNA structure mapping methods has enabled the rapid in vivo structural interrogation of entire cellular transcriptomes. Collectively, these studies are shedding new light on the long underestimated complexity of the structural organization of the transcriptome - the RNA structurome. Moreover, recent analyses are challenging the view that the RNA structurome is a static entity by revealing how RNA molecules establish intricate networks of alternative intramolecular and intermolecular interactions and that these ensembles of RNA structures are dynamically regulated to finely tune RNA functions in living cells. This new understanding of how RNA can shape cell phenotypes has important implications for the development of RNA-targeted therapeutic strategies.
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Affiliation(s)
- Robert C. Spitale
- grid.266093.80000 0001 0668 7243Department of Pharmaceutical Sciences, University of California, Irvine, 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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37
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Klotz K, Radwan Y, Chakrabarti K. Dissecting Functional Biological Interactions Using Modular RNA Nanoparticles. Molecules 2022; 28:molecules28010228. [PMID: 36615420 PMCID: PMC9821959 DOI: 10.3390/molecules28010228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/22/2022] [Accepted: 12/25/2022] [Indexed: 12/29/2022] Open
Abstract
Nucleic acid nanoparticles (NANPs) are an exciting and innovative technology in the context of both basic and biomedical research. Made of DNA, RNA, or their chemical analogs, NANPs are programmed for carrying out specific functions within human cells. NANPs are at the forefront of preventing, detecting, and treating disease. Their nucleic acid composition lends them biocompatibility that provides their cargo with enhanced opportunity for coordinated delivery. Of course, the NANP system of targeting specific cells and tissues is not without its disadvantages. Accumulation of NANPs outside of the target tissue and the potential for off-target effects of NANP-mediated cargo delivery present challenges to research and medical professionals and these challenges must be effectively addressed to provide safe treatment to patients. Importantly, development of NANPs with regulated biological activities and immunorecognition becomes a promising route for developing versatile nucleic acid therapeutics. In a basic research context, NANPs can assist investigators in fine-tuning the structure-function relationship of final formulations and in this review, we explore the practical applications of NANPs in laboratory and clinical settings and discuss how we can use established nucleic acid research techniques to design effective NANPs.
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Affiliation(s)
- Kaitlin Klotz
- Department of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA
| | - Yasmine Radwan
- Nanoscale Science Program, Department of Chemistry, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Kausik Chakrabarti
- Department of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA
- Correspondence:
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38
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O’Leary CA, Tompkins VS, Rouse WB, Nam G, Moss W. Thermodynamic and structural characterization of an EBV infected B-cell lymphoma transcriptome. NAR Genom Bioinform 2022; 4:lqac082. [PMID: 36285286 PMCID: PMC9585548 DOI: 10.1093/nargab/lqac082] [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: 09/13/2022] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 11/12/2022] Open
Abstract
Epstein-Barr virus (EBV) is a widely prevalent human herpes virus infecting over 95% of all adults and is associated with a variety of B-cell cancers and induction of multiple sclerosis. EBV accomplishes this in part by expression of coding and noncoding RNAs and alteration of the host cell transcriptome. To better understand the structures which are forming in the viral and host transcriptomes of infected cells, the RNA structure probing technique Structure-seq2 was applied to the BJAB-B1 cell line (an EBV infected B-cell lymphoma). This resulted in reactivity profiles and secondary structural analyses for over 10000 human mRNAs and lncRNAs, along with 19 lytic and latent EBV transcripts. We report in-depth structural analyses for the human MYC mRNA and the human lncRNA CYTOR. Additionally, we provide a new model for the EBV noncoding RNA EBER2 and provide the first reported model for the EBV tandem terminal repeat RNA. In-depth thermodynamic and structural analyses were carried out with the motif discovery tool ScanFold and RNAfold prediction tool; subsequent covariation analyses were performed on resulting models finding various levels of support. ScanFold results for all analyzed transcripts are made available for viewing and download on the user-friendly RNAStructuromeDB.
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Affiliation(s)
- Collin A O’Leary
- Roy J. Carver Department of Biophysics, Biochemistry and Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Van S Tompkins
- Roy J. Carver Department of Biophysics, Biochemistry and Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Warren B Rouse
- Roy J. Carver Department of Biophysics, Biochemistry and Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Gijong Nam
- Roy J. Carver Department of Biophysics, Biochemistry and Molecular Biology, Iowa State University, Ames, IA 50011, USA
| | - Walter N Moss
- Roy J. Carver Department of Biophysics, Biochemistry and Molecular Biology, Iowa State University, Ames, IA 50011, USA
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39
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Yu B, Li P, Zhang QC, Hou L. Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure. Nat Commun 2022; 13:4227. [PMID: 35869080 PMCID: PMC9307511 DOI: 10.1038/s41467-022-31875-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 07/05/2022] [Indexed: 11/09/2022] Open
Abstract
RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome and a subsequent motif enrichment analysis suggest potential links of RNA structural variation and mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome. The authors present DiffScan, an advanced tool for normalization and differential analysis of RNA structure probing experiments, combining their power in deciphering the dynamic RNA structurome and facilitating the discovery of RNA regulatory functions.
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40
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Andrews RJ, Rouse WB, O’Leary CA, Booher NJ, Moss WN. ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes. PeerJ 2022; 10:e14361. [PMID: 36389431 PMCID: PMC9651051 DOI: 10.7717/peerj.14361] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
A major limiting factor in target discovery for both basic research and therapeutic intervention is the identification of structural and/or functional RNA elements in genomes and transcriptomes. This was the impetus for the original ScanFold algorithm, which provides maps of local RNA structural stability, evidence of sequence-ordered (potentially evolved) structure, and unique model structures comprised of recurring base pairs with the greatest structural bias. A key step in quantifying this propensity for ordered structure is the prediction of secondary structural stability for randomized sequences which, in the original implementation of ScanFold, is explicitly evaluated. This slow process has limited the rapid identification of ordered structures in large genomes/transcriptomes, which we seek to overcome in this current work introducing ScanFold 2.0. In this revised version of ScanFold, we no longer explicitly evaluate randomized sequence folding energy, but rather estimate it using a machine learning approach. For high randomization numbers, this can increase prediction speeds over 100-fold compared to ScanFold 1.0, allowing for the analysis of large sequences, as well as the use of additional folding algorithms that may be computationally expensive. In the testing of ScanFold 2.0, we re-evaluate the Zika, HIV, and SARS-CoV-2 genomes and compare both the consistency of results and the time of each run to ScanFold 1.0. We also re-evaluate the SARS-CoV-2 genome to assess the quality of ScanFold 2.0 predictions vs several biochemical structure probing datasets and compare the results to those of the original ScanFold program.
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Affiliation(s)
- Ryan J. Andrews
- Department of Biochemistry, University of Utah, Salt Lake City, UT, United States
| | - Warren B. Rouse
- The Roy J Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, United States
| | - Collin A. O’Leary
- The Roy J Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, United States
| | - Nicholas J. Booher
- Infrastructure and Research IT Services, Iowa State University, Ames, IA, United States
| | - Walter N. Moss
- The Roy J Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, United States
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41
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Bizuayehu TT, Labun K, Jakubec M, Jefimov K, Niazi A, Valen E. Long-read single-molecule RNA structure sequencing using nanopore. Nucleic Acids Res 2022; 50:e120. [PMID: 36166000 PMCID: PMC9723614 DOI: 10.1093/nar/gkac775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/16/2022] [Accepted: 08/29/2022] [Indexed: 12/24/2022] Open
Abstract
RNA molecules can form secondary and tertiary structures that can regulate their localization and function. Using enzymatic or chemical probing together with high-throughput sequencing, secondary structure can be mapped across the entire transcriptome. However, a limiting factor is that only population averages can be obtained since each read is an independent measurement. Although long-read sequencing has recently been used to determine RNA structure, these methods still used aggregate signals across the strands to detect structure. Averaging across the population also means that only limited information about structural heterogeneity across molecules or dependencies within each molecule can be obtained. Here, we present Single-Molecule Structure sequencing (SMS-seq) that combines structural probing with native RNA sequencing to provide non-amplified, structural profiles of individual molecules with novel analysis methods. Our new approach using mutual information enabled single molecule structural interrogation. Each RNA is probed at numerous bases enabling the discovery of dependencies and heterogeneity of structural features. We also show that SMS-seq can capture tertiary interactions, dynamics of riboswitch ligand binding, and mRNA structural features.
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Affiliation(s)
- Teshome Tilahun Bizuayehu
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway
- Sars International Center for Marine Molecular Biology, University of Bergen, Norway
| | - Kornel Labun
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway
| | | | - Kirill Jefimov
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway
| | - Adnan Muhammad Niazi
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway
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42
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Yang SL, Ponti RD, Wan Y, Huber RG. Computational and Experimental Approaches to Study the RNA Secondary Structures of RNA Viruses. Viruses 2022; 14:v14081795. [PMID: 36016417 PMCID: PMC9415818 DOI: 10.3390/v14081795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/12/2022] [Accepted: 08/13/2022] [Indexed: 11/16/2022] Open
Abstract
Most pandemics of recent decades can be traced to RNA viruses, including HIV, SARS, influenza, dengue, Zika, and SARS-CoV-2. These RNA viruses impose considerable social and economic burdens on our society, resulting in a high number of deaths and high treatment costs. As these RNA viruses utilize an RNA genome, which is important for different stages of the viral life cycle, including replication, translation, and packaging, studying how the genome folds is important to understand virus function. In this review, we summarize recent advances in computational and high-throughput RNA structure-mapping approaches and their use in understanding structures within RNA virus genomes. In particular, we focus on the genome structures of the dengue, Zika, and SARS-CoV-2 viruses due to recent significant outbreaks of these viruses around the world.
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Affiliation(s)
- Siwy Ling Yang
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore 138672, Singapore
| | - Riccardo Delli Ponti
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore
| | - Yue Wan
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore 138672, Singapore
- Correspondence: (Y.W.); (R.G.H.)
| | - Roland G. Huber
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore
- Correspondence: (Y.W.); (R.G.H.)
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43
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Tong Y, Gibaut QMR, Rouse W, Childs-Disney JL, Suresh BM, Abegg D, Choudhary S, Akahori Y, Adibekian A, Moss WN, Disney MD. Transcriptome-Wide Mapping of Small-Molecule RNA-Binding Sites in Cells Informs an Isoform-Specific Degrader of QSOX1 mRNA. J Am Chem Soc 2022; 144:11620-11625. [PMID: 35737519 PMCID: PMC9594100 DOI: 10.1021/jacs.2c01929] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The interactions between cellular RNAs in MDA-MB-231 triple negative breast cancer cells and a panel of small molecules appended with a diazirine cross-linking moiety and an alkyne tag were probed transcriptome-wide in live cells. The alkyne tag allows for facile pull-down of cellular RNAs bound by each small molecule, and the enrichment of each RNA target defines the compound's molecular footprint. Among the 34 chemically diverse small molecules studied, six bound and enriched cellular RNAs. The most highly enriched interaction occurs between the novel RNA-binding compound F1 and a structured region in the 5' untranslated region of quiescin sulfhydryl oxidase 1 isoform a (QSOX1-a), not present in isoform b. Additional studies show that F1 specifically bound RNA over DNA and protein; that is, we studied the entire DNA, RNA, and protein interactome. This interaction was used to design a ribonuclease targeting chimera (RIBOTAC) to locally recruit Ribonuclease L to degrade QSOX1 mRNA in an isoform-specific manner, as QSOX1-a, but not QSOX1-b, mRNA and protein levels were reduced. The RIBOTAC alleviated QSOX1-mediated phenotypes in cancer cells. This approach can be broadly applied to discover ligands that bind RNA in cells, which could be bioactive themselves or augmented with functionality such as targeted degradation.
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Affiliation(s)
- Yuquan Tong
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Quentin M R Gibaut
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Warren Rouse
- Roy J. Carver Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
| | - Jessica L Childs-Disney
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Blessy M Suresh
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Daniel Abegg
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Shruti Choudhary
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Yoshihiro Akahori
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Alexander Adibekian
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
| | - Walter N Moss
- Roy J. Carver Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, Iowa 50011, United States
| | - Matthew D Disney
- Department of Chemistry, The Scripps Research Institute, Jupiter, Florida 33458, United States
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44
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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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45
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Georgakopoulos-Soares I, Parada GE, Wong HY, Medhi R, Furlan G, Munita R, Miska EA, Kwok CK, Hemberg M. Alternative splicing modulation by G-quadruplexes. Nat Commun 2022; 13:2404. [PMID: 35504902 PMCID: PMC9065059 DOI: 10.1038/s41467-022-30071-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 03/30/2022] [Indexed: 12/14/2022] Open
Abstract
Alternative splicing is central to metazoan gene regulation, but the regulatory mechanisms are incompletely understood. Here, we show that G-quadruplex (G4) motifs are enriched ~3-fold near splice junctions. The importance of G4s in RNA is emphasised by a higher enrichment for the non-template strand. RNA-seq data from mouse and human neurons reveals an enrichment of G4s at exons that were skipped following depolarisation induced by potassium chloride. We validate the formation of stable RNA G4s for three candidate splice sites by circular dichroism spectroscopy, UV-melting and fluorescence measurements. Moreover, we find that sQTLs are enriched at G4s, and a minigene experiment provides further support for their role in promoting exon inclusion. Analysis of >1,800 high-throughput experiments reveals multiple RNA binding proteins associated with G4s. Finally, exploration of G4 motifs across eleven species shows strong enrichment at splice sites in mammals and birds, suggesting an evolutionary conserved splice regulatory mechanism. Here the authors shows that G-quadruplexes, non-canonical DNA/RNA structures, can have a direct impact on alternative splicing and that binding of splicing regulators is affected by their presence.
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Affiliation(s)
- Ilias Georgakopoulos-Soares
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK.,Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Guillermo E Parada
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK.,Wellcome Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK.,Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, M5A 1A8, Canada
| | - Hei Yuen Wong
- Department of Chemistry and State Key Laboratory of Marine Pollution, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Ragini Medhi
- Wellcome Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK.,Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK
| | - Giulia Furlan
- Wellcome Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK.,Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK
| | - Roberto Munita
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund Stem Cell Center, Faculty of Medicine, Lund University, Lund, Sweden
| | - Eric A Miska
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK.,Wellcome Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK.,Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK
| | - 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, China
| | - Martin Hemberg
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK. .,Wellcome Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QN, UK. .,Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, 02115, USA.
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46
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Zhou Y, Sotcheff SL, Routh AL. Next-generation sequencing: A new avenue to understand viral RNA-protein interactions. J Biol Chem 2022; 298:101924. [PMID: 35413291 PMCID: PMC8994257 DOI: 10.1016/j.jbc.2022.101924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 04/01/2022] [Accepted: 04/02/2022] [Indexed: 10/25/2022] Open
Abstract
The genomes of RNA viruses present an astonishing source of both sequence and structural diversity. From intracellular viral RNA-host interfaces to interactions between the RNA genome and structural proteins in virus particles themselves, almost the entire viral lifecycle is accompanied by a myriad of RNA-protein interactions that are required to fulfill their replicative potential. It is therefore important to characterize such rich and dynamic collections of viral RNA-protein interactions to understand virus evolution and their adaptation to their hosts and environment. Recent advances in next-generation sequencing technologies have allowed the characterization of viral RNA-protein interactions, including both transient and conserved interactions, where molecular and structural approaches have fallen short. In this review, we will provide a methodological overview of the high-throughput techniques used to study viral RNA-protein interactions, their biochemical mechanisms, and how they evolved from classical methods as well as one another. We will discuss how different techniques have fueled virus research to characterize how viral RNA and proteins interact, both locally and on a global scale. Finally, we will present examples on how these techniques influence the studies of clinically important pathogens such as HIV-1 and SARS-CoV-2.
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Affiliation(s)
- Yiyang Zhou
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA.
| | - Stephanea L Sotcheff
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA
| | - Andrew L Routh
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, Texas, USA; Sealy Center for Structural Biology and Molecular Biophysics, The University of Texas Medical Branch, Galveston, Texas, USA; Institute for Human Infections and Immunity, University of Texas Medical Branch, Galveston, Texas, USA
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47
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Abstract
Omics-based technologies, which have developed rapidly over the last few decades, have generated increasing evidence demonstrating pervasive divergent transcription from RNA polymerase II (Pol II) promoters of eukaryotic genome, and indeed have raised considerable discussion as to their potential physiopathological function. Unlike many other long non-coding RNAs (lncRNAs), promoter antisense RNAs (PAS RNAs) were initially considered to be merely passive transcription by-products of active promoters. However, recent studies have begun to reveal their critical importance in a broad spectrum of biological processes. In this Review, I summarize recent technological advances that enable accurate detection of PAS RNA and discuss the mechanisms of PAS RNA biogenesis emphasizing the functional importance of its structure enabling the diverse functions of PAS RNA in transcription and chromatin regulation.
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Affiliation(s)
- Fan Yang
- Department of Medicine, University of California, San Diego, La Jolla, California, USA
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48
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Morandi E, van Hemert MJ, Incarnato D. SHAPE-guided RNA structure homology search and motif discovery. Nat Commun 2022; 13:1722. [PMID: 35361788 PMCID: PMC8971488 DOI: 10.1038/s41467-022-29398-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/11/2022] [Indexed: 01/13/2023] Open
Abstract
The rapidly growing popularity of RNA structure probing methods is leading to increasingly large amounts of available RNA structure information. This demands the development of efficient tools for the identification of RNAs sharing regions of structural similarity by direct comparison of their reactivity profiles, hence enabling the discovery of conserved structural features. We here introduce SHAPEwarp, a largely sequence-agnostic SHAPE-guided algorithm for the identification of structurally-similar regions in RNA molecules. Analysis of Dengue, Zika and coronavirus genomes recapitulates known regulatory RNA structures and identifies novel highly-conserved structural elements. This work represents a preliminary step towards the model-free search and identification of shared and conserved RNA structural features within transcriptomes.
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Affiliation(s)
- Edoardo Morandi
- Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, The Netherlands
| | - Martijn J van Hemert
- Department of Medical Microbiology, Molecular Virology Laboratory, Leiden University Medical Center, Leiden, The Netherlands
| | - Danny Incarnato
- Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, The Netherlands.
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49
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Solayman M, Litfin T, Singh J, Paliwal K, Zhou Y, Zhan J. Probing RNA structures and functions by solvent accessibility: an overview from experimental and computational perspectives. Brief Bioinform 2022; 23:6554125. [PMID: 35348613 PMCID: PMC9116373 DOI: 10.1093/bib/bbac112] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/03/2022] [Accepted: 03/04/2022] [Indexed: 12/30/2022] Open
Abstract
Characterizing RNA structures and functions have mostly been focused on 2D, secondary and 3D, tertiary structures. Recent advances in experimental and computational techniques for probing or predicting RNA solvent accessibility make this 1D representation of tertiary structures an increasingly attractive feature to explore. Here, we provide a survey of these recent developments, which indicate the emergence of solvent accessibility as a simple 1D property, adding to secondary and tertiary structures for investigating complex structure–function relations of RNAs.
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Affiliation(s)
- Md Solayman
- Institute for Glycomics, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Thomas Litfin
- Institute for Glycomics, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Jaswinder Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Yaoqi Zhou
- Institute for Glycomics, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.,Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China.,Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Jian Zhan
- Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
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50
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Fu L, Cao Y, Wu J, Peng Q, Nie Q, Xie X. UFold: fast and accurate RNA secondary structure prediction with deep learning. Nucleic Acids Res 2022; 50:e14. [PMID: 34792173 PMCID: PMC8860580 DOI: 10.1093/nar/gkab1074] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/15/2021] [Accepted: 10/19/2021] [Indexed: 11/13/2022] Open
Abstract
For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. Here, we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data and base-pairing rules. UFold proposes a novel image-like representation of RNA sequences, which can be efficiently processed by Fully Convolutional Networks (FCNs). We benchmark the performance of UFold on both within- and cross-family RNA datasets. It significantly outperforms previous methods on within-family datasets, while achieving a similar performance as the traditional methods when trained and tested on distinct RNA families. UFold is also able to predict pseudoknots accurately. Its prediction is fast with an inference time of about 160 ms per sequence up to 1500 bp in length. An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold.
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Affiliation(s)
- Laiyi Fu
- Systems Engineering Institute, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Yingxin Cao
- Department of Computer Science, University of California, Irvine, CA 92697, USA
- Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
| | - Jie Wu
- Department of Biological Chemistry, University of California, Irvine, CA 92697, USA
| | - Qinke Peng
- Systems Engineering Institute, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, CA 92697, USA
- Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
| | - Xiaohui Xie
- Department of Computer Science, University of California, Irvine, CA 92697, USA
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